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Shuaibi A, Chitra U, Raphael BJ. A latent variable model for evaluating mutual exclusivity and co-occurrence between driver mutations in cancer. bioRxiv 2024:2024.04.24.590995. [PMID: 38712136 PMCID: PMC11071465 DOI: 10.1101/2024.04.24.590995] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/08/2024]
Abstract
A key challenge in cancer genomics is understanding the functional relationships and dependencies between combinations of somatic mutations that drive cancer development. Such driver mutations frequently exhibit patterns of mutual exclusivity or co-occurrence across tumors, and many methods have been developed to identify such dependency patterns from bulk DNA sequencing data of a cohort of patients. However, while mutual exclusivity and co-occurrence are described as properties of driver mutations, existing methods do not explicitly disentangle functional, driver mutations from neutral, passenger mutations. In particular, nearly all existing methods evaluate mutual exclusivity or co-occurrence at the gene level, marking a gene as mutated if any mutation - driver or passenger - is present. Since some genes have a large number of passenger mutations, existing methods either restrict their analyses to a small subset of suspected driver genes - limiting their ability to identify novel dependencies - or make spurious inferences of mutual exclusivity and co-occurrence involving genes with many passenger mutations. We introduce DIALECT, an algorithm to identify dependencies between pairs of driver mutations from somatic mutation counts. We derive a latent variable mixture model for drivers and passengers that combines existing probabilistic models of passenger mutation rates with a latent variable describing the unknown status of a mutation as a driver or passenger. We use an expectation maximization (EM) algorithm to estimate the parameters of our model, including the rates of mutually exclusivity and co-occurrence between drivers. We demonstrate that DIALECT more accurately infers mutual exclusivity and co-occurrence between driver mutations compared to existing methods on both simulated mutation data and somatic mutation data from 5 cancer types in The Cancer Genome Atlas (TCGA).
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2
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Mai U, Chu G, Raphael BJ. Maximum Likelihood Inference of Time-scaled Cell Lineage Trees with Mixed-type Missing Data. bioRxiv 2024:2024.03.05.583638. [PMID: 38496496 PMCID: PMC10942411 DOI: 10.1101/2024.03.05.583638] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/19/2024]
Abstract
Recent dynamic lineage tracing technologies combine CRISPR-based genome editing with single-cell sequencing to track cell divisions during development. A key computational problem in dynamic lineage tracing is to infer a cell lineage tree from the measured CRISPR-induced mutations. Three features of dynamic lineage tracing data distinguish this problem from standard phylogenetic tree inference. First, the CRISPR-editing process modifies a genomic location exactly once. This non-modifiable property is not well described by the time-reversible models commonly used in phylogenetics. Second, as a consequence of non-modifiability, the number of mutations per time unit decreases over time. Third, CRISPR-based genome-editing and single-cell sequencing results in high rates of both heritable and non-heritable (dropout) missing data. To model these features, we introduce the Probabilistic Mixed-type Missing (PMM) model. We describe an algorithm, LAML (Lineage Analysis via Maximum Likelihood), to search for the maximum likelihood (ML) tree under the PMM model. LAML combines an Expectation Maximization (EM) algorithm with a heuristic tree search to jointly estimate tree topology, branch lengths and missing data parameters. We derive a closed-form solution for the M-step in the case of no heritable missing data, and a block coordinate ascent approach in the general case which is more efficient than the standard General Time Reversible (GTR) phylogenetic model. On simulated data, LAML infers more accurate tree topologies and branch lengths than existing methods, with greater advantages on datasets with higher ratios of heritable to non-heritable missing data. We show that LAML provides unbiased time-scaled estimates of branch lengths. In contrast, we demonstrate that maximum parsimony methods for lineage tracing data not only underestimate branch lengths, but also yield branch lengths which are not proportional to time, due to the nonlinear decay in the number of mutations on branches further from the root. On lineage tracing data from a mouse model of lung adenocarcinoma, we show that LAML infers phylogenetic distances that are more concordant with gene expression data compared to distances derived from maximum parsimony. The LAML tree topology is more plausible than existing published trees, with fewer total cell migrations between distant metastases and fewer reseeding events where cells migrate back to the primary tumor. Crucially, we identify three distinct time epochs of metastasis progression, which includes a burst of metastasis events to various anatomical sites during a single month.
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Affiliation(s)
| | | | - Benjamin J. Raphael
- Department of Computer Science, Princeton University, Princeton, NJ 08544, USA
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3
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Halmos P, Liu X, Gold J, Chen F, Ding L, Raphael BJ. DeST-OT: Alignment of Spatiotemporal Transcriptomics Data. bioRxiv 2024:2024.03.05.583575. [PMID: 38496660 PMCID: PMC10942402 DOI: 10.1101/2024.03.05.583575] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/19/2024]
Abstract
Spatially resolved transcriptomics (SRT) measures mRNA transcripts at thousands of locations within a tissue slice, revealing spatial variations in gene expression and distribution of cell types. In recent studies, SRT has been applied to tissue slices from multiple timepoints during the development of an organism. Alignment of this spatiotemporal transcriptomics data can provide insights into the gene expression programs governing the growth and differentiation of cells over space and time. We introduce DeST-OT (Developmental SpatioTemporal Optimal Transport), a method to align SRT slices from pairs of developmental timepoints using the framework of optimal transport (OT). DeST-OT uses semi-relaxed optimal transport to precisely model cellular growth, death, and differentiation processes that are not well-modeled by existing alignment methods. We demonstrate the advantage of DeST-OT on simulated slices. We further introduce two metrics to quantify the plausibility of a spatiotemporal alignment: a growth distortion metric which quantifies the discrepancy between the inferred and the true cell type growth rates, and a migration metric which quantifies the distance traveled between ancestor and descendant cells. DeST-OT outperforms existing methods on these metrics in the alignment of spatiotemporal transcriptomics data from the development of axolotl brain.
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Affiliation(s)
- Peter Halmos
- Department of Computer Science, Princeton University, 35 Olden St, Princeton, NJ 08544
| | - Xinhao Liu
- Department of Computer Science, Princeton University, 35 Olden St, Princeton, NJ 08544
| | - Julian Gold
- Center for Statistics and Machine Learning, Princeton University, 26 Prospect Ave, Princeton, NJ 08544
| | - Feng Chen
- Departments of Medicine and Genetics, Siteman Cancer Center, Washington University in St. Louis, St. Louis, MO 63110
| | - Li Ding
- Departments of Medicine and Genetics, Siteman Cancer Center, Washington University in St. Louis, St. Louis, MO 63110
- McDonnell Genome Institute, Washington University in St. Louis, St. Louis, MO 63108
| | - Benjamin J. Raphael
- Department of Computer Science, Princeton University, 35 Olden St, Princeton, NJ 08544
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Sashittal P, Schmidt H, Chan M, Raphael BJ. Startle: A star homoplasy approach for CRISPR-Cas9 lineage tracing. Cell Syst 2023; 14:1113-1121.e9. [PMID: 38128483 DOI: 10.1016/j.cels.2023.11.005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2023] [Revised: 10/31/2023] [Accepted: 11/17/2023] [Indexed: 12/23/2023]
Abstract
CRISPR-Cas9-based genome editing combined with single-cell sequencing enables the tracing of the history of cell divisions, or cellular lineage, in tissues and whole organisms. Although standard phylogenetic approaches may be applied to reconstruct cellular lineage trees from this data, the unique features of the CRISPR-Cas9 editing process motivate the development of specialized models that describe the evolution of CRISPR-Cas9-induced mutations. Here, we introduce the "star homoplasy" evolutionary model that constrains a phylogenetic character to mutate at most once along a lineage, capturing the "non-modifiability" property of CRISPR-Cas9 mutations. We derive a combinatorial characterization of star homoplasy phylogenies and use this characterization to develop an algorithm, "Startle", that computes a maximum parsimony star homoplasy phylogeny. We demonstrate that Startle infers more accurate phylogenies on simulated lineage tracing data compared with existing methods and finds parsimonious phylogenies with fewer metastatic migrations on lineage tracing data from mouse metastatic lung adenocarcinoma.
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Affiliation(s)
- Palash Sashittal
- Department of Computer Science, Princeton University, Princeton, NJ, USA
| | - Henri Schmidt
- Department of Computer Science, Princeton University, Princeton, NJ, USA
| | - Michelle Chan
- Department of Molecular Biology, Princeton University, Princeton, NJ, USA
| | - Benjamin J Raphael
- Department of Computer Science, Princeton University, Princeton, NJ, USA.
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5
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Sashittal P, Zhang H, Iacobuzio-Donahue CA, Raphael BJ. ConDoR: tumor phylogeny inference with a copy-number constrained mutation loss model. Genome Biol 2023; 24:272. [PMID: 38037115 PMCID: PMC10688497 DOI: 10.1186/s13059-023-03106-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/27/2022] [Accepted: 11/07/2023] [Indexed: 12/02/2023] Open
Abstract
A tumor contains a diverse collection of somatic mutations that reflect its past evolutionary history and that range in scale from single nucleotide variants (SNVs) to large-scale copy-number aberrations (CNAs). However, no current single-cell DNA sequencing (scDNA-seq) technology produces accurate measurements of both SNVs and CNAs, complicating the inference of tumor phylogenies. We introduce a new evolutionary model, the constrained k-Dollo model, that uses SNVs as phylogenetic markers but constrains losses of SNVs according to clusters of cells. We derive an algorithm, ConDoR, that infers phylogenies from targeted scDNA-seq data using this model. We demonstrate the advantages of ConDoR on simulated and real scDNA-seq data.
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Affiliation(s)
| | - Haochen Zhang
- Gerstner Sloan Kettering Graduate School of Biomedical Sciences, Memorial Sloan Kettering Cancer Center, NY, USA
| | - Christine A Iacobuzio-Donahue
- Human Oncology and Pathogenesis Program, Memorial Sloan Kettering Cancer Center, NY, USA
- David M. Rubenstein Center for Pancreatic Cancer Research, Memorial Sloan Kettering Cancer Center, NY, USA
- Department of Pathology and Laboratory Medicine, Memorial Sloan Kettering Cancer Center, NY, USA
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Terekhanova NV, Karpova A, Liang WW, Strzalkowski A, Chen S, Li Y, Southard-Smith AN, Iglesia MD, Wendl MC, Jayasinghe RG, Liu J, Song Y, Cao S, Houston A, Liu X, Wyczalkowski MA, Lu RJH, Caravan W, Shinkle A, Naser Al Deen N, Herndon JM, Mudd J, Ma C, Sarkar H, Sato K, Ibrahim OM, Mo CK, Chasnoff SE, Porta-Pardo E, Held JM, Pachynski R, Schwarz JK, Gillanders WE, Kim AH, Vij R, DiPersio JF, Puram SV, Chheda MG, Fuh KC, DeNardo DG, Fields RC, Chen F, Raphael BJ, Ding L. Epigenetic regulation during cancer transitions across 11 tumour types. Nature 2023; 623:432-441. [PMID: 37914932 PMCID: PMC10632147 DOI: 10.1038/s41586-023-06682-5] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/15/2022] [Accepted: 09/27/2023] [Indexed: 11/03/2023]
Abstract
Chromatin accessibility is essential in regulating gene expression and cellular identity, and alterations in accessibility have been implicated in driving cancer initiation, progression and metastasis1-4. Although the genetic contributions to oncogenic transitions have been investigated, epigenetic drivers remain less understood. Here we constructed a pan-cancer epigenetic and transcriptomic atlas using single-nucleus chromatin accessibility data (using single-nucleus assay for transposase-accessible chromatin) from 225 samples and matched single-cell or single-nucleus RNA-sequencing expression data from 206 samples. With over 1 million cells from each platform analysed through the enrichment of accessible chromatin regions, transcription factor motifs and regulons, we identified epigenetic drivers associated with cancer transitions. Some epigenetic drivers appeared in multiple cancers (for example, regulatory regions of ABCC1 and VEGFA; GATA6 and FOX-family motifs), whereas others were cancer specific (for example, regulatory regions of FGF19, ASAP2 and EN1, and the PBX3 motif). Among epigenetically altered pathways, TP53, hypoxia and TNF signalling were linked to cancer initiation, whereas oestrogen response, epithelial-mesenchymal transition and apical junction were tied to metastatic transition. Furthermore, we revealed a marked correlation between enhancer accessibility and gene expression and uncovered cooperation between epigenetic and genetic drivers. This atlas provides a foundation for further investigation of epigenetic dynamics in cancer transitions.
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Affiliation(s)
- Nadezhda V Terekhanova
- Department of Medicine, Washington University in St Louis, St Louis, MO, USA
- McDonnell Genome Institute, Washington University in St Louis, St Louis, MO, USA
| | - Alla Karpova
- Department of Medicine, Washington University in St Louis, St Louis, MO, USA
- McDonnell Genome Institute, Washington University in St Louis, St Louis, MO, USA
| | - Wen-Wei Liang
- Department of Medicine, Washington University in St Louis, St Louis, MO, USA
- McDonnell Genome Institute, Washington University in St Louis, St Louis, MO, USA
| | | | - Siqi Chen
- Department of Medicine, Washington University in St Louis, St Louis, MO, USA
- McDonnell Genome Institute, Washington University in St Louis, St Louis, MO, USA
| | - Yize Li
- Department of Medicine, Washington University in St Louis, St Louis, MO, USA
- McDonnell Genome Institute, Washington University in St Louis, St Louis, MO, USA
| | - Austin N Southard-Smith
- Department of Medicine, Washington University in St Louis, St Louis, MO, USA
- McDonnell Genome Institute, Washington University in St Louis, St Louis, MO, USA
| | - Michael D Iglesia
- Department of Medicine, Washington University in St Louis, St Louis, MO, USA
- McDonnell Genome Institute, Washington University in St Louis, St Louis, MO, USA
| | - Michael C Wendl
- Department of Medicine, Washington University in St Louis, St Louis, MO, USA
- McDonnell Genome Institute, Washington University in St Louis, St Louis, MO, USA
| | - Reyka G Jayasinghe
- Department of Medicine, Washington University in St Louis, St Louis, MO, USA
- McDonnell Genome Institute, Washington University in St Louis, St Louis, MO, USA
| | - Jingxian Liu
- Department of Medicine, Washington University in St Louis, St Louis, MO, USA
- McDonnell Genome Institute, Washington University in St Louis, St Louis, MO, USA
| | - Yizhe Song
- Department of Medicine, Washington University in St Louis, St Louis, MO, USA
- McDonnell Genome Institute, Washington University in St Louis, St Louis, MO, USA
| | - Song Cao
- Department of Medicine, Washington University in St Louis, St Louis, MO, USA
- McDonnell Genome Institute, Washington University in St Louis, St Louis, MO, USA
| | - Andrew Houston
- Department of Medicine, Washington University in St Louis, St Louis, MO, USA
- McDonnell Genome Institute, Washington University in St Louis, St Louis, MO, USA
| | - Xiuting Liu
- Department of Medicine, Washington University in St Louis, St Louis, MO, USA
| | - Matthew A Wyczalkowski
- Department of Medicine, Washington University in St Louis, St Louis, MO, USA
- McDonnell Genome Institute, Washington University in St Louis, St Louis, MO, USA
| | - Rita Jui-Hsien Lu
- Department of Medicine, Washington University in St Louis, St Louis, MO, USA
- McDonnell Genome Institute, Washington University in St Louis, St Louis, MO, USA
| | - Wagma Caravan
- Department of Medicine, Washington University in St Louis, St Louis, MO, USA
- McDonnell Genome Institute, Washington University in St Louis, St Louis, MO, USA
| | - Andrew Shinkle
- Department of Medicine, Washington University in St Louis, St Louis, MO, USA
| | - Nataly Naser Al Deen
- Department of Medicine, Washington University in St Louis, St Louis, MO, USA
- McDonnell Genome Institute, Washington University in St Louis, St Louis, MO, USA
| | - John M Herndon
- Department of Surgery, Washington University in St Louis, St Louis, MO, USA
- Siteman Cancer Center, Washington University in St Louis, St Louis, MO, USA
| | - Jacqueline Mudd
- Department of Surgery, Washington University in St Louis, St Louis, MO, USA
| | - Cong Ma
- Department of Computer Science, Princeton University, Princeton, NJ, USA
| | - Hirak Sarkar
- Department of Computer Science, Princeton University, Princeton, NJ, USA
| | - Kazuhito Sato
- Department of Medicine, Washington University in St Louis, St Louis, MO, USA
- McDonnell Genome Institute, Washington University in St Louis, St Louis, MO, USA
| | - Omar M Ibrahim
- Department of Medicine, Washington University in St Louis, St Louis, MO, USA
- McDonnell Genome Institute, Washington University in St Louis, St Louis, MO, USA
| | - Chia-Kuei Mo
- Department of Medicine, Washington University in St Louis, St Louis, MO, USA
- McDonnell Genome Institute, Washington University in St Louis, St Louis, MO, USA
| | - Sara E Chasnoff
- Department of Surgery, Washington University in St Louis, St Louis, MO, USA
- Siteman Cancer Center, Washington University in St Louis, St Louis, MO, USA
| | - Eduard Porta-Pardo
- Josep Carreras Leukaemia Research Institute, Barcelona, Spain
- Barcelona Supercomputing Center, Barcelona, Spain
| | - Jason M Held
- Department of Medicine, Washington University in St Louis, St Louis, MO, USA
- Siteman Cancer Center, Washington University in St Louis, St Louis, MO, USA
| | - Russell Pachynski
- Department of Medicine, Washington University in St Louis, St Louis, MO, USA
- Siteman Cancer Center, Washington University in St Louis, St Louis, MO, USA
| | - Julie K Schwarz
- Department of Radiation Oncology, Washington University in St Louis, St Louis, MO, USA
| | - William E Gillanders
- Department of Surgery, Washington University in St Louis, St Louis, MO, USA
- Siteman Cancer Center, Washington University in St Louis, St Louis, MO, USA
| | - Albert H Kim
- Siteman Cancer Center, Washington University in St Louis, St Louis, MO, USA
- Department of Neurological Surgery, Washington University in St Louis, St Louis, MO, USA
| | - Ravi Vij
- Department of Medicine, Washington University in St Louis, St Louis, MO, USA
- Siteman Cancer Center, Washington University in St Louis, St Louis, MO, USA
| | - John F DiPersio
- Department of Medicine, Washington University in St Louis, St Louis, MO, USA
- Siteman Cancer Center, Washington University in St Louis, St Louis, MO, USA
| | - Sidharth V Puram
- Department of Otolaryngology-Head & Neck Surgery, Washington University in St Louis, St Louis, MO, USA
| | - Milan G Chheda
- Department of Medicine, Washington University in St Louis, St Louis, MO, USA
- Siteman Cancer Center, Washington University in St Louis, St Louis, MO, USA
| | - Katherine C Fuh
- Department of Obstetrics and Gynecology, University of California, San Francisco, San Francisco, CA, USA
- Department of Obstetrics and Gynecology, Washington University in St Louis, St Louis, MO, USA
| | - David G DeNardo
- Department of Medicine, Washington University in St Louis, St Louis, MO, USA
- Siteman Cancer Center, Washington University in St Louis, St Louis, MO, USA
| | - Ryan C Fields
- Department of Surgery, Washington University in St Louis, St Louis, MO, USA.
- Siteman Cancer Center, Washington University in St Louis, St Louis, MO, USA.
| | - Feng Chen
- Department of Medicine, Washington University in St Louis, St Louis, MO, USA.
- Siteman Cancer Center, Washington University in St Louis, St Louis, MO, USA.
| | - Benjamin J Raphael
- Department of Computer Science, Princeton University, Princeton, NJ, USA.
| | - Li Ding
- Department of Medicine, Washington University in St Louis, St Louis, MO, USA.
- McDonnell Genome Institute, Washington University in St Louis, St Louis, MO, USA.
- Siteman Cancer Center, Washington University in St Louis, St Louis, MO, USA.
- Department of Genetics, Washington University in St Louis, St Louis, MO, USA.
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7
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Schmidt H, Sashittal P, Raphael BJ. A zero-agnostic model for copy number evolution in cancer. PLoS Comput Biol 2023; 19:e1011590. [PMID: 37943952 PMCID: PMC10662746 DOI: 10.1371/journal.pcbi.1011590] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/18/2023] [Revised: 11/21/2023] [Accepted: 10/11/2023] [Indexed: 11/12/2023] Open
Abstract
MOTIVATION New low-coverage single-cell DNA sequencing technologies enable the measurement of copy number profiles from thousands of individual cells within tumors. From this data, one can infer the evolutionary history of the tumor by modeling transformations of the genome via copy number aberrations. Copy number aberrations alter multiple adjacent genomic loci, violating the standard phylogenetic assumption that loci evolve independently. Thus, specialized models to infer copy number phylogenies have been introduced. A widely used model is the copy number transformation (CNT) model in which a genome is represented by an integer vector and a copy number aberration is an event that either increases or decreases the number of copies of a contiguous segment of the genome. The CNT distance between a pair of copy number profiles is the minimum number of events required to transform one profile to another. While this distance can be computed efficiently, no efficient algorithm has been developed to find the most parsimonious phylogeny under the CNT model. RESULTS We introduce the zero-agnostic copy number transformation (ZCNT) model, a simplification of the CNT model that allows the amplification or deletion of regions with zero copies. We derive a closed form expression for the ZCNT distance between two copy number profiles and show that, unlike the CNT distance, the ZCNT distance forms a metric. We leverage the closed-form expression for the ZCNT distance and an alternative characterization of copy number profiles to derive polynomial time algorithms for two natural relaxations of the small parsimony problem on copy number profiles. While the alteration of zero copy number regions allowed under the ZCNT model is not biologically realistic, we show on both simulated and real datasets that the ZCNT distance is a close approximation to the CNT distance. Extending our polynomial time algorithm for the ZCNT small parsimony problem, we develop an algorithm, Lazac, for solving the large parsimony problem on copy number profiles. We demonstrate that Lazac outperforms existing methods for inferring copy number phylogenies on both simulated and real data.
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Affiliation(s)
- Henri Schmidt
- Department of Computer Science, Princeton University, Princeton, New Jersey, United States of America
| | - Palash Sashittal
- Department of Computer Science, Princeton University, Princeton, New Jersey, United States of America
| | - Benjamin J. Raphael
- Department of Computer Science, Princeton University, Princeton, New Jersey, United States of America
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Chitra U, Arnold BJ, Sarkar H, Ma C, Lopez-Darwin S, Sanno K, Raphael BJ. Mapping the topography of spatial gene expression with interpretable deep learning. bioRxiv 2023:2023.10.10.561757. [PMID: 37873258 PMCID: PMC10592770 DOI: 10.1101/2023.10.10.561757] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/25/2023]
Abstract
Spatially resolved transcriptomics technologies provide high-throughput measurements of gene expression in a tissue slice, but the sparsity of this data complicates the analysis of spatial gene expression patterns such as gene expression gradients. We address these issues by deriving a topographic map of a tissue slice-analogous to a map of elevation in a landscape-using a novel quantity called the isodepth. Contours of constant isodepth enclose spatial domains with distinct cell type composition, while gradients of the isodepth indicate spatial directions of maximum change in gene expression. We develop GASTON, an unsupervised and interpretable deep learning algorithm that simultaneously learns the isodepth, spatial gene expression gradients, and piecewise linear functions of the isodepth that model both continuous gradients and discontinuous spatial variation in the expression of individual genes. We validate GASTON by showing that it accurately identifies spatial domains and marker genes across several biological systems. In SRT data from the brain, GASTON reveals gradients of neuronal differentiation and firing, and in SRT data from a tumor sample, GASTON infers gradients of metabolic activity and epithelial-mesenchymal transition (EMT)-related gene expression in the tumor microenvironment.
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Affiliation(s)
- Uthsav Chitra
- Department of Computer Science, Princeton University, Princeton, NJ, USA
| | - Brian J. Arnold
- Department of Computer Science, Princeton University, Princeton, NJ, USA
- Center for Statistics and Machine Learning, Princeton University, Princeton, NJ, USA
| | - Hirak Sarkar
- Department of Computer Science, Princeton University, Princeton, NJ, USA
- Ludwig Cancer Institute, Princeton Branch, Princeton University, Princeton, NJ, USA
| | - Cong Ma
- Department of Computer Science, Princeton University, Princeton, NJ, USA
| | | | - Kohei Sanno
- Department of Computer Science, Princeton University, Princeton, NJ, USA
- Center for Statistics and Machine Learning, Princeton University, Princeton, NJ, USA
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Myers MA, Arnold BJ, Bansal V, Mullen KM, Zaccaria S, Raphael BJ. HATCHet2: clone- and haplotype-specific copy number inference from bulk tumor sequencing data. bioRxiv 2023:2023.07.13.548855. [PMID: 37502835 PMCID: PMC10370020 DOI: 10.1101/2023.07.13.548855] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/29/2023]
Abstract
Multi-region DNA sequencing of primary tumors and metastases from individual patients helps identify somatic aberrations driving cancer development. However, most methods to infer copy-number aberrations (CNAs) analyze individual samples. We introduce HATCHet2 to identify haplotype- and clone-specific CNAs simultaneously from multiple bulk samples. HATCHet2 introduces a novel statistic, the mirrored haplotype B-allele frequency (mhBAF), to identify mirrored-subclonal CNAs having different numbers of copies of parental haplotypes in different tumor clones. HATCHet2 also has high accuracy in identifying focal CNAs and extends the earlier HATCHet method in several directions. We demonstrate HATCHet2's improved accuracy using simulations and a single-cell sequencing dataset. HATCHet2 analysis of 50 prostate cancer samples from 10 patients reveals previously-unreported mirrored-subclonal CNAs affecting cancer genes.
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Affiliation(s)
- Matthew A. Myers
- Department of Computer Science, Princeton University, Princeton, USA
| | - Brian J. Arnold
- Center for Statistics and Machine Learning, Princeton University, Princeton, USA
| | - Vineet Bansal
- Princeton Research Computing, Princeton University, Princeton, NJ, USA
| | - Katelyn M. Mullen
- Human Oncology & Pathogenesis Program, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Simone Zaccaria
- Computational Cancer Genomics Research Group, University College London Cancer Institute, London, UK
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10
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Abstract
Spatially resolved transcriptomics (SRT) technologies measure messenger RNA (mRNA) expression at thousands of locations in a tissue slice. However, nearly all SRT technologies measure expression in two-dimensional (2D) slices extracted from a 3D tissue, thus losing information that is shared across multiple slices from the same tissue. Integrating SRT data across multiple slices can help recover this information and improve downstream expression analyses, but multislice alignment and integration remains a challenging task. Existing methods for integrating SRT data either do not use spatial information or assume that the morphology of the tissue is largely preserved across slices, an assumption that is often violated because of biological or technical reasons. We introduce PASTE2, a method for partial alignment and 3D reconstruction of multislice SRT data sets, allowing only partial overlap between aligned slices and/or slice-specific cell types. PASTE2 formulates a novel partial fused Gromov-Wasserstein optimal transport problem, which we solve using a conditional gradient algorithm. PASTE2 includes a model selection procedure to estimate the fraction of overlap between slices, and optionally uses information from histological images that accompany some SRT experiments. We show on both simulated and real data that PASTE2 obtains more accurate alignments than existing methods. We further use PASTE2 to reconstruct a 3D map of gene expression in a Drosophila embryo from a 16 slice Stereo-seq data set. PASTE2 produces accurate alignments of multislice data sets from multiple SRT technologies, enabling detailed studies of spatial gene expression across a wide range of biological applications.
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Affiliation(s)
- Xinhao Liu
- Department of Computer Science, Princeton University, Princeton, New Jersey 08540, USA
| | - Ron Zeira
- Verily Life Sciences, Tel Aviv 6789141, Israel
| | - Benjamin J Raphael
- Department of Computer Science, Princeton University, Princeton, New Jersey 08540, USA;
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11
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Schmidt H, Sashittal P, Raphael BJ. A zero-agnostic model for copy number evolution in cancer. bioRxiv 2023:2023.04.10.536302. [PMID: 37090633 PMCID: PMC10120719 DOI: 10.1101/2023.04.10.536302] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/25/2023]
Abstract
Motivation New low-coverage single-cell DNA sequencing technologies enable the measurement of copy number profiles from thousands of individual cells within tumors. From this data, one can infer the evolutionary history of the tumor by modeling transformations of the genome via copy number aberrations. A widely used model to infer such copy number phylogenies is the copy number transformation (CNT) model in which a genome is represented by an integer vector and a copy number aberration is an event that either increases or decreases the number of copies of a contiguous segment of the genome. The CNT distance between a pair of copy number profiles is the minimum number of events required to transform one profile to another. While this distance can be computed efficiently, no efficient algorithm has been developed to find the most parsimonious phylogeny under the CNT model. Results We introduce the zero-agnostic copy number transformation (ZCNT) model, a simplification of the CNT model that allows the amplification or deletion of regions with zero copies. We derive a closed form expression for the ZCNT distance between two copy number profiles and show that, unlike the CNT distance, the ZCNT distance forms a metric. We leverage the closed-form expression for the ZCNT distance and an alternative characterization of copy number profiles to derive polynomial time algorithms for two natural relaxations of the small parsimony problem on copy number profiles. While the alteration of zero copy number regions allowed under the ZCNT model is not biologically realistic, we show on both simulated and real datasets that the ZCNT distance is a close approximation to the CNT distance. Extending our polynomial time algorithm for the ZCNT small parsimony problem, we develop an algorithm, Lazac, for solving the large parsimony problem on copy number profiles. We demonstrate that Lazac outperforms existing methods for inferring copy number phylogenies on both simulated and real data.
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Affiliation(s)
- Henri Schmidt
- Department of Computer Science, Princeton University, NJ, USA
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Zhang H, Sashittal P, Karnoub ER, Raphael BJ, Iacobuzio-Donahue CA. Abstract 1169: Genomic evolution of pancreatic cancer at single-cell resolution. Cancer Res 2023. [DOI: 10.1158/1538-7445.am2023-1169] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/07/2023]
Abstract
Abstract
As the breadth of genomic sequencing datasets increases, we can engage more directly with the evolutionary principles governing cancer progression. But as clonal evolution happens at the single-cell level, one must rely on strong assumptions when making conclusions with bulk-sequencing results, which fails in capturing clonal heterogeneity to its fullest.
Herein we have developed and optimized a high-throughput, high-depth targeted single-nucleus DNA-seq (snDNA-seq) technique (doi.org/10.1101/2022.03.06.483206) for archival primary solid tumor samples. We focused on pancreatic ductal adenocarcinoma (PDAC), well known as one of the most lethal cancers, and sequenced over 200,000 single nuclei from 80 archival primary samples of 25 PDAC patients. The samples included both early- and late-stage diagnoses and multiregional sampling from primary tumors and metastasis to capture clonal heterogeneity on both temporal and spatial scales.
With significant increase in sensitivity than bulk (down to mutations in 0.1% cells), we discovered thousands of novel mutations per sample on our 120,000 base-pair-long panel regions, suggesting a mutation rate higher than previously estimated. A small fraction of these mutations is in 1-10% single cells and are enriched in early-stage samples. They form mutually exclusive clones which functionally target key pathways including TGF-β, homologous recombination, suggesting subclonal evolution under positive selection at the early stage of cancer. Most novel mutations are in <1% single cells sampled and enabled us to measure convergent evolution and positive/negative selection within each tumor.
We next revisited PDAC’s genomic evolution model established by bulk studies. It posits that PDAC often arises when KRAS hotspot mutation-bearing precursors acquire TP53 and/or CDKN2A inactivation through stepwise and punctuated evolution. While it assumes that TGF-β inactivating mutations are present in all cancer cells (clonal), we found that they are targeted in a highly subclonal manner; moreover, short mutations and focal copy number variations occur in a stepwise manner over time leading to the most “fit” genotype. In many PDACs whose bulk results show no alteration to the TGF-β pathway, snDNA-seq shows focal deletions that are likely below bulk’s sensitivity.
Ongoing studies have begun to extend these analyses to longitudinal samples to study treatment response; normal pancreas tissues to study pancreas cells’ clonal evolution in aging and chronic disease conditions; blood samples to investigate circulating tumor cells in PDAC patients. Computational pipelines and analysis tools are being built as platform for more in-depth analyses. Overall, the high-throughput snDNA-seq technique brings genomic study of PDAC to a much higher resolution and holds the promise to not only inform precision medicine but also shed light on many fundamental questions on cancer evolution.
Citation Format: Haochen Zhang, Palash Sashittal, Elias-Ramzey Karnoub, Benjamin J. Raphael, Christine A. Iacobuzio-Donahue. Genomic evolution of pancreatic cancer at single-cell resolution [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 1169.
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Affiliation(s)
- Haochen Zhang
- 1Memorial Sloan Kettering Cancer Center, New York, NY
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13
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Zhang D, Deng Y, Kukanja P, Agirre E, Bartosovic M, Dong M, Ma C, Ma S, Su G, Bao S, Liu Y, Xiao Y, Rosoklija GB, Dwork AJ, Mann JJ, Leong KW, Boldrini M, Wang L, Haeussler M, Raphael BJ, Kluger Y, Castelo-Branco G, Fan R. Spatial epigenome-transcriptome co-profiling of mammalian tissues. Nature 2023; 616:113-122. [PMID: 36922587 PMCID: PMC10076218 DOI: 10.1038/s41586-023-05795-1] [Citation(s) in RCA: 59] [Impact Index Per Article: 59.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/06/2022] [Accepted: 02/03/2023] [Indexed: 03/17/2023]
Abstract
Emerging spatial technologies, including spatial transcriptomics and spatial epigenomics, are becoming powerful tools for profiling of cellular states in the tissue context1-5. However, current methods capture only one layer of omics information at a time, precluding the possibility of examining the mechanistic relationship across the central dogma of molecular biology. Here, we present two technologies for spatially resolved, genome-wide, joint profiling of the epigenome and transcriptome by cosequencing chromatin accessibility and gene expression, or histone modifications (H3K27me3, H3K27ac or H3K4me3) and gene expression on the same tissue section at near-single-cell resolution. These were applied to embryonic and juvenile mouse brain, as well as adult human brain, to map how epigenetic mechanisms control transcriptional phenotype and cell dynamics in tissue. Although highly concordant tissue features were identified by either spatial epigenome or spatial transcriptome we also observed distinct patterns, suggesting their differential roles in defining cell states. Linking epigenome to transcriptome pixel by pixel allows the uncovering of new insights in spatial epigenetic priming, differentiation and gene regulation within the tissue architecture. These technologies are of great interest in life science and biomedical research.
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Affiliation(s)
- Di Zhang
- Department of Biomedical Engineering, Yale University, New Haven, CT, USA
| | - Yanxiang Deng
- Department of Biomedical Engineering, Yale University, New Haven, CT, USA.
- Yale Stem Cell Center and Yale Cancer Center, Yale School of Medicine, New Haven, CT, USA.
- Department of Pathology and Laboratory Medicine, Epigenetics Institute, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
| | - Petra Kukanja
- Laboratory of Molecular Neurobiology, Department of Medical Biochemistry and Biophysics, Karolinska Institutet, Stockholm, Sweden
| | - Eneritz Agirre
- Laboratory of Molecular Neurobiology, Department of Medical Biochemistry and Biophysics, Karolinska Institutet, Stockholm, Sweden
| | - Marek Bartosovic
- Laboratory of Molecular Neurobiology, Department of Medical Biochemistry and Biophysics, Karolinska Institutet, Stockholm, Sweden
| | - Mingze Dong
- Department of Pathology, Yale University School of Medicine, New Haven, CT, USA
- Interdepartmental Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT, USA
| | - Cong Ma
- Department of Computer Science, Princeton University, Princeton, NJ, USA
| | - Sai Ma
- Klarman Cell Observatory, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Graham Su
- Department of Biomedical Engineering, Yale University, New Haven, CT, USA
- Yale Stem Cell Center and Yale Cancer Center, Yale School of Medicine, New Haven, CT, USA
| | - Shuozhen Bao
- Department of Biomedical Engineering, Yale University, New Haven, CT, USA
| | - Yang Liu
- Department of Biomedical Engineering, Yale University, New Haven, CT, USA
- Yale Stem Cell Center and Yale Cancer Center, Yale School of Medicine, New Haven, CT, USA
| | - Yang Xiao
- Department of Biomedical Engineering, Columbia University, New York, NY, USA
| | - Gorazd B Rosoklija
- Department of Psychiatry, Columbia University, New York, NY, USA
- Division of Molecular Imaging and Neuropathology, New York State Psychiatric Institute, New York, NY, USA
- Macedonian Academy of Sciences & Arts, Skopje, Republic of Macedonia
| | - Andrew J Dwork
- Department of Psychiatry, Columbia University, New York, NY, USA
- Division of Molecular Imaging and Neuropathology, New York State Psychiatric Institute, New York, NY, USA
- Macedonian Academy of Sciences & Arts, Skopje, Republic of Macedonia
- Department of Pathology and Cell Biology, Columbia University, New York, NY, USA
| | - J John Mann
- Department of Psychiatry, Columbia University, New York, NY, USA
- Division of Molecular Imaging and Neuropathology, New York State Psychiatric Institute, New York, NY, USA
- Department of Radiology, Columbia University, New York, NY, USA
| | - Kam W Leong
- Department of Biomedical Engineering, Columbia University, New York, NY, USA
- Department of Systems Biology, Columbia University Irving Medical Center, New York, NY, USA
| | - Maura Boldrini
- Department of Psychiatry, Columbia University, New York, NY, USA
- Division of Molecular Imaging and Neuropathology, New York State Psychiatric Institute, New York, NY, USA
| | - Liya Wang
- AtlasXomics, Inc., New Haven, CT, USA
| | | | - Benjamin J Raphael
- Department of Computer Science, Princeton University, Princeton, NJ, USA
| | - Yuval Kluger
- Department of Pathology, Yale University School of Medicine, New Haven, CT, USA
- Interdepartmental Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT, USA
- Applied Mathematics Program, Yale University, New Haven, CT, USA
| | - Gonçalo Castelo-Branco
- Laboratory of Molecular Neurobiology, Department of Medical Biochemistry and Biophysics, Karolinska Institutet, Stockholm, Sweden.
- Ming Wai Lau Centre for Reparative Medicine, Stockholm Node, Karolinska Institutet, Stockholm, Sweden.
| | - Rong Fan
- Department of Biomedical Engineering, Yale University, New Haven, CT, USA.
- Yale Stem Cell Center and Yale Cancer Center, Yale School of Medicine, New Haven, CT, USA.
- Department of Pathology, Yale University School of Medicine, New Haven, CT, USA.
- Human and Translational Immunology Program, Yale School of Medicine, New Haven, CT, USA.
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Rajan S, Zaccaria S, Cannon M, Cam M, Gross AC, Raphael BJ, Roberts RD. Structurally complex osteosarcoma genomes exhibit limited heterogeneity within individual tumors and across evolutionary time. Cancer Research Communications 2023; 3:564-575. [PMID: 37066022 PMCID: PMC10093779 DOI: 10.1158/2767-9764.crc-22-0348] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/13/2022] [Revised: 01/18/2023] [Accepted: 03/06/2023] [Indexed: 03/11/2023]
Abstract
Abstract
Osteosarcoma is an aggressive malignancy characterized by high genomic complexity. Identification of few recurrent mutations in protein coding genes suggests that somatic copy-number aberrations (SCNAs) are the genetic drivers of disease. Models around genomic instability conflict - it is unclear if osteosarcomas result from pervasive ongoing clonal evolution with continuous optimization of the fitness landscape or an early catastrophic event followed by stable maintenance of an abnormal genome. We address this question by investigating SCNAs in >12,000 tumor cells obtained from human osteosarcomas using single cell DNA sequencing, with a degree of precision and accuracy not possible when inferring single cell states using bulk sequencing. Using the CHISEL algorithm, we inferred allele- and haplotype-specific SCNAs from this whole-genome single cell DNA sequencing data. Surprisingly, despite extensive structural complexity, these tumors exhibit a high degree of cell-cell homogeneity with little sub-clonal diversification. Longitudinal analysis of patient samples obtained at distant therapeutic time points (diagnosis, relapse) demonstrated remarkable conservation of SCNA profiles over tumor evolution. Phylogenetic analysis suggests that the majority of SCNAs were acquired early in the oncogenic process, with relatively few structure-altering events arising in response to therapy or during adaptation to growth in metastatic tissues. These data further support the emerging hypothesis that early catastrophic events, rather than sustained genomic instability, give rise to structural complexity, which is then preserved over long periods of tumor developmental time.
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Affiliation(s)
- Sanjana Rajan
- Janssen Immunology R&D, South San Francisco, California, United States
| | | | | | - Maren Cam
- Nationwide Children's Hospital, Columbus, OH, United States
| | - Amy C Gross
- Nationwide Children's Hospital, Columbus, Ohio, United States
| | | | - Ryan D. Roberts
- Nationwide Children's Hospital, Columbus, Ohio, United States
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15
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Abstract
Spatially resolved transcriptomics (SRT) technologies measure mRNA expression at thousands of locations in a tissue slice. However, nearly all SRT technologies measure expression in two dimensional slices extracted from a three-dimensional tissue, thus losing information that is shared across multiple slices from the same tissue. Integrating SRT data across multiple slices can help recover this information and improve downstream expression analyses, but multi-slice alignment and integration remains a challenging task. Existing methods for integrating SRT data either do not use spatial information or assume that the morphology of the tissue is largely preserved across slices, an assumption that is often violated due to biological or technical reasons. We introduce PASTE2, a method for partial alignment and 3D reconstruction of multi-slice SRT datasets, allowing only partial overlap between aligned slices and/or slice-specific cell types. PASTE2 formulates a novel partial Fused Gromov-Wasserstein Optimal Transport problem, which we solve using a conditional gradient algorithm. PASTE2 includes a model selection procedure to estimate the fraction of overlap between slices, and optionally uses information from histological images that accompany some SRT experiments. We show on both simulated and real data that PASTE2 obtains more accurate alignments than existing methods. We further use PASTE2 to reconstruct a 3D map of gene expression in a Drosophila embryo from a 16 slice Stereo-seq dataset. PASTE2 produces accurate alignments of multi-slice datasets from multiple SRT technologies, enabling detailed studies of spatial gene expression across a wide range of biological applications. Code availability Software is available at https://github.com/raphael-group/paste2.
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Affiliation(s)
- Xinhao Liu
- Department of Computer Science, Princeton University, 35 Olden St, Princeton, NJ 08540
| | - Ron Zeira
- Verily Life Sciences, Tel Aviv, Israel
| | - Benjamin J. Raphael
- Department of Computer Science, Princeton University, 35 Olden St, Princeton, NJ 08540
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Sashittal P, Zhang H, Iacobuzio-Donahue CA, Raphael BJ. ConDoR: Tumor phylogeny inference with a copy-number constrained mutation loss model. bioRxiv 2023:2023.01.05.522408. [PMID: 36711528 PMCID: PMC9882003 DOI: 10.1101/2023.01.05.522408] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 01/09/2023]
Abstract
Tumors consist of subpopulations of cells that harbor distinct collections of somatic mutations. These mutations range in scale from single nucleotide variants (SNVs) to large-scale copy-number aberrations (CNAs). While many approaches infer tumor phylogenies using SNVs as phylogenetic markers, CNAs that overlap SNVs may lead to erroneous phylogenetic inference. Specifically, an SNV may be lost in a cell due to a deletion of the genomic segment containing the SNV. Unfortunately, no current single-cell DNA sequencing (scDNA-seq) technology produces accurate measurements of both SNVs and CNAs. For instance, recent targeted scDNA-seq technologies, such as Mission Bio Tapestri, measure SNVs with high fidelity in individual cells, but yield much less reliable measurements of CNAs. We introduce a new evolutionary model, the constrained k-Dollo model, that uses SNVs as phylogenetic markers and partial information about CNAs in the form of clustering of cells with similar copy-number profiles. This copy-number clustering constrains where loss of SNVs can occur in the phylogeny. We develop ConDoR (Constrained Dollo Reconstruction), an algorithm to infer tumor phylogenies from targeted scDNA-seq data using the constrained k-Dollo model. We show that ConDoR outperforms existing methods on simulated data. We use ConDoR to analyze a new multi-region targeted scDNA-seq dataset of 2153 cells from a pancreatic ductal adenocarcinoma (PDAC) tumor and produce a more plausible phylogeny compared to existing methods that conforms to histological results for the tumor from a previous study. We also analyze a metastatic colorectal cancer dataset, deriving a more parsimonious phylogeny than previously published analyses and with a simpler monoclonal origin of metastasis compared to the original study. Code availability Software is available at https://github.com/raphael-group/constrained-Dollo.
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Affiliation(s)
| | - Haochen Zhang
- Gerstner Sloan Kettering Graduate School of Biomedical Sciences, Memorial Sloan Kettering Cancer Center, NY, USA
| | - Christine A. Iacobuzio-Donahue
- Human Oncology and Pathogenesis Program, Memorial Sloan Kettering Cancer Center, NY, USA
- David M. Rubenstein Center for Pancreatic Cancer Research, Memorial Sloan Kettering Cancer Center, NY, USA
- Department of Pathology and Laboratory Medicine, Memorial Sloan Kettering Cancer Center, NY, USA
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Gerstung M, Jolly C, Leshchiner I, Dentro SC, Gonzalez S, Rosebrock D, Mitchell TJ, Rubanova Y, Anur P, Yu K, Tarabichi M, Deshwar A, Wintersinger J, Kleinheinz K, Vázquez-García I, Haase K, Jerman L, Sengupta S, Macintyre G, Malikic S, Donmez N, Livitz DG, Cmero M, Demeulemeester J, Schumacher S, Fan Y, Yao X, Lee J, Schlesner M, Boutros PC, Bowtell DD, Zhu H, Getz G, Imielinski M, Beroukhim R, Sahinalp SC, Ji Y, Peifer M, Markowetz F, Mustonen V, Yuan K, Wang W, Morris QD, Spellman PT, Wedge DC, Van Loo P, Tarabichi M, Wintersinger J, Deshwar AG, Yu K, Gonzalez S, Rubanova Y, Macintyre G, Adams DJ, Anur P, Beroukhim R, Boutros PC, Bowtell DD, Campbell PJ, Cao S, Christie EL, Cmero M, Cun Y, Dawson KJ, Demeulemeester J, Donmez N, Drews RM, Eils R, Fan Y, Fittall M, Garsed DW, Getz G, Ha G, Imielinski M, Jerman L, Ji Y, Kleinheinz K, Lee J, Lee-Six H, Livitz DG, Malikic S, Markowetz F, Martincorena I, Mitchell TJ, Mustonen V, Oesper L, Peifer M, Peto M, Raphael BJ, Rosebrock D, Sahinalp SC, Salcedo A, Schlesner M, Schumacher S, Sengupta S, Shi R, Shin SJ, Spiro O, Pitkänen E, Pivot X, Piñeiro-Yáñez E, Planko L, Plass C, Polak P, Pons T, Popescu I, Potapova O, Prasad A, Stein LD, Preston SR, Prinz M, Pritchard AL, Prokopec SD, Provenzano E, Puente XS, Puig S, Puiggròs M, Pulido-Tamayo S, Pupo GM, Vázquez-García I, Purdie CA, Quinn MC, Rabionet R, Rader JS, Radlwimmer B, Radovic P, Raeder B, Raine KM, Ramakrishna M, Ramakrishnan K, Vembu S, Ramalingam S, Raphael BJ, Rathmell WK, Rausch T, Reifenberger G, Reimand J, Reis-Filho J, Reuter V, Reyes-Salazar I, Reyna MA, Wheeler DA, Reynolds SM, Rheinbay E, Riazalhosseini Y, Richardson AL, Richter J, Ringel M, Ringnér M, Rino Y, Rippe K, Roach J, Yang TP, Roberts LR, Roberts ND, Roberts SA, Robertson AG, Robertson AJ, Rodriguez JB, Rodriguez-Martin B, Rodríguez-González FG, Roehrl MHA, Rohde M, Yao X, Rokutan H, Romieu G, Rooman I, Roques T, Rosebrock D, Rosenberg M, Rosenstiel PC, Rosenwald A, Rowe EW, Royo R, Yuan K, Rozen SG, Rubanova Y, Rubin MA, Rubio-Perez C, Rudneva VA, Rusev BC, Ruzzenente A, Rätsch G, Sabarinathan R, Sabelnykova VY, Zhu H, Sadeghi S, Sahinalp SC, Saini N, Saito-Adachi M, Saksena G, Salcedo A, Salgado R, Salichos L, Sallari R, Saller C, Wang W, Salvia R, Sam M, Samra JS, Sanchez-Vega F, Sander C, Sanders G, Sarin R, Sarrafi I, Sasaki-Oku A, Sauer T, Morris QD, Sauter G, Saw RPM, Scardoni M, Scarlett CJ, Scarpa A, Scelo G, Schadendorf D, Schein JE, Schilhabel MB, Schlesner M, Spellman PT, Schlomm T, Schmidt HK, Schramm SJ, Schreiber S, Schultz N, Schumacher SE, Schwarz RF, Scolyer RA, Scott D, Scully R, Wedge DC, Seethala R, Segre AV, Selander I, Semple CA, Senbabaoglu Y, Sengupta S, Sereni E, Serra S, Sgroi DC, Shackleton M, Van Loo P, Shah NC, Shahabi S, Shang CA, Shang P, Shapira O, Shelton T, Shen C, Shen H, Shepherd R, Shi R, Spellman PT, Shi Y, Shiah YJ, Shibata T, Shih J, Shimizu E, Shimizu K, Shin SJ, Shiraishi Y, Shmaya T, Shmulevich I, Wedge DC, Shorser SI, Short C, Shrestha R, Shringarpure SS, Shriver C, Shuai S, Sidiropoulos N, Siebert R, Sieuwerts AM, Sieverling L, Van Loo P, Signoretti S, Sikora KO, Simbolo M, Simon R, Simons JV, Simpson JT, Simpson PT, Singer S, Sinnott-Armstrong N, Sipahimalani P, Aaltonen LA, Skelly TJ, Smid M, Smith J, Smith-McCune K, Socci ND, Sofia HJ, Soloway MG, Song L, Sood AK, Sothi S, Abascal F, Sotiriou C, Soulette CM, Span PN, Spellman PT, Sperandio N, Spillane AJ, Spiro O, Spring J, Staaf J, Stadler PF, Abeshouse A, Staib P, Stark SG, Stebbings L, Stefánsson ÓA, Stegle O, Stein LD, Stenhouse A, Stewart C, Stilgenbauer S, Stobbe MD, Aburatani H, Stratton MR, Stretch JR, Struck AJ, Stuart JM, Stunnenberg HG, Su H, Su X, Sun RX, Sungalee S, Susak H, Adams DJ, Suzuki A, Sweep F, Szczepanowski M, Sültmann H, Yugawa T, Tam A, Tamborero D, Tan BKT, Tan D, Tan P, Agrawal N, Tanaka H, Taniguchi H, Tanskanen TJ, Tarabichi M, Tarnuzzer R, Tarpey P, Taschuk ML, Tatsuno K, Tavaré S, Taylor DF, Ahn KS, Taylor-Weiner A, Teague JW, Teh BT, Tembe V, Temes J, Thai K, Thayer SP, Thiessen N, Thomas G, Thomas S, Ahn SM, Thompson A, Thompson AM, Thompson JFF, Thompson RH, Thorne H, Thorne LB, Thorogood A, Tiao G, Tijanic N, Timms LE, Aikata H, Tirabosco R, Tojo M, Tommasi S, Toon CW, Toprak UH, Torrents D, Tortora G, Tost J, Totoki Y, Townend D, Akbani R, Traficante N, Treilleux I, Trotta JR, Trümper LHP, Tsao M, Tsunoda T, Tubio JMC, Tucker O, Turkington R, Turner DJ, Akdemir KC, Tutt A, Ueno M, Ueno NT, Umbricht C, Umer HM, Underwood TJ, Urban L, Urushidate T, Ushiku T, Uusküla-Reimand L, Al-Ahmadie H, Valencia A, Van Den Berg DJ, Van Laere S, Van Loo P, Van Meir EG, Van den Eynden GG, Van der Kwast T, Vasudev N, Vazquez M, Vedururu R, Al-Sedairy ST, Veluvolu U, Vembu S, Verbeke LPC, Vermeulen P, Verrill C, Viari A, Vicente D, Vicentini C, VijayRaghavan K, Viksna J, Al-Shahrour F, Vilain RE, Villasante I, Vincent-Salomon A, Visakorpi T, Voet D, Vyas P, Vázquez-García I, Waddell NM, Waddell N, Wadelius C, Alawi M, Wadi L, Wagener R, Wala JA, Wang J, Wang J, Wang L, Wang Q, Wang W, Wang Y, Wang Z, Albert M, Waring PM, Warnatz HJ, Warrell J, Warren AY, Waszak SM, Wedge DC, Weichenhan D, Weinberger P, Weinstein JN, Weischenfeldt J, Aldape K, Weisenberger DJ, Welch I, Wendl MC, Werner J, Whalley JP, Wheeler DA, Whitaker HC, Wigle D, Wilkerson MD, Williams A, Alexandrov LB, Wilmott JS, Wilson GW, Wilson JM, Wilson RK, Winterhoff B, Wintersinger JA, Wiznerowicz M, Wolf S, Wong BH, Wong T, Ally A, Wong W, Woo Y, Wood S, Wouters BG, Wright AJ, Wright DW, Wright MH, Wu CL, Wu DY, Wu G, Alsop K, Wu J, Wu K, Wu Y, Wu Z, Xi L, Xia T, Xiang Q, Xiao X, Xing R, Xiong H, Alvarez EG, Xu Q, Xu Y, Xue H, Yachida S, Yakneen S, Yamaguchi R, Yamaguchi TN, Yamamoto M, Yamamoto S, Yamaue H, Amary F, Yang F, Yang H, Yang JY, Yang L, Yang L, Yang S, Yang TP, Yang Y, Yao X, Yaspo ML, Amin SB, Yates L, Yau C, Ye C, Ye K, Yellapantula VD, Yoon CJ, Yoon SS, Yousif F, Yu J, Yu K, Aminou B, Yu W, Yu Y, Yuan K, Yuan Y, Yuen D, Yung CK, Zaikova O, Zamora J, Zapatka M, Zenklusen JC, Ammerpohl O, Zenz T, Zeps N, Zhang CZ, Zhang F, Zhang H, Zhang H, Zhang H, Zhang J, Zhang J, Zhang J, Anderson MJ, Zhang X, Zhang X, Zhang Y, Zhang Z, Zhao Z, Zheng L, Zheng X, Zhou W, Zhou Y, Zhu B, Ang Y, Zhu H, Zhu J, Zhu S, Zou L, Zou X, deFazio A, van As N, van Deurzen CHM, van de Vijver MJ, van’t Veer L, Antonello D, von Mering C, Anur P, Aparicio S, Appelbaum EL, Arai Y, Aretz A, Arihiro K, Ariizumi SI, Armenia J, Arnould L, Asa S, Assenov Y, Atwal G, Aukema S, Auman JT, Aure MRR, Awadalla P, Aymerich M, Bader GD, Baez-Ortega A, Bailey MH, Bailey PJ, Balasundaram M, Balu S, Bandopadhayay P, Banks RE, Barbi S, Barbour AP, Barenboim J, Barnholtz-Sloan J, Barr H, Barrera E, Bartlett J, Bartolome J, Bassi C, Bathe OF, Baumhoer D, Bavi P, Baylin SB, Bazant W, Beardsmore D, Beck TA, Behjati S, Behren A, Niu B, Bell C, Beltran S, Benz C, Berchuck A, Bergmann AK, Bergstrom EN, Berman BP, Berney DM, Bernhart SH, Beroukhim R, 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Hilmarsdottir H, Hinton J, Hirano S, Hiraoka N, Hoadley KA, Hobolth A, Hodzic E, Hoell JI, Hoffmann S, Hofmann O, Holbrook A, Holik AZ, Hollingsworth MA, Holmes O, Holt RA, Hong C, Hong EP, Hong JH, Hooijer GK, Hornshøj H, Hosoda F, Hou Y, Hovestadt V, Howat W, Hoyle AP, Hruban RH, Hu J, Hu T, Hua X, Huang KL, Huang M, Huang MN, Huang V, Huang Y, Huber W, Hudson TJ, Hummel M, Hung JA, Huntsman D, Hupp TR, Huse J, Huska MR, Hutter B, Hutter CM, Hübschmann D, Iacobuzio-Donahue CA, Imbusch CD, Imielinski M, Imoto S, Isaacs WB, Isaev K, Ishikawa S, Iskar M, Islam SMA, Ittmann M, Ivkovic S, Izarzugaza JMG, Jacquemier J, Jakrot V, Jamieson NB, Jang GH, Jang SJ, Jayaseelan JC, Jayasinghe R, Jefferys SR, Jegalian K, Jennings JL, Jeon SH, Jerman L, Ji Y, Jiao W, Johansson PA, Johns AL, Johns J, Johnson R, Johnson TA, Jolly C, Joly Y, Jonasson JG, Jones CD, Jones DR, Jones DTW, Jones N, Jones SJM, Jonkers J, Ju YS, Juhl H, Jung J, Juul M, Juul RI, Juul S, Jäger N, Kabbe R, Kahles A, Kahraman A, 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D, Lee D, Lee EA, Lee HJ, Lee JJK, Lee JY, Lee J, Lee MTM, Lee-Six H, Lehmann KV, Lehrach H, Lenze D, Leonard CR, Leongamornlert DA, Leshchiner I, Letourneau L, Letunic I, Levine DA, Lewis L, Ley T, Li C, Li CH, Li HI, Li J, Li L, Li S, Li S, Li X, Li X, Li X, Li Y, Liang H, Liang SB, Lichter P, Lin P, Lin Z, Linehan WM, Lingjærde OC, Liu D, Liu EM, Liu FFF, Liu F, Liu J, Liu X, Livingstone J, Livitz D, Livni N, Lochovsky L, Loeffler M, Long GV, Lopez-Guillermo A, Lou S, Louis DN, Lovat LB, Lu Y, Lu YJ, Lu Y, Luchini C, Lungu I, Luo X, Luxton HJ, Lynch AG, Lype L, López C, López-Otín C, Ma EZ, Ma Y, MacGrogan G, MacRae S, Macintyre G, Madsen T, Maejima K, Mafficini A, Maglinte DT, Maitra A, Majumder PP, Malcovati L, Malikic S, Malleo G, Mann GJ, Mantovani-Löffler L, Marchal K, Marchegiani G, Mardis ER, Margolin AA, Marin MG, Markowetz F, Markowski J, Marks J, Marques-Bonet T, Marra MA, Marsden L, Martens JWM, Martin S, Martin-Subero JI, Martincorena I, Martinez-Fundichely A, Maruvka YE, Mashl RJ, Massie CE, Matthew TJ, Matthews L, Mayer E, Mayes S, Mayo M, Mbabaali F, McCune K, McDermott U, McGillivray PD, McLellan MD, McPherson JD, McPherson JR, McPherson TA, Meier SR, Meng A, Meng S, Menzies A, Merrett ND, Merson S, Meyerson M, Meyerson W, Mieczkowski PA, Mihaiescu GL, Mijalkovic S, Mikkelsen T, Milella M, Mileshkin L, Miller CA, Miller DK, Miller JK, Mills GB, Milovanovic A, Minner S, Miotto M, Arnau GM, Mirabello L, Mitchell C, Mitchell TJ, Miyano S, Miyoshi N, Mizuno S, Molnár-Gábor F, Moore MJ, Moore RA, Morganella S, Morris QD, Morrison C, Mose LE, Moser CD, Muiños F, Mularoni L, Mungall AJ, Mungall K, Musgrove EA, Mustonen V, Mutch D, Muyas F, Muzny DM, Muñoz A, Myers J, Myklebost O, Möller P, Nagae G, Nagrial AM, Nahal-Bose HK, Nakagama H, Nakagawa H, Nakamura H, Nakamura T, Nakano K, Nandi T, Nangalia J, Nastic M, Navarro A, Navarro FCP, Neal DE, Nettekoven G, Newell F, Newhouse SJ, Newton Y, Ng AWT, Ng A, Nicholson J, Nicol D, Nie Y, Nielsen GP, Nielsen MM, Nik-Zainal S, Noble MS, Nones K, Northcott PA, Notta F, O’Connor BD, O’Donnell P, O’Donovan M, O’Meara S, O’Neill BP, O’Neill JR, Ocana D, Ochoa A, Oesper L, Ogden C, Ohdan H, Ohi K, Ohno-Machado L, Oien KA, Ojesina AI, Ojima H, Okusaka T, Omberg L, Ong CK, Ossowski S, Ott G, Ouellette BFF, P’ng C, Paczkowska M, Paiella S, Pairojkul C, Pajic M, Pan-Hammarström Q, Papaemmanuil E, Papatheodorou I, Paramasivam N, Park JW, Park JW, Park K, Park K, Park PJ, Parker JS, Parsons SL, Pass H, Pasternack D, Pastore A, Patch AM, Pauporté I, Pea A, Pearson JV, Pedamallu CS, Pedersen JS, Pederzoli P, Peifer M, Pennell NA, Perou CM, Perry MD, Petersen GM, Peto M, Petrelli N, Petryszak R, Pfister SM, Phillips M, Pich O, Pickett HA, Pihl TD, Pillay N, Pinder S, Pinese M, Pinho AV. Author Correction: The evolutionary history of 2,658 cancers. Nature 2023; 614:E42. [PMID: 36697833 PMCID: PMC9931577 DOI: 10.1038/s41586-022-05601-4] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/26/2023]
Affiliation(s)
- Moritz Gerstung
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Cambridge, UK. .,European Molecular Biology Laboratory, Genome Biology Unit, Heidelberg, Germany. .,Wellcome Sanger Institute, Cambridge, UK.
| | - Clemency Jolly
- grid.451388.30000 0004 1795 1830The Francis Crick Institute, London, UK
| | - Ignaty Leshchiner
- grid.66859.340000 0004 0546 1623Broad Institute of MIT and Harvard, Cambridge, MA USA
| | - Stefan C. Dentro
- grid.10306.340000 0004 0606 5382Wellcome Sanger Institute, Cambridge, UK ,grid.451388.30000 0004 1795 1830The Francis Crick Institute, London, UK ,grid.4991.50000 0004 1936 8948Big Data Institute, University of Oxford, Oxford, UK
| | - Santiago Gonzalez
- grid.225360.00000 0000 9709 7726European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Cambridge, UK
| | - Daniel Rosebrock
- grid.66859.340000 0004 0546 1623Broad Institute of MIT and Harvard, Cambridge, MA USA
| | - Thomas J. Mitchell
- grid.10306.340000 0004 0606 5382Wellcome Sanger Institute, Cambridge, UK ,grid.5335.00000000121885934University of Cambridge, Cambridge, UK
| | - Yulia Rubanova
- grid.17063.330000 0001 2157 2938University of Toronto, Toronto, Ontario Canada ,grid.494618.6Vector Institute, Toronto, Ontario Canada
| | - Pavana Anur
- grid.5288.70000 0000 9758 5690Molecular and Medical Genetics, Oregon Health & Science University, Portland, OR USA
| | - Kaixian Yu
- grid.240145.60000 0001 2291 4776The University of Texas MD Anderson Cancer Center, Houston, TX USA
| | - Maxime Tarabichi
- grid.10306.340000 0004 0606 5382Wellcome Sanger Institute, Cambridge, UK ,grid.451388.30000 0004 1795 1830The Francis Crick Institute, London, UK
| | - Amit Deshwar
- grid.17063.330000 0001 2157 2938University of Toronto, Toronto, Ontario Canada ,grid.494618.6Vector Institute, Toronto, Ontario Canada
| | - Jeff Wintersinger
- grid.17063.330000 0001 2157 2938University of Toronto, Toronto, Ontario Canada ,grid.494618.6Vector Institute, Toronto, Ontario Canada
| | - Kortine Kleinheinz
- grid.7497.d0000 0004 0492 0584German Cancer Research Center (DKFZ), Heidelberg, Germany ,grid.7700.00000 0001 2190 4373Heidelberg University, Heidelberg, Germany
| | - Ignacio Vázquez-García
- grid.10306.340000 0004 0606 5382Wellcome Sanger Institute, Cambridge, UK ,grid.5335.00000000121885934University of Cambridge, Cambridge, UK
| | - Kerstin Haase
- grid.451388.30000 0004 1795 1830The Francis Crick Institute, London, UK
| | - Lara Jerman
- grid.225360.00000 0000 9709 7726European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Cambridge, UK ,grid.8954.00000 0001 0721 6013University of Ljubljana, Ljubljana, Slovenia
| | - Subhajit Sengupta
- grid.240372.00000 0004 0400 4439NorthShore University HealthSystem, Evanston, IL USA
| | - Geoff Macintyre
- grid.5335.00000000121885934Cancer Research UK Cambridge Institute, University of Cambridge, Cambridge, UK
| | - Salem Malikic
- grid.61971.380000 0004 1936 7494Simon Fraser University, Burnaby, British Columbia Canada ,grid.412541.70000 0001 0684 7796Vancouver Prostate Centre, Vancouver, British Columbia Canada
| | - Nilgun Donmez
- grid.61971.380000 0004 1936 7494Simon Fraser University, Burnaby, British Columbia Canada ,grid.412541.70000 0001 0684 7796Vancouver Prostate Centre, Vancouver, British Columbia Canada
| | - Dimitri G. Livitz
- grid.66859.340000 0004 0546 1623Broad Institute of MIT and Harvard, Cambridge, MA USA
| | - Marek Cmero
- grid.1008.90000 0001 2179 088XUniversity of Melbourne, Melbourne, Victoria Australia ,grid.1042.70000 0004 0432 4889Walter and Eliza Hall Institute, Melbourne, Victoria Australia
| | - Jonas Demeulemeester
- grid.451388.30000 0004 1795 1830The Francis Crick Institute, London, UK ,grid.5596.f0000 0001 0668 7884University of Leuven, Leuven, Belgium
| | - Steven Schumacher
- grid.66859.340000 0004 0546 1623Broad Institute of MIT and Harvard, Cambridge, MA USA
| | - Yu Fan
- grid.240145.60000 0001 2291 4776The University of Texas MD Anderson Cancer Center, Houston, TX USA
| | - Xiaotong Yao
- grid.5386.8000000041936877XWeill Cornell Medicine, New York, NY USA ,grid.429884.b0000 0004 1791 0895New York Genome Center, New York, NY USA
| | - Juhee Lee
- grid.205975.c0000 0001 0740 6917University of California Santa Cruz, Santa Cruz, CA USA
| | - Matthias Schlesner
- grid.7497.d0000 0004 0492 0584German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Paul C. Boutros
- grid.17063.330000 0001 2157 2938University of Toronto, Toronto, Ontario Canada ,grid.419890.d0000 0004 0626 690XOntario Institute for Cancer Research, Toronto, Ontario Canada ,grid.19006.3e0000 0000 9632 6718University of California, Los Angeles, CA USA
| | - David D. Bowtell
- grid.1055.10000000403978434Peter MacCallum Cancer Centre, Melbourne, Victoria Australia
| | - Hongtu Zhu
- grid.240145.60000 0001 2291 4776The University of Texas MD Anderson Cancer Center, Houston, TX USA
| | - Gad Getz
- grid.66859.340000 0004 0546 1623Broad Institute of MIT and Harvard, Cambridge, MA USA ,grid.32224.350000 0004 0386 9924Center for Cancer Research, Massachusetts General Hospital, Charlestown, MA USA ,grid.32224.350000 0004 0386 9924Department of Pathology, Massachusetts General Hospital, Boston, MA USA ,grid.38142.3c000000041936754XHarvard Medical School, Boston, MA USA
| | - Marcin Imielinski
- grid.5386.8000000041936877XWeill Cornell Medicine, New York, NY USA ,grid.429884.b0000 0004 1791 0895New York Genome Center, New York, NY USA
| | - Rameen Beroukhim
- grid.66859.340000 0004 0546 1623Broad Institute of MIT and Harvard, Cambridge, MA USA ,grid.65499.370000 0001 2106 9910Dana-Farber Cancer Institute, Boston, MA USA
| | - S. Cenk Sahinalp
- grid.412541.70000 0001 0684 7796Vancouver Prostate Centre, Vancouver, British Columbia Canada ,grid.411377.70000 0001 0790 959XIndiana University, Bloomington, IN USA
| | - Yuan Ji
- grid.240372.00000 0004 0400 4439NorthShore University HealthSystem, Evanston, IL USA ,grid.170205.10000 0004 1936 7822The University of Chicago, Chicago, IL USA
| | - Martin Peifer
- grid.6190.e0000 0000 8580 3777University of Cologne, Cologne, Germany
| | - Florian Markowetz
- grid.5335.00000000121885934Cancer Research UK Cambridge Institute, University of Cambridge, Cambridge, UK
| | - Ville Mustonen
- grid.7737.40000 0004 0410 2071University of Helsinki, Helsinki, Finland
| | - Ke Yuan
- grid.5335.00000000121885934Cancer Research UK Cambridge Institute, University of Cambridge, Cambridge, UK ,grid.8756.c0000 0001 2193 314XUniversity of Glasgow, Glasgow, UK
| | - Wenyi Wang
- grid.240145.60000 0001 2291 4776The University of Texas MD Anderson Cancer Center, Houston, TX USA
| | - Quaid D. Morris
- grid.17063.330000 0001 2157 2938University of Toronto, Toronto, Ontario Canada ,grid.494618.6Vector Institute, Toronto, Ontario Canada
| | | | - Paul T. Spellman
- grid.5288.70000 0000 9758 5690Molecular and Medical Genetics, Oregon Health & Science University, Portland, OR USA
| | - David C. Wedge
- grid.4991.50000 0004 1936 8948Big Data Institute, University of Oxford, Oxford, UK ,grid.454382.c0000 0004 7871 7212Oxford NIHR Biomedical Research Centre, Oxford, UK
| | - Peter Van Loo
- The Francis Crick Institute, London, UK. .,University of Leuven, Leuven, Belgium.
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Calabrese C, Davidson NR, Demircioğlu D, Fonseca NA, He Y, Kahles A, Lehmann KV, Liu F, Shiraishi Y, Soulette CM, Urban L, Greger L, Li S, Liu D, Perry MD, Xiang Q, Zhang F, Zhang J, Bailey P, Erkek S, Hoadley KA, Hou Y, Huska MR, Kilpinen H, Korbel JO, Marin MG, Markowski J, Nandi T, Pan-Hammarström Q, Pedamallu CS, Siebert R, Stark SG, Su H, Tan P, Waszak SM, Yung C, Zhu S, Awadalla P, Creighton CJ, Meyerson M, Ouellette BFF, Wu K, Yang H, Brazma A, Brooks AN, Göke J, Rätsch G, Schwarz RF, Stegle O, Zhang Z, Wu K, Yang H, Fonseca NA, Kahles A, Lehmann KV, Urban L, Soulette CM, Shiraishi Y, Liu F, He Y, Demircioğlu D, Davidson NR, Calabrese C, Zhang J, Perry MD, Xiang Q, Greger L, Li S, Liu D, Stark SG, Zhang F, Amin SB, Bailey P, Chateigner A, Cortés-Ciriano I, Craft B, Erkek S, Frenkel-Morgenstern M, Goldman M, Hoadley KA, Hou Y, Huska MR, Khurana E, Kilpinen H, Korbel JO, Lamaze FC, Li C, Li X, Li X, Liu X, Marin MG, Markowski J, Nandi T, Nielsen MM, Ojesina AI, Pan-Hammarström Q, Park PJ, Pedamallu CS, Pedersen JS, Pederzoli P, Peifer M, Pennell NA, Perou CM, Perry MD, Petersen GM, Peto M, Petrelli N, Pedamallu CS, Petryszak R, Pfister SM, Phillips M, Pich O, Pickett HA, Pihl TD, Pillay N, Pinder S, Pinese M, Pinho AV, Pedersen JS, Pitkänen E, Pivot X, Piñeiro-Yáñez E, Planko L, Plass C, Polak P, Pons T, Popescu I, Potapova O, Prasad A, Siebert R, Preston SR, Prinz M, Pritchard AL, Prokopec SD, Provenzano E, Puente XS, Puig S, Puiggròs M, Pulido-Tamayo S, Pupo GM, Su H, Purdie CA, Quinn MC, Rabionet R, Rader JS, Radlwimmer B, Radovic P, Raeder B, Raine KM, Ramakrishna M, Ramakrishnan K, Tan P, Ramalingam S, Raphael BJ, Rathmell WK, Rausch T, Reifenberger G, Reimand J, Reis-Filho J, Reuter V, Reyes-Salazar I, Reyna MA, Teh BT, Reynolds SM, Rheinbay E, Riazalhosseini Y, Richardson AL, Richter J, Ringel M, Ringnér M, Rino Y, Rippe K, Roach J, Wang J, Roberts LR, Roberts ND, Roberts SA, Robertson AG, Robertson AJ, Rodriguez JB, Rodriguez-Martin B, Rodríguez-González FG, Roehrl MHA, Rohde M, Waszak SM, Rokutan H, Romieu G, Rooman I, Roques T, Rosebrock D, Rosenberg M, Rosenstiel PC, Rosenwald A, Rowe EW, Royo R, Xiong H, Rozen SG, Rubanova Y, Rubin MA, Rubio-Perez C, Rudneva VA, Rusev BC, Ruzzenente A, Rätsch G, Sabarinathan R, Sabelnykova VY, Yakneen S, Sadeghi S, Sahinalp SC, Saini N, Saito-Adachi M, Saksena G, Salcedo A, Salgado R, Salichos L, Sallari R, Saller C, Ye C, Salvia R, Sam M, Samra JS, Sanchez-Vega F, Sander C, Sanders G, Sarin R, Sarrafi I, Sasaki-Oku A, Sauer T, Yung C, Sauter G, Saw RPM, Scardoni M, Scarlett CJ, Scarpa A, Scelo G, Schadendorf D, Schein JE, Schilhabel MB, Schlesner M, Zhang X, Schlomm T, Schmidt HK, Schramm SJ, Schreiber S, Schultz N, Schumacher SE, Schwarz RF, Scolyer RA, Scott D, Scully R, Zheng L, Seethala R, Segre AV, Selander I, Semple CA, Senbabaoglu Y, Sengupta S, Sereni E, Serra S, Sgroi DC, Shackleton M, Zhu J, Shah NC, Shahabi S, Shang CA, Shang P, Shapira O, Shelton T, Shen C, Shen H, Shepherd R, Shi R, Zhu S, Shi Y, Shiah YJ, Shibata T, Shih J, Shimizu E, Shimizu K, Shin SJ, Shiraishi Y, Shmaya T, Shmulevich I, Awadalla P, Shorser SI, Short C, Shrestha R, Shringarpure SS, Shriver C, Shuai S, Sidiropoulos N, Siebert R, Sieuwerts AM, Sieverling L, Creighton CJ, Signoretti S, Sikora KO, Simbolo M, Simon R, Simons JV, Simpson JT, Simpson PT, Singer S, Sinnott-Armstrong N, Sipahimalani P, Meyerson M, Skelly TJ, Smid M, Smith J, Smith-McCune K, Socci ND, Sofia HJ, Soloway MG, Song L, Sood AK, Sothi S, Ouellette BFF, Sotiriou C, Soulette CM, Span PN, Spellman PT, Sperandio N, Spillane AJ, Spiro O, Spring J, Staaf J, Stadler PF, Wu K, Staib P, Stark SG, Stebbings L, Stefánsson ÓA, Stegle O, Stein LD, Stenhouse A, Stewart C, Stilgenbauer S, Stobbe MD, Yang H, Stratton MR, Stretch JR, Struck AJ, Stuart JM, Stunnenberg HG, Su H, Su X, Sun RX, Sungalee S, Susak H, Göke J, Suzuki A, Sweep F, Szczepanowski M, Sültmann H, Yugawa T, Tam A, Tamborero D, Tan BKT, Tan D, Tan P, Schwarz RF, Tanaka H, Taniguchi H, Tanskanen TJ, Tarabichi M, Tarnuzzer R, Tarpey P, Taschuk ML, Tatsuno K, Tavaré S, Taylor DF, Stegle O, Taylor-Weiner A, Teague JW, Teh BT, Tembe V, Temes J, Thai K, Thayer SP, Thiessen N, Thomas G, Thomas S, Zhang Z, Thompson A, Thompson AM, Thompson JFF, Thompson RH, Thorne H, Thorne LB, Thorogood A, Tiao G, Tijanic N, Timms LE, Brazma A, Tirabosco R, Tojo M, Tommasi S, Toon CW, Toprak UH, Torrents D, Tortora G, Tost J, Totoki Y, Townend D, Rätsch G, Traficante N, Treilleux I, Trotta JR, Trümper LHP, Tsao M, Tsunoda T, Tubio JMC, Tucker O, Turkington R, Turner DJ, Brooks AN, Tutt A, Ueno M, Ueno NT, Umbricht C, Umer HM, Underwood TJ, Urban L, Urushidate T, Ushiku T, Uusküla-Reimand L, Brazma A, Valencia A, Van Den Berg DJ, Van Laere S, Van Loo P, Van Meir EG, Van den Eynden GG, Van der Kwast T, Vasudev N, Vazquez M, Vedururu R, Brooks AN, Veluvolu U, Vembu S, Verbeke LPC, Vermeulen P, Verrill C, Viari A, Vicente D, Vicentini C, VijayRaghavan K, Viksna J, Göke J, Vilain RE, Villasante I, Vincent-Salomon A, Visakorpi T, Voet D, Vyas P, Vázquez-García I, Waddell NM, Waddell N, Wadelius C, Rätsch G, Wadi L, Wagener R, Wala JA, Wang J, Wang J, Wang L, Wang Q, Wang W, Wang Y, Wang Z, Schwarz RF, Waring PM, Warnatz HJ, Warrell J, Warren AY, Waszak SM, Wedge DC, Weichenhan D, Weinberger P, Weinstein JN, Weischenfeldt J, Stegle O, Weisenberger DJ, Welch I, Wendl MC, Werner J, Whalley JP, Wheeler DA, Whitaker HC, Wigle D, Wilkerson MD, Williams A, Zhang Z, Wilmott JS, Wilson GW, Wilson JM, Wilson RK, Winterhoff B, Wintersinger JA, Wiznerowicz M, Wolf S, Wong BH, Wong T, Aaltonen LA, Wong W, Woo Y, Wood S, Wouters BG, Wright AJ, Wright DW, Wright MH, Wu CL, Wu DY, Wu G, Abascal F, Wu J, Wu K, Wu Y, Wu Z, Xi L, Xia T, Xiang Q, Xiao X, Xing R, Xiong H, Abeshouse A, Xu Q, Xu Y, Xue H, Yachida S, Yakneen S, Yamaguchi R, Yamaguchi TN, Yamamoto M, Yamamoto S, Yamaue H, Aburatani H, Yang F, Yang H, Yang JY, Yang L, Yang L, Yang S, Yang TP, Yang Y, Yao X, Yaspo ML, Adams DJ, Yates L, Yau C, Ye C, Ye K, Yellapantula VD, Yoon CJ, Yoon SS, Yousif F, Yu J, Yu K, Agrawal N, Yu W, Yu Y, Yuan K, Yuan Y, Yuen D, Yung CK, Zaikova O, Zamora J, Zapatka M, Zenklusen JC, Ahn KS, Zenz T, Zeps N, Zhang CZ, Zhang F, Zhang H, Zhang H, Zhang H, Zhang J, Zhang J, Zhang J, Ahn SM, Zhang X, Zhang X, Zhang Y, Zhang Z, Zhao Z, Zheng L, Zheng X, Zhou W, Zhou Y, Zhu B, Aikata H, Zhu H, Zhu J, Zhu S, Zou L, Zou X, deFazio A, van As N, van Deurzen CHM, van de Vijver MJ, van’t Veer L, Akbani R, von Mering C, Akdemir KC, Al-Ahmadie H, Al-Sedairy ST, Al-Shahrour F, Alawi M, Albert M, Aldape K, Alexandrov LB, Ally A, Alsop K, Alvarez EG, Amary F, Amin SB, Aminou B, Ammerpohl O, Anderson MJ, Ang Y, Antonello D, Anur P, Aparicio S, Appelbaum EL, Arai Y, Aretz A, Arihiro K, Ariizumi SI, Armenia J, Arnould L, Asa S, Assenov Y, Atwal G, Aukema S, Auman JT, Aure MRR, Awadalla P, Aymerich M, Bader GD, Baez-Ortega A, Bailey MH, Bailey PJ, Balasundaram M, Balu S, Bandopadhayay P, Banks RE, Barbi S, Barbour AP, Barenboim J, Barnholtz-Sloan J, Barr H, Barrera E, Bartlett J, Bartolome J, Bassi C, Bathe OF, Baumhoer D, Bavi P, Baylin SB, Bazant W, Beardsmore D, Beck TA, Behjati S, Behren A, Niu B, Bell C, Beltran S, Benz C, Berchuck A, Bergmann AK, Bergstrom EN, Berman BP, Berney DM, Bernhart SH, Beroukhim R, Berrios M, Bersani S, Bertl J, Betancourt M, Bhandari V, Bhosle SG, Biankin AV, Bieg M, Bigner D, Binder H, Birney E, Birrer M, Biswas NK, Bjerkehagen B, Bodenheimer T, Boice L, Bonizzato G, De Bono JS, Boot A, Bootwalla MS, Borg A, Borkhardt A, Boroevich KA, Borozan I, Borst C, Bosenberg M, Bosio M, Boultwood J, Bourque G, Boutros PC, Bova GS, Bowen DT, Bowlby R, Bowtell DDL, Boyault S, Boyce R, Boyd J, Brazma A, Brennan P, Brewer DS, Brinkman AB, Bristow RG, Broaddus RR, Brock JE, Brock M, Broeks A, Brooks AN, Brooks D, Brors B, Brunak S, Bruxner TJC, Bruzos AL, Buchanan A, Buchhalter I, Buchholz C, Bullman S, Burke H, Burkhardt B, Burns KH, Busanovich J, Bustamante CD, Butler AP, Butte AJ, Byrne NJ, Børresen-Dale AL, Caesar-Johnson SJ, Cafferkey A, Cahill D, Calabrese C, Caldas C, Calvo F, Camacho N, Campbell PJ, Campo E, Cantù C, Cao S, Carey TE, Carlevaro-Fita J, Carlsen R, Cataldo I, Cazzola M, Cebon J, Cerfolio R, Chadwick DE, Chakravarty D, Chalmers D, Chan CWY, Chan K, Chan-Seng-Yue M, Chandan VS, Chang DK, Chanock SJ, Chantrill LA, Chateigner A, Chatterjee N, Chayama K, Chen HW, Chen J, Chen K, Chen Y, Chen Z, Cherniack AD, Chien J, Chiew YE, Chin SF, Cho J, Cho S, Choi JK, Choi W, Chomienne C, Chong Z, Choo SP, Chou A, Christ AN, Christie EL, Chuah E, Cibulskis C, Cibulskis K, Cingarlini S, Clapham P, Claviez A, Cleary S, Cloonan N, Cmero M, Collins CC, Connor AA, Cooke SL, Cooper CS, Cope L, Corbo V, Cordes MG, Cordner SM, Cortés-Ciriano I, Covington K, Cowin PA, Craft B, Craft D, Creighton CJ, Cun Y, Curley E, Cutcutache I, Czajka K, Czerniak B, Dagg RA, Danilova L, Davi MV, Davidson NR, Davies H, Davis IJ, Davis-Dusenbery BN, Dawson KJ, De La Vega FM, De Paoli-Iseppi R, Defreitas T, Tos APD, Delaneau O, Demchok JA, Demeulemeester J, Demidov GM, Demircioğlu D, Dennis NM, Denroche RE, Dentro SC, Desai N, Deshpande V, Deshwar AG, Desmedt C, Deu-Pons J, Dhalla N, Dhani NC, Dhingra P, Dhir R, DiBiase A, Diamanti K, Ding L, Ding S, Dinh HQ, Dirix L, Doddapaneni H, Donmez N, Dow MT, Drapkin R, Drechsel O, Drews RM, Serge S, Dudderidge T, Dueso-Barroso A, Dunford AJ, Dunn M, Dursi LJ, Duthie FR, Dutton-Regester K, Eagles J, Easton DF, Edmonds S, Edwards PA, Edwards SE, Eeles RA, Ehinger A, Eils J, Eils R, El-Naggar A, Eldridge M, Ellrott K, Erkek S, Escaramis G, Espiritu SMG, Estivill X, Etemadmoghadam D, Eyfjord JE, Faltas BM, Fan D, Fan Y, Faquin WC, Farcas C, Fassan M, Fatima A, Favero F, Fayzullaev N, Felau I, Fereday S, Ferguson ML, Ferretti V, Feuerbach L, Field MA, Fink JL, Finocchiaro G, Fisher C, Fittall MW, Fitzgerald A, Fitzgerald RC, Flanagan AM, Fleshner NE, Flicek P, Foekens JA, Fong KM, Fonseca NA, Foster CS, Fox NS, Fraser M, Frazer S, Frenkel-Morgenstern M, Friedman W, Frigola J, Fronick CC, Fujimoto A, Fujita M, Fukayama M, Fulton LA, Fulton RS, Furuta M, Futreal PA, Füllgrabe A, Gabriel SB, Gallinger S, Gambacorti-Passerini C, Gao J, Gao S, Garraway L, Garred Ø, Garrison E, Garsed DW, Gehlenborg N, Gelpi JLL, George J, Gerhard DS, Gerhauser C, Gershenwald JE, Gerstein M, Gerstung M, Getz G, Ghori M, Ghossein R, Giama NH, Gibbs RA, Gibson B, Gill AJ, Gill P, Giri DD, Glodzik D, Gnanapragasam VJ, Goebler ME, Goldman MJ, Gomez C, Gonzalez S, Gonzalez-Perez A, Gordenin DA, Gossage J, Gotoh K, Govindan R, Grabau D, Graham JS, Grant RC, Green AR, Green E, Greger L, Grehan N, Grimaldi S, Grimmond SM, Grossman RL, Grundhoff A, Gundem G, Guo Q, Gupta M, Gupta S, Gut IG, Gut M, Göke J, Ha G, Haake A, Haan D, Haas S, Haase K, Haber JE, Habermann N, Hach F, Haider S, Hama N, Hamdy FC, Hamilton A, Hamilton MP, Han L, Hanna GB, Hansmann M, Haradhvala NJ, Harismendy O, Harliwong I, Harmanci AO, Harrington E, Hasegawa T, Haussler D, Hawkins S, Hayami S, Hayashi S, Hayes DN, Hayes SJ, Hayward NK, Hazell S, He Y, Heath AP, Heath SC, Hedley D, Hegde AM, Heiman DI, Heinold MC, Heins Z, Heisler LE, Hellstrom-Lindberg E, Helmy M, Heo SG, Hepperla AJ, Heredia-Genestar JM, Herrmann C, Hersey P, Hess JM, Hilmarsdottir H, Hinton J, Hirano S, Hiraoka N, Hoadley KA, Hobolth A, Hodzic E, Hoell JI, Hoffmann S, Hofmann O, Holbrook A, Holik AZ, Hollingsworth MA, Holmes O, Holt RA, Hong C, Hong EP, Hong JH, Hooijer GK, Hornshøj H, Hosoda F, Hou Y, Hovestadt V, Howat W, Hoyle AP, Hruban RH, Hu J, Hu T, Hua X, Huang KL, Huang M, Huang MN, Huang V, Huang Y, Huber W, Hudson TJ, Hummel M, Hung JA, Huntsman D, Hupp TR, Huse J, Huska MR, Hutter B, Hutter CM, Hübschmann D, Iacobuzio-Donahue CA, Imbusch CD, Imielinski M, Imoto S, Isaacs WB, Isaev K, Ishikawa S, Iskar M, Islam SMA, Ittmann M, Ivkovic S, Izarzugaza JMG, Jacquemier J, Jakrot V, Jamieson NB, Jang GH, Jang SJ, Jayaseelan JC, Jayasinghe R, Jefferys SR, Jegalian K, Jennings JL, Jeon SH, Jerman L, Ji Y, Jiao W, Johansson PA, Johns AL, Johns J, Johnson R, Johnson TA, Jolly C, Joly Y, Jonasson JG, Jones CD, Jones DR, Jones DTW, Jones N, Jones SJM, Jonkers J, Ju YS, Juhl H, Jung J, Juul M, Juul RI, Juul S, Jäger N, Kabbe R, Kahles A, Kahraman A, Kaiser VB, Kakavand H, Kalimuthu S, von Kalle C, Kang KJ, Karaszi K, Karlan B, Karlić R, Karsch D, Kasaian K, Kassahn KS, Katai H, Kato M, Katoh H, Kawakami Y, Kay JD, Kazakoff SH, Kazanov MD, Keays M, Kebebew E, Kefford RF, Kellis M, Kench JG, Kennedy CJ, Kerssemakers JNA, Khoo D, Khoo V, Khuntikeo N, Khurana E, Kilpinen H, Kim HK, Kim HL, Kim HY, Kim H, Kim J, Kim J, Kim JK, Kim Y, King TA, Klapper W, Kleinheinz K, Klimczak LJ, Knappskog S, Kneba M, Knoppers BM, Koh Y, Komorowski J, Komura D, Komura M, Kong G, Kool M, Korbel JO, Korchina V, Korshunov A, Koscher M, Koster R, Kote-Jarai Z, Koures A, Kovacevic M, Kremeyer B, Kretzmer H, Kreuz M, Krishnamurthy S, Kube D, Kumar K, Kumar P, Kumar S, Kumar Y, Kundra R, Kübler K, Küppers R, Lagergren J, Lai PH, Laird PW, Lakhani SR, Lalansingh CM, Lalonde E, Lamaze FC, Lambert A, Lander E, Landgraf P, Landoni L, Langerød A, Lanzós A, Larsimont D, Larsson E, Lathrop M, Lau LMS, Lawerenz C, Lawlor RT, Lawrence MS, Lazar AJ, Lazic AM, Le X, Lee D, Lee D, Lee EA, Lee HJ, Lee JJK, Lee JY, Lee J, Lee MTM, Lee-Six H, Lehmann KV, Lehrach H, Lenze D, Leonard CR, Leongamornlert DA, Leshchiner I, Letourneau L, Letunic I, Levine DA, Lewis L, Ley T, Li C, Li CH, Li HI, Li J, Li L, Li S, Li S, Li X, Li X, Li X, Li Y, Liang H, Liang SB, Lichter P, Lin P, Lin Z, Linehan WM, Lingjærde OC, Liu D, Liu EM, Liu FFF, Liu F, Liu J, Liu X, Livingstone J, Livitz D, Livni N, Lochovsky L, Loeffler M, Long GV, Lopez-Guillermo A, Lou S, Louis DN, Lovat LB, Lu Y, Lu YJ, Lu Y, Luchini C, Lungu I, Luo X, Luxton HJ, Lynch AG, Lype L, López C, López-Otín C, Ma EZ, Ma Y, MacGrogan G, MacRae S, Macintyre G, Madsen T, Maejima K, Mafficini A, Maglinte DT, Maitra A, Majumder PP, Malcovati L, Malikic S, Malleo G, Mann GJ, Mantovani-Löffler L, Marchal K, Marchegiani G, Mardis ER, Margolin AA, Marin MG, Markowetz F, Markowski J, Marks J, Marques-Bonet T, Marra MA, Marsden L, Martens JWM, Martin S, Martin-Subero JI, Martincorena I, Martinez-Fundichely A, Maruvka YE, Mashl RJ, Massie CE, Matthew TJ, Matthews L, Mayer E, Mayes S, Mayo M, Mbabaali F, McCune K, McDermott U, McGillivray PD, McLellan MD, McPherson JD, McPherson JR, McPherson TA, Meier SR, Meng A, Meng S, Menzies A, Merrett ND, Merson S, Meyerson M, Meyerson W, Mieczkowski PA, Mihaiescu GL, Mijalkovic S, Mikkelsen T, Milella M, Mileshkin L, Miller CA, Miller DK, Miller JK, Mills GB, Milovanovic A, Minner S, Miotto M, Arnau GM, Mirabello L, Mitchell C, Mitchell TJ, Miyano S, Miyoshi N, Mizuno S, Molnár-Gábor F, Moore MJ, Moore RA, Morganella S, Morris QD, Morrison C, Mose LE, Moser CD, Muiños F, Mularoni L, Mungall AJ, Mungall K, Musgrove EA, Mustonen V, Mutch D, Muyas F, Muzny DM, Muñoz A, Myers J, Myklebost O, Möller P, Nagae G, Nagrial AM, Nahal-Bose HK, Nakagama H, Nakagawa H, Nakamura H, Nakamura T, Nakano K, Nandi T, Nangalia J, Nastic M, Navarro A, Navarro FCP, Neal DE, Nettekoven G, Newell F, Newhouse SJ, Newton Y, Ng AWT, Ng A, Nicholson J, Nicol D, Nie Y, Nielsen GP, Nielsen MM, Nik-Zainal S, Noble MS, Nones K, Northcott PA, Notta F, O’Connor BD, O’Donnell P, O’Donovan M, O’Meara S, O’Neill BP, O’Neill JR, Ocana D, Ochoa A, Oesper L, Ogden C, Ohdan H, Ohi K, Ohno-Machado L, Oien KA, Ojesina AI, Ojima H, Okusaka T, Omberg L, Ong CK, Ossowski S, Ott G, Ouellette BFF, P’ng C, Paczkowska M, Paiella S, Pairojkul C, Pajic M, Pan-Hammarström Q, Papaemmanuil E, Papatheodorou I, Paramasivam N, Park JW, Park JW, Park K, Park K, Park PJ, Parker JS, Parsons SL, Pass H, Pasternack D, Pastore A, Patch AM, Pauporté I, Pea A, Pearson JV. Author Correction: Genomic basis for RNA alterations in cancer. Nature 2023; 614:E37. [PMID: 36697831 PMCID: PMC9931574 DOI: 10.1038/s41586-022-05596-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/26/2023]
Affiliation(s)
| | - Claudia Calabrese
- grid.225360.00000 0000 9709 7726European Molecular Biology Laboratory, European Bioinformatics Institute, Hinxton, UK
| | - Natalie R. Davidson
- grid.5801.c0000 0001 2156 2780ETH Zurich, Zurich, Switzerland ,grid.51462.340000 0001 2171 9952Memorial Sloan Kettering Cancer Center, New York, NY USA ,grid.5386.8000000041936877XWeill Cornell Medical College, New York, NY USA ,grid.419765.80000 0001 2223 3006SIB Swiss Institute of Bioinformatics, Lausanne, Switzerland ,grid.412004.30000 0004 0478 9977University Hospital Zurich, Zurich, Switzerland
| | - Deniz Demircioğlu
- grid.4280.e0000 0001 2180 6431National University of Singapore, Singapore, Singapore ,grid.418377.e0000 0004 0620 715XGenome Institute of Singapore, Singapore, Singapore
| | - Nuno A. Fonseca
- grid.225360.00000 0000 9709 7726European Molecular Biology Laboratory, European Bioinformatics Institute, Hinxton, UK
| | - Yao He
- grid.11135.370000 0001 2256 9319Peking University, Beijing, China
| | - André Kahles
- grid.5801.c0000 0001 2156 2780ETH Zurich, Zurich, Switzerland ,grid.51462.340000 0001 2171 9952Memorial Sloan Kettering Cancer Center, New York, NY USA ,grid.419765.80000 0001 2223 3006SIB Swiss Institute of Bioinformatics, Lausanne, Switzerland ,grid.412004.30000 0004 0478 9977University Hospital Zurich, Zurich, Switzerland
| | - Kjong-Van Lehmann
- grid.5801.c0000 0001 2156 2780ETH Zurich, Zurich, Switzerland ,grid.51462.340000 0001 2171 9952Memorial Sloan Kettering Cancer Center, New York, NY USA ,grid.419765.80000 0001 2223 3006SIB Swiss Institute of Bioinformatics, Lausanne, Switzerland ,grid.412004.30000 0004 0478 9977University Hospital Zurich, Zurich, Switzerland
| | - Fenglin Liu
- grid.11135.370000 0001 2256 9319Peking University, Beijing, China
| | - Yuichi Shiraishi
- grid.26999.3d0000 0001 2151 536XThe University of Tokyo, Minato-ku, Japan
| | - Cameron M. Soulette
- grid.205975.c0000 0001 0740 6917University of California, Santa Cruz, Santa Cruz, CA USA
| | - Lara Urban
- grid.225360.00000 0000 9709 7726European Molecular Biology Laboratory, European Bioinformatics Institute, Hinxton, UK
| | - Liliana Greger
- grid.225360.00000 0000 9709 7726European Molecular Biology Laboratory, European Bioinformatics Institute, Hinxton, UK
| | - Siliang Li
- grid.21155.320000 0001 2034 1839BGI-Shenzhen, Shenzhen, China ,grid.507779.b0000 0004 4910 5858China National GeneBank-Shenzhen, Shenzhen, China
| | - Dongbing Liu
- grid.21155.320000 0001 2034 1839BGI-Shenzhen, Shenzhen, China ,grid.507779.b0000 0004 4910 5858China National GeneBank-Shenzhen, Shenzhen, China
| | - Marc D. Perry
- grid.17063.330000 0001 2157 2938Ontario Institute for Cancer Research, Toronto, Ontario, Canada ,grid.266102.10000 0001 2297 6811University of California, San Francisco, San Francisco, CA USA
| | - Qian Xiang
- grid.17063.330000 0001 2157 2938Ontario Institute for Cancer Research, Toronto, Ontario, Canada
| | - Fan Zhang
- grid.11135.370000 0001 2256 9319Peking University, Beijing, China
| | - Junjun Zhang
- grid.17063.330000 0001 2157 2938Ontario Institute for Cancer Research, Toronto, Ontario, Canada
| | - Peter Bailey
- grid.8756.c0000 0001 2193 314XUniversity of Glasgow, Glasgow, UK
| | - Serap Erkek
- grid.4709.a0000 0004 0495 846XEuropean Molecular Biology Laboratory, Genome Biology Unit, Heidelberg, Germany
| | - Katherine A. Hoadley
- grid.10698.360000000122483208The University of North Carolina at Chapel Hill, Chapel Hill, NC USA
| | - Yong Hou
- grid.21155.320000 0001 2034 1839BGI-Shenzhen, Shenzhen, China ,grid.507779.b0000 0004 4910 5858China National GeneBank-Shenzhen, Shenzhen, China
| | - Matthew R. Huska
- grid.419491.00000 0001 1014 0849Berlin Institute for Medical Systems Biology, Max Delbruck Center for Molecular Medicine, Berlin, Germany
| | - Helena Kilpinen
- grid.83440.3b0000000121901201University College London, London, UK
| | - Jan O. Korbel
- grid.4709.a0000 0004 0495 846XEuropean Molecular Biology Laboratory, Genome Biology Unit, Heidelberg, Germany
| | - Maximillian G. Marin
- grid.205975.c0000 0001 0740 6917University of California, Santa Cruz, Santa Cruz, CA USA
| | - Julia Markowski
- grid.419491.00000 0001 1014 0849Berlin Institute for Medical Systems Biology, Max Delbruck Center for Molecular Medicine, Berlin, Germany
| | - Tannistha Nandi
- grid.418377.e0000 0004 0620 715XGenome Institute of Singapore, Singapore, Singapore
| | - Qiang Pan-Hammarström
- grid.21155.320000 0001 2034 1839BGI-Shenzhen, Shenzhen, China ,grid.4714.60000 0004 1937 0626Karolinska Institutet, Stockholm, Sweden
| | - Chandra Sekhar Pedamallu
- grid.66859.340000 0004 0546 1623Broad Institute, Cambridge, MA USA ,grid.65499.370000 0001 2106 9910Dana-Farber Cancer Institute, Boston, MA USA ,grid.38142.3c000000041936754XHarvard Medical School, Boston, MA USA
| | - Reiner Siebert
- grid.410712.10000 0004 0473 882XUlm University and Ulm University Medical Center, Ulm, Germany
| | - Stefan G. Stark
- grid.5801.c0000 0001 2156 2780ETH Zurich, Zurich, Switzerland ,grid.51462.340000 0001 2171 9952Memorial Sloan Kettering Cancer Center, New York, NY USA ,grid.419765.80000 0001 2223 3006SIB Swiss Institute of Bioinformatics, Lausanne, Switzerland ,grid.412004.30000 0004 0478 9977University Hospital Zurich, Zurich, Switzerland
| | - Hong Su
- grid.21155.320000 0001 2034 1839BGI-Shenzhen, Shenzhen, China ,grid.507779.b0000 0004 4910 5858China National GeneBank-Shenzhen, Shenzhen, China
| | - Patrick Tan
- grid.418377.e0000 0004 0620 715XGenome Institute of Singapore, Singapore, Singapore ,grid.428397.30000 0004 0385 0924Duke-NUS Medical School, Singapore, Singapore
| | - Sebastian M. Waszak
- grid.4709.a0000 0004 0495 846XEuropean Molecular Biology Laboratory, Genome Biology Unit, Heidelberg, Germany
| | - Christina Yung
- grid.17063.330000 0001 2157 2938Ontario Institute for Cancer Research, Toronto, Ontario, Canada
| | - Shida Zhu
- grid.21155.320000 0001 2034 1839BGI-Shenzhen, Shenzhen, China ,grid.507779.b0000 0004 4910 5858China National GeneBank-Shenzhen, Shenzhen, China
| | - Philip Awadalla
- grid.17063.330000 0001 2157 2938Ontario Institute for Cancer Research, Toronto, Ontario, Canada ,grid.17063.330000 0001 2157 2938University of Toronto, Toronto, Ontario Canada
| | - Chad J. Creighton
- grid.39382.330000 0001 2160 926XBaylor College of Medicine, Houston, TX USA
| | - Matthew Meyerson
- grid.66859.340000 0004 0546 1623Broad Institute, Cambridge, MA USA ,grid.65499.370000 0001 2106 9910Dana-Farber Cancer Institute, Boston, MA USA ,grid.38142.3c000000041936754XHarvard Medical School, Boston, MA USA
| | | | - Kui Wu
- grid.21155.320000 0001 2034 1839BGI-Shenzhen, Shenzhen, China ,grid.507779.b0000 0004 4910 5858China National GeneBank-Shenzhen, Shenzhen, China
| | - Huanming Yang
- grid.21155.320000 0001 2034 1839BGI-Shenzhen, Shenzhen, China
| | | | - Alvis Brazma
- European Molecular Biology Laboratory, European Bioinformatics Institute, Hinxton, UK.
| | - Angela N. Brooks
- grid.205975.c0000 0001 0740 6917University of California, Santa Cruz, Santa Cruz, CA USA ,grid.66859.340000 0004 0546 1623Broad Institute, Cambridge, MA USA ,grid.65499.370000 0001 2106 9910Dana-Farber Cancer Institute, Boston, MA USA
| | - Jonathan Göke
- grid.418377.e0000 0004 0620 715XGenome Institute of Singapore, Singapore, Singapore ,grid.410724.40000 0004 0620 9745National Cancer Centre Singapore, Singapore, Singapore
| | - Gunnar Rätsch
- ETH Zurich, Zurich, Switzerland. .,Memorial Sloan Kettering Cancer Center, New York, NY, USA. .,Weill Cornell Medical College, New York, NY, USA. .,SIB Swiss Institute of Bioinformatics, Lausanne, Switzerland. .,University Hospital Zurich, Zurich, Switzerland.
| | - Roland F. Schwarz
- grid.225360.00000 0000 9709 7726European Molecular Biology Laboratory, European Bioinformatics Institute, Hinxton, UK ,grid.419491.00000 0001 1014 0849Berlin Institute for Medical Systems Biology, Max Delbruck Center for Molecular Medicine, Berlin, Germany ,grid.7497.d0000 0004 0492 0584German Cancer Consortium (DKTK), partner site Berlin, Germany ,grid.7497.d0000 0004 0492 0584German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Oliver Stegle
- grid.225360.00000 0000 9709 7726European Molecular Biology Laboratory, European Bioinformatics Institute, Hinxton, UK ,grid.4709.a0000 0004 0495 846XEuropean Molecular Biology Laboratory, Genome Biology Unit, Heidelberg, Germany ,grid.7497.d0000 0004 0492 0584German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Zemin Zhang
- grid.11135.370000 0001 2256 9319Peking University, Beijing, China
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Bose S, Barroso M, Chheda MG, Clevers H, Elez E, Kaochar S, Kopetz SE, Li XN, Meric-Bernstam F, Meyer CA, Mou H, Naegle KM, Pera MF, Perova Z, Politi KA, Raphael BJ, Robson P, Sears RC, Tabernero J, Tuveson DA, Welm AL, Welm BE, Willey CD, Salnikow K, Chuang JH, Shen X. A path to translation: How 3D patient tumor avatars enable next generation precision oncology. Cancer Cell 2022; 40:1448-1453. [PMID: 36270276 PMCID: PMC10576652 DOI: 10.1016/j.ccell.2022.09.017] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/09/2023]
Abstract
3D patient tumor avatars (3D-PTAs) hold promise for next-generation precision medicine. Here, we describe the benefits and challenges of 3D-PTA technologies and necessary future steps to realize their potential for clinical decision making. 3D-PTAs require standardization criteria and prospective trials to establish clinical benefits. Innovative trial designs that combine omics and 3D-PTA readouts may lead to more accurate clinical predictors, and an integrated platform that combines diagnostic and therapeutic development will accelerate new treatments for patients with refractory disease.
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Affiliation(s)
- Shree Bose
- Department of Pharmacology and Cancer Biology, Duke University School of Medicine, Durham, NC 27708, USA
| | - Margarida Barroso
- Department of Molecular and Cellular Physiology, Albany Medical College, Albany, NY 12208, USA
| | - Milan G Chheda
- Department of Medicine, Washington University in St. Louis, St. Louis, MO, 63110 USA
| | - Hans Clevers
- Oncode Institute, Hubrecht Institute, Royal Netherlands Academy of Arts and Sciences and University Medical Center, Uppsalalaan 8, Utrecht, 3584 CT, Netherlands; Research and Early Development (pRED) of F. Hoffmann-La Roche Ltd, Roche Pharma, Basel, Switzerland
| | - Elena Elez
- Vall d'Hebron Hospital Campus and Institute of Oncology, International Oncology Bureau-Quiron, University of Vic-Central University of Catalonia, Barcelona, 08035 Spain
| | - Salma Kaochar
- Department of Medicine, Dan L Duncan Comprehensive Cancer Center, Baylor College of Medicine, Houston, TX 77030, USA
| | - Scott E Kopetz
- The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
| | - Xiao-Nan Li
- Ann & Robert H. Lurie Children's Hospital of Chicago, Northwestern University Feinberg School of Medicine, Chicago, IL 60611, USA; Robert H. Lurie Comprehensive Cancer Center of Northwestern University, Chicago, IL 60611, USA
| | - Funda Meric-Bernstam
- Department of Investigational Cancer Therapeutics, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
| | - Clifford A Meyer
- Department of Data Science, Dana-Farber Cancer Institute, Boston, MA 02215, USA; Center for Functional Cancer Epigenetics, Dana-Farber Cancer Institute, Boston, MA 02215, USA; Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA 02215, USA; Department of Pharmacology and Cancer Biology, Duke University School of Medicine, Durham, NC 27708, USA
| | - Haiwei Mou
- The Wistar Institute, Philadelphia, PA 19104, USA
| | - Kristen M Naegle
- Department of Biomedical Engineering and the Center for Public Health Genomics, University of Virginia, Charlottesville, VA 22903, USA
| | | | - Zinaida Perova
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridge, CB10 1SD, UK
| | - Katerina A Politi
- Departments of Pathology and Internal Medicine (Medical Oncology), Yale School of Medicine and Yale Cancer Center, New Haven, CT 06510, USA
| | - Benjamin J Raphael
- Department of Computer Science, Princeton University, Princeton, NJ 08540, USA
| | - Paul Robson
- The Jackson Laboratory for Genomic Medicine, Farmington, CT 06032, USA; Department of Genetics and Genome Sciences, University of Connecticut School of Medicine, Farmington, CT 06032, USA; Institute for Systems Genomics, University of Connecticut, Farmington, CT 06032, USA
| | - Rosalie C Sears
- Department of Medical and Molecular Genetics, Oregon Health & Science University, Portland, OR 97201, USA
| | - Josep Tabernero
- Vall d'Hebron Hospital Campus and Institute of Oncology, International Oncology Bureau-Quiron, University of Vic-Central University of Catalonia, Barcelona, 08035 Spain
| | - David A Tuveson
- Lustgarten Foundation Pancreatic Cancer Research Laboratory at Cold Spring Harbor Laboratory, Cold Spring Harbor, NY 11724, USA
| | - Alana L Welm
- Department of Oncological Sciences, Huntsman Cancer Institute, University of Utah, Salt Lake City, UT 84112, USA
| | - Bryan E Welm
- Department of Surgery, Huntsman Cancer Institute, University of Utah, Salt Lake City, UT 84112, USA
| | - Christopher D Willey
- Department of Radiation Oncology, The University of Alabama at Birmingham, Birmingham, AL 35294, USA
| | - Konstantin Salnikow
- Division of Cancer Biology, National Cancer Institute, NIH, Rockville, MD 20850, USA.
| | - Jeffrey H Chuang
- The Jackson Laboratory for Genomic Medicine, Farmington, CT 06032, USA; Department of Genetics and Genome Sciences, University of Connecticut School of Medicine, Farmington, CT 06032, USA; Institute for Systems Genomics, University of Connecticut, Farmington, CT 06032, USA.
| | - Xiling Shen
- Terasaki Institute for Biomedical Innovation, Los Angeles, CA 90024, USA.
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20
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Reyna MA, Haan D, Paczkowska M, Verbeke LPC, Vazquez M, Kahraman A, Pulido-Tamayo S, Barenboim J, Wadi L, Dhingra P, Shrestha R, Getz G, Lawrence MS, Pedersen JS, Rubin MA, Wheeler DA, Brunak S, Izarzugaza JMG, Khurana E, Marchal K, von Mering C, Sahinalp SC, Valencia A, Reimand J, Stuart JM, Raphael BJ. Author Correction: Pathway and network analysis of more than 2500 whole cancer genomes. Nat Commun 2022; 13:7566. [PMID: 36481610 PMCID: PMC9732045 DOI: 10.1038/s41467-022-32334-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022] Open
Affiliation(s)
- Matthew A Reyna
- Department of Computer Science, Princeton University, Princeton, NJ, 08540, USA
- Department of Biomedical Informatics, Emory University, Atlanta, GA, 30322, USA
| | - David Haan
- Department of Biomolecular Engineering and UC Santa Cruz Genomics Institute, University of California, Santa Cruz, Santa Cruz, CA, 95060, USA
| | - Marta Paczkowska
- Computational Biology Program, Ontario Institute for Cancer Research, Toronto, Ontario, Canada
| | - Lieven P C Verbeke
- Department of Information Technology, IDLab, Ghent University, IMEC, Ghent, the Netherlands
- Department of Plant Biotechnology and Bioinformatics, Ghent University, Ghent, the Netherlands
| | - Miguel Vazquez
- Barcelona Supercomputing Center (BSC), Barcelona, 08034, Spain
- Department of Clinical and Molecular Medicine, Faculty of Medicine and Health Sciences, Norwegian University of Science and Technology, Trondheim, Norway
| | - Abdullah Kahraman
- Institute of Molecular Life Sciences and Swiss Institute of Bioinformatics, University of Zurich, CH-8057, Zurich, Switzerland
- Department of Pathology and Molecular Pathology, University Hospital Zurich, CH-8091, Zurich, Switzerland
| | - Sergio Pulido-Tamayo
- Department of Information Technology, IDLab, Ghent University, IMEC, Ghent, the Netherlands
- Department of Plant Biotechnology and Bioinformatics, Ghent University, Ghent, the Netherlands
| | - Jonathan Barenboim
- Computational Biology Program, Ontario Institute for Cancer Research, Toronto, Ontario, Canada
| | - Lina Wadi
- Computational Biology Program, Ontario Institute for Cancer Research, Toronto, Ontario, Canada
| | - Priyanka Dhingra
- Department of Physiology and Biophysics, Weill Cornell Medicine, New York, NY, 10065, USA
| | - Raunak Shrestha
- Vancouver Prostate Centre, 2660 Oak Street, Vancouver, BC, V6H 3Z6, Canada
| | - Gad Getz
- The Broad Institute of MIT and Harvard, Cambridge, MA, 02124, USA
- Massachusetts General Hospital Center for Cancer Research, Charlestown, MA, 02129, USA
- Harvard Medical School, 250 Longwood Avenue, Boston, MA, 02115, USA
- Massachusetts General Hospital, Department of Pathology, Boston, MA, 02114, USA
| | - Michael S Lawrence
- The Broad Institute of MIT and Harvard, Cambridge, MA, 02124, USA
- Massachusetts General Hospital Center for Cancer Research, Charlestown, MA, 02129, USA
| | - Jakob Skou Pedersen
- Department of Molecular Medicine (MOMA), Aarhus University Hospital, Aarhus, Denmark
- Bioinformatics Research Centre (BiRC), Aarhus University, Aarhus, Denmark
| | - Mark A Rubin
- Department of Physiology and Biophysics, Weill Cornell Medicine, New York, NY, 10065, USA
| | - David A Wheeler
- Human Genome Sequencing Center, Baylor College of Medicine, Houston, TX, 77030, USA
| | - Søren Brunak
- DTU Bioinformatics, Department of Bio and Health Informatics, Technical University of Denmark, Kemitorvet, 2800, Kongens Lyngby, Denmark
- Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, 2200, Copenhagen, Denmark
| | - Jose M G Izarzugaza
- DTU Bioinformatics, Department of Bio and Health Informatics, Technical University of Denmark, Kemitorvet, 2800, Kongens Lyngby, Denmark
- Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, 2200, Copenhagen, Denmark
| | - Ekta Khurana
- Department of Physiology and Biophysics, Weill Cornell Medicine, New York, NY, 10065, USA
| | - Kathleen Marchal
- Department of Information Technology, IDLab, Ghent University, IMEC, Ghent, the Netherlands
- Department of Plant Biotechnology and Bioinformatics, Ghent University, Ghent, the Netherlands
| | - Christian von Mering
- Institute of Molecular Life Sciences and Swiss Institute of Bioinformatics, University of Zurich, CH-8057, Zurich, Switzerland
| | - S Cenk Sahinalp
- Vancouver Prostate Centre, 2660 Oak Street, Vancouver, BC, V6H 3Z6, Canada
- Department of Computer Science, Indiana University, Bloomington, IN, 47405, USA
| | - Alfonso Valencia
- Barcelona Supercomputing Center (BSC), Barcelona, 08034, Spain
- ICREA, Barcelona, 08010, Spain
| | - Jüri Reimand
- Computational Biology Program, Ontario Institute for Cancer Research, Toronto, Ontario, Canada.
- Department of Medical Biophysics, University of Toronto, Toronto, Ontario, Canada.
| | - Joshua M Stuart
- Department of Biomolecular Engineering and UC Santa Cruz Genomics Institute, University of California, Santa Cruz, Santa Cruz, CA, 95060, USA.
| | - Benjamin J Raphael
- Department of Computer Science, Princeton University, Princeton, NJ, 08540, USA.
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Chitra U, Park TY, Raphael BJ. NetMix2: A Principled Network Propagation Algorithm for Identifying Altered Subnetworks. J Comput Biol 2022; 29:1305-1323. [PMID: 36525308 PMCID: PMC9917315 DOI: 10.1089/cmb.2022.0336] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/23/2022] Open
Abstract
A standard paradigm in computational biology is to leverage interaction networks as prior knowledge in analyzing high-throughput biological data, where the data give a score for each vertex in the network. One classical approach is the identification of altered subnetworks, or subnetworks of the interaction network that have both outlier vertex scores and a defined network topology. One class of algorithms for identifying altered subnetworks search for high-scoring subnetworks in subnetwork families with simple topological constraints, such as connected subnetworks, and have sound statistical guarantees. A second class of algorithms employ network propagation-the smoothing of vertex scores over the network using a random walk or diffusion process-and utilize the global structure of the network. However, network propagation algorithms often rely on ad hoc heuristics that lack a rigorous statistical foundation. In this work, we unify the subnetwork family and network propagation approaches by deriving the propagation family, a subnetwork family that approximates the sets of vertices ranked highly by network propagation approaches. We introduce NetMix2, a principled algorithm for identifying altered subnetworks from a wide range of subnetwork families. When using the propagation family, NetMix2 combines the advantages of the subnetwork family and network propagation approaches. NetMix2 outperforms other methods, including network propagation on simulated data, pan-cancer somatic mutation data, and genome-wide association data from multiple human diseases.
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Affiliation(s)
- Uthsav Chitra
- Department of Computer Science, Princeton University, Princeton, New Jersey, USA
| | - Tae Yoon Park
- Department of Computer Science, Princeton University, Princeton, New Jersey, USA
- Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, New Jersey, USA
| | - Benjamin J. Raphael
- Department of Computer Science, Princeton University, Princeton, New Jersey, USA
- Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, New Jersey, USA
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22
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Ma C, Chitra U, Zhang S, Raphael BJ. Belayer: Modeling discrete and continuous spatial variation in gene expression from spatially resolved transcriptomics. Cell Syst 2022; 13:786-797.e13. [PMID: 36265465 PMCID: PMC9814896 DOI: 10.1016/j.cels.2022.09.002] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2022] [Revised: 07/13/2022] [Accepted: 09/06/2022] [Indexed: 01/26/2023]
Abstract
Spatially resolved transcriptomics (SRT) technologies measure gene expression at known locations in a tissue slice, enabling the identification of spatially varying genes or cell types. Current approaches for these tasks assume either that gene expression varies continuously across a tissue or that a tissue contains a small number of regions with distinct cellular composition. We propose a model for SRT data from layered tissues that includes both continuous and discrete spatial variation in expression and an algorithm, Belayer, to learn the parameters of this model. Belayer models gene expression as a piecewise linear function of the relative depth of a tissue layer with possible discontinuities at layer boundaries. We use conformal maps to model relative depth and derive a dynamic programming algorithm to infer layer boundaries and gene expression functions. Belayer accurately identifies tissue layers and biologically meaningful spatially varying genes in SRT data from the brain and skin.
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Affiliation(s)
- Cong Ma
- Department of Computer Science, Princeton University, 35 Olden St, Princeton, NJ 08540, USA
| | - Uthsav Chitra
- Department of Computer Science, Princeton University, 35 Olden St, Princeton, NJ 08540, USA
| | - Shirley Zhang
- Department of Computer Science, Princeton University, 35 Olden St, Princeton, NJ 08540, USA
| | - Benjamin J Raphael
- Department of Computer Science, Princeton University, 35 Olden St, Princeton, NJ 08540, USA.
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23
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Arunachalam S, Szlachta K, Brady SW, Ma X, Ju B, Shaner B, Mulder HL, Easton J, Raphael BJ, Myers M, Tinkle C, Allen SJ, Orr BA, Wetmore CJ, Baker SJ, Zhang J. Convergent evolution and multi-wave clonal invasion in H3 K27-altered diffuse midline gliomas treated with a PDGFR inhibitor. Acta Neuropathol Commun 2022; 10:80. [PMID: 35642016 PMCID: PMC9153212 DOI: 10.1186/s40478-022-01381-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/10/2022] [Accepted: 05/12/2022] [Indexed: 11/11/2022] Open
Abstract
The majority of diffuse midline gliomas, H3 K27-altered (DMG-H3 K27-a), are infiltrating pediatric brain tumors that arise in the pons with no effective treatment. To understand how clonal evolution contributes to the tumor’s invasive spread, we performed exome sequencing and SNP array profiling on 49 multi-region autopsy samples from 11 patients with pontine DMG-H3 K27-a enrolled in a phase I clinical trial of PDGFR inhibitor crenolanib. For each patient, a phylogenetic tree was constructed by testing multiple possible clonal evolution models to select the one consistent with somatic mutations and copy number variations across all tumor regions. The tree was then used to deconvolute subclonal composition and prevalence at each tumor region to study convergent evolution and invasion patterns. Somatic variants in the PI3K pathway, a late event, are enriched in our cohort, affecting 70% of patients. Convergent evolution of PI3K at distinct phylogenetic branches was detected in 40% of the patients. 24 (~ 50%) of tumor regions were occupied by subclones of mixed lineages with varying molecular ages, indicating multiple waves of invasion across the pons and extrapontine. Subclones harboring a PDGFRA amplicon, including one that amplified a PDGRFAY849C mutant allele, were detected in four patients; their presence in extrapontine tumor and normal brain samples imply their involvement in extrapontine invasion. Our study expands the current knowledge on tumor invasion patterns in DMG-H3 K27-a, which may inform the design of future clinical trials.
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24
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Zeira R, Land M, Strzalkowski A, Raphael BJ. Alignment and integration of spatial transcriptomics data. Nat Methods 2022; 19:567-575. [PMID: 35577957 PMCID: PMC9334025 DOI: 10.1038/s41592-022-01459-6] [Citation(s) in RCA: 45] [Impact Index Per Article: 22.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/16/2021] [Accepted: 03/17/2022] [Indexed: 01/05/2023]
Abstract
Spatial transcriptomics (ST) measures mRNA expression across thousands of spots from a tissue slice while recording the two-dimensional (2D) coordinates of each spot. We introduce probabilistic alignment of ST experiments (PASTE), a method to align and integrate ST data from multiple adjacent tissue slices. PASTE computes pairwise alignments of slices using an optimal transport formulation that models both transcriptional similarity and physical distances between spots. PASTE further combines pairwise alignments to construct a stacked 3D alignment of a tissue. Alternatively, PASTE can integrate multiple ST slices into a single consensus slice. We show that PASTE accurately aligns spots across adjacent slices in both simulated and real ST data, demonstrating the advantages of using both transcriptional similarity and spatial information. We further show that the PASTE integrated slice improves the identification of cell types and differentially expressed genes compared with existing approaches that either analyze single ST slices or ignore spatial information.
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Affiliation(s)
- Ron Zeira
- Department of Computer Science, Princeton University, Princeton, NJ 08540
| | - Max Land
- Department of Computer Science, Princeton University, Princeton, NJ 08540
| | | | - Benjamin J. Raphael
- Department of Computer Science, Princeton University, Princeton, NJ 08540,Correspondence:
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25
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Park TY, Leiserson MD, Klau GW, Raphael BJ. SuperDendrix algorithm integrates genetic dependencies and genomic alterations across pathways and cancer types. Cell Genomics 2022; 2. [PMID: 35382456 PMCID: PMC8979493 DOI: 10.1016/j.xgen.2022.100099] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
Recent genome-wide CRISPR-Cas9 loss-of-function screens have identified genetic dependencies across many cancer cell lines. Associations between these dependencies and genomic alterations in the same cell lines reveal phenomena such as oncogene addiction and synthetic lethality. However, comprehensive identification of such associations is complicated by complex interactions between genes across genetically heterogeneous cancer types. We introduce and apply the algorithm SuperDendrix to CRISPR-Cas9 loss-of-function screens from 769 cancer cell lines, to identify differential dependencies across cell lines and to find associations between differential dependencies and combinations of genomic alterations and cell-type-specific markers. These associations respect the position and type of interactions within pathways: for example, we observe increased dependencies on downstream activators of pathways, such as NFE2L2, and decreased dependencies on upstream activators of pathways, such as CDK6. SuperDendrix also reveals dozens of dependencies on lineage-specific transcription factors, identifies cancer-type-specific correlations between dependencies, and enables annotation of individual mutated residues. Using SuperDendrix, Park et al. examine associations between genetic dependencies in 769 cancer cell lines. They report 127 genetic dependencies explained by combinations of mutually exclusive somatic mutations congregating into a few oncogenic pathways across cancer subtypes. These present a small number of prominent and highly specific genetic vulnerabilities in cancer. Graphical abstract
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26
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Sun H, Cao S, Mashl RJ, Mo CK, Zaccaria S, Wendl MC, Davies SR, Bailey MH, Primeau TM, Hoog J, Mudd JL, Dean DA, Patidar R, Chen L, Wyczalkowski MA, Jayasinghe RG, Rodrigues FM, Terekhanova NV, Li Y, Lim KH, Wang-Gillam A, Van Tine BA, Ma CX, Aft R, Fuh KC, Schwarz JK, Zevallos JP, Puram SV, Dipersio JF, Davis-Dusenbery B, Ellis MJ, Lewis MT, Davies MA, Herlyn M, Fang B, Roth JA, Welm AL, Welm BE, Meric-Bernstam F, Chen F, Fields RC, Li S, Govindan R, Doroshow JH, Moscow JA, Evrard YA, Chuang JH, Raphael BJ, Ding L. Author Correction: Comprehensive characterization of 536 patient-derived xenograft models prioritizes candidates for targeted treatment. Nat Commun 2022; 13:294. [PMID: 34996889 PMCID: PMC8742097 DOI: 10.1038/s41467-021-27678-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Key Words] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022] Open
Affiliation(s)
- Hua Sun
- Department of Medicine, Washington University in St. Louis, St. Louis, MO, USA
- McDonnell Genome Institute, Washington University in St. Louis, St. Louis, MO, USA
| | - Song Cao
- Department of Medicine, Washington University in St. Louis, St. Louis, MO, USA
- McDonnell Genome Institute, Washington University in St. Louis, St. Louis, MO, USA
| | - R Jay Mashl
- Department of Medicine, Washington University in St. Louis, St. Louis, MO, USA
- McDonnell Genome Institute, Washington University in St. Louis, St. Louis, MO, USA
| | - Chia-Kuei Mo
- Department of Medicine, Washington University in St. Louis, St. Louis, MO, USA
- McDonnell Genome Institute, Washington University in St. Louis, St. Louis, MO, USA
| | - Simone Zaccaria
- Department of Computer Science, Princeton University, Princeton, NJ, USA
- Computational Cancer Genomics Research Group and Cancer Research UK Lung Cancer Centre of Excellence, University College London Cancer Institute, London, UK
| | - Michael C Wendl
- McDonnell Genome Institute, Washington University in St. Louis, St. Louis, MO, USA
- Department of Mathematics, Washington University in St. Louis, St. Louis, MO, USA
- Department of Genetics, Washington University in St. Louis, St. Louis, MO, USA
| | - Sherri R Davies
- Department of Medicine, Washington University in St. Louis, St. Louis, MO, USA
| | - Matthew H Bailey
- Huntsman Cancer Institute, University of Utah, Salt Lake City, UT, USA
| | - Tina M Primeau
- Department of Medicine, Washington University in St. Louis, St. Louis, MO, USA
| | - Jeremy Hoog
- Department of Medicine, Washington University in St. Louis, St. Louis, MO, USA
| | - Jacqueline L Mudd
- Department of Medicine, Washington University in St. Louis, St. Louis, MO, USA
| | - Dennis A Dean
- Seven Bridges Genomics, Inc., Cambridge, Charlestown, MA, USA
| | - Rajesh Patidar
- Frederick National Laboratory for Cancer Research, Frederick, MD, USA
| | - Li Chen
- Frederick National Laboratory for Cancer Research, Frederick, MD, USA
| | - Matthew A Wyczalkowski
- Department of Medicine, Washington University in St. Louis, St. Louis, MO, USA
- McDonnell Genome Institute, Washington University in St. Louis, St. Louis, MO, USA
| | - Reyka G Jayasinghe
- Department of Medicine, Washington University in St. Louis, St. Louis, MO, USA
- McDonnell Genome Institute, Washington University in St. Louis, St. Louis, MO, USA
| | - Fernanda Martins Rodrigues
- Department of Medicine, Washington University in St. Louis, St. Louis, MO, USA
- McDonnell Genome Institute, Washington University in St. Louis, St. Louis, MO, USA
| | - Nadezhda V Terekhanova
- Department of Medicine, Washington University in St. Louis, St. Louis, MO, USA
- McDonnell Genome Institute, Washington University in St. Louis, St. Louis, MO, USA
| | - Yize Li
- Department of Medicine, Washington University in St. Louis, St. Louis, MO, USA
- McDonnell Genome Institute, Washington University in St. Louis, St. Louis, MO, USA
| | - Kian-Huat Lim
- Department of Medicine, Washington University in St. Louis, St. Louis, MO, USA
- Siteman Cancer Center, Washington University in St. Louis, St. Louis, MO, USA
| | - Andrea Wang-Gillam
- Department of Medicine, Washington University in St. Louis, St. Louis, MO, USA
- Siteman Cancer Center, Washington University in St. Louis, St. Louis, MO, USA
| | - Brian A Van Tine
- Department of Medicine, Washington University in St. Louis, St. Louis, MO, USA
- Siteman Cancer Center, Washington University in St. Louis, St. Louis, MO, USA
| | - Cynthia X Ma
- Department of Medicine, Washington University in St. Louis, St. Louis, MO, USA
- Siteman Cancer Center, Washington University in St. Louis, St. Louis, MO, USA
| | - Rebecca Aft
- Siteman Cancer Center, Washington University in St. Louis, St. Louis, MO, USA
| | - Katherine C Fuh
- Department of Medicine, Washington University in St. Louis, St. Louis, MO, USA
- Siteman Cancer Center, Washington University in St. Louis, St. Louis, MO, USA
| | - Julie K Schwarz
- Siteman Cancer Center, Washington University in St. Louis, St. Louis, MO, USA
- Department of Radiation Oncology, Washington University in St. Louis, St. Louis, MO, USA
| | - Jose P Zevallos
- Siteman Cancer Center, Washington University in St. Louis, St. Louis, MO, USA
- Department of Otolaryngology, Washington University St. Louis, St. Louis, MO, USA
| | - Sidharth V Puram
- Siteman Cancer Center, Washington University in St. Louis, St. Louis, MO, USA
- Department of Otolaryngology, Washington University St. Louis, St. Louis, MO, USA
| | - John F Dipersio
- Department of Medicine, Washington University in St. Louis, St. Louis, MO, USA
- Siteman Cancer Center, Washington University in St. Louis, St. Louis, MO, USA
| | | | - Matthew J Ellis
- Lester and Sue Smith Breast Center, Baylor College of Medicine, Houston, TX, USA
| | - Michael T Lewis
- Lester and Sue Smith Breast Center, Baylor College of Medicine, Houston, TX, USA
| | - Michael A Davies
- The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | | | - Bingliang Fang
- The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Jack A Roth
- The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Alana L Welm
- Huntsman Cancer Institute, University of Utah, Salt Lake City, UT, USA
| | - Bryan E Welm
- Huntsman Cancer Institute, University of Utah, Salt Lake City, UT, USA
| | | | - Feng Chen
- Department of Medicine, Washington University in St. Louis, St. Louis, MO, USA
| | - Ryan C Fields
- Siteman Cancer Center, Washington University in St. Louis, St. Louis, MO, USA
| | - Shunqiang Li
- Department of Medicine, Washington University in St. Louis, St. Louis, MO, USA
- Siteman Cancer Center, Washington University in St. Louis, St. Louis, MO, USA
| | - Ramaswamy Govindan
- Department of Medicine, Washington University in St. Louis, St. Louis, MO, USA
- Siteman Cancer Center, Washington University in St. Louis, St. Louis, MO, USA
| | - James H Doroshow
- Division of Cancer Treatment and Diagnosis, National Cancer Institute, Bethesda, MD, USA
| | - Jeffrey A Moscow
- Investigational Drug Branch, National Cancer Institute, Bethesda, MD, USA
| | - Yvonne A Evrard
- Frederick National Laboratory for Cancer Research, Frederick, MD, USA
| | - Jeffrey H Chuang
- The Jackson Laboratory for Genomic Medicine, Farmington, CT, USA
| | - Benjamin J Raphael
- Department of Computer Science, Princeton University, Princeton, NJ, USA
| | - Li Ding
- Department of Medicine, Washington University in St. Louis, St. Louis, MO, USA.
- McDonnell Genome Institute, Washington University in St. Louis, St. Louis, MO, USA.
- Department of Genetics, Washington University in St. Louis, St. Louis, MO, USA.
- Siteman Cancer Center, Washington University in St. Louis, St. Louis, MO, USA.
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27
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Satas G, Zaccaria S, El-Kebir M, Raphael BJ. DeCiFering the elusive cancer cell fraction in tumor heterogeneity and evolution. Cell Syst 2021; 12:1004-1018.e10. [PMID: 34416171 DOI: 10.1016/j.cels.2021.07.006] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2021] [Revised: 07/09/2021] [Accepted: 07/21/2021] [Indexed: 12/22/2022]
Abstract
The cancer cell fraction (CCF), or proportion of cancerous cells in a tumor containing a single-nucleotide variant (SNV), is a fundamental statistic used to quantify tumor heterogeneity and evolution. Existing CCF estimation methods from bulk DNA sequencing data assume that every cell with an SNV contains the same number of copies of the SNV. This assumption is unrealistic in tumors with copy-number aberrations that alter SNV multiplicities. Furthermore, the CCF does not account for SNV losses due to copy-number aberrations, confounding downstream phylogenetic analyses. We introduce DeCiFer, an algorithm that overcomes these limitations by clustering SNVs using a novel statistic, the descendant cell fraction (DCF). The DCF quantifies both the prevalence of an SNV at the present time and its past evolutionary history using an evolutionary model that allows mutation losses. We show that DeCiFer yields more parsimonious reconstructions of tumor evolution than previously reported for 49 prostate cancer samples.
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Affiliation(s)
- Gryte Satas
- Department of Computer Science, Princeton University, Princeton, NJ, USA
| | - Simone Zaccaria
- Department of Computer Science, Princeton University, Princeton, NJ, USA
| | - Mohammed El-Kebir
- Department of Computer Science, University of Illinois at Urbana-Champaign, Urbana, IL, USA.
| | - Benjamin J Raphael
- Department of Computer Science, Princeton University, Princeton, NJ, USA.
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28
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Sun H, Cao S, Mashl RJ, Mo CK, Zaccaria S, Wendl MC, Davies SR, Bailey MH, Primeau TM, Hoog J, Mudd JL, Dean DA, Patidar R, Chen L, Wyczalkowski MA, Jayasinghe RG, Rodrigues FM, Terekhanova NV, Li Y, Lim KH, Wang-Gillam A, Van Tine BA, Ma CX, Aft R, Fuh KC, Schwarz JK, Zevallos JP, Puram SV, Dipersio JF, Davis-Dusenbery B, Ellis MJ, Lewis MT, Davies MA, Herlyn M, Fang B, Roth JA, Welm AL, Welm BE, Meric-Bernstam F, Chen F, Fields RC, Li S, Govindan R, Doroshow JH, Moscow JA, Evrard YA, Chuang JH, Raphael BJ, Ding L. Comprehensive characterization of 536 patient-derived xenograft models prioritizes candidatesfor targeted treatment. Nat Commun 2021; 12:5086. [PMID: 34429404 PMCID: PMC8384880 DOI: 10.1038/s41467-021-25177-3] [Citation(s) in RCA: 44] [Impact Index Per Article: 14.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/16/2021] [Accepted: 07/14/2021] [Indexed: 02/07/2023] Open
Abstract
Development of candidate cancer treatments is a resource-intensive process, with the research community continuing to investigate options beyond static genomic characterization. Toward this goal, we have established the genomic landscapes of 536 patient-derived xenograft (PDX) models across 25 cancer types, together with mutation, copy number, fusion, transcriptomic profiles, and NCI-MATCH arms. Compared with human tumors, PDXs typically have higher purity and fit to investigate dynamic driver events and molecular properties via multiple time points from same case PDXs. Here, we report on dynamic genomic landscapes and pharmacogenomic associations, including associations between activating oncogenic events and drugs, correlations between whole-genome duplications and subclone events, and the potential PDX models for NCI-MATCH trials. Lastly, we provide a web portal having comprehensive pan-cancer PDX genomic profiles and source code to facilitate identification of more druggable events and further insights into PDXs' recapitulation of human tumors.
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Affiliation(s)
- Hua Sun
- grid.4367.60000 0001 2355 7002Department of Medicine, Washington University in St. Louis, St. Louis, MO USA ,grid.4367.60000 0001 2355 7002McDonnell Genome Institute, Washington University in St. Louis, St. Louis, MO USA
| | - Song Cao
- grid.4367.60000 0001 2355 7002Department of Medicine, Washington University in St. Louis, St. Louis, MO USA ,grid.4367.60000 0001 2355 7002McDonnell Genome Institute, Washington University in St. Louis, St. Louis, MO USA
| | - R. Jay Mashl
- grid.4367.60000 0001 2355 7002Department of Medicine, Washington University in St. Louis, St. Louis, MO USA ,grid.4367.60000 0001 2355 7002McDonnell Genome Institute, Washington University in St. Louis, St. Louis, MO USA
| | - Chia-Kuei Mo
- grid.4367.60000 0001 2355 7002Department of Medicine, Washington University in St. Louis, St. Louis, MO USA ,grid.4367.60000 0001 2355 7002McDonnell Genome Institute, Washington University in St. Louis, St. Louis, MO USA
| | - Simone Zaccaria
- grid.16750.350000 0001 2097 5006Department of Computer Science, Princeton University, Princeton, NJ USA ,grid.83440.3b0000000121901201Computational Cancer Genomics Research Group and Cancer Research UK Lung Cancer Centre of Excellence, University College London Cancer Institute, London, UK
| | - Michael C. Wendl
- grid.4367.60000 0001 2355 7002McDonnell Genome Institute, Washington University in St. Louis, St. Louis, MO USA ,grid.4367.60000 0001 2355 7002Department of Mathematics, Washington University in St. Louis, St. Louis, MO USA ,grid.4367.60000 0001 2355 7002Department of Genetics, Washington University in St. Louis, St. Louis, MO USA
| | - Sherri R. Davies
- grid.4367.60000 0001 2355 7002Department of Medicine, Washington University in St. Louis, St. Louis, MO USA
| | - Matthew H. Bailey
- grid.412722.00000 0004 0515 3663Huntsman Cancer Institute, University of Utah, Salt Lake City, UT USA
| | - Tina M. Primeau
- grid.4367.60000 0001 2355 7002Department of Medicine, Washington University in St. Louis, St. Louis, MO USA
| | - Jeremy Hoog
- grid.4367.60000 0001 2355 7002Department of Medicine, Washington University in St. Louis, St. Louis, MO USA
| | - Jacqueline L. Mudd
- grid.4367.60000 0001 2355 7002Department of Medicine, Washington University in St. Louis, St. Louis, MO USA
| | - Dennis A. Dean
- grid.492568.4Seven Bridges Genomics, Inc., Cambridge, Charlestown, MA USA
| | - Rajesh Patidar
- grid.418021.e0000 0004 0535 8394Frederick National Laboratory for Cancer Research, Frederick, MD USA
| | - Li Chen
- grid.418021.e0000 0004 0535 8394Frederick National Laboratory for Cancer Research, Frederick, MD USA
| | - Matthew A. Wyczalkowski
- grid.4367.60000 0001 2355 7002Department of Medicine, Washington University in St. Louis, St. Louis, MO USA ,grid.4367.60000 0001 2355 7002McDonnell Genome Institute, Washington University in St. Louis, St. Louis, MO USA
| | - Reyka G. Jayasinghe
- grid.4367.60000 0001 2355 7002Department of Medicine, Washington University in St. Louis, St. Louis, MO USA ,grid.4367.60000 0001 2355 7002McDonnell Genome Institute, Washington University in St. Louis, St. Louis, MO USA
| | - Fernanda Martins Rodrigues
- grid.4367.60000 0001 2355 7002Department of Medicine, Washington University in St. Louis, St. Louis, MO USA ,grid.4367.60000 0001 2355 7002McDonnell Genome Institute, Washington University in St. Louis, St. Louis, MO USA
| | - Nadezhda V. Terekhanova
- grid.4367.60000 0001 2355 7002Department of Medicine, Washington University in St. Louis, St. Louis, MO USA ,grid.4367.60000 0001 2355 7002McDonnell Genome Institute, Washington University in St. Louis, St. Louis, MO USA
| | - Yize Li
- grid.4367.60000 0001 2355 7002Department of Medicine, Washington University in St. Louis, St. Louis, MO USA ,grid.4367.60000 0001 2355 7002McDonnell Genome Institute, Washington University in St. Louis, St. Louis, MO USA
| | - Kian-Huat Lim
- grid.4367.60000 0001 2355 7002Department of Medicine, Washington University in St. Louis, St. Louis, MO USA ,grid.4367.60000 0001 2355 7002Siteman Cancer Center, Washington University in St. Louis, St. Louis, MO USA
| | - Andrea Wang-Gillam
- grid.4367.60000 0001 2355 7002Department of Medicine, Washington University in St. Louis, St. Louis, MO USA ,grid.4367.60000 0001 2355 7002Siteman Cancer Center, Washington University in St. Louis, St. Louis, MO USA
| | - Brian A. Van Tine
- grid.4367.60000 0001 2355 7002Department of Medicine, Washington University in St. Louis, St. Louis, MO USA ,grid.4367.60000 0001 2355 7002Siteman Cancer Center, Washington University in St. Louis, St. Louis, MO USA
| | - Cynthia X. Ma
- grid.4367.60000 0001 2355 7002Department of Medicine, Washington University in St. Louis, St. Louis, MO USA ,grid.4367.60000 0001 2355 7002Siteman Cancer Center, Washington University in St. Louis, St. Louis, MO USA
| | - Rebecca Aft
- grid.4367.60000 0001 2355 7002Siteman Cancer Center, Washington University in St. Louis, St. Louis, MO USA
| | - Katherine C. Fuh
- grid.4367.60000 0001 2355 7002Department of Medicine, Washington University in St. Louis, St. Louis, MO USA ,grid.4367.60000 0001 2355 7002Siteman Cancer Center, Washington University in St. Louis, St. Louis, MO USA
| | - Julie K. Schwarz
- grid.4367.60000 0001 2355 7002Siteman Cancer Center, Washington University in St. Louis, St. Louis, MO USA ,grid.4367.60000 0001 2355 7002Department of Radiation Oncology, Washington University in St. Louis, St. Louis, MO USA
| | - Jose P. Zevallos
- grid.4367.60000 0001 2355 7002Siteman Cancer Center, Washington University in St. Louis, St. Louis, MO USA ,grid.4367.60000 0001 2355 7002Department of Otolaryngology, Washington University St. Louis, St. Louis, MO USA
| | - Sidharth V. Puram
- grid.4367.60000 0001 2355 7002Siteman Cancer Center, Washington University in St. Louis, St. Louis, MO USA ,grid.4367.60000 0001 2355 7002Department of Otolaryngology, Washington University St. Louis, St. Louis, MO USA
| | - John F. Dipersio
- grid.4367.60000 0001 2355 7002Department of Medicine, Washington University in St. Louis, St. Louis, MO USA ,grid.4367.60000 0001 2355 7002Siteman Cancer Center, Washington University in St. Louis, St. Louis, MO USA
| | | | | | - Matthew J. Ellis
- grid.39382.330000 0001 2160 926XLester and Sue Smith Breast Center, Baylor College of Medicine, Houston, TX USA
| | - Michael T. Lewis
- grid.39382.330000 0001 2160 926XLester and Sue Smith Breast Center, Baylor College of Medicine, Houston, TX USA
| | - Michael A. Davies
- grid.240145.60000 0001 2291 4776The University of Texas MD Anderson Cancer Center, Houston, TX USA
| | - Meenhard Herlyn
- grid.251075.40000 0001 1956 6678The Wistar Institute, Philadelphia, PA USA
| | - Bingliang Fang
- grid.240145.60000 0001 2291 4776The University of Texas MD Anderson Cancer Center, Houston, TX USA
| | - Jack A. Roth
- grid.240145.60000 0001 2291 4776The University of Texas MD Anderson Cancer Center, Houston, TX USA
| | - Alana L. Welm
- grid.412722.00000 0004 0515 3663Huntsman Cancer Institute, University of Utah, Salt Lake City, UT USA
| | - Bryan E. Welm
- grid.412722.00000 0004 0515 3663Huntsman Cancer Institute, University of Utah, Salt Lake City, UT USA
| | - Funda Meric-Bernstam
- grid.240145.60000 0001 2291 4776The University of Texas MD Anderson Cancer Center, Houston, TX USA
| | - Feng Chen
- grid.4367.60000 0001 2355 7002Department of Medicine, Washington University in St. Louis, St. Louis, MO USA
| | - Ryan C. Fields
- grid.4367.60000 0001 2355 7002Siteman Cancer Center, Washington University in St. Louis, St. Louis, MO USA
| | - Shunqiang Li
- grid.4367.60000 0001 2355 7002Department of Medicine, Washington University in St. Louis, St. Louis, MO USA ,grid.4367.60000 0001 2355 7002Siteman Cancer Center, Washington University in St. Louis, St. Louis, MO USA
| | - Ramaswamy Govindan
- grid.4367.60000 0001 2355 7002Department of Medicine, Washington University in St. Louis, St. Louis, MO USA ,grid.4367.60000 0001 2355 7002Siteman Cancer Center, Washington University in St. Louis, St. Louis, MO USA
| | - James H. Doroshow
- grid.48336.3a0000 0004 1936 8075Division of Cancer Treatment and Diagnosis, National Cancer Institute, Bethesda, MD USA
| | - Jeffrey A. Moscow
- grid.48336.3a0000 0004 1936 8075Investigational Drug Branch, National Cancer Institute, Bethesda, MD USA
| | - Yvonne A. Evrard
- grid.418021.e0000 0004 0535 8394Frederick National Laboratory for Cancer Research, Frederick, MD USA
| | - Jeffrey H. Chuang
- grid.249880.f0000 0004 0374 0039The Jackson Laboratory for Genomic Medicine, Farmington, CT USA
| | - Benjamin J. Raphael
- grid.16750.350000 0001 2097 5006Department of Computer Science, Princeton University, Princeton, NJ USA
| | - Li Ding
- grid.4367.60000 0001 2355 7002Department of Medicine, Washington University in St. Louis, St. Louis, MO USA ,grid.4367.60000 0001 2355 7002McDonnell Genome Institute, Washington University in St. Louis, St. Louis, MO USA ,grid.4367.60000 0001 2355 7002Department of Genetics, Washington University in St. Louis, St. Louis, MO USA ,grid.4367.60000 0001 2355 7002Siteman Cancer Center, Washington University in St. Louis, St. Louis, MO USA
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29
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Abstract
Tumors are highly heterogeneous, consisting of cell populations with both transcriptional and genetic diversity. These diverse cell populations are spatially organized within a tumor, creating a distinct tumor microenvironment. A new technology called spatial transcriptomics can measure spatial patterns of gene expression within a tissue by sequencing RNA transcripts from a grid of spots, each containing a small number of cells. In tumor cells, these gene expression patterns represent the combined contribution of regulatory mechanisms, which alter the rate at which a gene is transcribed, and genetic diversity, particularly copy number aberrations (CNAs) which alter the number of copies of a gene in the genome. CNAs are common in tumors and often promote cancer growth through upregulation of oncogenes or downregulation of tumor-suppressor genes. We introduce a new method STARCH (spatial transcriptomics algorithm reconstructing copy-number heterogeneity) to infer CNAs from spatial transcriptomics data. STARCH overcomes challenges in inferring CNAs from RNA-sequencing data by leveraging the observation that cells located nearby in a tumor are likely to share similar CNAs. We find that STARCH outperforms existing methods for inferring CNAs from RNA-sequencing data without incorporating spatial information.
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Affiliation(s)
- Rebecca Elyanow
- Center for Computational Molecular Biology, Brown University, Providence, RI 029012, USA,Department of Computer Science, Princeton University, Princeton, NJ 08540
| | - Ron Zeira
- Department of Computer Science, Princeton University, Princeton, NJ 08540
| | - Max Land
- Department of Computer Science, Princeton University, Princeton, NJ 08540
| | - Benjamin J. Raphael
- Department of Computer Science, Princeton University, Princeton, NJ 08540,Correspondence:
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30
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Zaccaria S, Raphael BJ. Characterizing allele- and haplotype-specific copy numbers in single cells with CHISEL. Nat Biotechnol 2021; 39:207-214. [PMID: 32879467 PMCID: PMC9876616 DOI: 10.1038/s41587-020-0661-6] [Citation(s) in RCA: 50] [Impact Index Per Article: 16.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/17/2019] [Accepted: 08/04/2020] [Indexed: 01/28/2023]
Abstract
Single-cell barcoding technologies enable genome sequencing of thousands of individual cells in parallel, but with extremely low sequencing coverage (<0.05×) per cell. While the total copy number of large multi-megabase segments can be derived from such data, important allele-specific mutations-such as copy-neutral loss of heterozygosity (LOH) in cancer-are missed. We introduce copy-number haplotype inference in single cells using evolutionary links (CHISEL), a method to infer allele- and haplotype-specific copy numbers in single cells and subpopulations of cells by aggregating sparse signal across hundreds or thousands of individual cells. We applied CHISEL to ten single-cell sequencing datasets of ~2,000 cells from two patients with breast cancer. We identified extensive allele-specific copy-number aberrations (CNAs) in these samples, including copy-neutral LOHs, whole-genome duplications (WGDs) and mirrored-subclonal CNAs. These allele-specific CNAs affect genomic regions containing well-known breast-cancer genes. We also refined the reconstruction of tumor evolution, timing allele-specific CNAs before and after WGDs, identifying low-frequency subpopulations distinguished by unique CNAs and uncovering evidence of convergent evolution.
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Affiliation(s)
- Simone Zaccaria
- Department of Computer Science, Princeton University, Princeton, NJ, USA
| | - Benjamin J Raphael
- Department of Computer Science, Princeton University, Princeton, NJ, USA.
- Rutgers Cancer Institute of New Jersey, New Brunswick, NJ, USA.
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31
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Abstract
A classic problem in computational biology is the identification of altered subnetworks: subnetworks of an interaction network that contain genes/proteins that are differentially expressed, highly mutated, or otherwise aberrant compared with other genes/proteins. Numerous methods have been developed to solve this problem under various assumptions, but the statistical properties of these methods are often unknown. For example, some widely used methods are reported to output very large subnetworks that are difficult to interpret biologically. In this work, we formulate the identification of altered subnetworks as the problem of estimating the parameters of a class of probability distributions that we call the Altered Subset Distribution (ASD). We derive a connection between a popular method, jActiveModules, and the maximum likelihood estimator (MLE) of the ASD. We show that the MLE is statistically biased, explaining the large subnetworks output by jActiveModules. Based on these insights, we introduce NetMix, an algorithm that uses Gaussian mixture models to obtain less biased estimates of the parameters of the ASD. We demonstrate that NetMix outperforms existing methods in identifying altered subnetworks on both simulated and real data, including the identification of differentially expressed genes from both microarray and RNA-seq experiments and the identification of cancer driver genes in somatic mutation data.
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Affiliation(s)
- Matthew A Reyna
- Department of Biomedical Informatics, Emory University, Atlanta, Georgia, USA
| | - Uthsav Chitra
- Department of Computer Science, Princeton University, Princeton, New Jersey, USA
| | - Rebecca Elyanow
- Department of Computer Science, Princeton University, Princeton, New Jersey, USA
- Department of Computer Science, Brown University, Providence, Rhode Island, USA
| | - Benjamin J Raphael
- Department of Computer Science, Princeton University, Princeton, New Jersey, USA
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32
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Aganezov S, Raphael BJ. Reconstruction of clone- and haplotype-specific cancer genome karyotypes from bulk tumor samples. Genome Res 2020; 30:1274-1290. [PMID: 32887685 PMCID: PMC7545144 DOI: 10.1101/gr.256701.119] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/03/2019] [Accepted: 08/07/2020] [Indexed: 12/25/2022]
Abstract
Many cancer genomes are extensively rearranged with aberrant chromosomal karyotypes. Deriving these karyotypes from high-throughput DNA sequencing of bulk tumor samples is complicated because most tumors are a heterogeneous mixture of normal cells and subpopulations of cancer cells, or clones, that harbor distinct somatic mutations. We introduce a new algorithm, Reconstructing Cancer Karyotypes (RCK), to reconstruct haplotype-specific karyotypes of one or more rearranged cancer genomes from DNA sequencing data from a bulk tumor sample. RCK leverages evolutionary constraints on the somatic mutational process in cancer to reduce ambiguity in the deconvolution of admixed sequencing data into multiple haplotype-specific cancer karyotypes. RCK models mixtures containing an arbitrary number of derived genomes and allows the incorporation of information both from short-read and long-read DNA sequencing technologies. We compare RCK to existing approaches on 17 primary and metastatic prostate cancer samples. We find that RCK infers cancer karyotypes that better explain the DNA sequencing data and conform to a reasonable evolutionary model. RCK's reconstructions of clone- and haplotype-specific karyotypes will aid further studies of the role of intra-tumor heterogeneity in cancer development and response to treatment. RCK is freely available as open source software.
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Affiliation(s)
- Sergey Aganezov
- Department of Computer Science, Princeton University, Princeton, New Jersey 08540, USA
| | - Benjamin J Raphael
- Department of Computer Science, Princeton University, Princeton, New Jersey 08540, USA
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33
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Zaccaria S, Raphael BJ. Accurate quantification of copy-number aberrations and whole-genome duplications in multi-sample tumor sequencing data. Nat Commun 2020; 11:4301. [PMID: 32879317 PMCID: PMC7468132 DOI: 10.1038/s41467-020-17967-y] [Citation(s) in RCA: 48] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/06/2020] [Accepted: 07/28/2020] [Indexed: 12/12/2022] Open
Abstract
Copy-number aberrations (CNAs) and whole-genome duplications (WGDs) are frequent somatic mutations in cancer but their quantification from DNA sequencing of bulk tumor samples is challenging. Standard methods for CNA inference analyze tumor samples individually; however, DNA sequencing of multiple samples from a cancer patient has recently become more common. We introduce HATCHet (Holistic Allele-specific Tumor Copy-number Heterogeneity), an algorithm that infers allele- and clone-specific CNAs and WGDs jointly across multiple tumor samples from the same patient. We show that HATCHet outperforms current state-of-the-art methods on multi-sample DNA sequencing data that we simulate using MASCoTE (Multiple Allele-specific Simulation of Copy-number Tumor Evolution). Applying HATCHet to 84 tumor samples from 14 prostate and pancreas cancer patients, we identify subclonal CNAs and WGDs that are more plausible than previously published analyses and more consistent with somatic single-nucleotide variants (SNVs) and small indels in the same samples.
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Affiliation(s)
- Simone Zaccaria
- Department of Computer Science, Princeton University, Princeton, NJ, 08540, USA
| | - Benjamin J Raphael
- Department of Computer Science, Princeton University, Princeton, NJ, 08540, USA.
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34
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Abstract
MOTIVATION Copy number aberrations (CNAs), which delete or amplify large contiguous segments of the genome, are a common type of somatic mutation in cancer. Copy number profiles, representing the number of copies of each region of a genome, are readily obtained from whole-genome sequencing or microarrays. However, modeling copy number evolution is a substantial challenge, because different CNAs may overlap with one another on the genome. A recent popular model for copy number evolution is the copy number distance (CND), defined as the length of a shortest sequence of deletions and amplifications of contiguous segments that transforms one profile into the other. In the CND, all events contribute equally; however, it is well known that rates of CNAs vary by length, genomic position and type (amplification versus deletion). RESULTS We introduce a weighted CND that allows events to have varying weights, or probabilities, based on their length, position and type. We derive an efficient algorithm to compute the weighted CND as well as the associated transformation. This algorithm is based on the observation that the constraint matrix of the underlying optimization problem is totally unimodular. We show that the weighted CND improves phylogenetic reconstruction on simulated data where CNAs occur with varying probabilities, aids in the derivation of phylogenies from ultra-low-coverage single-cell DNA sequencing data and helps estimate CNA rates in a large pan-cancer dataset. AVAILABILITY AND IMPLEMENTATION Code is available at https://github.com/raphael-group/WCND. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Ron Zeira
- Department of Computer Science, Princeton University, Princeton, NJ 08544, USA
| | - Benjamin J Raphael
- Department of Computer Science, Princeton University, Princeton, NJ 08544, USA
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35
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Abstract
MOTIVATION Recent single-cell DNA sequencing technologies enable whole-genome sequencing of hundreds to thousands of individual cells. However, these technologies have ultra-low sequencing coverage (<0.5× per cell) which has limited their use to the analysis of large copy-number aberrations (CNAs) in individual cells. While CNAs are useful markers in cancer studies, single-nucleotide mutations are equally important, both in cancer studies and in other applications. However, ultra-low coverage sequencing yields single-nucleotide mutation data that are too sparse for current single-cell analysis methods. RESULTS We introduce SBMClone, a method to infer clusters of cells, or clones, that share groups of somatic single-nucleotide mutations. SBMClone uses a stochastic block model to overcome sparsity in ultra-low coverage single-cell sequencing data, and we show that SBMClone accurately infers the true clonal composition on simulated datasets with coverage at low as 0.2×. We applied SBMClone to single-cell whole-genome sequencing data from two breast cancer patients obtained using two different sequencing technologies. On the first patient, sequenced using the 10X Genomics CNV solution with sequencing coverage ≈0.03×, SBMClone recovers the major clonal composition when incorporating a small amount of additional information. On the second patient, where pre- and post-treatment tumor samples were sequenced using DOP-PCR with sequencing coverage ≈0.5×, SBMClone shows that tumor cells are present in the post-treatment sample, contrary to published analysis of this dataset. AVAILABILITY AND IMPLEMENTATION SBMClone is available on the GitHub repository https://github.com/raphael-group/SBMClone. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Matthew A Myers
- Department of Computer Science, Princeton University, Princeton, NJ 08544, USA
| | - Simone Zaccaria
- Department of Computer Science, Princeton University, Princeton, NJ 08544, USA
| | - Benjamin J Raphael
- Department of Computer Science, Princeton University, Princeton, NJ 08544, USA
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36
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Abstract
A small number of somatic mutations drive the development of cancer, but all somatic mutations are markers of the evolutionary history of a tumor. Prominent methods to construct phylogenies from single-cell sequencing data use single-nucleotide variants (SNVs) as markers but fail to adequately account for copy-number aberrations (CNAs), which can overlap SNVs and result in SNV losses. Here, we introduce SCARLET, an algorithm that infers tumor phylogenies from single-cell DNA sequencing data while accounting for both CNA-driven loss of SNVs and sequencing errors. SCARLET outperforms existing methods on simulated data, with more accurate inference of the order in which mutations were acquired and the mutations present in individual cells. Using a single-cell dataset from a patient with colorectal cancer, SCARLET constructs a tumor phylogeny that is consistent with the observed CNAs and suggests an alternate origin for the patient's metastases. SCARLET is available at: github.com/raphael-group/scarlet.
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Affiliation(s)
- Gryte Satas
- Department of Computer Science, Brown University, Providence, RI 02912
- Department of Computer Science, Princeton University, Princeton, NJ 08540
| | - Simone Zaccaria
- Department of Computer Science, Princeton University, Princeton, NJ 08540
| | - Geoffrey Mon
- Department of Computer Science, Princeton University, Princeton, NJ 08540
| | - Benjamin J Raphael
- Department of Computer Science, Princeton University, Princeton, NJ 08540
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37
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Lähnemann D, Köster J, Szczurek E, McCarthy DJ, Hicks SC, Robinson MD, Vallejos CA, Campbell KR, Beerenwinkel N, Mahfouz A, Pinello L, Skums P, Stamatakis A, Attolini CSO, Aparicio S, Baaijens J, Balvert M, Barbanson BD, Cappuccio A, Corleone G, Dutilh BE, Florescu M, Guryev V, Holmer R, Jahn K, Lobo TJ, Keizer EM, Khatri I, Kielbasa SM, Korbel JO, Kozlov AM, Kuo TH, Lelieveldt BP, Mandoiu II, Marioni JC, Marschall T, Mölder F, Niknejad A, Rączkowska A, Reinders M, Ridder JD, Saliba AE, Somarakis A, Stegle O, Theis FJ, Yang H, Zelikovsky A, McHardy AC, Raphael BJ, Shah SP, Schönhuth A. Eleven grand challenges in single-cell data science. Genome Biol 2020; 21:31. [PMID: 32033589 PMCID: PMC7007675 DOI: 10.1186/s13059-020-1926-6] [Citation(s) in RCA: 520] [Impact Index Per Article: 130.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2019] [Accepted: 01/02/2020] [Indexed: 02/08/2023] Open
Abstract
The recent boom in microfluidics and combinatorial indexing strategies, combined with low sequencing costs, has empowered single-cell sequencing technology. Thousands-or even millions-of cells analyzed in a single experiment amount to a data revolution in single-cell biology and pose unique data science problems. Here, we outline eleven challenges that will be central to bringing this emerging field of single-cell data science forward. For each challenge, we highlight motivating research questions, review prior work, and formulate open problems. This compendium is for established researchers, newcomers, and students alike, highlighting interesting and rewarding problems for the coming years.
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Affiliation(s)
- David Lähnemann
- Algorithms for Reproducible Bioinformatics, Genome Informatics, Institute of Human Genetics, University Hospital Essen, University of Duisburg-Essen, Essen, Germany
- Department of Paediatric Oncology, Haematology and Immunology, Medical Faculty, Heinrich Heine University, University Hospital, Düsseldorf, Germany
- Computational Biology of Infection Research Group, Helmholtz Centre for Infection Research, Braunschweig, Germany
| | - Johannes Köster
- Algorithms for Reproducible Bioinformatics, Genome Informatics, Institute of Human Genetics, University Hospital Essen, University of Duisburg-Essen, Essen, Germany
- Medical Oncology, Dana-Farber Cancer Institute, Harvard Medical School, Boston, USA
| | - Ewa Szczurek
- Institute of Informatics, Faculty of Mathematics, Informatics and Mechanics, University of Warsaw, Warszawa, Poland
| | - Davis J. McCarthy
- Bioinformatics and Cellular Genomics, St Vincent’s Institute of Medical Research, Fitzroy, Australia
- Melbourne Integrative Genomics, School of BioSciences–School of Mathematics & Statistics, Faculty of Science, University of Melbourne, Melbourne, Australia
| | - Stephanie C. Hicks
- Department of Biostatistics, Johns Hopkins University, Baltimore, MD USA
| | - Mark D. Robinson
- Institute of Molecular Life Sciences and SIB Swiss Institute of Bioinformatics, University of Zürich, Zürich, Switzerland
| | - Catalina A. Vallejos
- MRC Human Genetics Unit, Institute of Genetics and Molecular Medicine, University of Edinburgh, Western General Hospital, Edinburgh, UK
- The Alan Turing Institute, British Library, London, UK
| | - Kieran R. Campbell
- Department of Statistics, University of British Columbia, Vancouver, Canada
- Department of Molecular Oncology, BC Cancer Agency, Vancouver, Canada
- Data Science Institute, University of British Columbia, Vancouver, Canada
| | - Niko Beerenwinkel
- Department of Biosystems Science and Engineering, ETH Zurich, Basel, Switzerland
- SIB Swiss Institute of Bioinformatics, Lausanne, Switzerland
| | - Ahmed Mahfouz
- Leiden Computational Biology Center, Leiden University Medical Center, Leiden, The Netherlands
- Delft Bioinformatics Lab, Faculty of Electrical Engineering, Mathematics and Computer Science, Delft University of Technology, Delft, The Netherlands
| | - Luca Pinello
- Molecular Pathology Unit and Center for Cancer Research, Massachusetts General Hospital Research Institute, Charlestown, USA
- Department of Pathology, Harvard Medical School, Boston, USA
- Broad Institute of Harvard and MIT, Cambridge, MA USA
| | - Pavel Skums
- Department of Computer Science, Georgia State University, Atlanta, USA
| | - Alexandros Stamatakis
- Computational Molecular Evolution Group, Heidelberg Institute for Theoretical Studies, Heidelberg, Germany
- Institute for Theoretical Informatics, Karlsruhe Institute of Technology, Karlsruhe, Germany
| | | | - Samuel Aparicio
- Department of Molecular Oncology, BC Cancer Agency, Vancouver, Canada
- Department of Pathology and Laboratory Medicine, University of British Columbia, Vancouver, Canada
| | - Jasmijn Baaijens
- Life Sciences and Health, Centrum Wiskunde & Informatica, Amsterdam, The Netherlands
| | - Marleen Balvert
- Life Sciences and Health, Centrum Wiskunde & Informatica, Amsterdam, The Netherlands
- Theoretical Biology and Bioinformatics, Science for Life, Utrecht University, Utrecht, The Netherlands
| | - Buys de Barbanson
- Center for Molecular Medicine, University Medical Center Utrecht, Utrecht, The Netherlands
- Oncode Institute, Utrecht, The Netherlands
- Quantitative biology, Hubrecht Institute, Utrecht, The Netherlands
| | - Antonio Cappuccio
- Institute for Advanced Study, University of Amsterdam, Amsterdam, The Netherlands
| | - Giacomo Corleone
- Department of Surgery and Cancer, The Imperial Centre for Translational and Experimental Medicine, Imperial College London, London, UK
| | - Bas E. Dutilh
- Theoretical Biology and Bioinformatics, Science for Life, Utrecht University, Utrecht, The Netherlands
- Centre for Molecular and Biomolecular Informatics, Radboud University Medical Center, Nijmegen, The Netherlands
| | - Maria Florescu
- Center for Molecular Medicine, University Medical Center Utrecht, Utrecht, The Netherlands
- Oncode Institute, Utrecht, The Netherlands
- Quantitative biology, Hubrecht Institute, Utrecht, The Netherlands
| | - Victor Guryev
- European Research Institute for the Biology of Ageing, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands
| | - Rens Holmer
- Bioinformatics Group, Wageningen University, Wageningen, The Netherlands
| | - Katharina Jahn
- Department of Biosystems Science and Engineering, ETH Zurich, Basel, Switzerland
- SIB Swiss Institute of Bioinformatics, Lausanne, Switzerland
| | - Thamar Jessurun Lobo
- European Research Institute for the Biology of Ageing, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands
| | - Emma M. Keizer
- Biometris, Wageningen University & Research, Wageningen, The Netherlands
| | - Indu Khatri
- Department of Immunohematology and Blood Transfusion, Leiden University Medical Center, Leiden, The Netherlands
| | - Szymon M. Kielbasa
- Department of Biomedical Data Sciences, Leiden University Medical Center, Leiden, The Netherlands
| | - Jan O. Korbel
- Genome Biology Unit, European Molecular Biology Laboratory, Heidelberg, Germany
| | - Alexey M. Kozlov
- Computational Molecular Evolution Group, Heidelberg Institute for Theoretical Studies, Heidelberg, Germany
| | - Tzu-Hao Kuo
- Computational Biology of Infection Research Group, Helmholtz Centre for Infection Research, Braunschweig, Germany
| | - Boudewijn P.F. Lelieveldt
- PRB lab, Delft University of Technology, Delft, The Netherlands
- Division of Image Processing, Department of Radiology, Leiden University Medical Center, Leiden, The Netherlands
| | - Ion I. Mandoiu
- Computer Science & Engineering Department, University of Connecticut, Storrs, USA
| | - John C. Marioni
- Cancer Research UK Cambridge Institute, Li Ka Shing Centre, University of Cambridge, Cambridge, UK
- Wellcome Trust Sanger Institute, Wellcome Genome Campus, Hinxton, UK
- European Molecular Biology Laboratory, European Bioinformatics Institute, Hinxton, UK
| | - Tobias Marschall
- Center for Bioinformatics, Saarland University, Saarbrücken, Germany
- Max Planck Institute for Informatics, Saarbrücken, Germany
| | - Felix Mölder
- Algorithms for Reproducible Bioinformatics, Genome Informatics, Institute of Human Genetics, University Hospital Essen, University of Duisburg-Essen, Essen, Germany
- Institute of Pathology, University Hospital Essen, University of Duisburg-Essen, Essen, Germany
| | - Amir Niknejad
- Computation molecular design, Zuse Institute Berlin, Berlin, Germany
- Mathematics Department, Mount Saint Vincent, New York, USA
| | - Alicja Rączkowska
- Institute of Informatics, Faculty of Mathematics, Informatics and Mechanics, University of Warsaw, Warszawa, Poland
| | - Marcel Reinders
- Leiden Computational Biology Center, Leiden University Medical Center, Leiden, The Netherlands
- Delft Bioinformatics Lab, Faculty of Electrical Engineering, Mathematics and Computer Science, Delft University of Technology, Delft, The Netherlands
| | - Jeroen de Ridder
- Center for Molecular Medicine, University Medical Center Utrecht, Utrecht, The Netherlands
- Oncode Institute, Utrecht, The Netherlands
| | - Antoine-Emmanuel Saliba
- Helmholtz Institute for RNA-based Infection Research, Helmholtz-Center for Infection Research, Würzburg, Germany
| | - Antonios Somarakis
- Division of Image Processing, Department of Radiology, Leiden University Medical Center, Leiden, The Netherlands
| | - Oliver Stegle
- Genome Biology Unit, European Molecular Biology Laboratory, Heidelberg, Germany
- European Molecular Biology Laboratory, European Bioinformatics Institute, Hinxton, UK
- Division of Computational Genomics and Systems Genetics, German Cancer Research Center–DKFZ, Heidelberg, Germany
| | - Fabian J. Theis
- Institute of Computational Biology, Helmholtz Zentrum München–German Research Center for Environmental Health, Neuherberg, Germany
| | - Huan Yang
- Division of Drug Discovery and Safety, Leiden Academic Center for Drug Research–LACDR–Leiden University, Leiden, The Netherlands
| | - Alex Zelikovsky
- Department of Computer Science, Georgia State University, Atlanta, USA
- The Laboratory of Bioinformatics, I.M. Sechenov First Moscow State Medical University, Moscow, Russia
| | - Alice C. McHardy
- Computational Biology of Infection Research Group, Helmholtz Centre for Infection Research, Braunschweig, Germany
| | | | - Sohrab P. Shah
- Computational Oncology, Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, USA
| | - Alexander Schönhuth
- Life Sciences and Health, Centrum Wiskunde & Informatica, Amsterdam, The Netherlands
- Theoretical Biology and Bioinformatics, Science for Life, Utrecht University, Utrecht, The Netherlands
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Reyna MA, Haan D, Paczkowska M, Verbeke LPC, Vazquez M, Kahraman A, Pulido-Tamayo S, Barenboim J, Wadi L, Dhingra P, Shrestha R, Getz G, Lawrence MS, Pedersen JS, Rubin MA, Wheeler DA, Brunak S, Izarzugaza JMG, Khurana E, Marchal K, von Mering C, Sahinalp SC, Valencia A, Reimand J, Stuart JM, Raphael BJ. Pathway and network analysis of more than 2500 whole cancer genomes. Nat Commun 2020; 11:729. [PMID: 32024854 PMCID: PMC7002574 DOI: 10.1038/s41467-020-14367-0] [Citation(s) in RCA: 56] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/12/2018] [Accepted: 12/18/2019] [Indexed: 12/14/2022] Open
Abstract
The catalog of cancer driver mutations in protein-coding genes has greatly expanded in the past decade. However, non-coding cancer driver mutations are less well-characterized and only a handful of recurrent non-coding mutations, most notably TERT promoter mutations, have been reported. Here, as part of the ICGC/TCGA Pan-Cancer Analysis of Whole Genomes (PCAWG) Consortium, which aggregated whole genome sequencing data from 2658 cancer across 38 tumor types, we perform multi-faceted pathway and network analyses of non-coding mutations across 2583 whole cancer genomes from 27 tumor types compiled by the ICGC/TCGA PCAWG project that was motivated by the success of pathway and network analyses in prioritizing rare mutations in protein-coding genes. While few non-coding genomic elements are recurrently mutated in this cohort, we identify 93 genes harboring non-coding mutations that cluster into several modules of interacting proteins. Among these are promoter mutations associated with reduced mRNA expression in TP53, TLE4, and TCF4. We find that biological processes had variable proportions of coding and non-coding mutations, with chromatin remodeling and proliferation pathways altered primarily by coding mutations, while developmental pathways, including Wnt and Notch, altered by both coding and non-coding mutations. RNA splicing is primarily altered by non-coding mutations in this cohort, and samples containing non-coding mutations in well-known RNA splicing factors exhibit similar gene expression signatures as samples with coding mutations in these genes. These analyses contribute a new repertoire of possible cancer genes and mechanisms that are altered by non-coding mutations and offer insights into additional cancer vulnerabilities that can be investigated for potential therapeutic treatments.
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Affiliation(s)
- Matthew A Reyna
- Department of Computer Science, Princeton University, Princeton, NJ, 08540, USA
- Department of Biomedical Informatics, Emory University, Atlanta, GA, 30322, USA
| | - David Haan
- Department of Biomolecular Engineering and UC Santa Cruz Genomics Institute, University of California, Santa Cruz, Santa Cruz, CA, 95060, USA
| | - Marta Paczkowska
- Computational Biology Program, Ontario Institute for Cancer Research, Toronto, Ontario, Canada
| | - Lieven P C Verbeke
- Department of Information Technology, IDLab, Ghent University, IMEC, Ghent, the Netherlands
- Department of Plant Biotechnology and Bioinformatics, Ghent University, Ghent, the Netherlands
| | - Miguel Vazquez
- Barcelona Supercomputing Center (BSC), Barcelona, 08034, Spain
- Department of Clinical and Molecular Medicine, Faculty of Medicine and Health Sciences, Norwegian University of Science and Technology, Trondheim, Norway
| | - Abdullah Kahraman
- Institute of Molecular Life Sciences and Swiss Institute of Bioinformatics, University of Zurich, CH-8057, Zurich, Switzerland
- Department of Pathology and Molecular Pathology, University Hospital Zurich, CH-8091, Zurich, Switzerland
| | - Sergio Pulido-Tamayo
- Department of Information Technology, IDLab, Ghent University, IMEC, Ghent, the Netherlands
- Department of Plant Biotechnology and Bioinformatics, Ghent University, Ghent, the Netherlands
| | - Jonathan Barenboim
- Computational Biology Program, Ontario Institute for Cancer Research, Toronto, Ontario, Canada
| | - Lina Wadi
- Computational Biology Program, Ontario Institute for Cancer Research, Toronto, Ontario, Canada
| | - Priyanka Dhingra
- Department of Physiology and Biophysics, Weill Cornell Medicine, New York, NY, 10065, USA
| | - Raunak Shrestha
- Vancouver Prostate Centre, 2660 Oak Street, Vancouver, BC, V6H 3Z6, Canada
| | - Gad Getz
- The Broad Institute of MIT and Harvard, Cambridge, MA, 02124, USA
- Massachusetts General Hospital Center for Cancer Research, Charlestown, MA, 02129, USA
- Harvard Medical School, 250 Longwood Avenue, Boston, MA, 02115, USA
- Massachusetts General Hospital, Department of Pathology, Boston, MA, 02114, USA
| | - Michael S Lawrence
- The Broad Institute of MIT and Harvard, Cambridge, MA, 02124, USA
- Massachusetts General Hospital Center for Cancer Research, Charlestown, MA, 02129, USA
| | - Jakob Skou Pedersen
- Department of Molecular Medicine (MOMA), Aarhus University Hospital, Aarhus, Denmark
- Bioinformatics Research Centre (BiRC), Aarhus University, Aarhus, Denmark
| | - Mark A Rubin
- Department of Physiology and Biophysics, Weill Cornell Medicine, New York, NY, 10065, USA
| | - David A Wheeler
- Human Genome Sequencing Center, Baylor College of Medicine, Houston, TX, 77030, USA
| | - Søren Brunak
- DTU Bioinformatics, Department of Bio and Health Informatics, Technical University of Denmark, Kemitorvet, 2800, Kongens Lyngby, Denmark
- Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, 2200, Copenhagen, Denmark
| | - Jose M G Izarzugaza
- DTU Bioinformatics, Department of Bio and Health Informatics, Technical University of Denmark, Kemitorvet, 2800, Kongens Lyngby, Denmark
- Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, 2200, Copenhagen, Denmark
| | - Ekta Khurana
- Department of Physiology and Biophysics, Weill Cornell Medicine, New York, NY, 10065, USA
| | - Kathleen Marchal
- Department of Information Technology, IDLab, Ghent University, IMEC, Ghent, the Netherlands
- Department of Plant Biotechnology and Bioinformatics, Ghent University, Ghent, the Netherlands
| | - Christian von Mering
- Institute of Molecular Life Sciences and Swiss Institute of Bioinformatics, University of Zurich, CH-8057, Zurich, Switzerland
| | - S Cenk Sahinalp
- Vancouver Prostate Centre, 2660 Oak Street, Vancouver, BC, V6H 3Z6, Canada
- Department of Computer Science, Indiana University, Bloomington, IN, 47405, USA
| | - Alfonso Valencia
- Barcelona Supercomputing Center (BSC), Barcelona, 08034, Spain
- ICREA, Barcelona, 08010, Spain
| | - Jüri Reimand
- Computational Biology Program, Ontario Institute for Cancer Research, Toronto, Ontario, Canada.
- Department of Medical Biophysics, University of Toronto, Toronto, Ontario, Canada.
| | - Joshua M Stuart
- Department of Biomolecular Engineering and UC Santa Cruz Genomics Institute, University of California, Santa Cruz, Santa Cruz, CA, 95060, USA.
| | - Benjamin J Raphael
- Department of Computer Science, Princeton University, Princeton, NJ, 08540, USA.
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Aaltonen LA, Abascal F, Abeshouse A, Aburatani H, Adams DJ, Agrawal N, Ahn KS, Ahn SM, Aikata H, Akbani R, Akdemir KC, Al-Ahmadie H, Al-Sedairy ST, Al-Shahrour F, Alawi M, Albert M, Aldape K, Alexandrov LB, Ally A, Alsop K, Alvarez EG, Amary F, Amin SB, Aminou B, Ammerpohl O, Anderson MJ, Ang Y, Antonello D, Anur P, Aparicio S, Appelbaum EL, Arai Y, Aretz A, Arihiro K, Ariizumi SI, Armenia J, Arnould L, Asa S, Assenov Y, Atwal G, Aukema S, Auman JT, Aure MRR, Awadalla P, Aymerich M, Bader GD, Baez-Ortega A, Bailey MH, Bailey PJ, Balasundaram M, Balu S, Bandopadhayay P, Banks RE, Barbi S, Barbour AP, Barenboim J, Barnholtz-Sloan J, Barr H, Barrera E, Bartlett J, Bartolome J, Bassi C, Bathe OF, Baumhoer D, Bavi P, Baylin SB, Bazant W, Beardsmore D, Beck TA, Behjati S, Behren A, Niu B, Bell C, Beltran S, Benz C, Berchuck A, Bergmann AK, Bergstrom EN, Berman BP, Berney DM, Bernhart SH, Beroukhim R, Berrios M, Bersani S, Bertl J, Betancourt M, Bhandari V, Bhosle SG, Biankin AV, Bieg M, Bigner D, Binder H, Birney E, Birrer M, Biswas NK, Bjerkehagen B, Bodenheimer T, Boice L, Bonizzato G, De Bono JS, Boot A, Bootwalla MS, Borg A, Borkhardt A, Boroevich KA, Borozan I, Borst C, Bosenberg M, Bosio M, Boultwood J, Bourque G, Boutros PC, Bova GS, Bowen DT, Bowlby R, Bowtell DDL, Boyault S, Boyce R, Boyd J, Brazma A, Brennan P, Brewer DS, Brinkman AB, Bristow RG, Broaddus RR, Brock JE, Brock M, Broeks A, Brooks AN, Brooks D, Brors B, Brunak S, Bruxner TJC, Bruzos AL, Buchanan A, Buchhalter I, Buchholz C, Bullman S, Burke H, Burkhardt B, Burns KH, Busanovich J, Bustamante CD, Butler AP, Butte AJ, Byrne NJ, Børresen-Dale AL, Caesar-Johnson SJ, Cafferkey A, Cahill D, Calabrese C, Caldas C, Calvo F, Camacho N, Campbell PJ, Campo E, Cantù C, Cao S, Carey TE, Carlevaro-Fita J, Carlsen R, Cataldo I, Cazzola M, Cebon J, Cerfolio R, Chadwick DE, Chakravarty D, Chalmers D, Chan CWY, Chan K, Chan-Seng-Yue M, Chandan VS, Chang DK, Chanock SJ, Chantrill LA, Chateigner A, Chatterjee N, Chayama K, Chen HW, Chen J, Chen K, Chen Y, Chen Z, Cherniack AD, Chien J, Chiew YE, Chin SF, Cho J, Cho S, Choi JK, Choi W, Chomienne C, Chong Z, Choo SP, Chou A, Christ AN, Christie EL, Chuah E, Cibulskis C, Cibulskis K, Cingarlini S, Clapham P, Claviez A, Cleary S, Cloonan N, Cmero M, Collins CC, Connor AA, Cooke SL, Cooper CS, Cope L, Corbo V, Cordes MG, Cordner SM, Cortés-Ciriano I, Covington K, Cowin PA, Craft B, Craft D, Creighton CJ, Cun Y, Curley E, Cutcutache I, Czajka K, Czerniak B, Dagg RA, Danilova L, Davi MV, Davidson NR, Davies H, Davis IJ, Davis-Dusenbery BN, Dawson KJ, De La Vega FM, De Paoli-Iseppi R, Defreitas T, Tos APD, Delaneau O, Demchok JA, Demeulemeester J, Demidov GM, Demircioğlu D, Dennis NM, Denroche RE, Dentro SC, Desai N, Deshpande V, Deshwar AG, Desmedt C, Deu-Pons J, Dhalla N, Dhani NC, Dhingra P, Dhir R, DiBiase A, Diamanti K, Ding L, Ding S, Dinh HQ, Dirix L, Doddapaneni H, Donmez N, Dow MT, Drapkin R, Drechsel O, Drews RM, Serge S, Dudderidge T, Dueso-Barroso A, Dunford AJ, Dunn M, Dursi LJ, Duthie FR, Dutton-Regester K, Eagles J, Easton DF, Edmonds S, Edwards PA, Edwards SE, Eeles RA, Ehinger A, Eils J, Eils R, El-Naggar A, Eldridge M, Ellrott K, Erkek S, Escaramis G, Espiritu SMG, Estivill X, Etemadmoghadam D, Eyfjord JE, Faltas BM, Fan D, Fan Y, Faquin WC, Farcas C, Fassan M, Fatima A, Favero F, Fayzullaev N, Felau I, Fereday S, Ferguson ML, Ferretti V, Feuerbach L, Field MA, Fink JL, Finocchiaro G, Fisher C, Fittall MW, Fitzgerald A, Fitzgerald RC, Flanagan AM, Fleshner NE, Flicek P, Foekens JA, Fong KM, Fonseca NA, Foster CS, Fox NS, Fraser M, Frazer S, Frenkel-Morgenstern M, Friedman W, Frigola J, Fronick CC, Fujimoto A, Fujita M, Fukayama M, Fulton LA, Fulton RS, Furuta M, Futreal PA, Füllgrabe A, Gabriel SB, Gallinger S, Gambacorti-Passerini C, Gao J, Gao S, Garraway L, Garred Ø, Garrison E, Garsed DW, Gehlenborg N, Gelpi JLL, George J, Gerhard DS, Gerhauser C, Gershenwald JE, Gerstein M, Gerstung M, Getz G, Ghori M, Ghossein R, Giama NH, Gibbs RA, Gibson B, Gill AJ, Gill P, Giri DD, Glodzik D, Gnanapragasam VJ, Goebler ME, Goldman MJ, Gomez C, Gonzalez S, Gonzalez-Perez A, Gordenin DA, Gossage J, Gotoh K, Govindan R, Grabau D, Graham JS, Grant RC, Green AR, Green E, Greger L, Grehan N, Grimaldi S, Grimmond SM, Grossman RL, Grundhoff A, Gundem G, Guo Q, Gupta M, Gupta S, Gut IG, Gut M, Göke J, Ha G, Haake A, Haan D, Haas S, Haase K, Haber JE, Habermann N, Hach F, Haider S, Hama N, Hamdy FC, Hamilton A, Hamilton MP, Han L, Hanna GB, Hansmann M, Haradhvala NJ, Harismendy O, Harliwong I, Harmanci AO, Harrington E, Hasegawa T, Haussler D, Hawkins S, Hayami S, Hayashi S, Hayes DN, Hayes SJ, Hayward NK, Hazell S, He Y, Heath AP, Heath SC, Hedley D, Hegde AM, Heiman DI, Heinold MC, Heins Z, Heisler LE, Hellstrom-Lindberg E, Helmy M, Heo SG, Hepperla AJ, Heredia-Genestar JM, Herrmann C, Hersey P, Hess JM, Hilmarsdottir H, Hinton J, Hirano S, Hiraoka N, Hoadley KA, Hobolth A, Hodzic E, Hoell JI, Hoffmann S, Hofmann O, Holbrook A, Holik AZ, Hollingsworth MA, Holmes O, Holt RA, Hong C, Hong EP, Hong JH, Hooijer GK, Hornshøj H, Hosoda F, Hou Y, Hovestadt V, Howat W, Hoyle AP, Hruban RH, Hu J, Hu T, Hua X, Huang KL, Huang M, Huang MN, Huang V, Huang Y, Huber W, Hudson TJ, Hummel M, Hung JA, Huntsman D, Hupp TR, Huse J, Huska MR, Hutter B, Hutter CM, Hübschmann D, Iacobuzio-Donahue CA, Imbusch CD, Imielinski M, Imoto S, Isaacs WB, Isaev K, Ishikawa S, Iskar M, Islam SMA, Ittmann M, Ivkovic S, Izarzugaza JMG, Jacquemier J, Jakrot V, Jamieson NB, Jang GH, Jang SJ, Jayaseelan JC, Jayasinghe R, Jefferys SR, Jegalian K, Jennings JL, Jeon SH, Jerman L, Ji Y, Jiao W, Johansson PA, Johns AL, Johns J, Johnson R, Johnson TA, Jolly C, Joly Y, Jonasson JG, Jones CD, Jones DR, Jones DTW, Jones N, Jones SJM, Jonkers J, Ju YS, Juhl H, Jung J, Juul M, Juul RI, Juul S, Jäger N, Kabbe R, Kahles A, Kahraman A, Kaiser VB, Kakavand H, Kalimuthu S, von Kalle C, Kang KJ, Karaszi K, Karlan B, Karlić R, Karsch D, Kasaian K, Kassahn KS, Katai H, Kato M, Katoh H, Kawakami Y, Kay JD, Kazakoff SH, Kazanov MD, Keays M, Kebebew E, Kefford RF, Kellis M, Kench JG, Kennedy CJ, Kerssemakers JNA, Khoo D, Khoo V, Khuntikeo N, Khurana E, Kilpinen H, Kim HK, Kim HL, Kim HY, Kim H, Kim J, Kim J, Kim JK, Kim Y, King TA, Klapper W, Kleinheinz K, Klimczak LJ, Knappskog S, Kneba M, Knoppers BM, Koh Y, Komorowski J, Komura D, Komura M, Kong G, Kool M, Korbel JO, Korchina V, Korshunov A, Koscher M, Koster R, Kote-Jarai Z, Koures A, Kovacevic M, Kremeyer B, Kretzmer H, Kreuz M, Krishnamurthy S, Kube D, Kumar K, Kumar P, Kumar S, Kumar Y, Kundra R, Kübler K, Küppers R, Lagergren J, Lai PH, Laird PW, Lakhani SR, Lalansingh CM, Lalonde E, Lamaze FC, Lambert A, Lander E, Landgraf P, Landoni L, Langerød A, Lanzós A, Larsimont D, Larsson E, Lathrop M, Lau LMS, Lawerenz C, Lawlor RT, Lawrence MS, Lazar AJ, Lazic AM, Le X, Lee D, Lee D, Lee EA, Lee HJ, Lee JJK, Lee JY, Lee J, Lee MTM, Lee-Six H, Lehmann KV, Lehrach H, Lenze D, Leonard CR, Leongamornlert DA, Leshchiner I, Letourneau L, Letunic I, Levine DA, Lewis L, Ley T, Li C, Li CH, Li HI, Li J, Li L, Li S, Li S, Li X, Li X, Li X, Li Y, Liang H, Liang SB, Lichter P, Lin P, Lin Z, Linehan WM, Lingjærde OC, Liu D, Liu EM, Liu FFF, Liu F, Liu J, Liu X, Livingstone J, Livitz D, Livni N, Lochovsky L, Loeffler M, Long GV, Lopez-Guillermo A, Lou S, Louis DN, Lovat LB, Lu Y, Lu YJ, Lu Y, Luchini C, Lungu I, Luo X, Luxton HJ, Lynch AG, Lype L, López C, López-Otín C, Ma EZ, Ma Y, MacGrogan G, MacRae S, Macintyre G, Madsen T, Maejima K, Mafficini A, Maglinte DT, Maitra A, Majumder PP, Malcovati L, Malikic S, Malleo G, Mann GJ, Mantovani-Löffler L, Marchal K, Marchegiani G, Mardis ER, Margolin AA, Marin MG, Markowetz F, Markowski J, Marks J, Marques-Bonet T, Marra MA, Marsden L, Martens JWM, Martin S, Martin-Subero JI, Martincorena I, Martinez-Fundichely A, Maruvka YE, Mashl RJ, Massie CE, Matthew TJ, Matthews L, Mayer E, Mayes S, Mayo M, Mbabaali F, McCune K, McDermott U, McGillivray PD, McLellan MD, McPherson JD, McPherson JR, McPherson TA, Meier SR, Meng A, Meng S, Menzies A, Merrett ND, Merson S, Meyerson M, Meyerson W, Mieczkowski PA, Mihaiescu GL, Mijalkovic S, Mikkelsen T, Milella M, Mileshkin L, Miller CA, Miller DK, Miller JK, Mills GB, Milovanovic A, Minner S, Miotto M, Arnau GM, Mirabello L, Mitchell C, Mitchell TJ, Miyano S, Miyoshi N, Mizuno S, Molnár-Gábor F, Moore MJ, Moore RA, Morganella S, Morris QD, Morrison C, Mose LE, Moser CD, Muiños F, Mularoni L, Mungall AJ, Mungall K, Musgrove EA, Mustonen V, Mutch D, Muyas F, Muzny DM, Muñoz A, Myers J, Myklebost O, Möller P, Nagae G, Nagrial AM, Nahal-Bose HK, Nakagama H, Nakagawa H, Nakamura H, Nakamura T, Nakano K, Nandi T, Nangalia J, Nastic M, Navarro A, Navarro FCP, Neal DE, Nettekoven G, Newell F, Newhouse SJ, Newton Y, Ng AWT, Ng A, Nicholson J, Nicol D, Nie Y, Nielsen GP, Nielsen MM, Nik-Zainal S, Noble MS, Nones K, Northcott PA, Notta F, O’Connor BD, O’Donnell P, O’Donovan M, O’Meara S, O’Neill BP, O’Neill JR, Ocana D, Ochoa A, Oesper L, Ogden C, Ohdan H, Ohi K, Ohno-Machado L, Oien KA, Ojesina AI, Ojima H, Okusaka T, Omberg L, Ong CK, Ossowski S, Ott G, Ouellette BFF, P’ng C, Paczkowska M, Paiella S, Pairojkul C, Pajic M, Pan-Hammarström Q, Papaemmanuil E, Papatheodorou I, Paramasivam N, Park JW, Park JW, Park K, Park K, Park PJ, Parker JS, Parsons SL, Pass H, Pasternack D, Pastore A, Patch AM, Pauporté I, Pea A, Pearson JV, Pedamallu CS, Pedersen JS, Pederzoli P, Peifer M, Pennell NA, Perou CM, Perry MD, Petersen GM, Peto M, Petrelli N, Petryszak R, Pfister SM, Phillips M, Pich O, Pickett HA, Pihl TD, Pillay N, Pinder S, Pinese M, Pinho AV, Pitkänen E, Pivot X, Piñeiro-Yáñez E, Planko L, Plass C, Polak P, Pons T, Popescu I, Potapova O, Prasad A, Preston SR, Prinz M, Pritchard AL, Prokopec SD, Provenzano E, Puente XS, Puig S, Puiggròs M, Pulido-Tamayo S, Pupo GM, Purdie CA, Quinn MC, Rabionet R, Rader JS, Radlwimmer B, Radovic P, Raeder B, Raine KM, Ramakrishna M, Ramakrishnan K, Ramalingam S, Raphael BJ, Rathmell WK, Rausch T, Reifenberger G, Reimand J, Reis-Filho J, Reuter V, Reyes-Salazar I, Reyna MA, Reynolds SM, Rheinbay E, Riazalhosseini Y, Richardson AL, Richter J, Ringel M, Ringnér M, Rino Y, Rippe K, Roach J, Roberts LR, Roberts ND, Roberts SA, Robertson AG, Robertson AJ, Rodriguez JB, Rodriguez-Martin B, Rodríguez-González FG, Roehrl MHA, Rohde M, Rokutan H, Romieu G, Rooman I, Roques T, Rosebrock D, Rosenberg M, Rosenstiel PC, Rosenwald A, Rowe EW, Royo R, Rozen SG, Rubanova Y, Rubin MA, Rubio-Perez C, Rudneva VA, Rusev BC, Ruzzenente A, Rätsch G, Sabarinathan R, Sabelnykova VY, Sadeghi S, Sahinalp SC, Saini N, Saito-Adachi M, Saksena G, Salcedo A, Salgado R, Salichos L, Sallari R, Saller C, Salvia R, Sam M, Samra JS, Sanchez-Vega F, Sander C, Sanders G, Sarin R, Sarrafi I, Sasaki-Oku A, Sauer T, Sauter G, Saw RPM, Scardoni M, Scarlett CJ, Scarpa A, Scelo G, Schadendorf D, Schein JE, Schilhabel MB, Schlesner M, Schlomm T, Schmidt HK, Schramm SJ, Schreiber S, Schultz N, Schumacher SE, Schwarz RF, Scolyer RA, Scott D, Scully R, Seethala R, Segre AV, Selander I, Semple CA, Senbabaoglu Y, Sengupta S, Sereni E, Serra S, Sgroi DC, Shackleton M, Shah NC, Shahabi S, Shang CA, Shang P, Shapira O, Shelton T, Shen C, Shen H, Shepherd R, Shi R, Shi Y, Shiah YJ, Shibata T, Shih J, Shimizu E, Shimizu K, Shin SJ, Shiraishi Y, Shmaya T, Shmulevich I, Shorser SI, Short C, Shrestha R, Shringarpure SS, Shriver C, Shuai S, Sidiropoulos N, Siebert R, Sieuwerts AM, Sieverling L, Signoretti S, Sikora KO, Simbolo M, Simon R, Simons JV, Simpson JT, Simpson PT, Singer S, Sinnott-Armstrong N, Sipahimalani P, Skelly TJ, Smid M, Smith J, Smith-McCune K, Socci ND, Sofia HJ, Soloway MG, Song L, Sood AK, Sothi S, Sotiriou C, Soulette CM, Span PN, Spellman PT, Sperandio N, Spillane AJ, Spiro O, Spring J, Staaf J, Stadler PF, Staib P, Stark SG, Stebbings L, Stefánsson ÓA, Stegle O, Stein LD, Stenhouse A, Stewart C, Stilgenbauer S, Stobbe MD, Stratton MR, Stretch JR, Struck AJ, Stuart JM, Stunnenberg HG, Su H, Su X, Sun RX, Sungalee S, Susak H, Suzuki A, Sweep F, Szczepanowski M, Sültmann H, Yugawa T, Tam A, Tamborero D, Tan BKT, Tan D, Tan P, Tanaka H, Taniguchi H, Tanskanen TJ, Tarabichi M, Tarnuzzer R, Tarpey P, Taschuk ML, Tatsuno K, Tavaré S, Taylor DF, Taylor-Weiner A, Teague JW, Teh BT, Tembe V, Temes J, Thai K, Thayer SP, Thiessen N, Thomas G, Thomas S, Thompson A, Thompson AM, Thompson JFF, Thompson RH, Thorne H, Thorne LB, Thorogood A, Tiao G, Tijanic N, Timms LE, Tirabosco R, Tojo M, Tommasi S, Toon CW, Toprak UH, Torrents D, Tortora G, Tost J, Totoki Y, Townend D, Traficante N, Treilleux I, Trotta JR, Trümper LHP, Tsao M, Tsunoda T, Tubio JMC, Tucker O, Turkington R, Turner DJ, Tutt A, Ueno M, Ueno NT, Umbricht C, Umer HM, Underwood TJ, Urban L, Urushidate T, Ushiku T, Uusküla-Reimand L, Valencia A, Van Den Berg DJ, Van Laere S, Van Loo P, Van Meir EG, Van den Eynden GG, Van der Kwast T, Vasudev N, Vazquez M, Vedururu R, Veluvolu U, Vembu S, Verbeke LPC, Vermeulen P, Verrill C, Viari A, Vicente D, Vicentini C, VijayRaghavan K, Viksna J, Vilain RE, Villasante I, Vincent-Salomon A, Visakorpi T, Voet D, Vyas P, Vázquez-García I, Waddell NM, Waddell N, Wadelius C, Wadi L, Wagener R, Wala JA, Wang J, Wang J, Wang L, Wang Q, Wang W, Wang Y, Wang Z, Waring PM, Warnatz HJ, Warrell J, Warren AY, Waszak SM, Wedge DC, Weichenhan D, Weinberger P, Weinstein JN, Weischenfeldt J, Weisenberger DJ, Welch I, Wendl MC, Werner J, Whalley JP, Wheeler DA, Whitaker HC, Wigle D, Wilkerson MD, Williams A, Wilmott JS, Wilson GW, Wilson JM, Wilson RK, Winterhoff B, Wintersinger JA, Wiznerowicz M, Wolf S, Wong BH, Wong T, Wong W, Woo Y, Wood S, Wouters BG, Wright AJ, Wright DW, Wright MH, Wu CL, Wu DY, Wu G, Wu J, Wu K, Wu Y, Wu Z, Xi L, Xia T, Xiang Q, Xiao X, Xing R, Xiong H, Xu Q, Xu Y, Xue H, Yachida S, Yakneen S, Yamaguchi R, Yamaguchi TN, Yamamoto M, Yamamoto S, Yamaue H, Yang F, Yang H, Yang JY, Yang L, Yang L, Yang S, Yang TP, Yang Y, Yao X, Yaspo ML, Yates L, Yau C, Ye C, Ye K, Yellapantula VD, Yoon CJ, Yoon SS, Yousif F, Yu J, Yu K, Yu W, Yu Y, Yuan K, Yuan Y, Yuen D, Yung CK, Zaikova O, Zamora J, Zapatka M, Zenklusen JC, Zenz T, Zeps N, Zhang CZ, Zhang F, Zhang H, Zhang H, Zhang H, Zhang J, Zhang J, Zhang J, Zhang X, Zhang X, Zhang Y, Zhang Z, Zhao Z, Zheng L, Zheng X, Zhou W, Zhou Y, Zhu B, Zhu H, Zhu J, Zhu S, Zou L, Zou X, deFazio A, van As N, van Deurzen CHM, van de Vijver MJ, van’t Veer L, von Mering C. Pan-cancer analysis of whole genomes. Nature 2020; 578:82-93. [PMID: 32025007 PMCID: PMC7025898 DOI: 10.1038/s41586-020-1969-6] [Citation(s) in RCA: 1435] [Impact Index Per Article: 358.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/29/2018] [Accepted: 12/11/2019] [Indexed: 02/07/2023]
Abstract
Cancer is driven by genetic change, and the advent of massively parallel sequencing has enabled systematic documentation of this variation at the whole-genome scale1-3. Here we report the integrative analysis of 2,658 whole-cancer genomes and their matching normal tissues across 38 tumour types from the Pan-Cancer Analysis of Whole Genomes (PCAWG) Consortium of the International Cancer Genome Consortium (ICGC) and The Cancer Genome Atlas (TCGA). We describe the generation of the PCAWG resource, facilitated by international data sharing using compute clouds. On average, cancer genomes contained 4-5 driver mutations when combining coding and non-coding genomic elements; however, in around 5% of cases no drivers were identified, suggesting that cancer driver discovery is not yet complete. Chromothripsis, in which many clustered structural variants arise in a single catastrophic event, is frequently an early event in tumour evolution; in acral melanoma, for example, these events precede most somatic point mutations and affect several cancer-associated genes simultaneously. Cancers with abnormal telomere maintenance often originate from tissues with low replicative activity and show several mechanisms of preventing telomere attrition to critical levels. Common and rare germline variants affect patterns of somatic mutation, including point mutations, structural variants and somatic retrotransposition. A collection of papers from the PCAWG Consortium describes non-coding mutations that drive cancer beyond those in the TERT promoter4; identifies new signatures of mutational processes that cause base substitutions, small insertions and deletions and structural variation5,6; analyses timings and patterns of tumour evolution7; describes the diverse transcriptional consequences of somatic mutation on splicing, expression levels, fusion genes and promoter activity8,9; and evaluates a range of more-specialized features of cancer genomes8,10-18.
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Rheinbay E, Nielsen MM, Abascal F, Wala JA, Shapira O, Tiao G, Hornshøj H, Hess JM, Juul RI, Lin Z, Feuerbach L, Sabarinathan R, Madsen T, Kim J, Mularoni L, Shuai S, Lanzós A, Herrmann C, Maruvka YE, Shen C, Amin SB, Bandopadhayay P, Bertl J, Boroevich KA, Busanovich J, Carlevaro-Fita J, Chakravarty D, Chan CWY, Craft D, Dhingra P, Diamanti K, Fonseca NA, Gonzalez-Perez A, Guo Q, Hamilton MP, Haradhvala NJ, Hong C, Isaev K, Johnson TA, Juul M, Kahles A, Kahraman A, Kim Y, Komorowski J, Kumar K, Kumar S, Lee D, Lehmann KV, Li Y, Liu EM, Lochovsky L, Park K, Pich O, Roberts ND, Saksena G, Schumacher SE, Sidiropoulos N, Sieverling L, Sinnott-Armstrong N, Stewart C, Tamborero D, Tubio JMC, Umer HM, Uusküla-Reimand L, Wadelius C, Wadi L, Yao X, Zhang CZ, Zhang J, Haber JE, Hobolth A, Imielinski M, Kellis M, Lawrence MS, von Mering C, Nakagawa H, Raphael BJ, Rubin MA, Sander C, Stein LD, Stuart JM, Tsunoda T, Wheeler DA, Johnson R, Reimand J, Gerstein M, Khurana E, Campbell PJ, López-Bigas N, Weischenfeldt J, Beroukhim R, Martincorena I, Pedersen JS, Getz G. Analyses of non-coding somatic drivers in 2,658 cancer whole genomes. Nature 2020; 578:102-111. [PMID: 32025015 PMCID: PMC7054214 DOI: 10.1038/s41586-020-1965-x] [Citation(s) in RCA: 332] [Impact Index Per Article: 83.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2018] [Accepted: 12/02/2019] [Indexed: 01/28/2023]
Abstract
The discovery of drivers of cancer has traditionally focused on protein-coding genes1-4. Here we present analyses of driver point mutations and structural variants in non-coding regions across 2,658 genomes from the Pan-Cancer Analysis of Whole Genomes (PCAWG) Consortium5 of the International Cancer Genome Consortium (ICGC) and The Cancer Genome Atlas (TCGA). For point mutations, we developed a statistically rigorous strategy for combining significance levels from multiple methods of driver discovery that overcomes the limitations of individual methods. For structural variants, we present two methods of driver discovery, and identify regions that are significantly affected by recurrent breakpoints and recurrent somatic juxtapositions. Our analyses confirm previously reported drivers6,7, raise doubts about others and identify novel candidates, including point mutations in the 5' region of TP53, in the 3' untranslated regions of NFKBIZ and TOB1, focal deletions in BRD4 and rearrangements in the loci of AKR1C genes. We show that although point mutations and structural variants that drive cancer are less frequent in non-coding genes and regulatory sequences than in protein-coding genes, additional examples of these drivers will be found as more cancer genomes become available.
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Affiliation(s)
- Esther Rheinbay
- The Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Center for Cancer Research, Massachusetts General Hospital, Charlestown, MA, USA
- Harvard Medical School, Boston, MA, USA
| | - Morten Muhlig Nielsen
- Department of Molecular Medicine (MOMA), Aarhus University Hospital, Aarhus, Denmark
| | | | - Jeremiah A Wala
- The Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Bioinformatics and Integrative Genomics, Harvard University, Cambridge, MA, USA
| | - Ofer Shapira
- The Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, MA, USA
| | - Grace Tiao
- The Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Henrik Hornshøj
- Department of Molecular Medicine (MOMA), Aarhus University Hospital, Aarhus, Denmark
| | - Julian M Hess
- The Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Randi Istrup Juul
- Department of Molecular Medicine (MOMA), Aarhus University Hospital, Aarhus, Denmark
| | - Ziao Lin
- The Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Harvard University, Cambridge, MA, USA
| | - Lars Feuerbach
- Division of Applied Bioinformatics, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Radhakrishnan Sabarinathan
- Institute for Research in Biomedicine (IRB Barcelona), The Barcelona Institute of Science and Technology, Barcelona, Spain
- Research Program on Biomedical Informatics, Universitat Pompeu Fabra, Barcelona, Spain
| | - Tobias Madsen
- Department of Molecular Medicine (MOMA), Aarhus University Hospital, Aarhus, Denmark
| | - Jaegil Kim
- The Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Loris Mularoni
- Institute for Research in Biomedicine (IRB Barcelona), The Barcelona Institute of Science and Technology, Barcelona, Spain
- Research Program on Biomedical Informatics, Universitat Pompeu Fabra, Barcelona, Spain
| | - Shimin Shuai
- Computational Biology Program, Ontario Institute for Cancer Research, Toronto, Ontario, Canada
- Ontario Institute for Cancer Research, Toronto, Ontario, Canada
| | - Andrés Lanzós
- Department for BioMedical Research, University of Bern, Bern, Switzerland
- Graduate School of Cellular and Biomedical Sciences, University of Bern, Bern, Switzerland
- Department of Medical Oncology, Bern University Hospital, University of Bern, Bern, Switzerland
| | - Carl Herrmann
- Division of Theoretical Bioinformatics, German Cancer Research Center (DKFZ), Heidelberg, Germany
- Bioquant Center, Institute of Pharmacy and Molecular Biotechnology, University of Heidelberg, Heidelberg, Germany
| | - Yosef E Maruvka
- The Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Center for Cancer Research, Massachusetts General Hospital, Charlestown, MA, USA
| | - Ciyue Shen
- Department of Cell Biology, Harvard Medical School, Boston, MA, USA
- cBio Center, Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA, USA
| | - Samirkumar B Amin
- Department of Genomic Medicine, University of Texas MD Anderson Cancer Center, Houston, TX, USA
- Graduate Program in Structural and Computational Biology and Molecular Biophysics, Baylor College of Medicine, Houston, TX, USA
| | - Pratiti Bandopadhayay
- The Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, MA, USA
| | - Johanna Bertl
- Department of Molecular Medicine (MOMA), Aarhus University Hospital, Aarhus, Denmark
| | - Keith A Boroevich
- Laboratory for Medical Science Mathematics, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan
| | - John Busanovich
- The Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, MA, USA
| | - Joana Carlevaro-Fita
- Department for BioMedical Research, University of Bern, Bern, Switzerland
- Graduate School of Cellular and Biomedical Sciences, University of Bern, Bern, Switzerland
- Department of Medical Oncology, Bern University Hospital, University of Bern, Bern, Switzerland
| | - Dimple Chakravarty
- Department of Genitourinary Medical Oncology - Research, Division of Cancer Medicine, University of Texas MD Anderson Cancer Center, Houston, TX, USA
- Department of Urology, Icahn school of Medicine at Mount Sinai, New York, NY, USA
| | - Calvin Wing Yiu Chan
- Division of Theoretical Bioinformatics, German Cancer Research Center (DKFZ), Heidelberg, Germany
- Faculty of Biosciences, Heidelberg University, Heidelberg, Germany
| | - David Craft
- Department of Radiation Oncology, Harvard Medical School, Massachusetts General Hospital, Boston, MA, USA
| | - Priyanka Dhingra
- Department of Physiology and Biophysics, Weill Cornell Medicine, New York, NY, USA
- Institute for Computational Biomedicine, Weill Cornell Medicine, New York, NY, USA
| | - Klev Diamanti
- Science for Life Laboratory, Department of Cell and Molecular Biology, Uppsala University, Uppsala, Sweden
| | - Nuno A Fonseca
- European Bioinformatics Institute, European Molecular Biology Laboratory, Hinxton, UK
| | - Abel Gonzalez-Perez
- Institute for Research in Biomedicine (IRB Barcelona), The Barcelona Institute of Science and Technology, Barcelona, Spain
- Research Program on Biomedical Informatics, Universitat Pompeu Fabra, Barcelona, Spain
| | - Qianyun Guo
- Bioinformatics Research Centre (BiRC), Aarhus University, Aarhus, Denmark
| | - Mark P Hamilton
- Department of Molecular and Cellular Biology, Baylor College of Medicine, Houston, TX, USA
| | - Nicholas J Haradhvala
- The Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Center for Cancer Research, Massachusetts General Hospital, Charlestown, MA, USA
| | - Chen Hong
- Division of Applied Bioinformatics, German Cancer Research Center (DKFZ), Heidelberg, Germany
- Faculty of Biosciences, Heidelberg University, Heidelberg, Germany
| | - Keren Isaev
- Computational Biology Program, Ontario Institute for Cancer Research, Toronto, Ontario, Canada
- Department of Medical Biophysics, University of Toronto, Toronto, Ontario, Canada
| | - Todd A Johnson
- Laboratory for Medical Science Mathematics, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan
| | - Malene Juul
- Department of Molecular Medicine (MOMA), Aarhus University Hospital, Aarhus, Denmark
| | - Andre Kahles
- Division of Computational Biology, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Abdullah Kahraman
- Institute of Molecular Life Sciences and Swiss Institute of Bioinformatics, University of Zurich, Zurich, Switzerland
| | - Youngwook Kim
- Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, South Korea
| | - Jan Komorowski
- Science for Life Laboratory, Department of Cell and Molecular Biology, Uppsala University, Uppsala, Sweden
- Institute of Computer Science, Polish Academy of Sciences, Warsaw, Poland
| | - Kiran Kumar
- The Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, MA, USA
| | - Sushant Kumar
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT, USA
| | - Donghoon Lee
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT, USA
| | - Kjong-Van Lehmann
- Division of Computational Biology, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Yilong Li
- SBGD Inc, Cambridge, MA, USA
- Department of Haematology, University of Cambridge, Cambridge, UK
| | - Eric Minwei Liu
- Department of Physiology and Biophysics, Weill Cornell Medicine, New York, NY, USA
- Institute for Computational Biomedicine, Weill Cornell Medicine, New York, NY, USA
| | - Lucas Lochovsky
- Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT, USA
| | - Keunchil Park
- Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, South Korea
| | - Oriol Pich
- Institute for Research in Biomedicine (IRB Barcelona), The Barcelona Institute of Science and Technology, Barcelona, Spain
- Research Program on Biomedical Informatics, Universitat Pompeu Fabra, Barcelona, Spain
| | - Nicola D Roberts
- Department of Haematology, University of Cambridge, Cambridge, UK
| | - Gordon Saksena
- The Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Steven E Schumacher
- The Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, MA, USA
| | - Nikos Sidiropoulos
- Biotech Research & Innovation Centre (BRIC), The Finsen Laboratory, Rigshospitalet, University of Copenhagen, Copenhagen, Denmark
| | - Lina Sieverling
- Division of Applied Bioinformatics, German Cancer Research Center (DKFZ), Heidelberg, Germany
- Faculty of Biosciences, Heidelberg University, Heidelberg, Germany
| | | | - Chip Stewart
- The Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - David Tamborero
- Institute for Research in Biomedicine (IRB Barcelona), The Barcelona Institute of Science and Technology, Barcelona, Spain
- Research Program on Biomedical Informatics, Universitat Pompeu Fabra, Barcelona, Spain
| | - Jose M C Tubio
- Department of Zoology, Genetics and Physical Anthropology, Universidade de Santiago de Compostela, Santiago de Compostela, Spain
- Centre for Research in Molecular Medicine and Chronic Diseases (CIMUS), Universidade de Santiago de Compostela, Santiago de Compostela, Spain
- The Biomedical Research Centre (CINBIO), Universidade de Vigo, Vigo, Spain
| | - Husen M Umer
- Science for Life Laboratory, Department of Cell and Molecular Biology, Uppsala University, Uppsala, Sweden
- Department of Oncology-Pathology, Science for Life Laboratory, Karolinska Institute, Stockholm, Sweden
| | - Liis Uusküla-Reimand
- Genetics and Genome Biology Program, SickKids Research Institute, Toronto, Ontario, Canada
- Department of Gene Technology, Tallinn University of Technology, Tallinn, Estonia
| | - Claes Wadelius
- Science for Life Laboratory, Department of Immunology, Genetics and Pathology, Uppsala University, Uppsala, Sweden
| | - Lina Wadi
- Computational Biology Program, Ontario Institute for Cancer Research, Toronto, Ontario, Canada
| | | | - Cheng-Zhong Zhang
- Department of Biostatistics and Computational Biology, Dana-Farber Cancer Institute, Boston, MA, USA
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
| | - Jing Zhang
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT, USA
| | - James E Haber
- Department of Biology and Rosenstiel Basic Medical Sciences Research Center, Brandeis University, Waltham, MA, USA
| | - Asger Hobolth
- Bioinformatics Research Centre (BiRC), Aarhus University, Aarhus, Denmark
| | - Marcin Imielinski
- New York Genome Center, New York, NY, USA
- Department of Pathology and Laboratory Medicine, and Englander Institute for Precision Medicine, and Institute for Computational Biomedicine, and Meyer Cancer Center, Weill Cornell Medicine, New York, NY, USA
| | - Manolis Kellis
- The Broad Institute of MIT and Harvard, Cambridge, MA, USA
- MIT Computer Science and Artificial Intelligence Laboratory, Cambridge, MA, USA
| | - Michael S Lawrence
- The Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Center for Cancer Research, Massachusetts General Hospital, Charlestown, MA, USA
| | - Christian von Mering
- Institute of Molecular Life Sciences and Swiss Institute of Bioinformatics, University of Zurich, Zurich, Switzerland
| | - Hidewaki Nakagawa
- Laboratory for Cancer Genomics, RIKEN Center for Integrative Medical Sciences, Tokyo, Japan
| | - Benjamin J Raphael
- Department of Computer Science, Princeton University, Princeton, NJ, USA
| | - Mark A Rubin
- Department of Pathology and Laboratory Medicine, Weill Cornell Medicine, New York, NY, USA
- Englander Institute for Precision Medicine, Weill Cornell Medicine, New York, NY, USA
- Meyer Cancer Center, Weill Cornell Medicine, New York, NY, USA
| | - Chris Sander
- Department of Cell Biology, Harvard Medical School, Boston, MA, USA
- cBio Center, Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA, USA
| | - Lincoln D Stein
- Computational Biology Program, Ontario Institute for Cancer Research, Toronto, Ontario, Canada
- Ontario Institute for Cancer Research, Toronto, Ontario, Canada
| | - Joshua M Stuart
- Center for Biomolecular Science and Engineering, University of California at Santa Cruz, Santa Cruz, CA, USA
| | - Tatsuhiko Tsunoda
- Laboratory for Medical Science Mathematics, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan
- Department of Medical Science Mathematics, Medical Research Institute, Tokyo Medical and Dental University, Tokyo, Japan
- Laboratory for Medical Science Mathematics, Department of Biological Sciences, Graduate School of Science, The University of Tokyo, Tokyo, Japan
| | - David A Wheeler
- Human Genome Sequencing Center, Baylor College of Medicine, Houston, TX, USA
| | - Rory Johnson
- Department for BioMedical Research, University of Bern, Bern, Switzerland
- Department of Medical Oncology, Bern University Hospital, University of Bern, Bern, Switzerland
| | - Jüri Reimand
- Computational Biology Program, Ontario Institute for Cancer Research, Toronto, Ontario, Canada
- Department of Medical Biophysics, University of Toronto, Toronto, Ontario, Canada
| | - Mark Gerstein
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT, USA
- Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT, USA
- Department of Computer Science, Yale University, New Haven, CT, USA
| | - Ekta Khurana
- Department of Physiology and Biophysics, Weill Cornell Medicine, New York, NY, USA
- Institute for Computational Biomedicine, Weill Cornell Medicine, New York, NY, USA
- Englander Institute for Precision Medicine, Weill Cornell Medicine, New York, NY, USA
- Meyer Cancer Center, Weill Cornell Medicine, New York, NY, USA
| | - Peter J Campbell
- Wellcome Trust Sanger Institute, Hinxton, UK
- Department of Haematology, University of Cambridge, Cambridge, UK
| | - Núria López-Bigas
- Institute for Research in Biomedicine (IRB Barcelona), The Barcelona Institute of Science and Technology, Barcelona, Spain
- Research Program on Biomedical Informatics, Universitat Pompeu Fabra, Barcelona, Spain
- Catalan Institution for Research and Advanced Studies (ICREA), Barcelona, Spain
| | - Joachim Weischenfeldt
- Biotech Research & Innovation Centre (BRIC), The Finsen Laboratory, Rigshospitalet, University of Copenhagen, Copenhagen, Denmark.
- Genome Biology Unit, European Molecular Biology Laboratory (EMBL), Heidelberg, Germany.
| | - Rameen Beroukhim
- The Broad Institute of MIT and Harvard, Cambridge, MA, USA.
- Bioinformatics and Integrative Genomics, Harvard University, Cambridge, MA, USA.
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA.
| | | | - Jakob Skou Pedersen
- Department of Molecular Medicine (MOMA), Aarhus University Hospital, Aarhus, Denmark.
- Bioinformatics Research Centre (BiRC), Aarhus University, Aarhus, Denmark.
| | - Gad Getz
- The Broad Institute of MIT and Harvard, Cambridge, MA, USA.
- Center for Cancer Research, Massachusetts General Hospital, Charlestown, MA, USA.
- Harvard Medical School, Boston, MA, USA.
- Department of Pathology, Massachusetts General Hospital, Boston, MA, USA.
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41
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Elyanow R, Dumitrascu B, Engelhardt BE, Raphael BJ. netNMF-sc: leveraging gene-gene interactions for imputation and dimensionality reduction in single-cell expression analysis. Genome Res 2020; 30:195-204. [PMID: 31992614 PMCID: PMC7050525 DOI: 10.1101/gr.251603.119] [Citation(s) in RCA: 36] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/18/2019] [Accepted: 11/19/2019] [Indexed: 02/06/2023]
Abstract
Single-cell RNA-sequencing (scRNA-seq) enables high-throughput measurement of RNA expression in single cells. However, because of technical limitations, scRNA-seq data often contain zero counts for many transcripts in individual cells. These zero counts, or dropout events, complicate the analysis of scRNA-seq data using standard methods developed for bulk RNA-seq data. Current scRNA-seq analysis methods typically overcome dropout by combining information across cells in a lower-dimensional space, leveraging the observation that cells generally occupy a small number of RNA expression states. We introduce netNMF-sc, an algorithm for scRNA-seq analysis that leverages information across both cells and genes. netNMF-sc learns a low-dimensional representation of scRNA-seq transcript counts using network-regularized non-negative matrix factorization. The network regularization takes advantage of prior knowledge of gene–gene interactions, encouraging pairs of genes with known interactions to be nearby each other in the low-dimensional representation. The resulting matrix factorization imputes gene abundance for both zero and nonzero counts and can be used to cluster cells into meaningful subpopulations. We show that netNMF-sc outperforms existing methods at clustering cells and estimating gene–gene covariance using both simulated and real scRNA-seq data, with increasing advantages at higher dropout rates (e.g., >60%). We also show that the results from netNMF-sc are robust to variation in the input network, with more representative networks leading to greater performance gains.
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Affiliation(s)
- Rebecca Elyanow
- Center for Computational Molecular Biology, Brown University, Providence, Rhode Island 02912, USA.,Department of Computer Science, Princeton University, Princeton, New Jersey 08540, USA
| | - Bianca Dumitrascu
- Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, New Jersey 08540, USA
| | - Barbara E Engelhardt
- Department of Computer Science, Princeton University, Princeton, New Jersey 08540, USA.,Center for Statistics and Machine Learning, Princeton University, Princeton, New Jersey 08540, USA
| | - Benjamin J Raphael
- Department of Computer Science, Princeton University, Princeton, New Jersey 08540, USA
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42
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Li B, Brady SW, Ma X, Shen S, Zhang Y, Li Y, Szlachta K, Dong L, Liu Y, Yang F, Wang N, Flasch DA, Myers MA, Mulder HL, Ding L, Liu Y, Tian L, Hagiwara K, Xu K, Zhou X, Sioson E, Wang T, Yang L, Zhao J, Zhang H, Shao Y, Sun H, Sun L, Cai J, Sun HY, Lin TN, Du L, Li H, Rusch M, Edmonson MN, Easton J, Zhu X, Zhang J, Cheng C, Raphael BJ, Tang J, Downing JR, Alexandrov LB, Zhou BBS, Pui CH, Yang JJ, Zhang J. Therapy-induced mutations drive the genomic landscape of relapsed acute lymphoblastic leukemia. Blood 2020; 135:41-55. [PMID: 31697823 PMCID: PMC6940198 DOI: 10.1182/blood.2019002220] [Citation(s) in RCA: 145] [Impact Index Per Article: 36.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/27/2019] [Accepted: 10/10/2019] [Indexed: 12/23/2022] Open
Abstract
To study the mechanisms of relapse in acute lymphoblastic leukemia (ALL), we performed whole-genome sequencing of 103 diagnosis-relapse-germline trios and ultra-deep sequencing of 208 serial samples in 16 patients. Relapse-specific somatic alterations were enriched in 12 genes (NR3C1, NR3C2, TP53, NT5C2, FPGS, CREBBP, MSH2, MSH6, PMS2, WHSC1, PRPS1, and PRPS2) involved in drug response. Their prevalence was 17% in very early relapse (<9 months from diagnosis), 65% in early relapse (9-36 months), and 32% in late relapse (>36 months) groups. Convergent evolution, in which multiple subclones harbor mutations in the same drug resistance gene, was observed in 6 relapses and confirmed by single-cell sequencing in 1 case. Mathematical modeling and mutational signature analysis indicated that early relapse resistance acquisition was frequently a 2-step process in which a persistent clone survived initial therapy and later acquired bona fide resistance mutations during therapy. In contrast, very early relapses arose from preexisting resistant clone(s). Two novel relapse-specific mutational signatures, one of which was caused by thiopurine treatment based on in vitro drug exposure experiments, were identified in early and late relapses but were absent from 2540 pan-cancer diagnosis samples and 129 non-ALL relapses. The novel signatures were detected in 27% of relapsed ALLs and were responsible for 46% of acquired resistance mutations in NT5C2, PRPS1, NR3C1, and TP53. These results suggest that chemotherapy-induced drug resistance mutations facilitate a subset of pediatric ALL relapses.
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Affiliation(s)
- Benshang Li
- Key Laboratory of Pediatric Hematology & Oncology Ministry of Health, Department of Hematology & Oncology, Shanghai Children's Medical Center-National Children's Medical Center, and
- Pediatric Translational Medicine Institute, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Samuel W Brady
- Department of Computational Biology, St. Jude Children's Research Hospital, Memphis, TN
| | - Xiaotu Ma
- Department of Computational Biology, St. Jude Children's Research Hospital, Memphis, TN
| | - Shuhong Shen
- Key Laboratory of Pediatric Hematology & Oncology Ministry of Health, Department of Hematology & Oncology, Shanghai Children's Medical Center-National Children's Medical Center, and
- Pediatric Translational Medicine Institute, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Yingchi Zhang
- State Key Laboratory of Experimental Hematology, Institute of Hematology and Blood Diseases Hospital-Center for Stem Cell Medicine, Chinese Academy of Medical Sciences and Peking Union Medical College, Tianjin, China
| | - Yongjin Li
- Department of Computational Biology, St. Jude Children's Research Hospital, Memphis, TN
| | - Karol Szlachta
- Department of Computational Biology, St. Jude Children's Research Hospital, Memphis, TN
| | - Li Dong
- Department of Computational Biology, St. Jude Children's Research Hospital, Memphis, TN
| | - Yu Liu
- Department of Computational Biology, St. Jude Children's Research Hospital, Memphis, TN
| | - Fan Yang
- Key Laboratory of Pediatric Hematology & Oncology Ministry of Health, Department of Hematology & Oncology, Shanghai Children's Medical Center-National Children's Medical Center, and
- Pediatric Translational Medicine Institute, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Ningling Wang
- Department of Pediatrics, the Second Hospital of Anhui Medical University, Hefei, China
| | - Diane A Flasch
- Department of Computational Biology, St. Jude Children's Research Hospital, Memphis, TN
| | - Matthew A Myers
- Department of Computer Science, Princeton University, Princeton, NJ
| | - Heather L Mulder
- Department of Computational Biology, St. Jude Children's Research Hospital, Memphis, TN
| | - Lixia Ding
- Key Laboratory of Pediatric Hematology & Oncology Ministry of Health, Department of Hematology & Oncology, Shanghai Children's Medical Center-National Children's Medical Center, and
- Pediatric Translational Medicine Institute, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Yanling Liu
- Department of Computational Biology, St. Jude Children's Research Hospital, Memphis, TN
| | - Liqing Tian
- Department of Computational Biology, St. Jude Children's Research Hospital, Memphis, TN
| | - Kohei Hagiwara
- Department of Computational Biology, St. Jude Children's Research Hospital, Memphis, TN
| | - Ke Xu
- Department of Computational Biology, St. Jude Children's Research Hospital, Memphis, TN
| | - Xin Zhou
- Department of Computational Biology, St. Jude Children's Research Hospital, Memphis, TN
| | - Edgar Sioson
- Department of Computational Biology, St. Jude Children's Research Hospital, Memphis, TN
| | - Tianyi Wang
- Key Laboratory of Pediatric Hematology & Oncology Ministry of Health, Department of Hematology & Oncology, Shanghai Children's Medical Center-National Children's Medical Center, and
| | - Liu Yang
- Key Laboratory of Pediatric Hematology & Oncology Ministry of Health, Department of Hematology & Oncology, Shanghai Children's Medical Center-National Children's Medical Center, and
| | - Jie Zhao
- Key Laboratory of Pediatric Hematology & Oncology Ministry of Health, Department of Hematology & Oncology, Shanghai Children's Medical Center-National Children's Medical Center, and
| | - Hui Zhang
- Department of Pediatric Hematology/Oncology, Guangzhou Women and Children's Medical Center, Guangzhou, Guangdong, China
| | - Ying Shao
- Department of Computational Biology, St. Jude Children's Research Hospital, Memphis, TN
| | | | - Lele Sun
- WuXi NextCODE Co., Ltd, Shanghai, China
| | - Jiaoyang Cai
- Key Laboratory of Pediatric Hematology & Oncology Ministry of Health, Department of Hematology & Oncology, Shanghai Children's Medical Center-National Children's Medical Center, and
| | - Hui-Ying Sun
- Key Laboratory of Pediatric Hematology & Oncology Ministry of Health, Department of Hematology & Oncology, Shanghai Children's Medical Center-National Children's Medical Center, and
| | | | - Lijuan Du
- Key Laboratory of Pediatric Hematology & Oncology Ministry of Health, Department of Hematology & Oncology, Shanghai Children's Medical Center-National Children's Medical Center, and
- Pediatric Translational Medicine Institute, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Hui Li
- Key Laboratory of Pediatric Hematology & Oncology Ministry of Health, Department of Hematology & Oncology, Shanghai Children's Medical Center-National Children's Medical Center, and
- Pediatric Translational Medicine Institute, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Michael Rusch
- Department of Computational Biology, St. Jude Children's Research Hospital, Memphis, TN
| | - Michael N Edmonson
- Department of Computational Biology, St. Jude Children's Research Hospital, Memphis, TN
| | - John Easton
- Department of Computational Biology, St. Jude Children's Research Hospital, Memphis, TN
| | - Xiaofan Zhu
- State Key Laboratory of Experimental Hematology, Institute of Hematology and Blood Diseases Hospital-Center for Stem Cell Medicine, Chinese Academy of Medical Sciences and Peking Union Medical College, Tianjin, China
| | - Jingliao Zhang
- State Key Laboratory of Experimental Hematology, Institute of Hematology and Blood Diseases Hospital-Center for Stem Cell Medicine, Chinese Academy of Medical Sciences and Peking Union Medical College, Tianjin, China
| | | | | | - Jingyan Tang
- Key Laboratory of Pediatric Hematology & Oncology Ministry of Health, Department of Hematology & Oncology, Shanghai Children's Medical Center-National Children's Medical Center, and
| | - James R Downing
- Department of Pathology, St. Jude Children's Research Hospital, Memphis, Tennessee, TN
| | - Ludmil B Alexandrov
- Department of Cellular and Molecular Medicine, University of California, San Diego, La Jolla, CA; and
| | - Bin-Bing S Zhou
- Key Laboratory of Pediatric Hematology & Oncology Ministry of Health, Department of Hematology & Oncology, Shanghai Children's Medical Center-National Children's Medical Center, and
- Pediatric Translational Medicine Institute, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Ching-Hon Pui
- Department of Oncology, St. Jude Children's Research Hospital, Memphis, TN
| | | | - Jinghui Zhang
- Department of Computational Biology, St. Jude Children's Research Hospital, Memphis, TN
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Abstract
Motivation The analysis of high-dimensional ‘omics data is often informed by the use of biological interaction networks. For example, protein–protein interaction networks have been used to analyze gene expression data, to prioritize germline variants, and to identify somatic driver mutations in cancer. In these and other applications, the underlying computational problem is to identify altered subnetworks containing genes that are both highly altered in an ‘omics dataset and are topologically close (e.g. connected) on an interaction network. Results We introduce Hierarchical HotNet, an algorithm that finds a hierarchy of altered subnetworks. Hierarchical HotNet assesses the statistical significance of the resulting subnetworks over a range of biological scales and explicitly controls for ascertainment bias in the network. We evaluate the performance of Hierarchical HotNet and several other algorithms that identify altered subnetworks on the problem of predicting cancer genes and significantly mutated subnetworks. On somatic mutation data from The Cancer Genome Atlas, Hierarchical HotNet outperforms other methods and identifies significantly mutated subnetworks containing both well-known cancer genes and candidate cancer genes that are rarely mutated in the cohort. Hierarchical HotNet is a robust algorithm for identifying altered subnetworks across different ‘omics datasets. Availability and implementation http://github.com/raphael-group/hierarchical-hotnet. Supplementary information Supplementary material are available at Bioinformatics online.
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Affiliation(s)
- Matthew A Reyna
- Department of Computer Science, Princeton University, Princeton, NJ, USA
| | - Mark D M Leiserson
- Department of Computer Science, University of Maryland, College Park, MD, USA
| | - Benjamin J Raphael
- Department of Computer Science, Princeton University, Princeton, NJ, USA
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44
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Myers MA, Satas G, Raphael BJ. CALDER: Inferring Phylogenetic Trees from Longitudinal Tumor Samples. Cell Syst 2019; 8:514-522.e5. [PMID: 31229560 DOI: 10.1016/j.cels.2019.05.010] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2019] [Accepted: 05/23/2019] [Indexed: 02/07/2023]
Abstract
Longitudinal DNA sequencing of cancer patients yields insight into how tumors evolve over time or in response to treatment. However, sequencing data from bulk tumor samples often have considerable ambiguity in clonal composition, complicating the inference of ancestral relationships between clones. We introduce Cancer Analysis of Longitudinal Data through Evolutionary Reconstruction (CALDER), an algorithm to infer phylogenetic trees from longitudinal bulk DNA sequencing data. CALDER explicitly models a longitudinally observed phylogeny incorporating constraints that longitudinal sampling imposes on phylogeny reconstruction. We show on simulated bulk tumor data that longitudinal constraints substantially reduce ambiguity in phylogeny reconstruction and that CALDER outperforms existing methods that do not leverage this longitudinal information. On real data from two chronic lymphocytic leukemia patients, we find that CALDER reconstructs more plausible and parsimonious phylogenies than existing methods, with CALDER phylogenies containing fewer tumor clones per sample. CALDER's use of longitudinal information will be advantageous in further studies of tumor heterogeneity and evolution.
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Affiliation(s)
- Matthew A Myers
- Department of Computer Science, Princeton University, Princeton, NJ 08540, USA
| | - Gryte Satas
- Department of Computer Science, Princeton University, Princeton, NJ 08540, USA; Department of Computer Science, Brown University, Providence, RI 02912, USA
| | - Benjamin J Raphael
- Department of Computer Science, Princeton University, Princeton, NJ 08540, USA.
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45
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Abstract
A major ambition of artificial intelligence lies in translating patient data to successful therapies. Machine learning models face particular challenges in biomedicine, however, including handling of extreme data heterogeneity and lack of mechanistic insight into predictions. Here, we argue for "visible" approaches that guide model structure with experimental biology.
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Affiliation(s)
- Michael K Yu
- Division of Genetics, Department of Medicine, University of California San Diego, La Jolla, CA, USA; Cancer Cell Map Initiative, University of California San Diego, La Jolla, CA, USA; UCSD Program in Bioinformatics and Systems Biology, University of California San Diego, La Jolla, CA, USA
| | - Jianzhu Ma
- Division of Genetics, Department of Medicine, University of California San Diego, La Jolla, CA, USA; Cancer Cell Map Initiative, University of California San Diego, La Jolla, CA, USA
| | - Jasmin Fisher
- Department of Biochemistry, University of Cambridge, Cambridge, UK
| | - Jason F Kreisberg
- Division of Genetics, Department of Medicine, University of California San Diego, La Jolla, CA, USA; Cancer Cell Map Initiative, University of California San Diego, La Jolla, CA, USA
| | - Benjamin J Raphael
- Department of Computer Science, Princeton University, Princeton, NJ, USA.
| | - Trey Ideker
- Division of Genetics, Department of Medicine, University of California San Diego, La Jolla, CA, USA; Cancer Cell Map Initiative, University of California San Diego, La Jolla, CA, USA; UCSD Program in Bioinformatics and Systems Biology, University of California San Diego, La Jolla, CA, USA.
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46
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Brady SW, Ma X, Bahrami A, Satas G, Wu G, Newman S, Rusch M, Putnam DK, Mulder HL, Yergeau DA, Edmonson MN, Easton J, Alexandrov LB, Chen X, Mardis ER, Wilson RK, Downing JR, Pappo AS, Raphael BJ, Dyer MA, Zhang J. The Clonal Evolution of Metastatic Osteosarcoma as Shaped by Cisplatin Treatment. Mol Cancer Res 2019; 17:895-906. [PMID: 30651371 DOI: 10.1158/1541-7786.mcr-18-0620] [Citation(s) in RCA: 27] [Impact Index Per Article: 5.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/11/2018] [Revised: 10/17/2018] [Accepted: 01/07/2019] [Indexed: 11/16/2022]
Abstract
To investigate the genomic evolution of metastatic pediatric osteosarcoma, we performed whole-genome and targeted deep sequencing on 14 osteosarcoma metastases and two primary tumors from four patients (two to eight samples per patient). All four patients harbored ancestral (truncal) somatic variants resulting in TP53 inactivation and cell-cycle aberrations, followed by divergence into relapse-specific lineages exhibiting a cisplatin-induced mutation signature. In three of the four patients, the cisplatin signature accounted for >40% of mutations detected in the metastatic samples. Mutations potentially acquired during cisplatin treatment included NF1 missense mutations of uncertain significance in two patients and a KIT G565R activating mutation in one patient. Three of four patients demonstrated widespread ploidy differences between samples from the sample patient. Single-cell seeding of metastasis was detected in most metastatic samples. Cross-seeding between metastatic sites was observed in one patient, whereas in another patient a minor clone from the primary tumor seeded both metastases analyzed. These results reveal extensive clonal heterogeneity in metastatic osteosarcoma, much of which is likely cisplatin-induced. IMPLICATIONS: The extent and consequences of chemotherapy-induced damage in pediatric cancers is unknown. We found that cisplatin treatment can potentially double the mutational burden in osteosarcoma, which has implications for optimizing therapy for recurrent, chemotherapy-resistant disease.
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Affiliation(s)
- Samuel W Brady
- Department of Computational Biology, St. Jude Children's Research Hospital, Memphis, Tennessee
| | - Xiaotu Ma
- Department of Computational Biology, St. Jude Children's Research Hospital, Memphis, Tennessee
| | - Armita Bahrami
- Department of Pathology, St. Jude Children's Research Hospital, Memphis, Tennessee
| | - Gryte Satas
- Department of Computer Science, Princeton University, Princeton, New Jersey
| | - Gang Wu
- Department of Computational Biology, St. Jude Children's Research Hospital, Memphis, Tennessee
| | - Scott Newman
- Department of Computational Biology, St. Jude Children's Research Hospital, Memphis, Tennessee
| | - Michael Rusch
- Department of Computational Biology, St. Jude Children's Research Hospital, Memphis, Tennessee
| | - Daniel K Putnam
- Department of Computational Biology, St. Jude Children's Research Hospital, Memphis, Tennessee
| | - Heather L Mulder
- Department of Computational Biology, St. Jude Children's Research Hospital, Memphis, Tennessee
| | - Donald A Yergeau
- UB Genomics and Bioinformatics Core, University at Buffalo, Buffalo, New York
| | - Michael N Edmonson
- Department of Computational Biology, St. Jude Children's Research Hospital, Memphis, Tennessee
| | - John Easton
- Department of Computational Biology, St. Jude Children's Research Hospital, Memphis, Tennessee
| | - Ludmil B Alexandrov
- Department of Cellular and Molecular Medicine, University of California, San Diego, La Jolla, California
| | - Xiang Chen
- Department of Computational Biology, St. Jude Children's Research Hospital, Memphis, Tennessee
| | - Elaine R Mardis
- Institute for Genomic Medicine, Nationwide Children's Hospital and The Ohio State University College of Medicine, Columbus, Ohio
| | - Richard K Wilson
- Institute for Genomic Medicine, Nationwide Children's Hospital and The Ohio State University College of Medicine, Columbus, Ohio
| | - James R Downing
- Department of Pathology, St. Jude Children's Research Hospital, Memphis, Tennessee
| | - Alberto S Pappo
- Department of Oncology, St. Jude Children's Research Hospital, Memphis, Tennessee
| | - Benjamin J Raphael
- Department of Computer Science, Princeton University, Princeton, New Jersey
| | - Michael A Dyer
- Department of Developmental Neurobiology, St. Jude Children's Research Hospital, Memphis, Tennessee.
| | - Jinghui Zhang
- Department of Computational Biology, St. Jude Children's Research Hospital, Memphis, Tennessee.
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47
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Parks MM, Raphael BJ, Lawrence CE. Using controls to limit false discovery in the era of big data. BMC Bioinformatics 2018; 19:323. [PMID: 30217148 PMCID: PMC6137876 DOI: 10.1186/s12859-018-2356-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/19/2018] [Accepted: 09/03/2018] [Indexed: 12/04/2022] Open
Abstract
Background Procedures for controlling the false discovery rate (FDR) are widely applied as a solution to the multiple comparisons problem of high-dimensional statistics. Current FDR-controlling procedures require accurately calculated p-values and rely on extrapolation into the unknown and unobserved tails of the null distribution. Both of these intermediate steps are challenging and can compromise the reliability of the results. Results We present a general method for controlling the FDR that capitalizes on the large amount of control data often found in big data studies to avoid these frequently problematic intermediate steps. The method utilizes control data to empirically construct the distribution of the test statistic under the null hypothesis and directly compares this distribution to the empirical distribution of the test data. By not relying on p-values, our control data-based empirical FDR procedure more closely follows the foundational principles of the scientific method: that inference is drawn by comparing test data to control data. The method is demonstrated through application to a problem in structural genomics. Conclusions The method described here provides a general statistical framework for controlling the FDR that is specifically tailored for the big data setting. By relying on empirically constructed distributions and control data, it forgoes potentially problematic modeling steps and extrapolation into the unknown tails of the null distribution. This procedure is broadly applicable insofar as controlled experiments or internal negative controls are available, as is increasingly common in the big data setting. Electronic supplementary material The online version of this article (10.1186/s12859-018-2356-2) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Matthew M Parks
- Department of Physiology and Biophysics, Weill Cornell Medicine, 1300 York Ave, New York, NY, 10065, USA
| | - Benjamin J Raphael
- Department of Computer Science, Princeton University, 35 Olden Street, Princeton, NJ, 08540, USA
| | - Charles E Lawrence
- Center for Computational Molecular Biology, Brown University, 115 Waterman Street, Providence, RI, 02912, USA. .,Division of Applied Mathematics, Brown University, 182 George Street, Providence, RI, 02912, USA.
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48
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Abstract
Motivation Current technologies for single-cell DNA sequencing require whole-genome amplification (WGA), as a single cell contains too little DNA for direct sequencing. Unfortunately, WGA introduces biases in the resulting sequencing data, including non-uniformity in genome coverage and high rates of allele dropout. These biases complicate many downstream analyses, including the detection of genomic variants. Results We show that amplification biases have a potential upside: long-range correlations in rates of allele dropout provide a signal for phasing haplotypes at the lengths of amplicons from WGA, lengths which are generally longer than than individual sequence reads. We describe a statistical test to measure concurrent allele dropout between single-nucleotide polymorphisms (SNPs) across multiple sequenced single cells. We use results of this test to perform haplotype assembly across a collection of single cells. We demonstrate that the algorithm predicts phasing between pairs of SNPs with higher accuracy than phasing from reads alone. Using whole-genome sequencing data from only seven neural cells, we obtain haplotype blocks that are orders of magnitude longer than with sequence reads alone (median length 10.2 kb versus 312 bp), with error rates <2%. We demonstrate similar advantages on whole-exome data from 16 cells, where we obtain haplotype blocks with median length 9.2 kb-comparable to typical gene lengths-compared with median lengths of 41 bp with sequence reads alone, with error rates <4%. Our algorithm will be useful for haplotyping of rare alleles and studies of allele-specific somatic aberrations. Availability and implementation Source code is available at https://www.github.com/raphael-group. Supplementary information Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Gryte Satas
- Department of Computer Science, Princeton University, Princeton, NJ, USA
- Department of Computer Science, Brown University, Providence, RI, USA
| | - Benjamin J Raphael
- Department of Computer Science, Princeton University, Princeton, NJ, USA
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49
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El-Kebir M, Satas G, Raphael BJ. Abstract 5307: Inferring parsimonious migration histories for metastatic cancers. Cancer Res 2018. [DOI: 10.1158/1538-7445.am2018-5307] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Abstract
Recent studies have attempted to infer the pattern of cellular migrations between a primary tumor and distant metastases using phylogenetic trees constructed from somatic mutations. Several of these studies have challenged the conventional view of monoclonal seeding of a metastasis, and reported complex patterns of migration between primary tumors and metastases, including polyclonal seeding and reseeding.
Most current analyses that attempt to infer migration patterns from somatic mutations rely on two key assumptions, sample homogeneity and mutation-migration concordance, that do not typically hold for cancer sequencing datasets. Consequently, standard phylogenetic analysis of mutations may result in incorrect or misleading patterns of metastasis.
We introduce a computational model to evaluate migration patterns in a rigorous manner. We use this model as a basis for Metastatic And Clonal History INtegrative Analysis (MACHINA), an algorithm that jointly infers parsimonious clone trees and migration histories of metastatic cancers from DNA sequencing data. We show that MACHINA accurately recovers clone trees and migration histories on simulated data. We apply MACHINA to sequencing data from metastatic ovarian, breast, prostate and skin cancer samples. In several cases, we find simpler migration histories than previously reported, and these alter conclusions regarding metastasis-to-metastasis spread, the anatomical site of the primary tumor, or the occurrence of polyclonal seeding and reseeding in individual patients. MACHINA enables researchers to rigorously assess the validity of different migration patterns in individual patients with metastatic disease and evaluate the prevalence of different migration patterns across large cohorts of patients and tumor types.
Citation Format: Mohammed El-Kebir, Gryte Satas, Benjamin J. Raphael. Inferring parsimonious migration histories for metastatic cancers [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2018; 2018 Apr 14-18; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2018;78(13 Suppl):Abstract nr 5307.
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50
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Abstract
Cancer is an evolutionary process driven by somatic mutations. This process can be represented as a phylogenetic tree. Constructing such a phylogenetic tree from genome sequencing data is a challenging task due to the many types of mutations in cancer and the fact that nearly all cancer sequencing is of a bulk tumor, measuring a superposition of somatic mutations present in different cells. We study the problem of reconstructing tumor phylogenies from copy-number aberrations (CNAs) measured in bulk-sequencing data. We introduce the Copy-Number Tree Mixture Deconvolution (CNTMD) problem, which aims to find the phylogenetic tree with the fewest number of CNAs that explain the copy-number data from multiple samples of a tumor. We design an algorithm for solving the CNTMD problem and apply the algorithm to both simulated and real data. On simulated data, we find that our algorithm outperforms existing approaches that either perform deconvolution/factorization of mixed tumor samples or build phylogenetic trees assuming homogeneous tumor samples. On real data, we analyze multiple samples from a prostate cancer patient, identifying clones within these samples and a phylogenetic tree that relates these clones and their differing proportions across samples. This phylogenetic tree provides a higher resolution view of copy-number evolution of this cancer than published analyses.
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Affiliation(s)
- Simone Zaccaria
- Department of Computer Science, Princeton University, Princeton, New Jersey
- Dipartimento di Informatica Sistemistica e Comunicazione (DISCo), Università degli Studi di Milano-Bicocca, Milan, Italy
| | - Mohammed El-Kebir
- Department of Computer Science, Princeton University, Princeton, New Jersey
| | - Gunnar W. Klau
- Algorithmic Bioinformatics, Heinrich Heine University, Düsseldorf, Germany
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