1
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Salcedo A, Tarabichi M, Buchanan A, Espiritu SMG, Zhang H, Zhu K, Ou Yang TH, Leshchiner I, Anastassiou D, Guan Y, Jang GH, Mootor MFE, Haase K, Deshwar AG, Zou W, Umar I, Dentro S, Wintersinger JA, Chiotti K, Demeulemeester J, Jolly C, Sycza L, Ko M, Wedge DC, Morris QD, Ellrott K, Van Loo P, Boutros PC. Crowd-sourced benchmarking of single-sample tumor subclonal reconstruction. Nat Biotechnol 2025; 43:581-592. [PMID: 38862616 PMCID: PMC11994449 DOI: 10.1038/s41587-024-02250-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/11/2022] [Accepted: 04/17/2024] [Indexed: 06/13/2024]
Abstract
Subclonal reconstruction algorithms use bulk DNA sequencing data to quantify parameters of tumor evolution, allowing an assessment of how cancers initiate, progress and respond to selective pressures. We launched the ICGC-TCGA (International Cancer Genome Consortium-The Cancer Genome Atlas) DREAM Somatic Mutation Calling Tumor Heterogeneity and Evolution Challenge to benchmark existing subclonal reconstruction algorithms. This 7-year community effort used cloud computing to benchmark 31 subclonal reconstruction algorithms on 51 simulated tumors. Algorithms were scored on seven independent tasks, leading to 12,061 total runs. Algorithm choice influenced performance substantially more than tumor features but purity-adjusted read depth, copy-number state and read mappability were associated with the performance of most algorithms on most tasks. No single algorithm was a top performer for all seven tasks and existing ensemble strategies were unable to outperform the best individual methods, highlighting a key research need. All containerized methods, evaluation code and datasets are available to support further assessment of the determinants of subclonal reconstruction accuracy and development of improved methods to understand tumor evolution.
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Affiliation(s)
- Adriana Salcedo
- Department of Human Genetics, University of California, Los Angeles, CA, USA.
- Jonsson Comprehensive Cancer Center, University of California, Los Angeles, CA, USA.
- Institute for Precision Health, University of California, Los Angeles, CA, USA.
- Department of Medical Biophysics, University of Toronto, Toronto, Ontario, Canada.
- Ontario Institute for Cancer Research, Toronto, Ontario, Canada.
| | - Maxime Tarabichi
- The Francis Crick Institute, London, UK.
- Wellcome Sanger Institute, Hinxton, UK.
- Institute for Interdisciplinary Research, Université Libre de Bruxelles, Brussels, Belgium.
| | - Alex Buchanan
- Oregon Health and Sciences University, Portland, OR, USA
| | | | - Hongjiu Zhang
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA
| | - Kaiyi Zhu
- Department of Systems Biology, Columbia University, New York, NY, USA
- Center for Cancer Systems Therapeutics, Columbia University, New York, NY, USA
- Department of Electrical Engineering, Columbia University, New York, NY, USA
| | - Tai-Hsien Ou Yang
- Department of Systems Biology, Columbia University, New York, NY, USA
- Center for Cancer Systems Therapeutics, Columbia University, New York, NY, USA
- Department of Electrical Engineering, Columbia University, New York, NY, USA
| | | | - Dimitris Anastassiou
- Department of Systems Biology, Columbia University, New York, NY, USA
- Center for Cancer Systems Therapeutics, Columbia University, New York, NY, USA
- Department of Electrical Engineering, Columbia University, New York, NY, USA
- Herbert Irving Comprehensive Cancer Center, Columbia University, New York, NY, USA
| | - Yuanfang Guan
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA
- Department of Internal Medicine, University of Michigan, Ann Arbor, MI, USA
- Department of Electronic Engineering and Computer Science, University of Michigan, Ann Arbor, MI, USA
| | - Gun Ho Jang
- Ontario Institute for Cancer Research, Toronto, Ontario, Canada
| | - Mohammed F E Mootor
- Department of Human Genetics, University of California, Los Angeles, CA, USA
- Jonsson Comprehensive Cancer Center, University of California, Los Angeles, CA, USA
- Institute for Precision Health, University of California, Los Angeles, CA, USA
| | | | - Amit G Deshwar
- Department of Computer Science, University of Toronto, Toronto, Ontario, Canada
| | - William Zou
- Ontario Institute for Cancer Research, Toronto, Ontario, Canada
| | - Imaad Umar
- Ontario Institute for Cancer Research, Toronto, Ontario, Canada
| | - Stefan Dentro
- The Francis Crick Institute, London, UK
- Wellcome Sanger Institute, Hinxton, UK
| | - Jeff A Wintersinger
- Department of Computer Science, University of Toronto, Toronto, Ontario, Canada
| | - Kami Chiotti
- Oregon Health and Sciences University, Portland, OR, USA
| | - Jonas Demeulemeester
- The Francis Crick Institute, London, UK
- VIB Center for Cancer Biology, Leuven, Belgium
- Department of Oncology, KU Leuven, Leuven, Belgium
| | | | - Lesia Sycza
- Ontario Institute for Cancer Research, Toronto, Ontario, Canada
| | - Minjeong Ko
- Ontario Institute for Cancer Research, Toronto, Ontario, Canada
| | - David C Wedge
- Big Data Institute, University of Oxford, Oxford, UK
- Manchester Cancer Research Center, University of Manchester, Manchester, UK
| | - Quaid D Morris
- Department of Computer Science, University of Toronto, Toronto, Ontario, Canada
- Vector Institute, Toronto, Ontario, Canada
- Computational and Systems Biology Program, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Kyle Ellrott
- Oregon Health and Sciences University, Portland, OR, USA.
| | - Peter Van Loo
- The Francis Crick Institute, London, UK.
- Department of Genetics, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.
- Department of Genomic Medicine, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.
| | - Paul C Boutros
- Department of Human Genetics, University of California, Los Angeles, CA, USA.
- Jonsson Comprehensive Cancer Center, University of California, Los Angeles, CA, USA.
- Institute for Precision Health, University of California, Los Angeles, CA, USA.
- Department of Medical Biophysics, University of Toronto, Toronto, Ontario, Canada.
- Department of Pharmacology and Toxicology, University of Toronto, Toronto, Ontario, Canada.
- Department of Urology, University of California, Los Angeles, CA, USA.
- Broad Stem Cell Research Center, University of California, Los Angeles, CA, USA.
- California NanoSystems Institute, University of California, Los Angeles, CA, USA.
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2
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Morrill Gavarró L, Couturier DL, Markowetz F. A Dirichlet-multinomial mixed model for determining differential abundance of mutational signatures. BMC Bioinformatics 2025; 26:59. [PMID: 39966709 PMCID: PMC11837616 DOI: 10.1186/s12859-025-06055-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/07/2024] [Accepted: 01/16/2025] [Indexed: 02/20/2025] Open
Abstract
BACKGROUND Mutational processes of diverse origin leave their imprints in the genome during tumour evolution. These imprints are called mutational signatures and they have been characterised for point mutations, structural variants and copy number changes. Each signature has an exposure, or abundance, per sample, which indicates how much a process has contributed to the overall genomic change. Mutational processes are not static, and a better understanding of their dynamics is key to characterise tumour evolution and identify cancer cell vulnerabilities that can be exploited during treatment. However, the structure of the data typically collected in this context makes it difficult to test whether signature exposures differ between conditions or time-points when comparing groups of samples. In general, the data consists of multivariate count mutational data (e.g. signature exposures) with two observations per patient, each reflecting a group. RESULTS We propose a mixed-effects Dirichlet-multinomial model: within-patient correlations are taken into account with random effects, possible correlations between signatures by making such random effects multivariate, and a group-specific dispersion parameter can deal with particularities of the groups. Moreover, the model is flexible in its fixed-effects structure, so that the two-group comparison can be generalised to several groups, or to a regression setting. We apply our approach to characterise differences of mutational processes between clonal and subclonal mutations across 23 cancer types of the PCAWG cohort. We find ubiquitous differential abundance of clonal and subclonal signatures across cancer types, and higher dispersion of signatures in the subclonal group, indicating higher variability between patients at subclonal level, possibly due to the presence of different clones with distinct active mutational processes. CONCLUSIONS Mutational signature analysis is an expanding field and we envision our framework to be used widely to detect global changes in mutational process activity. Our methodology is available in the R package CompSign and offers an ample toolkit for the analysis and visualisation of differential abundance of compositional data such as, but not restricted to, mutational signatures.
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Affiliation(s)
- Lena Morrill Gavarró
- Cancer Research UK Cambridge Institute, University of Cambridge, Cambridge, UK
- MRC Weatherall Institute of Molecular Medicine, University of Oxford, Oxford, UK
- Human Oncology & Pathogenesis Program, Memorial Sloan Kettering Cancer Center, New York, USA
| | - Dominique-Laurent Couturier
- Cancer Research UK Cambridge Institute, University of Cambridge, Cambridge, UK
- Medical Research Council Biostatistics Unit, University of Cambridge, Cambridge, UK
| | - Florian Markowetz
- Cancer Research UK Cambridge Institute, University of Cambridge, Cambridge, UK.
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3
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Jakobsdottir GM, Dentro SC, Bristow RG, Wedge DC. AmplificationTimeR: an R package for timing sequential amplification events. Bioinformatics 2024; 40:btae281. [PMID: 38656989 PMCID: PMC11153944 DOI: 10.1093/bioinformatics/btae281] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/08/2023] [Revised: 02/23/2024] [Accepted: 04/23/2024] [Indexed: 04/26/2024] Open
Abstract
MOTIVATION Few methods exist for timing individual amplification events in regions of focal amplification. Current methods are also limited in the copy number states that they are able to time. Here we introduce AmplificationTimeR, a method for timing higher level copy number gains and inferring the most parsimonious order of events for regions that have undergone both single gains and whole genome duplication. Our method is an extension of established approaches for timing genomic gains. RESULTS We can time more copy number states, and in states covered by other methods our results are comparable to previously published methods. AVAILABILITY AND IMPLEMENTATION AmplificationTimer is freely available as an R package hosted at https://github.com/Wedge-lab/AmplificationTimeR.
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Affiliation(s)
- G Maria Jakobsdottir
- Division of Cancer Sciences, University of Manchester, Manchester Academic Health Science Centre, Manchester M13 9PL, United Kingdom
- Christie Hospital, The Christie NHS Foundation Trust, Manchester Academic Health Science Centre, Manchester M20 4BX, United Kingdom
| | - Stefan C Dentro
- Division of AI in Oncology, German Cancer Research Center (DKFZ), Heidelberg, Germany
- Christie Hospital, The Christie NHS Foundation Trust, Manchester Academic Health Science Centre, Manchester M20 4BX, United Kingdom
| | - Robert G Bristow
- Division of Cancer Sciences, University of Manchester, Manchester Academic Health Science Centre, Manchester M13 9PL, United Kingdom
- CRUK Manchester Institute and Manchester Cancer Research Centre, Manchester M20 4GJ, United Kingdom
- Christie Hospital, The Christie NHS Foundation Trust, Manchester Academic Health Science Centre, Manchester M20 4BX, United Kingdom
| | - David C Wedge
- Division of Cancer Sciences, University of Manchester, Manchester Academic Health Science Centre, Manchester M13 9PL, United Kingdom
- Christie Hospital, The Christie NHS Foundation Trust, Manchester Academic Health Science Centre, Manchester M20 4BX, United Kingdom
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4
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Dayton TL, Alcala N, Moonen L, den Hartigh L, Geurts V, Mangiante L, Lap L, Dost AFM, Beumer J, Levy S, van Leeuwaarde RS, Hackeng WM, Samsom K, Voegele C, Sexton-Oates A, Begthel H, Korving J, Hillen L, Brosens LAA, Lantuejoul S, Jaksani S, Kok NFM, Hartemink KJ, Klomp HM, Borel Rinkes IHM, Dingemans AM, Valk GD, Vriens MR, Buikhuisen W, van den Berg J, Tesselaar M, Derks J, Speel EJ, Foll M, Fernández-Cuesta L, Clevers H. Druggable growth dependencies and tumor evolution analysis in patient-derived organoids of neuroendocrine neoplasms from multiple body sites. Cancer Cell 2023; 41:2083-2099.e9. [PMID: 38086335 DOI: 10.1016/j.ccell.2023.11.007] [Citation(s) in RCA: 20] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/11/2022] [Revised: 09/06/2023] [Accepted: 11/08/2023] [Indexed: 12/18/2023]
Abstract
Neuroendocrine neoplasms (NENs) comprise well-differentiated neuroendocrine tumors (NETs) and poorly differentiated neuroendocrine carcinomas (NECs). Treatment options for patients with NENs are limited, in part due to lack of accurate models. We establish patient-derived tumor organoids (PDTOs) from pulmonary NETs and derive PDTOs from an understudied subtype of NEC, large cell neuroendocrine carcinoma (LCNEC), arising from multiple body sites. PDTOs maintain the gene expression patterns, intra-tumoral heterogeneity, and evolutionary processes of parental tumors. Through hypothesis-driven drug sensitivity analyses, we identify ASCL1 as a potential biomarker for response of LCNEC to treatment with BCL-2 inhibitors. Additionally, we discover a dependency on EGF in pulmonary NET PDTOs. Consistent with these findings, we find that, in an independent cohort, approximately 50% of pulmonary NETs express EGFR. This study identifies an actionable vulnerability for a subset of pulmonary NETs, emphasizing the utility of these PDTO models.
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Affiliation(s)
- Talya L Dayton
- Hubrecht Institute, Royal Netherlands Academy of Arts and Sciences (KNAW) and UMC Utrecht, 3584 CT Utrecht, the Netherlands; Oncode Institute, Hubrecht Institute, 3584 CT Utrecht, the Netherlands.
| | - Nicolas Alcala
- Rare Cancers Genomics Team (RCG), Genomic Epidemiology Branch (GEM), International Agency for Research on Cancer/World Health Organisation (IARC/WHO), 69007 Lyon, France
| | - Laura Moonen
- Department of Pathology, GROW School for Oncology and Reproduction, Maastricht University Medical Centre, 6229 ER Maastricht, the Netherlands
| | - Lisanne den Hartigh
- Hubrecht Institute, Royal Netherlands Academy of Arts and Sciences (KNAW) and UMC Utrecht, 3584 CT Utrecht, the Netherlands
| | - Veerle Geurts
- Hubrecht Institute, Royal Netherlands Academy of Arts and Sciences (KNAW) and UMC Utrecht, 3584 CT Utrecht, the Netherlands
| | - Lise Mangiante
- Rare Cancers Genomics Team (RCG), Genomic Epidemiology Branch (GEM), International Agency for Research on Cancer/World Health Organisation (IARC/WHO), 69007 Lyon, France
| | - Lisa Lap
- Department of Pathology, GROW School for Oncology and Reproduction, Maastricht University Medical Centre, 6229 ER Maastricht, the Netherlands
| | - Antonella F M Dost
- Hubrecht Institute, Royal Netherlands Academy of Arts and Sciences (KNAW) and UMC Utrecht, 3584 CT Utrecht, the Netherlands; Oncode Institute, Hubrecht Institute, 3584 CT Utrecht, the Netherlands
| | - Joep Beumer
- Hubrecht Institute, Royal Netherlands Academy of Arts and Sciences (KNAW) and UMC Utrecht, 3584 CT Utrecht, the Netherlands; Oncode Institute, Hubrecht Institute, 3584 CT Utrecht, the Netherlands
| | - Sonja Levy
- Department of Medical Oncology, Netherlands Cancer Institute, 1066 CX Amsterdam, the Netherlands
| | - Rachel S van Leeuwaarde
- Department of Endocrine Oncology, University Medical Center Utrecht, 3584 CX Utrecht, the Netherlands
| | - Wenzel M Hackeng
- Department of Pathology, University Medical Center Utrecht, Utrecht University, 3584 CX Utrecht, the Netherlands
| | - Kris Samsom
- Department of Pathology, Netherlands Cancer Institute, Amsterdam 1066 CX, the Netherlands
| | - Catherine Voegele
- Rare Cancers Genomics Team (RCG), Genomic Epidemiology Branch (GEM), International Agency for Research on Cancer/World Health Organisation (IARC/WHO), 69007 Lyon, France
| | - Alexandra Sexton-Oates
- Rare Cancers Genomics Team (RCG), Genomic Epidemiology Branch (GEM), International Agency for Research on Cancer/World Health Organisation (IARC/WHO), 69007 Lyon, France
| | - Harry Begthel
- Hubrecht Institute, Royal Netherlands Academy of Arts and Sciences (KNAW) and UMC Utrecht, 3584 CT Utrecht, the Netherlands
| | - Jeroen Korving
- Hubrecht Institute, Royal Netherlands Academy of Arts and Sciences (KNAW) and UMC Utrecht, 3584 CT Utrecht, the Netherlands
| | - Lisa Hillen
- Department of Pathology, GROW School for Oncology and Reproduction, Maastricht University Medical Centre, 6229 ER Maastricht, the Netherlands
| | - Lodewijk A A Brosens
- Department of Pathology, University Medical Center Utrecht, Utrecht University, 3584 CX Utrecht, the Netherlands
| | - Sylvie Lantuejoul
- Department of Biopathology, Pathology Research Platform- Synergie Lyon Cancer- CRCL, Centre Léon Bérard Unicancer, 69008 Lyon, France; Université Grenoble Alpes, Grenoble, France
| | - Sridevi Jaksani
- Hubrecht Organoid Technology, Utrecht 3584 CM, the Netherlands
| | - Niels F M Kok
- Department of Surgery, Netherlands Cancer Institute, Amsterdam 1066 CX, the Netherlands
| | - Koen J Hartemink
- Department of Surgery, Netherlands Cancer Institute, Amsterdam 1066 CX, the Netherlands
| | - Houke M Klomp
- Department of Surgery, Netherlands Cancer Institute, Amsterdam 1066 CX, the Netherlands
| | - Inne H M Borel Rinkes
- Department of Endocrine Surgical Oncology, University Medical Center Utrecht, Utrecht 3508 GA, the Netherlands
| | - Anne-Marie Dingemans
- Department of Pulmonary Diseases, GROW School for Oncology and and Reproduction, Maastricht University Medical Centre, Maastricht, the Netherlands; Department of Pulmonary Medicine, Erasmus MC Cancer Institute, University Medical Center, Rotterdam 3015 GD, the Netherlands
| | - Gerlof D Valk
- Department of Endocrine Oncology, University Medical Center Utrecht, 3584 CX Utrecht, the Netherlands
| | - Menno R Vriens
- Department of Endocrine Surgical Oncology, University Medical Center Utrecht, Utrecht 3508 GA, the Netherlands
| | - Wieneke Buikhuisen
- Department of Thoracic Oncology, Netherlands Cancer Institute, Amsterdam 1066 CX, the Netherlands
| | - José van den Berg
- Department of Pathology, Netherlands Cancer Institute, Amsterdam 1066 CX, the Netherlands
| | - Margot Tesselaar
- Department of Medical Oncology, Netherlands Cancer Institute, 1066 CX Amsterdam, the Netherlands
| | - Jules Derks
- Department of Pulmonary Diseases, GROW School for Oncology and and Reproduction, Maastricht University Medical Centre, Maastricht, the Netherlands
| | - Ernst Jan Speel
- Department of Pathology, GROW School for Oncology and Reproduction, Maastricht University Medical Centre, 6229 ER Maastricht, the Netherlands
| | - Matthieu Foll
- Rare Cancers Genomics Team (RCG), Genomic Epidemiology Branch (GEM), International Agency for Research on Cancer/World Health Organisation (IARC/WHO), 69007 Lyon, France
| | - Lynnette Fernández-Cuesta
- Rare Cancers Genomics Team (RCG), Genomic Epidemiology Branch (GEM), International Agency for Research on Cancer/World Health Organisation (IARC/WHO), 69007 Lyon, France.
| | - Hans Clevers
- Hubrecht Institute, Royal Netherlands Academy of Arts and Sciences (KNAW) and UMC Utrecht, 3584 CT Utrecht, the Netherlands; Oncode Institute, Hubrecht Institute, 3584 CT Utrecht, the Netherlands.
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5
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Legrand C, Andriantsoa R, Lichter P, Raddatz G, Lyko F. Time-resolved, integrated analysis of clonally evolving genomes. PLoS Genet 2023; 19:e1011085. [PMID: 38096267 PMCID: PMC10754456 DOI: 10.1371/journal.pgen.1011085] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/27/2023] [Revised: 12/28/2023] [Accepted: 11/27/2023] [Indexed: 12/29/2023] Open
Abstract
Clonal genome evolution is a key feature of asexually reproducing species and human cancer development. While many studies have described the landscapes of clonal genome evolution in cancer, few determine the underlying evolutionary parameters from molecular data, and even fewer integrate theory with data. We derived theoretical results linking mutation rate, time, expansion dynamics, and biological/clinical parameters. Subsequently, we inferred time-resolved estimates of evolutionary parameters from mutation accumulation, mutational signatures and selection. We then applied this framework to predict the time of speciation of the marbled crayfish, an enigmatic, globally invasive parthenogenetic freshwater crayfish. The results predict that speciation occurred between 1986 and 1990, which is consistent with biological records. We also used our framework to analyze whole-genome sequencing datasets from primary and relapsed glioblastoma, an aggressive brain tumor. The results identified evolutionary subgroups and showed that tumor cell survival could be inferred from genomic data that was generated during the resection of the primary tumor. In conclusion, our framework allowed a time-resolved, integrated analysis of key parameters in clonally evolving genomes, and provided novel insights into the evolutionary age of marbled crayfish and the progression of glioblastoma.
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Affiliation(s)
- Carine Legrand
- Division of Epigenetics, DKFZ-ZMBH Alliance, German Cancer Research Center, Heidelberg, Germany
- Université Paris Cité, Génomes, biologie cellulaire et thérapeutique U944, INSERM, CNRS, Paris, France
| | - Ranja Andriantsoa
- Division of Epigenetics, DKFZ-ZMBH Alliance, German Cancer Research Center, Heidelberg, Germany
| | - Peter Lichter
- Division of Molecular Genetics, German Cancer Research Consortium (DKTK), German Cancer Research Center (DKFZ), Heidelberg, Germany
- Molecular Precision Oncology, National Center for Tumor Diseases, Heidelberg, Germany
| | - Günter Raddatz
- Division of Epigenetics, DKFZ-ZMBH Alliance, German Cancer Research Center, Heidelberg, Germany
| | - Frank Lyko
- Division of Epigenetics, DKFZ-ZMBH Alliance, German Cancer Research Center, Heidelberg, Germany
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6
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Rodriguez-Fos E, Planas-Fèlix M, Burkert M, Puiggròs M, Toedling J, Thiessen N, Blanc E, Szymansky A, Hertwig F, Ishaque N, Beule D, Torrents D, Eggert A, Koche RP, Schwarz RF, Haase K, Schulte JH, Henssen AG. Mutational topography reflects clinical neuroblastoma heterogeneity. CELL GENOMICS 2023; 3:100402. [PMID: 37868040 PMCID: PMC10589636 DOI: 10.1016/j.xgen.2023.100402] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 04/28/2023] [Revised: 06/13/2023] [Accepted: 08/11/2023] [Indexed: 10/24/2023]
Abstract
Neuroblastoma is a pediatric solid tumor characterized by strong clinical heterogeneity. Although clinical risk-defining genomic alterations exist in neuroblastomas, the mutational processes involved in their generation remain largely unclear. By examining the topography and mutational signatures derived from all variant classes, we identified co-occurring mutational footprints, which we termed mutational scenarios. We demonstrate that clinical neuroblastoma heterogeneity is associated with differences in the mutational processes driving these scenarios, linking risk-defining pathognomonic variants to distinct molecular processes. Whereas high-risk MYCN-amplified neuroblastomas were characterized by signs of replication slippage and stress, homologous recombination-associated signatures defined high-risk non-MYCN-amplified patients. Non-high-risk neuroblastomas were marked by footprints of chromosome mis-segregation and TOP1 mutational activity. Furthermore, analysis of subclonal mutations uncovered differential activity of these processes through neuroblastoma evolution. Thus, clinical heterogeneity of neuroblastoma patients can be linked to differences in the mutational processes that are active in their tumors.
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Affiliation(s)
- Elias Rodriguez-Fos
- Experimental and Clinical Research Center (ECRC) of the MDC and Charité Berlin, Berlin, Germany
- Department of Pediatric Oncology and Hematology, Charité – Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin, Berlin, Germany
| | - Mercè Planas-Fèlix
- Experimental and Clinical Research Center (ECRC) of the MDC and Charité Berlin, Berlin, Germany
- German Cancer Consortium (DKTK), Partner Site Berlin, and German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Martin Burkert
- Berlin Institute for Medical Systems Biology (BIMSB), Max Delbrück Center for Molecular Medicine in the Helmholtz Association (MDC), Berlin, Germany
| | - Montserrat Puiggròs
- Barcelona Supercomputing Center, Joint Barcelona Supercomputing Center – Center for Genomic Regulation – Institute for Research in Biomedicine Research Program in Computational Biology, Barcelona, Spain
| | - Joern Toedling
- Department of Pediatric Oncology and Hematology, Charité – Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin, Berlin, Germany
| | - Nina Thiessen
- Berlin Institute of Health at Charité – Universitätsmedizin Berlin, Digital Health Center, Berlin, Germany
| | - Eric Blanc
- Berlin Institute of Health at Charité – Universitätsmedizin Berlin, Digital Health Center, Berlin, Germany
| | - Annabell Szymansky
- Department of Pediatric Oncology and Hematology, Charité – Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin, Berlin, Germany
| | - Falk Hertwig
- Department of Pediatric Oncology and Hematology, Charité – Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin, Berlin, Germany
| | - Naveed Ishaque
- Berlin Institute of Health at Charité – Universitätsmedizin Berlin, Digital Health Center, Berlin, Germany
| | - Dieter Beule
- Berlin Institute of Health at Charité – Universitätsmedizin Berlin, Digital Health Center, Berlin, Germany
| | - David Torrents
- Barcelona Supercomputing Center, Joint Barcelona Supercomputing Center – Center for Genomic Regulation – Institute for Research in Biomedicine Research Program in Computational Biology, Barcelona, Spain
- Institució Catalana de Recerca i Estudis Avançats (ICREA), Barcelona, Spain
| | - Angelika Eggert
- Experimental and Clinical Research Center (ECRC) of the MDC and Charité Berlin, Berlin, Germany
- Department of Pediatric Oncology and Hematology, Charité – Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin, Berlin, Germany
| | - Richard P. Koche
- Center for Epigenetics Research, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Roland F. Schwarz
- Berlin Institute for Medical Systems Biology (BIMSB), Max Delbrück Center for Molecular Medicine in the Helmholtz Association (MDC), Berlin, Germany
- Center for Integrated Oncology (CIO), Cancer Research Center Cologne Essen (CCCE), Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany
- BIFOLD – Berlin Institute for the Foundations of Learning and Data, Berlin, Germany
| | - Kerstin Haase
- Experimental and Clinical Research Center (ECRC) of the MDC and Charité Berlin, Berlin, Germany
- German Cancer Consortium (DKTK), Partner Site Berlin, and German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Johannes H. Schulte
- Department of Pediatric Oncology and Hematology, Charité – Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin, Berlin, Germany
| | - Anton G. Henssen
- Experimental and Clinical Research Center (ECRC) of the MDC and Charité Berlin, Berlin, Germany
- Department of Pediatric Oncology and Hematology, Charité – Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin, Berlin, Germany
- Berlin Institute for Medical Systems Biology (BIMSB), Max Delbrück Center for Molecular Medicine in the Helmholtz Association (MDC), Berlin, Germany
- Berlin Institute of Health at Charité – Universitätsmedizin Berlin, Digital Health Center, Berlin, Germany
- German Cancer Consortium (DKTK), Partner Site Berlin, and German Cancer Research Center (DKFZ), Heidelberg, Germany
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7
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Abbas S, Pich O, Devonshire G, Zamani SA, Katz-Summercorn A, Killcoyne S, Cheah C, Nutzinger B, Grehan N, Lopez-Bigas N, Fitzgerald RC, Secrier M. Mutational signature dynamics shaping the evolution of oesophageal adenocarcinoma. Nat Commun 2023; 14:4239. [PMID: 37454136 PMCID: PMC10349863 DOI: 10.1038/s41467-023-39957-6] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/13/2023] [Accepted: 07/03/2023] [Indexed: 07/18/2023] Open
Abstract
A variety of mutational processes drive cancer development, but their dynamics across the entire disease spectrum from pre-cancerous to advanced neoplasia are poorly understood. We explore the mutagenic processes shaping oesophageal adenocarcinoma tumorigenesis in 997 instances comprising distinct stages of this malignancy, from Barrett Oesophagus to primary tumours and advanced metastatic disease. The mutational landscape is dominated by the C[T > C/G]T substitution enriched signatures SBS17a/b, which are linked with TP53 mutations, increased proliferation, genomic instability and disease progression. The APOBEC mutagenesis signature is a weak but persistent signal amplified in primary tumours. We also identify prevalent alterations in DNA damage repair pathways, with homologous recombination, base and nucleotide excision repair and translesion synthesis mutated in up to 50% of the cohort, and surprisingly uncoupled from transcriptional activity. Among these, the presence of base excision repair deficiencies show remarkably poor prognosis in the cohort. In this work, we provide insights on the mutational aetiology and changes enabling the transition from pre-neoplastic to advanced oesophageal adenocarcinoma.
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Affiliation(s)
- Sujath Abbas
- Early Cancer Institute, University of Cambridge, Cambridge, UK
| | - Oriol Pich
- Institute for Research in Biomedicine (IRB Barcelona), The Barcelona Institute of Science and Technology, Barcelona, Spain
| | - Ginny Devonshire
- Cancer Research UK Cambridge Institute, University of Cambridge, Cambridge, UK
| | | | | | - Sarah Killcoyne
- Early Cancer Institute, University of Cambridge, Cambridge, UK
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Hinxton, UK
| | - Calvin Cheah
- Early Cancer Institute, University of Cambridge, Cambridge, UK
| | | | - Nicola Grehan
- Early Cancer Institute, University of Cambridge, Cambridge, UK
| | - Nuria Lopez-Bigas
- Institute for Research in Biomedicine (IRB Barcelona), The Barcelona Institute of Science and Technology, Barcelona, Spain
- Institució Catalana de Recerca i Estudis Avançats (ICREA), Barcelona, Spain
- Centro de Investigación Biomédica en Red en Cáncer (CIBERONC), Instituto de Salud Carlos III, Madrid, Spain
| | | | - Maria Secrier
- UCL Genetics Institute, Department of Genetics, Evolution and Environment, University College London, London, UK.
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8
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Long X, Lu H, Cai MC, Zang J, Zhang Z, Wu J, Liu X, Cheng L, Cheng J, Cheung LWT, Shen Z, Zhou Y, Di W, Zhuang G, Yin X. APOBEC3B stratifies ovarian clear cell carcinoma with distinct immunophenotype and prognosis. Br J Cancer 2023; 128:2054-2062. [PMID: 36997661 PMCID: PMC10206171 DOI: 10.1038/s41416-023-02239-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/18/2022] [Revised: 03/07/2023] [Accepted: 03/16/2023] [Indexed: 04/01/2023] Open
Abstract
BACKGROUND Ovarian clear cell carcinoma (OCCC) is a challenging disease due to its intrinsic chemoresistance. Immunotherapy is an emerging treatment option but currently impeded by insufficient understanding of OCCC immunophenotypes and their molecular determinants. METHODS Whole-genome sequencing on 23 pathologically confirmed patients was employed to depict the genomic profile of primary OCCCs. APOBEC3B expression and digital pathology-based Immunoscore were assessed by performing immunohistochemistry and correlated with clinical outcomes. RESULTS An APOBEC-positive (APOBEC+) subtype was identified based on the characteristic mutational signature and prevalent kataegis events. APOBEC + OCCC displayed favourable prognosis across one internal and two external patient cohorts. The improved outcome was ascribable to increased lymphocytic infiltration. Similar phenomena of APOBEC3B expression and T-cell accumulation were observed in endometriotic tissues, suggesting that APOBEC-induced mutagenesis and immunogenicity could occur early during OCCC pathogenesis. Corroborating these results, a case report was presented for an APOBEC + patient demonstrating inflamed tumour microenvironment and clinical response to immune checkpoint blockade. CONCLUSIONS Our findings implicate APOBEC3B as a novel mechanism of OCCC stratification with prognostic value and as a potential predictive biomarker that may inform immunotherapeutic opportunities.
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Affiliation(s)
- Xiaoran Long
- State Key Laboratory of Oncogenes and Related Genes, Department of Obstetrics and Gynecology, Shanghai Cancer Institute, Ren Ji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
- Shanghai Key Laboratory of Gynecologic Oncology, Ren Ji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Huaiwu Lu
- Department of Gynecologic Oncology, Sun Yat-sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, China
| | - Mei-Chun Cai
- State Key Laboratory of Oncogenes and Related Genes, Department of Obstetrics and Gynecology, Shanghai Cancer Institute, Ren Ji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Jingyu Zang
- State Key Laboratory of Oncogenes and Related Genes, Department of Obstetrics and Gynecology, Shanghai Cancer Institute, Ren Ji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Zhuqing Zhang
- State Key Laboratory of Oncogenes and Related Genes, Department of Obstetrics and Gynecology, Shanghai Cancer Institute, Ren Ji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
- Shanghai Key Laboratory of Gynecologic Oncology, Ren Ji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Jie Wu
- Department of Pathology, The Affiliated Hospital of Qingdao University, Qingdao, China
| | - Xiaoshi Liu
- State Key Laboratory of Oncogenes and Related Genes, Department of Obstetrics and Gynecology, Shanghai Cancer Institute, Ren Ji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
- Shanghai Key Laboratory of Gynecologic Oncology, Ren Ji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Lin Cheng
- State Key Laboratory of Oncogenes and Related Genes, Department of Obstetrics and Gynecology, Shanghai Cancer Institute, Ren Ji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
- Shanghai Key Laboratory of Gynecologic Oncology, Ren Ji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Jiejun Cheng
- Department of Radiology, Ren Ji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Lydia W T Cheung
- School of Biomedical Sciences, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, China
| | - Zhen Shen
- Department of Obstetrics and Gynecology, The First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, China
| | - Ying Zhou
- Department of Obstetrics and Gynecology, The First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, China
| | - Wen Di
- State Key Laboratory of Oncogenes and Related Genes, Department of Obstetrics and Gynecology, Shanghai Cancer Institute, Ren Ji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
- Shanghai Key Laboratory of Gynecologic Oncology, Ren Ji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
| | - Guanglei Zhuang
- State Key Laboratory of Oncogenes and Related Genes, Department of Obstetrics and Gynecology, Shanghai Cancer Institute, Ren Ji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
- Shanghai Key Laboratory of Gynecologic Oncology, Ren Ji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
| | - Xia Yin
- State Key Laboratory of Oncogenes and Related Genes, Department of Obstetrics and Gynecology, Shanghai Cancer Institute, Ren Ji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
- Shanghai Key Laboratory of Gynecologic Oncology, Ren Ji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
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9
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Timmons C, Morris Q, Harrigan CF. Regional mutational signature activities in cancer genomes. PLoS Comput Biol 2022; 18:e1010733. [PMID: 36469539 PMCID: PMC9754594 DOI: 10.1371/journal.pcbi.1010733] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/07/2022] [Revised: 12/15/2022] [Accepted: 11/14/2022] [Indexed: 12/12/2022] Open
Abstract
Cancer genomes harbor a catalog of somatic mutations. The type and genomic context of these mutations depend on their causes and allow their attribution to particular mutational signatures. Previous work has shown that mutational signature activities change over the course of tumor development, but investigations of genomic region variability in mutational signatures have been limited. Here, we expand upon this work by constructing regional profiles of mutational signature activities over 2,203 whole genomes across 25 tumor types, using data aggregated by the Pan-Cancer Analysis of Whole Genomes (PCAWG) consortium. We present GenomeTrackSig as an extension to the TrackSig R package to construct regional signature profiles using optimal segmentation and the expectation-maximization (EM) algorithm. We find that 426 genomes from 20 tumor types display at least one change in mutational signature activities (changepoint), and 306 genomes contain at least one of 54 recurrent changepoints shared by seven or more genomes of the same tumor type. Five recurrent changepoint locations are shared by multiple tumor types. Within these regions, the particular signature changes are often consistent across samples of the same type and some, but not all, are characterized by signatures associated with subclonal expansion. The changepoints we found cannot strictly be explained by gene density, mutation density, or cell-of-origin chromatin state. We hypothesize that they reflect a confluence of factors including evolutionary timing of mutational processes, regional differences in somatic mutation rate, large-scale changes in chromatin state that may be tissue type-specific, and changes in chromatin accessibility during subclonal expansion. These results provide insight into the regional effects of DNA damage and repair processes, and may help us localize genomic and epigenomic changes that occur during cancer development.
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Affiliation(s)
- Caitlin Timmons
- Computational and Systems Biology Program, Sloan Kettering Institute, New York, New York, United States of America
- Department of Biological Sciences, Smith College, Northampton, Massachusetts, United States of America
| | - Quaid Morris
- Computational and Systems Biology Program, Sloan Kettering Institute, New York, New York, United States of America
- Vector Institute for Artificial Intelligence, Toronto, Canada
| | - Caitlin F. Harrigan
- Vector Institute for Artificial Intelligence, Toronto, Canada
- Department of Computer Science, University of Toronto, Toronto, Canada
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10
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Lee ND, Bozic I. Inferring parameters of cancer evolution in chronic lymphocytic leukemia. PLoS Comput Biol 2022; 18:e1010677. [PMID: 36331987 PMCID: PMC9668150 DOI: 10.1371/journal.pcbi.1010677] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/26/2022] [Revised: 11/16/2022] [Accepted: 10/24/2022] [Indexed: 11/06/2022] Open
Abstract
As a cancer develops, its cells accrue new mutations, resulting in a heterogeneous, complex genomic profile. We make use of this heterogeneity to derive simple, analytic estimates of parameters driving carcinogenesis and reconstruct the timeline of selective events following initiation of an individual cancer, where two longitudinal samples are available for sequencing. Using stochastic computer simulations of cancer growth, we show that we can accurately estimate mutation rate, time before and after a driver event occurred, and growth rates of both initiated cancer cells and subsequently appearing subclones. We demonstrate that in order to obtain accurate estimates of mutation rate and timing of events, observed mutation counts should be corrected to account for clonal mutations that occurred after the founding of the tumor, as well as sequencing coverage. Chronic lymphocytic leukemia (CLL), which often does not require treatment for years after diagnosis, presents an optimal system to study the untreated, natural evolution of cancer cell populations. When we apply our methodology to reconstruct the individual evolutionary histories of CLL patients, we find that the parental leukemic clone typically appears within the first fifteen years of life.
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Affiliation(s)
- Nathan D. Lee
- Department of Applied Mathematics, University of Washington, Seattle, Washington, United States of America
| | - Ivana Bozic
- Department of Applied Mathematics, University of Washington, Seattle, Washington, United States of America
- Herbold Computational Biology Program, Fred Hutchinson Cancer Research Center, Seattle, Washington, United States of America
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11
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The shaping of cancer genomes with the regional impact of mutation processes. EXPERIMENTAL & MOLECULAR MEDICINE 2022; 54:1049-1060. [PMID: 35902761 PMCID: PMC9355972 DOI: 10.1038/s12276-022-00808-x] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/03/2022] [Revised: 04/03/2022] [Accepted: 04/28/2022] [Indexed: 11/09/2022]
Abstract
Mutation signature analysis has been used to infer the contributions of various DNA mutagenic-repair events in individual cancer genomes. Here, we build a statistical framework using a multinomial distribution to assign individual mutations to their cognate mutation signatures. We applied it to 47 million somatic mutations in 1925 publicly available cancer genomes to obtain a mutation signature map at the resolution of individual somatic mutations. Based on mutation signature-level genetic-epigenetic correlative analyses, mutations with transcriptional and replicative strand asymmetries show different enrichment patterns across genomes, and “transcribed” chromatin states and gene boundaries are particularly vulnerable to transcription-coupled repair activities. While causative processes of cancer-driving mutations can be diverse, as shown for converging effects of multiple mutational processes on TP53 mutations, the substantial fraction of recurrently mutated amino acids points to specific mutational processes, e.g., age-related C-to-T transition for KRAS p.G12 mutations. Our investigation of evolutionary trajectories with respect to mutation signatures further revealed that candidate pairs of early- vs. late-operative mutation processes in cancer genomes represent evolutionary dynamics of multiple mutational processes in the shaping of cancer genomes. We also observed that the local mutation clusters of kataegis often include mutations arising from multiple mutational processes, suggestive of a locally synchronous impact of multiple mutational processes on cancer genomes. Taken together, our examination of the genome-wide landscape of mutation signatures at the resolution of individual somatic mutations shows the spatially and temporally distinct mutagenesis-repair-replication histories of various mutational processes and their effects on shaping cancer genomes. A statistical model that assigns non-hereditary DNA alterations known as somatic mutations to mutation “signatures” (groups of mutations arising from a specific biological process) on cancer genomes provides novel insights into disease evolution. Somatic mutations result from exposure to factors often linked to cancer development, such as tobacco or ultraviolet radiation. However, assigning a somatic mutation to a particular mutation “signature” remains challenging. The model created by Ruibin Xi (Peking University, China) and Tae-Min Kim (Catholic University of Korea, Seoul, South Korea) and co-workers grouped 47 million somatic mutations in 1925 cancer genomes into localized clusters before connecting them with mutation signatures. This strategy highlights the spatial and temporal patterns related to the origins of mutations, how the DNA strands are repaired and replicated, and how this influences the emerging cancer genome.
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12
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Angaroni F, Guidi A, Ascolani G, d'Onofrio A, Antoniotti M, Graudenzi A. J-SPACE: a Julia package for the simulation of spatial models of cancer evolution and of sequencing experiments. BMC Bioinformatics 2022; 23:269. [PMID: 35804300 PMCID: PMC9270769 DOI: 10.1186/s12859-022-04779-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2022] [Accepted: 06/09/2022] [Indexed: 11/15/2022] Open
Abstract
Background The combined effects of biological variability and measurement-related errors on cancer sequencing data remain largely unexplored. However, the spatio-temporal simulation of multi-cellular systems provides a powerful instrument to address this issue. In particular, efficient algorithmic frameworks are needed to overcome the harsh trade-off between scalability and expressivity, so to allow one to simulate both realistic cancer evolution scenarios and the related sequencing experiments, which can then be used to benchmark downstream bioinformatics methods. Result We introduce a Julia package for SPAtial Cancer Evolution (J-SPACE), which allows one to model and simulate a broad set of experimental scenarios, phenomenological rules and sequencing settings.Specifically, J-SPACE simulates the spatial dynamics of cells as a continuous-time multi-type birth-death stochastic process on a arbitrary graph, employing different rules of interaction and an optimised Gillespie algorithm. The evolutionary dynamics of genomic alterations (single-nucleotide variants and indels) is simulated either under the Infinite Sites Assumption or several different substitution models, including one based on mutational signatures. After mimicking the spatial sampling of tumour cells, J-SPACE returns the related phylogenetic model, and allows one to generate synthetic reads from several Next-Generation Sequencing (NGS) platforms, via the ART read simulator. The results are finally returned in standard FASTA, FASTQ, SAM, ALN and Newick file formats. Conclusion J-SPACE is designed to efficiently simulate the heterogeneous behaviour of a large number of cancer cells and produces a rich set of outputs. Our framework is useful to investigate the emergent spatial dynamics of cancer subpopulations, as well as to assess the impact of incomplete sampling and of experiment-specific errors. Importantly, the output of J-SPACE is designed to allow the performance assessment of downstream bioinformatics pipelines processing NGS data. J-SPACE is freely available at: https://github.com/BIMIB-DISCo/J-Space.jl.
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Affiliation(s)
- Fabrizio Angaroni
- Dept. of Informatics, Systems and Communication, Univ. of Milan-Bicocca, Milan, Italy.
| | - Alessandro Guidi
- Dept. of Informatics, Systems and Communication, Univ. of Milan-Bicocca, Milan, Italy
| | - Gianluca Ascolani
- Dept. of Informatics, Systems and Communication, Univ. of Milan-Bicocca, Milan, Italy
| | - Alberto d'Onofrio
- Department of Mathematics and Geosciences, Univ. of Trieste, Trieste, Italy
| | - Marco Antoniotti
- Dept. of Informatics, Systems and Communication, Univ. of Milan-Bicocca, Milan, Italy.,Bicocca Bioinformatics, Biostatistics and Bioimaging Centre (B4), Milan, Italy
| | - Alex Graudenzi
- Dept. of Informatics, Systems and Communication, Univ. of Milan-Bicocca, Milan, Italy.,Bicocca Bioinformatics, Biostatistics and Bioimaging Centre (B4), Milan, Italy.,Inst. of Molecular Bioimaging and Physiology, National Research Council (IBFM-CNR), Segrate, Italy
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13
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Stella GM, Scialò F, Bortolotto C, Agustoni F, Sanci V, Saddi J, Casali L, Corsico AG, Bianco A. Pragmatic Expectancy on Microbiota and Non-Small Cell Lung Cancer: A Narrative Review. Cancers (Basel) 2022; 14:cancers14133131. [PMID: 35804901 PMCID: PMC9264919 DOI: 10.3390/cancers14133131] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/05/2022] [Revised: 06/08/2022] [Accepted: 06/21/2022] [Indexed: 11/16/2022] Open
Abstract
It is well known that lung cancer relies on a number of genes aberrantly expressed because of somatic lesions. Indeed, the lungs, based on their anatomical features, are organs at a high risk of development of extremely heterogeneous tumors due to the exposure to several environmental toxic agents. In this context, the microbiome identifies the whole assemblage of microorganisms present in the lungs, as well as in distant organs, together with their structural elements and metabolites, which actively interact with normal and transformed cells. A relevant amount of data suggest that the microbiota plays a role not only in cancer disease predisposition and risk but also in its initiation and progression, with an impact on patients’ prognosis. Here, we discuss the mechanistic insights of the complex interaction between lung cancer and microbiota as a relevant component of the microenvironment, mainly focusing on novel diagnostic and therapeutic objectives.
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Affiliation(s)
- Giulia Maria Stella
- Department of Internal Medicine and Medical Therapeutics, University of Pavia Medical School, 27100 Pavia, Italy; (V.S.); (A.G.C.)
- Unit of Respiratory Diseases IRCCS Policlinico San Matteo Foundation, Department of Medical Sciences and Infective Diseases, 27100 Pavia, Italy
- Correspondence:
| | - Filippo Scialò
- Department of Translational Medical Sciences, University of Campania “L. Vanvitelli”, 80138 Naples, Italy; (F.S.); (A.B.)
- Ceinge Biotecnologie Avanzate s.c.a.r.l., 80145 Naples, Italy
| | - Chandra Bortolotto
- Department of Clinical-Surgical, Diagnostic and Pediatric Sciences, University of Pavia Medical School, 27100 Pavia, Italy;
- Unit of Radiology, Department of Intensive Medicine, IRCCS Policlinico San Matteo Foundation, 27100 Pavia, Italy
| | - Francesco Agustoni
- Unit of Oncology, Department of Medical Sciences and Infective Diseases, IRCCS Policlinico San Matteo Foundation, 27100 Pavia, Italy;
| | - Vincenzo Sanci
- Department of Internal Medicine and Medical Therapeutics, University of Pavia Medical School, 27100 Pavia, Italy; (V.S.); (A.G.C.)
- Unit of Respiratory Diseases IRCCS Policlinico San Matteo Foundation, Department of Medical Sciences and Infective Diseases, 27100 Pavia, Italy
| | - Jessica Saddi
- Radiation Therapy IRCCS Unit, Department of Medical Sciences and Infective Diseases, Policlinico San Matteo Foundation, 27100 Pavia, Italy;
- University of Milano-Bicocca, 20900 Monza, Italy
| | - Lucio Casali
- Honorary Consultant Student Support and Services, University of Pavia, 27100 Pavia, Italy;
| | - Angelo Guido Corsico
- Department of Internal Medicine and Medical Therapeutics, University of Pavia Medical School, 27100 Pavia, Italy; (V.S.); (A.G.C.)
- Unit of Respiratory Diseases IRCCS Policlinico San Matteo Foundation, Department of Medical Sciences and Infective Diseases, 27100 Pavia, Italy
| | - Andrea Bianco
- Department of Translational Medical Sciences, University of Campania “L. Vanvitelli”, 80138 Naples, Italy; (F.S.); (A.B.)
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14
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A phylogenetic approach to study the evolution of somatic mutational processes in cancer. Commun Biol 2022; 5:617. [PMID: 35732905 PMCID: PMC9217972 DOI: 10.1038/s42003-022-03560-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2021] [Accepted: 06/07/2022] [Indexed: 11/09/2022] Open
Abstract
Cancer cell genomes change continuously due to mutations, and mutational processes change over time in patients, leaving dynamic signatures in the accumulated genomic variation in tumors. Many computational methods detect the relative activities of known mutation signatures. However, these methods may produce erroneous signatures when applied to individual branches in cancer cell phylogenies. Here, we show that the inference of branch-specific mutational signatures can be improved through a joint analysis of the collections of mutations mapped on proximal branches of the cancer cell phylogeny. This approach reduces the false-positive discovery rate of branch-specific signatures and can sometimes detect faint signatures. An analysis of empirical data from 61 lung cancer patients supports trends based on computer-simulated datasets for which the correct signatures are known. In lung cancer somatic variation, we detect a decreasing trend of smoking-related mutational processes over time and an increasing influence of APOBEC mutational processes as the tumor evolution progresses. These analyses also reveal patterns of conservation and divergence of mutational processes in cell lineages within patients.
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15
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Georgeson P, Harrison TA, Pope BJ, Zaidi SH, Qu C, Steinfelder RS, Lin Y, Joo JE, Mahmood K, Clendenning M, Walker R, Amitay EL, Berndt SI, Brenner H, Campbell PT, Cao Y, Chan AT, Chang-Claude J, Doheny KF, Drew DA, Figueiredo JC, French AJ, Gallinger S, Giannakis M, Giles GG, Gsur A, Gunter MJ, Hoffmeister M, Hsu L, Huang WY, Limburg P, Manson JE, Moreno V, Nassir R, Nowak JA, Obón-Santacana M, Ogino S, Phipps AI, Potter JD, Schoen RE, Sun W, Toland AE, Trinh QM, Ugai T, Macrae FA, Rosty C, Hudson TJ, Jenkins MA, Thibodeau SN, Winship IM, Peters U, Buchanan DD. Identifying colorectal cancer caused by biallelic MUTYH pathogenic variants using tumor mutational signatures. Nat Commun 2022; 13:3254. [PMID: 35668106 PMCID: PMC9170691 DOI: 10.1038/s41467-022-30916-1] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2021] [Accepted: 05/24/2022] [Indexed: 01/11/2023] Open
Abstract
Carriers of germline biallelic pathogenic variants in the MUTYH gene have a high risk of colorectal cancer. We test 5649 colorectal cancers to evaluate the discriminatory potential of a tumor mutational signature specific to MUTYH for identifying biallelic carriers and classifying variants of uncertain clinical significance (VUS). Using a tumor and matched germline targeted multi-gene panel approach, our classifier identifies all biallelic MUTYH carriers and all known non-carriers in an independent test set of 3019 colorectal cancers (accuracy = 100% (95% confidence interval 99.87-100%)). All monoallelic MUTYH carriers are classified with the non-MUTYH carriers. The classifier provides evidence for a pathogenic classification for two VUS and a benign classification for five VUS. Somatic hotspot mutations KRAS p.G12C and PIK3CA p.Q546K are associated with colorectal cancers from biallelic MUTYH carriers compared with non-carriers (p = 2 × 10-23 and p = 6 × 10-11, respectively). Here, we demonstrate the potential application of mutational signatures to tumor sequencing workflows to improve the identification of biallelic MUTYH carriers.
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Affiliation(s)
- Peter Georgeson
- Colorectal Oncogenomics Group, Department of Clinical Pathology, The University of Melbourne, Parkville, VIC, 3010, Australia
- University of Melbourne Centre for Cancer Research, Victorian Comprehensive Cancer Centre, Parkville, VIC, 3010, Australia
| | - Tabitha A Harrison
- Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, WA, USA
| | - Bernard J Pope
- Colorectal Oncogenomics Group, Department of Clinical Pathology, The University of Melbourne, Parkville, VIC, 3010, Australia
- University of Melbourne Centre for Cancer Research, Victorian Comprehensive Cancer Centre, Parkville, VIC, 3010, Australia
- Melbourne Bioinformatics, The University of Melbourne, Carlton, VIC, Australia
| | - Syed H Zaidi
- Ontario Institute for Cancer Research, Toronto, ON, Canada
| | - Conghui Qu
- Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, WA, USA
| | - Robert S Steinfelder
- Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, WA, USA
| | - Yi Lin
- Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, WA, USA
| | - Jihoon E Joo
- Colorectal Oncogenomics Group, Department of Clinical Pathology, The University of Melbourne, Parkville, VIC, 3010, Australia
- University of Melbourne Centre for Cancer Research, Victorian Comprehensive Cancer Centre, Parkville, VIC, 3010, Australia
| | - Khalid Mahmood
- Colorectal Oncogenomics Group, Department of Clinical Pathology, The University of Melbourne, Parkville, VIC, 3010, Australia
- University of Melbourne Centre for Cancer Research, Victorian Comprehensive Cancer Centre, Parkville, VIC, 3010, Australia
- Melbourne Bioinformatics, The University of Melbourne, Carlton, VIC, Australia
| | - Mark Clendenning
- Colorectal Oncogenomics Group, Department of Clinical Pathology, The University of Melbourne, Parkville, VIC, 3010, Australia
- University of Melbourne Centre for Cancer Research, Victorian Comprehensive Cancer Centre, Parkville, VIC, 3010, Australia
| | - Romy Walker
- Colorectal Oncogenomics Group, Department of Clinical Pathology, The University of Melbourne, Parkville, VIC, 3010, Australia
- University of Melbourne Centre for Cancer Research, Victorian Comprehensive Cancer Centre, Parkville, VIC, 3010, Australia
| | - Efrat L Amitay
- Division of Clinical Epidemiology and Aging Research, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Sonja I Berndt
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | - Hermann Brenner
- Division of Clinical Epidemiology and Aging Research, German Cancer Research Center (DKFZ), Heidelberg, Germany
- Division of Preventive Oncology, German Cancer Research Center (DKFZ) and National Center for Tumor Diseases (NCT), Heidelberg, Germany
- German Cancer Consortium (DKTK), German Cancer Research Center(DKFZ), Heidelberg, Germany
| | - Peter T Campbell
- Department of Epidemiology and Population Health, Albert Einstein College of Medicine, Bronx, NY, USA
| | - Yin Cao
- Division of Public Health Sciences, Department of Surgery, Washington University School of Medicine, St Louis, MO, USA
- Alvin J. Siteman Cancer Center at Barnes-Jewish Hospital and Washington University School of Medicine, St. Louis, MO, USA
- Division of Gastroenterology, Department of Medicine, Washington University School of Medicine, St. Louis, MO, USA
| | - Andrew T Chan
- Clinical and Translational Epidemiology Unit, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
- Division of Gastroenterology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
- Channing Division of Network Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA
- Department of Immunology and Infectious Diseases, Harvard T.H. Chan School of Public Health, Harvard University, Boston, MA, USA
| | - Jenny Chang-Claude
- Division of Cancer Epidemiology, German Cancer Research Center (DKFZ), Heidelberg, Germany
- University Medical Centre Hamburg-Eppendorf, University Cancer Centre Hamburg (UCCH), Hamburg, Germany
| | - Kimberly F Doheny
- Center for Inherited Disease Research (CIDR), Department of Genetic Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - David A Drew
- Clinical and Translational Epidemiology Unit, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
- Division of Gastroenterology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
| | - Jane C Figueiredo
- Department of Medicine, Samuel Oschin Comprehensive Cancer Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA
- Department of Preventive Medicine, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Amy J French
- Division of Laboratory Genetics, Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, MN, USA
| | - Steven Gallinger
- Ontario Institute for Cancer Research, Toronto, ON, Canada
- Lunenfeld Tanenbaum Research Institute, Mount Sinai Hospital, University of Toronto, Toronto, ON, Canada
| | - Marios Giannakis
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA
- Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Graham G Giles
- Cancer Epidemiology Division, Cancer Council Victoria, Melbourne, VIC, Australia
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Melbourne, VIC, Australia
- Precision Medicine, School of Clinical Sciences at Monash Health, Monash University, Clayton, VIC, Australia
| | - Andrea Gsur
- Institute of Cancer Research, Department of Medicine I, Medical University Vienna, Vienna, Austria
| | - Marc J Gunter
- Nutrition and Metabolism Branch, International Agency for Research on Cancer, World Health Organization, Lyon, France
| | - Michael Hoffmeister
- Division of Clinical Epidemiology and Aging Research, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Li Hsu
- Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, WA, USA
- Department of Biostatistics, University of Washington, Seattle, WA, USA
| | - Wen-Yi Huang
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | - Paul Limburg
- Division of Gastroenterology & Hepatology, Mayo Clinic, Rochester, MN, USA
| | - JoAnn E Manson
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA
- Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Victor Moreno
- Oncology Data Analytics Program, Catalan Institute of Oncology-IDIBELL, L'Hospitalet de Llobregat, Barcelona, Spain
- CIBER Epidemiología y Salud Pública (CIBERESP), Madrid, Spain
- Department of Clinical Sciences, Faculty of Medicine, University of Barcelona, Barcelona, Spain
- ONCOBEL Program, Bellvitge Biomedical Research Institute (IDIBELL), L'Hospitalet de Llobregat, Barcelona, Spain
| | - Rami Nassir
- Department of Pathology, College of Medicine, Umm Al-Qura University, Mecca, Saudi Arabia
| | - Jonathan A Nowak
- Program in MPE Molecular Pathological Epidemiology, Department of Pathology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Mireia Obón-Santacana
- Oncology Data Analytics Program, Catalan Institute of Oncology-IDIBELL, L'Hospitalet de Llobregat, Barcelona, Spain
- CIBER Epidemiología y Salud Pública (CIBERESP), Madrid, Spain
| | - Shuji Ogino
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA
- Program in MPE Molecular Pathological Epidemiology, Department of Pathology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
- Cancer Immunology Program, Dana-Farber Harvard Cancer Center, Boston, MA, USA
| | - Amanda I Phipps
- Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, WA, USA
- Department of Epidemiology, University of Washington, Seattle, WA, USA
| | - John D Potter
- Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, WA, USA
- Research Centre for Hauora and Health, Massey University, Wellington, New Zealand
| | - Robert E Schoen
- Department of Medicine and Epidemiology, University of Pittsburgh Medical Center, Pittsburgh, PA, USA
| | - Wei Sun
- Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, WA, USA
| | - Amanda E Toland
- Departments of Cancer Biology and Genetics and Internal Medicine, Comprehensive Cancer Center, The Ohio State University, Columbus, OH, USA
| | - Quang M Trinh
- Ontario Institute for Cancer Research, Toronto, ON, Canada
| | - Tomotaka Ugai
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA
- Program in MPE Molecular Pathological Epidemiology, Department of Pathology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Finlay A Macrae
- Parkville Familial Cancer Centre, Royal Melbourne Hospital, Parkville, VIC, Australia
- Colorectal Medicine and Genetics, Royal Melbourne Hospital, Parkville, VIC, Australia
- Genomic Medicine and Family Cancer Clinic, Royal Melbourne Hospital, Parkville, VIC, Australia
| | - Christophe Rosty
- Colorectal Oncogenomics Group, Department of Clinical Pathology, The University of Melbourne, Parkville, VIC, 3010, Australia
- University of Melbourne Centre for Cancer Research, Victorian Comprehensive Cancer Centre, Parkville, VIC, 3010, Australia
- Envoi Specialist Pathologists, Brisbane, QLD, Australia
- University of Queensland, Brisbane, QLD, Australia
| | | | - Mark A Jenkins
- University of Melbourne Centre for Cancer Research, Victorian Comprehensive Cancer Centre, Parkville, VIC, 3010, Australia
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Melbourne, VIC, Australia
| | - Stephen N Thibodeau
- Division of Laboratory Genetics, Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, MN, USA
| | - Ingrid M Winship
- Genomic Medicine and Family Cancer Clinic, Royal Melbourne Hospital, Parkville, VIC, Australia
- Department of Medicine, The University of Melbourne, Parkville, VIC, Australia
| | - Ulrike Peters
- Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, WA, USA
- Department of Epidemiology, University of Washington, Seattle, WA, USA
| | - Daniel D Buchanan
- Colorectal Oncogenomics Group, Department of Clinical Pathology, The University of Melbourne, Parkville, VIC, 3010, Australia.
- University of Melbourne Centre for Cancer Research, Victorian Comprehensive Cancer Centre, Parkville, VIC, 3010, Australia.
- Genomic Medicine and Family Cancer Clinic, Royal Melbourne Hospital, Parkville, VIC, Australia.
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16
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Abstract
The evolutionary history of hepatobiliary cancers is embedded in their genomes. By analysing their catalogue of somatic mutations and the DNA sequence context in which they occur, it is possible to infer the mechanisms underpinning tumorigenesis. These mutational signatures reflect the exogenous and endogenous origins of genetic damage as well as the capacity of hepatobiliary cells to repair and replicate DNA. Genomic analysis of thousands of patients with hepatobiliary cancers has highlighted the diversity of mutagenic processes active in these malignancies, highlighting a prominent source of the inter-cancer-type, inter-patient, intertumour and intratumoural heterogeneity that is observed clinically. However, a substantial proportion of mutational signatures detected in hepatocellular carcinoma and biliary tract cancer remain of unknown cause, emphasizing the important contribution of processes yet to be identified. Exploiting mutational signatures to retrospectively understand hepatobiliary carcinogenesis could advance preventative management of these aggressive tumours as well as potentially predict treatment response and guide the development of therapies targeting tumour evolution.
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17
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Li Z, Spoelstra NS, Sikora MJ, Sams SB, Elias A, Richer JK, Lee AV, Oesterreich S. Mutual exclusivity of ESR1 and TP53 mutations in endocrine resistant metastatic breast cancer. NPJ Breast Cancer 2022; 8:62. [PMID: 35538119 PMCID: PMC9090919 DOI: 10.1038/s41523-022-00426-w] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2021] [Accepted: 03/31/2022] [Indexed: 12/12/2022] Open
Abstract
Both TP53 and ESR1 mutations occur frequently in estrogen receptor positive (ER+) metastatic breast cancers (MBC) and their distinct roles in breast cancer tumorigenesis and progression are well appreciated. Recent clinical studies discovered mutual exclusivity between TP53 and ESR1 mutations in metastatic breast cancers; however, mechanisms underlying this intriguing clinical observation remain largely understudied and unknown. Here, we explored the interplay between TP53 and ESR1 mutations using publicly available clinical and experimental data sets. We first confirmed the robust mutational exclusivity using six independent cohorts with 1,056 ER+ MBC samples and found that the exclusivity broadly applies to all ER+ breast tumors regardless of their clinical and distinct mutational features. ESR1 mutant tumors do not exhibit differential p53 pathway activity, whereas we identified attenuated ER activity and expression in TP53 mutant tumors, driven by a p53-associated E2 response gene signature. Further, 81% of these p53-associated E2 response genes are either direct targets of wild-type (WT) p53-regulated transactivation or are mutant p53-associated microRNAs, representing bimodal mechanisms of ER suppression. Lastly, we analyzed the very rare cases with co-occurrences of TP53 and ESR1 mutations and found that their simultaneous presence was also associated with reduced ER activity. In addition, tumors with dual mutations showed higher levels of total and PD-L1 positive macrophages. In summary, our study utilized multiple publicly available sources to explore the mechanism underlying the mutual exclusivity between ESR1 and TP53 mutations, providing further insights and testable hypotheses of the molecular interplay between these two pivotal genes in ER+ MBC.
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Affiliation(s)
- Zheqi Li
- Department of Pharmacology and Chemical Biology, University of Pittsburgh, Pittsburgh, PA, USA
- Women's Cancer Research Center, Magee Women's Research Institute, UPMC Hillman Cancer Center, Pittsburgh, PA, USA
| | - Nicole S Spoelstra
- Department of Pathology, University of Colorado Anschutz Medical Campus, Aurora, CO, USA
| | - Matthew J Sikora
- Department of Pathology, University of Colorado Anschutz Medical Campus, Aurora, CO, USA
| | - Sharon B Sams
- Department of Pathology, University of Colorado Anschutz Medical Campus, Aurora, CO, USA
| | - Anthony Elias
- School of Medicine, Division of Oncology, University of Colorado Anschutz Medical Campus, Aurora, CO, USA
| | - Jennifer K Richer
- Department of Pathology, University of Colorado Anschutz Medical Campus, Aurora, CO, USA
| | - Adrian V Lee
- Department of Pharmacology and Chemical Biology, University of Pittsburgh, Pittsburgh, PA, USA
- Women's Cancer Research Center, Magee Women's Research Institute, UPMC Hillman Cancer Center, Pittsburgh, PA, USA
| | - Steffi Oesterreich
- Department of Pharmacology and Chemical Biology, University of Pittsburgh, Pittsburgh, PA, USA.
- Women's Cancer Research Center, Magee Women's Research Institute, UPMC Hillman Cancer Center, Pittsburgh, PA, USA.
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18
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Matsutani T, Hamada M. Clone decomposition based on mutation signatures provides novel insights into mutational processes. NAR Genom Bioinform 2021; 3:lqab093. [PMID: 34734181 PMCID: PMC8559167 DOI: 10.1093/nargab/lqab093] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/08/2021] [Revised: 09/17/2021] [Accepted: 10/19/2021] [Indexed: 11/24/2022] Open
Abstract
Intra-tumor heterogeneity is a phenomenon in which mutation profiles differ from cell to cell within the same tumor and is observed in almost all tumors. Understanding intra-tumor heterogeneity is essential from the clinical perspective. Numerous methods have been developed to predict this phenomenon based on variant allele frequency. Among the methods, CloneSig models the variant allele frequency and mutation signatures simultaneously and provides an accurate clone decomposition. However, this method has limitations in terms of clone number selection and modeling. We propose SigTracer, a novel hierarchical Bayesian approach for analyzing intra-tumor heterogeneity based on mutation signatures to tackle these issues. We show that SigTracer predicts more reasonable clone decompositions than the existing methods against artificial data that mimic cancer genomes. We applied SigTracer to whole-genome sequences of blood cancer samples. The results were consistent with past findings that single base substitutions caused by a specific signature (previously reported as SBS9) related to the activation-induced cytidine deaminase intensively lie within immunoglobulin-coding regions for chronic lymphocytic leukemia samples. Furthermore, we showed that this signature mutates regions responsible for cell-cell adhesion. Accurate assignments of mutations to signatures by SigTracer can provide novel insights into signature origins and mutational processes.
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Affiliation(s)
- Taro Matsutani
- Graduate School of Advanced Science and Engineering, Waseda University, 55N-06-10, 3-4-1, Okubo Shinjuku-ku, Tokyo 169–8555, Japan
- Computational Bio Big-Data Open Innovation Laboratory (CBBD-OIL), National Institute of Advanced Industrial Science and Technology (AIST), Tokyo 169–8555, Japan
| | - Michiaki Hamada
- Graduate School of Advanced Science and Engineering, Waseda University, 55N-06-10, 3-4-1, Okubo Shinjuku-ku, Tokyo 169–8555, Japan
- Computational Bio Big-Data Open Innovation Laboratory (CBBD-OIL), National Institute of Advanced Industrial Science and Technology (AIST), Tokyo 169–8555, Japan
- Graduate School of Medicine, Nippon Medical School, Sendagi, Bunkyo, Tokyo 113-8602, Japan
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19
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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: 20] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [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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20
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Koh G, Degasperi A, Zou X, Momen S, Nik-Zainal S. Mutational signatures: emerging concepts, caveats and clinical applications. Nat Rev Cancer 2021; 21:619-637. [PMID: 34316057 DOI: 10.1038/s41568-021-00377-7] [Citation(s) in RCA: 147] [Impact Index Per Article: 36.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 06/08/2021] [Indexed: 02/05/2023]
Abstract
Whole-genome sequencing has brought the cancer genomics community into new territory. Thanks to the sheer power provided by the thousands of mutations present in each patient's cancer, we have been able to discern generic patterns of mutations, termed 'mutational signatures', that arise during tumorigenesis. These mutational signatures provide new insights into the causes of individual cancers, revealing both endogenous and exogenous factors that have influenced cancer development. This Review brings readers up to date in a field that is expanding in computational, experimental and clinical directions. We focus on recent conceptual advances, underscoring some of the caveats associated with using the mutational signature frameworks and highlighting the latest experimental insights. We conclude by bringing attention to areas that are likely to see advancements in clinical applications.
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Affiliation(s)
- Gene Koh
- Department of Medical Genetics, School of Clinical Medicine, University of Cambridge, Cambridge, UK
- MRC Cancer Unit, School of Clinical Medicine, University of Cambridge, Cambridge, UK
| | - Andrea Degasperi
- Department of Medical Genetics, School of Clinical Medicine, University of Cambridge, Cambridge, UK
- MRC Cancer Unit, School of Clinical Medicine, University of Cambridge, Cambridge, UK
| | - Xueqing Zou
- Department of Medical Genetics, School of Clinical Medicine, University of Cambridge, Cambridge, UK
- MRC Cancer Unit, School of Clinical Medicine, University of Cambridge, Cambridge, UK
| | - Sophie Momen
- Department of Medical Genetics, School of Clinical Medicine, University of Cambridge, Cambridge, UK
- MRC Cancer Unit, School of Clinical Medicine, University of Cambridge, Cambridge, UK
| | - Serena Nik-Zainal
- Department of Medical Genetics, School of Clinical Medicine, University of Cambridge, Cambridge, UK.
- MRC Cancer Unit, School of Clinical Medicine, University of Cambridge, Cambridge, UK.
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21
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Abécassis J, Reyal F, Vert JP. CloneSig can jointly infer intra-tumor heterogeneity and mutational signature activity in bulk tumor sequencing data. Nat Commun 2021; 12:5352. [PMID: 34504064 PMCID: PMC8429716 DOI: 10.1038/s41467-021-24992-y] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/12/2019] [Accepted: 07/12/2021] [Indexed: 02/07/2023] Open
Abstract
Systematic DNA sequencing of cancer samples has highlighted the importance of two aspects of cancer genomics: intra-tumor heterogeneity (ITH) and mutational processes. These two aspects may not always be independent, as different mutational processes could be involved in different stages or regions of the tumor, but existing computational approaches to study them largely ignore this potential dependency. Here, we present CloneSig, a computational method to jointly infer ITH and mutational processes in a tumor from bulk-sequencing data. Extensive simulations show that CloneSig outperforms current methods for ITH inference and detection of mutational processes when the distribution of mutational signatures changes between clones. Applied to a large cohort of 8,951 tumors with whole-exome sequencing data from The Cancer Genome Atlas, and on a pan-cancer dataset of 2,632 whole-genome sequencing tumor samples from the Pan-Cancer Analysis of Whole Genomes initiative, CloneSig obtains results overall coherent with previous studies.
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Affiliation(s)
- Judith Abécassis
- Institut Curie, PSL Research University, Translational Research Department, INSERM, U932 Immunity and Cancer, Residual Tumor & Response to Treatment Laboratory (RT2Lab), Paris, France
- MINES ParisTech, PSL University, CBIO - Centre for Computational Biology, Paris, France
- Institut Curie, PSL Research University, Paris, France
| | - Fabien Reyal
- Institut Curie, PSL Research University, Translational Research Department, INSERM, U932 Immunity and Cancer, Residual Tumor & Response to Treatment Laboratory (RT2Lab), Paris, France
- Department of Surgery, Institut Curie, Paris, France
| | - Jean-Philippe Vert
- MINES ParisTech, PSL University, CBIO - Centre for Computational Biology, Paris, France.
- Google Research, Brain team, Paris, France.
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22
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Giunta S. Decoding human cancer with whole genome sequencing: a review of PCAWG Project studies published in February 2020. Cancer Metastasis Rev 2021; 40:909-924. [PMID: 34097189 PMCID: PMC8180541 DOI: 10.1007/s10555-021-09969-z] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/26/2021] [Accepted: 04/21/2021] [Indexed: 12/15/2022]
Abstract
Cancer is underlined by genetic changes. In an unprecedented international effort, the Pan-Cancer Analysis of Whole Genomes (PCAWG) of the International Cancer Genome Consortium (ICGC) and The Cancer Genome Atlas (TCGA) sequenced the tumors of over two thousand five hundred patients across 38 different cancer types, as well as the corresponding healthy tissue, with the aim of identifying genome-wide mutations exclusively found in cancer and uncovering new genetic changes that drive tumor formation. What set this project apart from earlier efforts is the use of whole genome sequencing (WGS) that enabled to explore alterations beyond the coding DNA, into cancer's non-coding genome. WGS of the entire cohort allowed to tease apart driving mutations that initiate and support carcinogenesis from passenger mutations that do not play an overt role in the disease. At least one causative mutation was found in 95% of all cancers, with many tumors showing an average of 5 driver mutations. The PCAWG Project also assessed the transcriptional output altered in cancer and rebuilt the evolutionary history of each tumor showing that initial driver mutations can occur years if not decades prior to a diagnosis. Here, I provide a concise review of the Pan-Cancer Project papers published on February 2020, along with key computational tools and the digital framework generated as part of the project. This represents an historic effort by hundreds of international collaborators, which provides a comprehensive understanding of cancer genetics, with publicly available data and resources representing a treasure trove of information to advance cancer research for years to come.
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Affiliation(s)
- Simona Giunta
- Laboratory of Genome Evolution, Department of Biology & Biotechnology "Charles Darwin", University of Rome Sapienza, Rome, Italy.
- The Rockefeller University, 1230 York Avenue, New York, NY, USA.
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23
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Kim YA, Leiserson MDM, Moorjani P, Sharan R, Wojtowicz D, Przytycka TM. Mutational Signatures: From Methods to Mechanisms. Annu Rev Biomed Data Sci 2021; 4:189-206. [PMID: 34465178 DOI: 10.1146/annurev-biodatasci-122320-120920] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
Mutations are the driving force of evolution, yet they underlie many diseases, in particular, cancer. They are thought to arise from a combination of stochastic errors in DNA processing, naturally occurring DNA damage (e.g., the spontaneous deamination of methylated CpG sites), replication errors, and dysregulation of DNA repair mechanisms. High-throughput sequencing has made it possible to generate large datasets to study mutational processes in health and disease. Since the emergence of the first mutational process studies in 2012, this field is gaining increasing attention and has already accumulated a host of computational approaches and biomedical applications.
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Affiliation(s)
- Yoo-Ah Kim
- National Center of Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, Maryland 20894, USA;
| | - Mark D M Leiserson
- Department of Computer Science and Center for Bioinformatics and Computational Biology, University of Maryland, College Park, Maryland 20742, USA
| | - Priya Moorjani
- Department of Molecular and Cell Biology and Center for Computational Biology, University of California, Berkeley, California 94720, USA
| | - Roded Sharan
- Blavatnik School of Computer Science, Tel Aviv University, Tel Aviv 69978, Israel
| | - Damian Wojtowicz
- National Center of Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, Maryland 20894, USA;
| | - Teresa M Przytycka
- National Center of Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, Maryland 20894, USA;
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24
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Vavoulis DV, Cutts A, Taylor JC, Schuh A. A statistical approach for tracking clonal dynamics in cancer using longitudinal next-generation sequencing data. Bioinformatics 2021; 37:147-154. [PMID: 32722772 PMCID: PMC8055230 DOI: 10.1093/bioinformatics/btaa672] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/29/2020] [Revised: 05/13/2020] [Accepted: 07/20/2020] [Indexed: 01/22/2023] Open
Abstract
MOTIVATION Tumours are composed of distinct cancer cell populations (clones), which continuously adapt to their local micro-environment. Standard methods for clonal deconvolution seek to identify groups of mutations and estimate the prevalence of each group in the tumour, while considering its purity and copy number profile. These methods have been applied on cross-sectional data and on longitudinal data after discarding information on the timing of sample collection. Two key questions are how can we incorporate such information in our analyses and is there any benefit in doing so? RESULTS We developed a clonal deconvolution method, which incorporates explicitly the temporal spacing of longitudinally sampled tumours. By merging a Dirichlet Process Mixture Model with Gaussian Process priors and using as input a sequence of several sparsely collected samples, our method can reconstruct the temporal profile of the abundance of any mutation cluster supported by the data as a continuous function of time. We benchmarked our method on whole genome, whole exome and targeted sequencing data from patients with chronic lymphocytic leukaemia, on liquid biopsy data from a patient with melanoma and on synthetic data and we found that incorporating information on the timing of tissue collection improves model performance, as long as data of sufficient volume and complexity are available for estimating free model parameters. Thus, our approach is particularly useful when collecting a relatively long sequence of tumour samples is feasible, as in liquid cancers (e.g. leukaemia) and liquid biopsies. AVAILABILITY AND IMPLEMENTATION The statistical methodology presented in this paper is freely available at github.com/dvav/clonosGP. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Dimitrios V Vavoulis
- Department of Oncology, University of Oxford, Oxford, OX3 7DQ, UK
- Nuffield Department of Medicine, Wellcome Centre for Human Genetics, University of Oxford, Oxford, OX3 7BN, UK
- NIHR Oxford Biomedical Research Centre, Oxford University Hospitals NHS Trust, Oxford, OX3 9DU, UK
- Department of Oncology, Molecular Diagnostic Centre, University of Oxford, Oxford OX3 9DU, UK
| | - Anthony Cutts
- Department of Oncology, University of Oxford, Oxford, OX3 7DQ, UK
- Department of Oncology, Molecular Diagnostic Centre, University of Oxford, Oxford OX3 9DU, UK
| | - Jenny C Taylor
- Nuffield Department of Medicine, Wellcome Centre for Human Genetics, University of Oxford, Oxford, OX3 7BN, UK
- NIHR Oxford Biomedical Research Centre, Oxford University Hospitals NHS Trust, Oxford, OX3 9DU, UK
| | - Anna Schuh
- Department of Oncology, University of Oxford, Oxford, OX3 7DQ, UK
- NIHR Oxford Biomedical Research Centre, Oxford University Hospitals NHS Trust, Oxford, OX3 9DU, UK
- Department of Oncology, Molecular Diagnostic Centre, University of Oxford, Oxford OX3 9DU, UK
- Department of Haematology, Oxford University Hospitals NHS Trust, Oxford OX3 9DU, UK
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25
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Dentro SC, Leshchiner I, Haase K, Tarabichi M, Wintersinger J, Deshwar AG, Yu K, Rubanova Y, Macintyre G, Demeulemeester J, Vázquez-García I, Kleinheinz K, Livitz DG, Malikic S, Donmez N, Sengupta S, Anur P, Jolly C, Cmero M, Rosebrock D, Schumacher SE, Fan Y, Fittall M, Drews RM, Yao X, Watkins TBK, Lee J, Schlesner M, Zhu H, Adams DJ, McGranahan N, Swanton C, Getz G, Boutros PC, Imielinski M, Beroukhim R, Sahinalp SC, Ji Y, Peifer M, Martincorena I, Markowetz F, Mustonen V, Yuan K, Gerstung M, Spellman PT, Wang W, Morris QD, Wedge DC, Van Loo P. Characterizing genetic intra-tumor heterogeneity across 2,658 human cancer genomes. Cell 2021; 184:2239-2254.e39. [PMID: 33831375 PMCID: PMC8054914 DOI: 10.1016/j.cell.2021.03.009] [Citation(s) in RCA: 311] [Impact Index Per Article: 77.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/06/2020] [Revised: 09/21/2020] [Accepted: 03/03/2021] [Indexed: 02/07/2023]
Abstract
Intra-tumor heterogeneity (ITH) is a mechanism of therapeutic resistance and therefore an important clinical challenge. However, the extent, origin, and drivers of ITH across cancer types are poorly understood. To address this, we extensively characterize ITH across whole-genome sequences of 2,658 cancer samples spanning 38 cancer types. Nearly all informative samples (95.1%) contain evidence of distinct subclonal expansions with frequent branching relationships between subclones. We observe positive selection of subclonal driver mutations across most cancer types and identify cancer type-specific subclonal patterns of driver gene mutations, fusions, structural variants, and copy number alterations as well as dynamic changes in mutational processes between subclonal expansions. Our results underline the importance of ITH and its drivers in tumor evolution and provide a pan-cancer resource of comprehensively annotated subclonal events from whole-genome sequencing data.
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Affiliation(s)
- Stefan C Dentro
- Cancer Genomics Laboratory, The Francis Crick Institute, London NW1 1AT, UK; Wellcome Trust Sanger Institute, Cambridge CB10 1SA, UK; Big Data Institute, University of Oxford, Oxford OX3 7LF, UK
| | | | - Kerstin Haase
- Cancer Genomics Laboratory, The Francis Crick Institute, London NW1 1AT, UK
| | - Maxime Tarabichi
- Cancer Genomics Laboratory, The Francis Crick Institute, London NW1 1AT, UK; Wellcome Trust Sanger Institute, Cambridge CB10 1SA, UK
| | - Jeff Wintersinger
- University of Toronto, Toronto, ON M5S 3E1, Canada; Vector Institute, Toronto, ON M5G 1L7, Canada
| | - Amit G Deshwar
- University of Toronto, Toronto, ON M5S 3E1, Canada; Vector Institute, Toronto, ON M5G 1L7, Canada
| | - Kaixian Yu
- The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
| | - Yulia Rubanova
- University of Toronto, Toronto, ON M5S 3E1, Canada; Vector Institute, Toronto, ON M5G 1L7, Canada
| | - Geoff Macintyre
- Cancer Research UK Cambridge Institute, University of Cambridge, Cambridge CB2 0RE, UK
| | - Jonas Demeulemeester
- Cancer Genomics Laboratory, The Francis Crick Institute, London NW1 1AT, UK; Department of Human Genetics, University of Leuven, 3000 Leuven, Belgium
| | - Ignacio Vázquez-García
- Wellcome Trust Sanger Institute, Cambridge CB10 1SA, UK; University of Cambridge, Cambridge CB2 0QQ, UK; Computational Oncology, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA; Irving Institute for Cancer Dynamics, Columbia University, New York, NY 10027, USA
| | - Kortine Kleinheinz
- German Cancer Research Center (DKFZ), 69120 Heidelberg, Germany; Heidelberg University, 69120 Heidelberg, Germany
| | | | - Salem Malikic
- Cancer Data Science Laboratory, National Cancer Institute, NIH, Bethesda, MD 20892, USA
| | - Nilgun Donmez
- Simon Fraser University, Burnaby, BC V5A 1S6, Canada; Vancouver Prostate Centre, Vancouver, BC V6H 3Z6, Canada
| | | | - Pavana Anur
- Molecular and Medical Genetics, Oregon Health & Science University, Portland, OR 97231, USA
| | - Clemency Jolly
- Cancer Genomics Laboratory, The Francis Crick Institute, London NW1 1AT, UK
| | - Marek Cmero
- University of Melbourne, Melbourne, VIC 3010, Australia; Walter + Eliza Hall Institute, Melbourne, VIC 3000, Australia
| | | | | | - Yu Fan
- The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
| | - Matthew Fittall
- Cancer Genomics Laboratory, The Francis Crick Institute, London NW1 1AT, UK
| | - Ruben M Drews
- Cancer Research UK Cambridge Institute, University of Cambridge, Cambridge CB2 0RE, UK
| | - Xiaotong Yao
- Weill Cornell Medicine, New York, NY 10065, USA; New York Genome Center, New York, NY 10013, USA
| | - Thomas B K Watkins
- Cancer Evolution and Genome Instability Laboratory, The Francis Crick Institute, London NW1 1AT, UK
| | - Juhee Lee
- University of California, Santa Cruz, Santa Cruz, CA 95064, USA
| | | | - Hongtu Zhu
- The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
| | - David J Adams
- Wellcome Trust Sanger Institute, Cambridge CB10 1SA, UK
| | - Nicholas McGranahan
- Cancer Research UK Lung Cancer Centre of Excellence, University College London Cancer Institute, London WC1E 6BT, UK; Cancer Genome Evolution Research Group, University College London Cancer Institute, London WC1E 6DD, UK
| | - Charles Swanton
- Cancer Evolution and Genome Instability Laboratory, The Francis Crick Institute, London NW1 1AT, UK; Cancer Research UK Lung Cancer Centre of Excellence, University College London Cancer Institute, London WC1E 6BT, UK; Department of Medical Oncology, University College London Hospitals, London NW1 2BU, UK
| | - Gad Getz
- Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA; Massachusetts General Hospital Center for Cancer Research, Charlestown, MA 02129, USA; Massachusetts General Hospital, Department of Pathology, Boston, MA 02114, USA; Harvard Medical School, Boston, MA 02215, USA
| | - Paul C Boutros
- University of Toronto, Toronto, ON M5S 3E1, Canada; Ontario Institute for Cancer Research, Toronto, ON M5G 0A3, Canada; University of California, Los Angeles, Los Angeles, CA 90095, USA
| | - Marcin Imielinski
- Weill Cornell Medicine, New York, NY 10065, USA; New York Genome Center, New York, NY 10013, USA
| | - Rameen Beroukhim
- Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA; Dana-Farber Cancer Institute, Boston, MA 02215, USA
| | - S Cenk Sahinalp
- Cancer Data Science Laboratory, National Cancer Institute, NIH, Bethesda, MD 20892, USA
| | - Yuan Ji
- NorthShore University HealthSystem, Evanston, IL 60201, USA; The University of Chicago, Chicago, IL 60637, USA
| | - Martin Peifer
- Department of Translational Genomics, Center for Integrated Oncology Cologne-Bonn, Medical Faculty, University of Cologne, 50931 Cologne, Germany
| | | | - Florian Markowetz
- Cancer Research UK Cambridge Institute, University of Cambridge, Cambridge CB2 0RE, UK
| | - Ville Mustonen
- Organismal and Evolutionary Biology Research Programme, Department of Computer Science, Institute of Biotechnology, University of Helsinki, 00014 Helsinki, Finland
| | - Ke Yuan
- Cancer Research UK Cambridge Institute, University of Cambridge, Cambridge CB2 0RE, UK; School of Computing Science, University of Glasgow, Glasgow G12 8RZ, UK
| | - Moritz Gerstung
- Wellcome Trust Sanger Institute, Cambridge CB10 1SA, UK; European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Cambridge CB10 1SD, UK; European Molecular Biology Laboratory, Genome Biology Unit, 69117 Heidelberg, Germany
| | - Paul T Spellman
- Molecular and Medical Genetics, Oregon Health & Science University, Portland, OR 97231, USA
| | - Wenyi Wang
- The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
| | - Quaid D Morris
- University of Toronto, Toronto, ON M5S 3E1, Canada; Vector Institute, Toronto, ON M5G 1L7, Canada; Ontario Institute for Cancer Research, Toronto, ON M5G 0A3, Canada; Computational and Systems Biology, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA
| | - David C Wedge
- Big Data Institute, University of Oxford, Oxford OX3 7LF, UK; Oxford NIHR Biomedical Research Centre, Oxford OX4 2PG, UK; Manchester Cancer Research Centre, University of Manchester, Manchester M20 4GJ, UK
| | - Peter Van Loo
- Cancer Genomics Laboratory, The Francis Crick Institute, London NW1 1AT, UK.
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26
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Zhang C, El-Kebir M, Ochoa I. Moss enables high sensitivity single-nucleotide variant calling from multiple bulk DNA tumor samples. Nat Commun 2021; 12:2204. [PMID: 33850139 PMCID: PMC8044184 DOI: 10.1038/s41467-021-22466-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2020] [Accepted: 03/05/2021] [Indexed: 11/17/2022] Open
Abstract
Intra-tumor heterogeneity renders the identification of somatic single-nucleotide variants (SNVs) a challenging problem. In particular, low-frequency SNVs are hard to distinguish from sequencing artifacts. While the increasing availability of multi-sample tumor DNA sequencing data holds the potential for more accurate variant calling, there is a lack of high-sensitivity multi-sample SNV callers that utilize these data. Here we report Moss, a method to identify low-frequency SNVs that recur in multiple sequencing samples from the same tumor. Moss provides any existing single-sample SNV caller the ability to support multiple samples with little additional time overhead. We demonstrate that Moss improves recall while maintaining high precision in a simulated dataset. On multi-sample hepatocellular carcinoma, acute myeloid leukemia and colorectal cancer datasets, Moss identifies new low-frequency variants that meet manual review criteria and are consistent with the tumor's mutational signature profile. In addition, Moss detects the presence of variants in more samples of the same tumor than reported by the single-sample caller. Moss' improved sensitivity in SNV calling will enable more detailed downstream analyses in cancer genomics.
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Affiliation(s)
- Chuanyi Zhang
- Department of Electrical and Computer Engineering, University of Illinois at Urbana-Champaign, Urbana, IL, USA
| | - Mohammed El-Kebir
- Department of Computer Science, University of Illinois at Urbana-Champaign, Urbana, IL, USA.
| | - Idoia Ochoa
- Department of Electrical and Computer Engineering, University of Illinois at Urbana-Champaign, Urbana, IL, USA.
- Department of Electrical Engineering, University of Navarra, Tecnun, San Sebastian, Spain.
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27
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Hübschmann D, Jopp-Saile L, Andresen C, Krämer S, Gu Z, Heilig CE, Kreutzfeldt S, Teleanu V, Fröhling S, Eils R, Schlesner M. Analysis of mutational signatures with yet another package for signature analysis. Genes Chromosomes Cancer 2020; 60:314-331. [PMID: 33222322 DOI: 10.1002/gcc.22918] [Citation(s) in RCA: 28] [Impact Index Per Article: 5.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2020] [Revised: 11/06/2020] [Accepted: 11/06/2020] [Indexed: 12/11/2022] Open
Abstract
Different mutational processes leave characteristic patterns of somatic mutations in the genome that can be identified as mutational signatures. Determining the contributions of mutational signatures to cancer genomes allows not only to reconstruct the etiology of somatic mutations, but can also be used for improved tumor classification and support therapeutic decisions. We here present the R package yet another package for signature analysis (YAPSA) to deconvolute the contributions of mutational signatures to tumor genomes. YAPSA provides in-built collections from the COSMIC and PCAWG SNV signature sets as well as the PCAWG Indel signatures and employs signature-specific cutoffs to increase sensitivity and specificity. Furthermore, YAPSA allows to determine 95% confidence intervals for signature exposures, to perform constrained stratified signature analyses to obtain enrichment and depletion patterns of the identified signatures and, when applied to whole exome sequencing data, to correct for the triplet content of individual target capture kits. With this functionality, YAPSA has proved to be a valuable tool for analysis of mutational signatures in molecular tumor boards in a precision oncology context. YAPSA is available at R/Bioconductor (http://bioconductor.org/packages/3.12/bioc/html/YAPSA.html).
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Affiliation(s)
- Daniel Hübschmann
- Computational Oncology, Molecular Diagnostics Program, National Center for Tumor Diseases (NCT) and German Cancer Research Center (DKFZ), Im Neuenheimer Feld 280, Heidelberg, 69120, Germany.,Pattern Recognition and Digital Medicine, Heidelberg Institute of Stem Cell Technology and Experimental Medicine (HI-STEM), Im Neuenheimer Feld 280, Heidelberg, 69120, Germany.,German Cancer Consortium (DKTK), Im Neuenheimer Feld 280, Heidelberg, 69120, Germany.,Department of Pediatric Immunology, Hematology and Oncology, University Hospital Heidelberg, Heidelberg, Germany
| | - Lea Jopp-Saile
- Computational Oncology, Molecular Diagnostics Program, National Center for Tumor Diseases (NCT) and German Cancer Research Center (DKFZ), Im Neuenheimer Feld 280, Heidelberg, 69120, Germany.,Pattern Recognition and Digital Medicine, Heidelberg Institute of Stem Cell Technology and Experimental Medicine (HI-STEM), Im Neuenheimer Feld 280, Heidelberg, 69120, Germany.,Bioinformatics and Omics Data Analytics, German Cancer Research Center (DKFZ), Im Neuenheimer Feld 280, Heidelberg, 69120, Germany
| | - Carolin Andresen
- Pattern Recognition and Digital Medicine, Heidelberg Institute of Stem Cell Technology and Experimental Medicine (HI-STEM), Im Neuenheimer Feld 280, Heidelberg, 69120, Germany.,Bioinformatics and Omics Data Analytics, German Cancer Research Center (DKFZ), Im Neuenheimer Feld 280, Heidelberg, 69120, Germany
| | - Stephen Krämer
- Bioinformatics and Omics Data Analytics, German Cancer Research Center (DKFZ), Im Neuenheimer Feld 280, Heidelberg, 69120, Germany
| | - Zuguang Gu
- Computational Oncology, Molecular Diagnostics Program, National Center for Tumor Diseases (NCT) and German Cancer Research Center (DKFZ), Im Neuenheimer Feld 280, Heidelberg, 69120, Germany.,DKFZ-HIPO (Heidelberg Center for Personalized Oncology), German Cancer Research Center (DKFZ), Im Neuenheimer Feld 280, Heidelberg, 69120, Germany
| | - Christoph E Heilig
- German Cancer Consortium (DKTK), Im Neuenheimer Feld 280, Heidelberg, 69120, Germany.,Department of Translational Medical Oncology, NCT Heidelberg and DKFZ, Heidelberg, Germany
| | - Simon Kreutzfeldt
- German Cancer Consortium (DKTK), Im Neuenheimer Feld 280, Heidelberg, 69120, Germany.,Department of Translational Medical Oncology, NCT Heidelberg and DKFZ, Heidelberg, Germany
| | - Veronica Teleanu
- German Cancer Consortium (DKTK), Im Neuenheimer Feld 280, Heidelberg, 69120, Germany.,Department of Translational Medical Oncology, NCT Heidelberg and DKFZ, Heidelberg, Germany
| | - Stefan Fröhling
- German Cancer Consortium (DKTK), Im Neuenheimer Feld 280, Heidelberg, 69120, Germany.,Department of Translational Medical Oncology, NCT Heidelberg and DKFZ, Heidelberg, Germany
| | - Roland Eils
- Center for Digital Health, Berlin Institute of Health and Charité - Universitätsmedizin Berlin, Berlin, Germany.,Health Data Science Unit, University Hospital Heidelberg, Heidelberg, Germany
| | - Matthias Schlesner
- Bioinformatics and Omics Data Analytics, German Cancer Research Center (DKFZ), Im Neuenheimer Feld 280, Heidelberg, 69120, Germany.,Chair of Biomedical Informatics, Data Mining and Data Analytics, Faculty of Applied Computer Science and Medical Faculty, University of Augsburg, Augsburg, Germany
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28
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Hu X, Xu Z, De S. Characteristics of mutational signatures of unknown etiology. NAR Cancer 2020; 2:zcaa026. [PMID: 33015626 PMCID: PMC7520824 DOI: 10.1093/narcan/zcaa026] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/21/2020] [Revised: 09/01/2020] [Accepted: 09/23/2020] [Indexed: 12/25/2022] Open
Abstract
Although not all somatic mutations are cancer drivers, their mutational signatures, i.e. the patterns of genomic alterations at a genome-wide scale, provide insights into past exposure to mutagens, DNA damage and repair processes. Computational deconvolution of somatic mutation patterns and expert curation pan-cancer studies have identified a number of mutational signatures associated with point mutations, dinucleotide substitutions, insertions and deletions, and rearrangements, and have established etiologies for a subset of these signatures. However, the mechanisms underlying nearly one-third of all mutational signatures are not yet understood. The signatures with established etiology and those with hitherto unknown origin appear to have some differences in strand bias, GC content and nucleotide context diversity. It is possible that some of the hitherto ‘unknown’ signatures predominantly occur outside gene regions. While nucleotide contexts might be adequate to establish etiologies of some mutational signatures, in other cases additional features, such as broader (epi)genomic contexts, including chromatin, replication timing, processivity and local mutational patterns, may help fully understand the underlying DNA damage and repair processes. Nonetheless, remarkable progress in characterization of mutational signatures has provided fundamental insights into the biology of cancer, informed disease etiology and opened up new opportunities for cancer prevention, risk management, and therapeutic decision making.
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Affiliation(s)
- Xiaoju Hu
- Rutgers Cancer Institute of New Jersey, New Brunswick, NJ 08901, USA
| | - Zhuxuan Xu
- Rutgers Cancer Institute of New Jersey, New Brunswick, NJ 08901, USA
| | - Subhajyoti De
- Rutgers Cancer Institute of New Jersey, New Brunswick, NJ 08901, USA
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29
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Emerging Precision Oncology Applications of Liquid Biopsy using Circulating Tumour DNA and Methylome Profiling. Clin Oncol (R Coll Radiol) 2020; 32:626-631. [PMID: 32586654 DOI: 10.1016/j.clon.2020.05.022] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/05/2020] [Revised: 05/13/2020] [Accepted: 05/29/2020] [Indexed: 01/13/2023]
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30
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Mannakee BK, Gutenkunst RN. BATCAVE: calling somatic mutations with a tumor- and site-specific prior. NAR Genom Bioinform 2020; 2:lqaa004. [PMID: 32051931 PMCID: PMC7003682 DOI: 10.1093/nargab/lqaa004] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/08/2019] [Revised: 01/13/2020] [Accepted: 01/23/2020] [Indexed: 02/06/2023] Open
Abstract
Detecting somatic mutations withins tumors is key to understanding treatment resistance, patient prognosis and tumor evolution. Mutations at low allelic frequency, those present in only a small portion of tumor cells, are particularly difficult to detect. Many algorithms have been developed to detect such mutations, but none models a key aspect of tumor biology. Namely, every tumor has its own profile of mutation types that it tends to generate. We present BATCAVE (Bayesian Analysis Tools for Context-Aware Variant Evaluation), an algorithm that first learns the individual tumor mutational profile and mutation rate then uses them in a prior for evaluating potential mutations. We also present an R implementation of the algorithm, built on the popular caller MuTect. Using simulations, we show that adding the BATCAVE algorithm to MuTect improves variant detection. It also improves the calibration of posterior probabilities, enabling more principled tradeoff between precision and recall. We also show that BATCAVE performs well on real data. Our implementation is computationally inexpensive and straightforward to incorporate into existing MuTect pipelines. More broadly, the algorithm can be added to other variant callers, and it can be extended to include additional biological features that affect mutation generation.
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Affiliation(s)
- Brian K Mannakee
- Mel and Enid Zuckerman College of Public Health, University of Arizona, Tucson, AZ 85721, USA
| | - Ryan N Gutenkunst
- Department of Molecular and Cellular Biology, University of Arizona, Tucson, AZ 85721, USA
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