The manatee variational autoencoder model for predicting gene expression alterations caused by transcription factor perturbations

We present the Manatee variational autoencoder model to predict transcription factor (TF) perturbation-induced transcriptomes. We demonstrate that the Manatee in silico perturbation analysis recapitulates target transcriptomic phenotypes in diverse cellular lineage transitions. We further propose the Manatee in silico screening analysis for prioritizing TF combinations targeting desired transcriptomic phenotypes.

CAGI, the Critical Assessment of Genome Interpretation, establishes progress and prospects for computational genetic variant interpretation methods

BACKGROUND

The Critical Assessment of Genome Interpretation (CAGI) aims to advance the state-of-the-art for computational prediction of genetic variant impact, particularly where relevant to disease. The five complete editions of the CAGI community experiment comprised 50 challenges, in which participants made blind predictions of phenotypes from genetic data, and these were evaluated by independent assessors.

RESULTS

Performance was particularly strong for clinical pathogenic variants, including some difficult-to-diagnose cases, and extends to interpretation of cancer-related variants. Missense variant interpretation methods were able to estimate biochemical effects with increasing accuracy. Assessment of methods for regulatory variants and complex trait disease risk was less definitive and indicates performance potentially suitable for auxiliary use in the clinic.

CONCLUSIONS

Results show that while current methods are imperfect, they have major utility for research and clinical applications. Emerging methods and increasingly large, robust datasets for training and assessment promise further progress ahead.

Analysis of germline-driven ancestry-associated gene expression in cancers

Differential mRNA expression between ancestry groups can be explained by both genetic and environmental factors. We outline a computational workflow to determine the extent to which germline genetic variation explains cancer-specific molecular differences across ancestry groups. Using multi-omics datasets from The Cancer Genome Atlas (TCGA), we enumerate ancestry-informative markers colocalized with cancer-type-specific expression quantitative trait loci (e-QTLs) at ancestry-associated genes. This approach is generalizable to other settings with paired germline genotyping and mRNA expression data for a multi-ethnic cohort.

For complete details on the use and execution of this protocol, please refer to Carrot-Zhang et al. (2020), Robertson et al. (2021), and Sayaman et al. (2021).

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Protocol for obtaining controlled access TCGA datasets

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Protocols for quality control analysis and genotype imputation of TCGA germline data

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Statistical analysis for determining ancestry-associated SNPs

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Determination of ancestry-associated germline genetic variation driving mRNA expression

Publisher’s note: Undertaking any experimental protocol requires adherence to local institutional guidelines for laboratory safety and ethics.

A community challenge to evaluate RNA-seq, fusion detection, and isoform quantification methods for cancer discovery

The accurate identification and quantitation of RNA isoforms present in the cancer transcriptome is key for analyses ranging from the inference of the impacts of somatic variants to pathway analysis to biomarker development and subtype discovery. The ICGC-TCGA DREAM Somatic Mutation Calling in RNA (SMC-RNA) challenge was a crowd-sourced effort to benchmark methods for RNA isoform quantification and fusion detection from bulk cancer RNA sequencing (RNA-seq) data. It concluded in 2018 with a comparison of 77 fusion detection entries and 65 isoform quantification entries on 51 synthetic tumors and 32 cell lines with spiked-in fusion constructs. We report the entries used to build this benchmark, the leaderboard results, and the experimental features associated with the accurate prediction of RNA species. This challenge required submissions to be in the form of containerized workflows, meaning each of the entries described is easily reusable through CWL and Docker containers at https://github.com/SMC-RNA-challenge. A record of this paper's transparent peer review process is included in the supplemental information.

Abstract 2287: Detection of RNA-Seq library preparation type via random forest

Library preparation protocols for RNA sequencing (RNA-Seq) libraries vary and affect the downstream analysis of RNA-Seq data. Available RNA sequencing datasets often lack library construction protocol information in the metadata. This information is necessary to be able to compare RNA sequencing datasets appropriately. For example, non-polyadenylated transcripts are measured when a ribosomal RNA-depletion method is used (riboD library preparation), but not when transcripts are selected by poly(A) tails (polyA library preparation). Without knowing the library preparation method, it would appear that the non-polyadenylated transcripts were expressed at much higher levels in the samples prepared via riboD. In order to tackle this issue, we have developed a Random Forest classifier that can delineate between riboD and polyA RNA-Seq datasets. A grid search method was applied to the number of trees, maximum depth, and the minimum datasets included in each leaf node, which determined the best parameters to be 100, 8, and 1 respectively. However, applying the model on the Pediatric Brain Tumor Atlas (PBTA) showed strong overfitting for polyA samples. We examined the performance of the model investigating each maximum depth increment from 1 to 8, and we determined that the best performance on our validation set was achieved with maximum depth of 1. We subsequently proceeded to train our classifier on our own curated compendiums of pediatric cancer polyA and riboD samples including 188 and 264 samples respectively, after balancing the two datasets in terms of disease prevalence, and selecting the top 5,000 most variable genes as the default input dimensionality of the model. For samples whose genes do not exactly match the predetermined 5,000 genes, we substitute the expression of the missing gene with the mean expression of the gene observed in the training data. We show that it achieves 100% classification accuracy of samples to their respective library preparation protocols in GTEX (all polyA), CCLE (all polyA), and for 7 of 9 SRA projects. Five of the SRA projects contained only riboD datasets, 3 contained only all polyA datasets, and one was 50% riboD, and 50% polyA. Notably the SRA datasets are not all cancer related datasets, showing the power of our model to distinguish between library preparation protocols in vastly different settings. The model will become available on Docker so that it is readily, and easily accessible for application on new samples. Our model serves as an important step towards robust library preparation identification. Including samples in the training procedure from diverse contexts would make our model more widely applicable. Need summary statement here and/or future work statement

Citation Format: Ioannis Anastopoulos, Holly Beale, Geoff Lyle, Allison Cheney, Olena M. Vaske, Joshua M. Stuart. Detection of RNA-Seq library preparation type via random forest [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2021; 2021 Apr 10-15 and May 17-21. Philadelphia (PA): AACR; Cancer Res 2021;81(13_Suppl):Abstract nr 2287.

Accurate cancer phenotype prediction with AKLIMATE, a stacked kernel learner integrating multimodal genomic data and pathway knowledge

Advancements in sequencing have led to the proliferation of multi-omic profiles of human cells under different conditions and perturbations. In addition, many databases have amassed information about pathways and gene "signatures"-patterns of gene expression associated with specific cellular and phenotypic contexts. An important current challenge in systems biology is to leverage such knowledge about gene coordination to maximize the predictive power and generalization of models applied to high-throughput datasets. However, few such integrative approaches exist that also provide interpretable results quantifying the importance of individual genes and pathways to model accuracy. We introduce AKLIMATE, a first kernel-based stacked learner that seamlessly incorporates multi-omics feature data with prior information in the form of pathways for either regression or classification tasks. AKLIMATE uses a novel multiple-kernel learning framework where individual kernels capture the prediction propensities recorded in random forests, each built from a specific pathway gene set that integrates all omics data for its member genes. AKLIMATE has comparable or improved performance relative to state-of-the-art methods on diverse phenotype learning tasks, including predicting microsatellite instability in endometrial and colorectal cancer, survival in breast cancer, and cell line response to gene knockdowns. We show how AKLIMATE is able to connect feature data across data platforms through their common pathways to identify examples of several known and novel contributors of cancer and synthetic lethality.

Modeling Human TBX5 Haploinsufficiency Predicts Regulatory Networks for Congenital Heart Disease

Haploinsufficiency of transcriptional regulators causes human congenital heart disease (CHD); however, the underlying CHD gene regulatory network (GRN) imbalances are unknown. Here, we define transcriptional consequences of reduced dosage of the CHD transcription factor, TBX5, in individual cells during cardiomyocyte differentiation from human induced pluripotent stem cells (iPSCs). We discovered highly sensitive dysregulation of TBX5-dependent pathways-including lineage decisions and genes associated with heart development, cardiomyocyte function, and CHD genetics-in discrete subpopulations of cardiomyocytes. Spatial transcriptomic mapping revealed chamber-restricted expression for many TBX5-sensitive transcripts. GRN analysis indicated that cardiac network stability, including vulnerable CHD-linked nodes, is sensitive to TBX5 dosage. A GRN-predicted genetic interaction between Tbx5 and Mef2c, manifesting as ventricular septation defects, was validated in mice. These results demonstrate exquisite and diverse sensitivity to TBX5 dosage in heterogeneous subsets of iPSC-derived cardiomyocytes and predicts candidate GRNs for human CHDs, with implications for quantitative transcriptional regulation in disease.

Prioritizing transcriptional factors in gene regulatory networks with PageRank

Biological states are controlled by orchestrated transcriptional factors (TFs) within gene regulatory networks. Here we show TFs responsible for the dynamic changes of biological states can be prioritized with temporal PageRank. We further show such TF prioritization can be extended by integrating gene regulatory networks reverse engineered from multi-omics profiles, e.g. gene expression, chromatin accessibility, and chromosome conformation assays, using multiplex PageRank.

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Temporal PageRank prioritizes TFs controlling cellular state dynamics

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Multiplex PageRank prioritizes TFs by integrating multi-omics GRNs

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Temporal and multiplex PageRank can be used in combination for TF prioritization

Copy Number Loss of 17q22 Is Associated with Enzalutamide Resistance and Poor Prognosis in Metastatic Castration-Resistant Prostate Cancer

PURPOSE

The purpose of this study was to measure genomic changes that emerge with enzalutamide treatment using analyses of whole-genome sequencing and RNA sequencing.

EXPERIMENTAL DESIGN

One hundred and one tumors from men with metastatic castration-resistant prostate cancer (mCRPC) who had not been treated with enzalutamide (n = 64) or who had enzalutamide-resistant mCRPC (n = 37) underwent whole genome sequencing. Ninety-nine of these tumors also underwent RNA sequencing. We analyzed the genomes and transcriptomes of these mCRPC tumors.

RESULTS

Copy number loss was more common than gain in enzalutamide-resistant tumors. Specially, we identified 124 protein-coding genes that were more commonly lost in enzalutamide-resistant samples. These 124 genes included eight putative tumor suppressors located at nine distinct genomic regions. We demonstrated that focal deletion of the 17q22 locus that includes RNF43 and SRSF1 was not present in any patient with enzalutamide-naïve mCRPC but was present in 16% (6/37) of patients with enzalutamide-resistant mCRPC. 17q22 loss was associated with lower RNF43 and SRSF1 expression and poor overall survival from time of biopsy [median overall survival of 19.3 months in 17q22 intact vs. 8.9 months in 17q22 loss, HR, 3.44 95% confidence interval (CI), 1.338-8.867, log-rank P = 0.006]. Finally, 17q22 loss was linked with activation of several targetable factors, including CDK1/2, Akt, and PLK1, demonstrating the potential therapeutic relevance of 17q22 loss in mCRPC.

CONCLUSIONS

Copy number loss is common in enzalutamide-resistant tumors. Focal deletion of chromosome 17q22 defines a previously unappreciated molecular subset of enzalutamide-resistant mCRPC associated with poor clinical outcome.

Clinical and genomic characterization of Low PSA Secretors: a unique subset of metastatic castration resistant prostate cancer

BACKGROUND

Metastatic disease burden out of proportion to serum PSA has been used as a marker of aggressive phenotype prostate cancer but is not well defined as a distinct subgroup. We sought to prospectively characterize the molecular features and clinical outcomes of Low PSA Secretors.

METHODS

Eligible metastatic castration resistant prostate cancer (mCRPC) patients without prior small cell histology underwent metastatic tumor biopsy with molecular characterization. Low PSA secretion was defined as serum PSA < 2, 5, or 10 ng/mL plus >5 metastases with radiographic progression at study entry. Clinical and molecular features were compared between low PSA vs. normal secretors in a post-hoc fashion.

RESULTS

183 patients were enrolled, including 15 (8%) identified as Low PSA Secretors using optimal PSA cut point of 5 ng/mL. Biopsies from Low PSA Secretors demonstrated higher t-SCNC and RB1 loss and lower AR transcriptional signature scores compared with normal secretors. Genomic loss of RB1 and/or TP53 was more common in Low PSA Secretors (80% vs. 41%). Overall survival (OS) was shorter in Low PSA Secretors (median OS = 26.7 vs. 46.0 months, hazard ratio = 2.465 (95% CI: 0.982-6.183). Progression-free survival (PFS) on post-biopsy treatment with AR-targeted therapy was shorter than with chemotherapy (median PFS 6.2 vs. 4.1 months).

CONCLUSIONS

Low PSA secretion in relation to metastatic tumor burden may be a readily available clinical selection tool for de-differentiated mCRPC with molecular features consistent with t-SCNC. Prospective validation is warranted.

Pathway and network analysis of more than 2500 whole cancer genomes

The catalog of cancer driver mutations in protein-coding genes has greatly expanded in the past decade. However, non-coding cancer driver mutations are less well-characterized and only a handful of recurrent non-coding mutations, most notably TERT promoter mutations, have been reported. Here, as part of the ICGC/TCGA Pan-Cancer Analysis of Whole Genomes (PCAWG) Consortium, which aggregated whole genome sequencing data from 2658 cancer across 38 tumor types, we perform multi-faceted pathway and network analyses of non-coding mutations across 2583 whole cancer genomes from 27 tumor types compiled by the ICGC/TCGA PCAWG project that was motivated by the success of pathway and network analyses in prioritizing rare mutations in protein-coding genes. While few non-coding genomic elements are recurrently mutated in this cohort, we identify 93 genes harboring non-coding mutations that cluster into several modules of interacting proteins. Among these are promoter mutations associated with reduced mRNA expression in TP53, TLE4, and TCF4. We find that biological processes had variable proportions of coding and non-coding mutations, with chromatin remodeling and proliferation pathways altered primarily by coding mutations, while developmental pathways, including Wnt and Notch, altered by both coding and non-coding mutations. RNA splicing is primarily altered by non-coding mutations in this cohort, and samples containing non-coding mutations in well-known RNA splicing factors exhibit similar gene expression signatures as samples with coding mutations in these genes. These analyses contribute a new repertoire of possible cancer genes and mechanisms that are altered by non-coding mutations and offer insights into additional cancer vulnerabilities that can be investigated for potential therapeutic treatments.

Pan-cancer analysis of whole genomes

Cancer is driven by genetic change, and the advent of massively parallel sequencing has enabled systematic documentation of this variation at the whole-genome scale1-3. Here we report the integrative analysis of 2,658 whole-cancer genomes and their matching normal tissues across 38 tumour types from the Pan-Cancer Analysis of Whole Genomes (PCAWG) Consortium of the International Cancer Genome Consortium (ICGC) and The Cancer Genome Atlas (TCGA). We describe the generation of the PCAWG resource, facilitated by international data sharing using compute clouds. On average, cancer genomes contained 4-5 driver mutations when combining coding and non-coding genomic elements; however, in around 5% of cases no drivers were identified, suggesting that cancer driver discovery is not yet complete. Chromothripsis, in which many clustered structural variants arise in a single catastrophic event, is frequently an early event in tumour evolution; in acral melanoma, for example, these events precede most somatic point mutations and affect several cancer-associated genes simultaneously. Cancers with abnormal telomere maintenance often originate from tissues with low replicative activity and show several mechanisms of preventing telomere attrition to critical levels. Common and rare germline variants affect patterns of somatic mutation, including point mutations, structural variants and somatic retrotransposition. A collection of papers from the PCAWG Consortium describes non-coding mutations that drive cancer beyond those in the TERT promoter4; identifies new signatures of mutational processes that cause base substitutions, small insertions and deletions and structural variation5,6; analyses timings and patterns of tumour evolution7; describes the diverse transcriptional consequences of somatic mutation on splicing, expression levels, fusion genes and promoter activity8,9; and evaluates a range of more-specialized features of cancer genomes8,10-18.

Analyses of non-coding somatic drivers in 2,658 cancer whole genomes

The discovery of drivers of cancer has traditionally focused on protein-coding genes1-4. Here we present analyses of driver point mutations and structural variants in non-coding regions across 2,658 genomes from the Pan-Cancer Analysis of Whole Genomes (PCAWG) Consortium5 of the International Cancer Genome Consortium (ICGC) and The Cancer Genome Atlas (TCGA). For point mutations, we developed a statistically rigorous strategy for combining significance levels from multiple methods of driver discovery that overcomes the limitations of individual methods. For structural variants, we present two methods of driver discovery, and identify regions that are significantly affected by recurrent breakpoints and recurrent somatic juxtapositions. Our analyses confirm previously reported drivers6,7, raise doubts about others and identify novel candidates, including point mutations in the 5' region of TP53, in the 3' untranslated regions of NFKBIZ and TOB1, focal deletions in BRD4 and rearrangements in the loci of AKR1C genes. We show that although point mutations and structural variants that drive cancer are less frequent in non-coding genes and regulatory sequences than in protein-coding genes, additional examples of these drivers will be found as more cancer genomes become available.

Exploring Integrative Analysis Using the BioMedical Evidence Graph

PURPOSE

The analysis of cancer biology data involves extremely heterogeneous data sets, including information from RNA sequencing, genome-wide copy number, DNA methylation data reporting on epigenetic regulation, somatic mutations from whole-exome or whole-genome analyses, pathology estimates from imaging sections or subtyping, drug response or other treatment outcomes, and various other clinical and phenotypic measurements. Bringing these different resources into a common framework, with a data model that allows for complex relationships as well as dense vectors of features, will unlock integrated data set analysis.

METHODS

We introduce the BioMedical Evidence Graph (BMEG), a graph database and query engine for discovery and analysis of cancer biology. The BMEG is unique from other biologic data graphs in that sample-level molecular and clinical information is connected to reference knowledge bases. It combines gene expression and mutation data with drug-response experiments, pathway information databases, and literature-derived associations.

RESULTS

The construction of the BMEG has resulted in a graph containing > 41 million vertices and 57 million edges. The BMEG system provides a graph query-based application programming interface to enable analysis, with client code available for Python, Javascript, and R, and a server online at bmeg.io. Using this system, we have demonstrated several forms of cross-data set analysis to show the utility of the system.

CONCLUSION

The BMEG is an evolving resource dedicated to enabling integrative analysis. We have demonstrated queries on the system that illustrate mutation significance analysis, drug-response machine learning, patient-level knowledge-base queries, and pathway level analysis. We have compared the resulting graph to other available integrated graph systems and demonstrated the former is unique in the scale of the graph and the type of data it makes available.

A community effort to create standards for evaluating tumor subclonal reconstruction

Tumor DNA sequencing data can be interpreted by computational methods that analyze genomic heterogeneity to infer evolutionary dynamics. A growing number of studies have used these approaches to link cancer evolution with clinical progression and response to therapy. Although the inference of tumor phylogenies is rapidly becoming standard practice in cancer genome analyses, standards for evaluating them are lacking. To address this need, we systematically assess methods for reconstructing tumor subclonality. First, we elucidate the main algorithmic problems in subclonal reconstruction and develop quantitative metrics for evaluating them. Then we simulate realistic tumor genomes that harbor all known clonal and subclonal mutation types and processes. Finally, we benchmark 580 tumor reconstructions, varying tumor read depth, tumor type and somatic variant detection. Our analysis provides a baseline for the establishment of gold-standard methods to analyze tumor heterogeneity.

Using Transcriptional Signatures to Find Cancer Drivers with LURE

Cancer genome projects have produced multidimensional datasets on thousands of samples. Yet, depending on the tumor type, 5-50% of samples have no known driving event. We introduce a semi-supervised method called Learning UnRealized Events (LURE) that uses a progressive label learning framework and minimum spanning analysis to predict cancer drivers based on their altered samples sharing a gene expression signature with the samples of a known event. We demonstrate the utility of the method on the TCGA Pan-Cancer Atlas dataset for which it produced a high-confidence result relating 59 new connections to 18 known mutation events including alterations in the same gene, family, and pathway. We give examples of predicted drivers involved in TP53, telomere maintenance, and MAPK/RTK signaling pathways. LURE identifies connections between genes with no known prior relationship, some of which may offer clues for targeting specific forms of cancer. Code and Supplemental Material are available on the LURE website: https://sysbiowiki.soe.ucsc.edu/lure.

Biological process activity transformation of single cell gene expression for cross-species alignment

The maintenance and transition of cellular states are controlled by biological processes. Here we present a gene set-based transformation of single cell RNA-Seq data into biological process activities that provides a robust description of cellular states. Moreover, as these activities represent species-independent descriptors, they facilitate the alignment of single cell states across different organisms.

Single cell RNA-Seq data can report on cellular types and states, but low signal-to noise and sparse data can make interpretation of cellular state difficult. Here the authors propose a transformation strategy to map RNA-Seq data to biological process activities that are species-agnostic and allow for comparison across species.

Reproducible biomedical benchmarking in the cloud: lessons from crowd-sourced data challenges

Challenges are achieving broad acceptance for addressing many biomedical questions and enabling tool assessment. But ensuring that the methods evaluated are reproducible and reusable is complicated by the diversity of software architectures, input and output file formats, and computing environments. To mitigate these problems, some challenges have leveraged new virtualization and compute methods, requiring participants to submit cloud-ready software packages. We review recent data challenges with innovative approaches to model reproducibility and data sharing, and outline key lessons for improving quantitative biomedical data analysis through crowd-sourced benchmarking challenges.

Genomic, Pathway Network, and Immunologic Features Distinguishing Squamous Carcinomas

This integrated, multiplatform PanCancer Atlas study co-mapped and identified distinguishing molecular features of squamous cell carcinomas (SCCs) from five sites associated with smoking and/or human papillomavirus (HPV). SCCs harbor 3q, 5p, and other recurrent chromosomal copy-number alterations (CNAs), DNA mutations, and/or aberrant methylation of genes and microRNAs, which are correlated with the expression of multi-gene programs linked to squamous cell stemness, epithelial-to-mesenchymal differentiation, growth, genomic integrity, oxidative damage, death, and inflammation. Low-CNA SCCs tended to be HPV(+) and display hypermethylation with repression of TET1 demethylase and FANCF, previously linked to predisposition to SCC, or harbor mutations affecting CASP8, RAS-MAPK pathways, chromatin modifiers, and immunoregulatory molecules. We uncovered hypomethylation of the alternative promoter that drives expression of the ΔNp63 oncogene and embedded miR944. Co-expression of immune checkpoint, T-regulatory, and Myeloid suppressor cells signatures may explain reduced efficacy of immune therapy. These findings support possibilities for molecular classification and therapeutic approaches.

Clinical and genomic hallmarks of low PSA secretors in metastatic castration-resistant prostate cancer (mCRPC)

5051

Background: Metastatic disease burden out of proportion to low serum PSA level is frequently used as a clinical surrogate for the diagnosis of treatment-emergent small cell neuroendocrine prostate cancer (t-SCNC), although many t-SCNC patients (pts) have normal or elevated PSA levels. The clinical and genomic characteristics of mCRPC pts who are Low PSA Secretors have not been previously described. Methods: Eligible mCRPC patients (pts) underwent image-guided needle biopsy. Formalin-fixed paraffin embedded tissue was evaluated with targeted next-generation DNA sequencing. Fresh frozen tissue from the same metastatic tumor underwent RNA-seq. A validated AR transcriptional signature was applied. Low PSA Secretors were defined as pts with PSA < 5 ng/mL plus ≥ 6 metastases on conventional imaging at the time of tumor biopsy. Clinical and genomic characteristics were compared between low PSA Secretors and all other pts. Results: Of 89 evaluable pts, 9 (10%) were identified as Low PSA Secretors. There was no difference between Low PSA Secretors and all other pts in: serum PSA at diagnosis, frequency of Gleason ≥ 8 adenocarcinoma at diagnosis, and serum level of LDH, alkaline phosphatase, or hemoglobin at the time of biopsy. Lung and/or liver metastases were more common in low PSA secretors (67% vs. 33%, p = 0.04). There was no difference in serum level of LDH, alkaline phosphatase, or hemoglobin. Tumor biopsies from Low PSA Secretors were more likely to fall within a previously defined t-SCNC transcriptional cluster (80% vs. 6%, p < 0.001). RB1 loss or inactivating mutations appeared to be enriched in Low PSA Secretors (40% vs. 12%, p = 0.09); there was no difference in frequency of TP53 alterations between subgroups. AR transcriptional signature scores were lower in the Low PSA Secretor group (median score -3.63 vs. 0.66, p < 0.001). Conclusions: Low serum PSA levels in relation to metastatic tumor burden may be a reliable surrogate for the detection of mCRPC that harbors the transcriptional and genomic hallmarks of t-SCNC. Validation studies are warranted.

Machine Learning Identifies Stemness Features Associated with Oncogenic Dedifferentiation

Cancer progression involves the gradual loss of a differentiated phenotype and acquisition of progenitor and stem-cell-like features. Here, we provide novel stemness indices for assessing the degree of oncogenic dedifferentiation. We used an innovative one-class logistic regression (OCLR) machine-learning algorithm to extract transcriptomic and epigenetic feature sets derived from non-transformed pluripotent stem cells and their differentiated progeny. Using OCLR, we were able to identify previously undiscovered biological mechanisms associated with the dedifferentiated oncogenic state. Analyses of the tumor microenvironment revealed unanticipated correlation of cancer stemness with immune checkpoint expression and infiltrating immune cells. We found that the dedifferentiated oncogenic phenotype was generally most prominent in metastatic tumors. Application of our stemness indices to single-cell data revealed patterns of intra-tumor molecular heterogeneity. Finally, the indices allowed for the identification of novel targets and possible targeted therapies aimed at tumor differentiation.

PLATYPUS: A Multiple-View Learning Predictive Framework for Cancer Drug Sensitivity Prediction

Cancer is a complex collection of diseases that are to some degree unique to each patient. Precision oncology aims to identify the best drug treatment regime using molecular data on tumor samples. While omics-level data is becoming more widely available for tumor specimens, the datasets upon which computational learning methods can be trained vary in coverage from sample to sample and from data type to data type. Methods that can 'connect the dots' to leverage more of the information provided by these studies could offer major advantages for maximizing predictive potential. We introduce a multi-view machinelearning strategy called PLATYPUS that builds 'views' from multiple data sources that are all used as features for predicting patient outcomes. We show that a learning strategy that finds agreement across the views on unlabeled data increases the performance of the learning methods over any single view. We illustrate the power of the approach by deriving signatures for drug sensitivity in a large cancer cell line database. Code and additional information are available from the PLATYPUS website https://sysbiowiki.soe.ucsc.edu/platypus.

Combining accurate tumor genome simulation with crowdsourcing to benchmark somatic structural variant detection

BACKGROUND

The phenotypes of cancer cells are driven in part by somatic structural variants. Structural variants can initiate tumors, enhance their aggressiveness, and provide unique therapeutic opportunities. Whole-genome sequencing of tumors can allow exhaustive identification of the specific structural variants present in an individual cancer, facilitating both clinical diagnostics and the discovery of novel mutagenic mechanisms. A plethora of somatic structural variant detection algorithms have been created to enable these discoveries; however, there are no systematic benchmarks of them. Rigorous performance evaluation of somatic structural variant detection methods has been challenged by the lack of gold standards, extensive resource requirements, and difficulties arising from the need to share personal genomic information.

RESULTS

To facilitate structural variant detection algorithm evaluations, we create a robust simulation framework for somatic structural variants by extending the BAMSurgeon algorithm. We then organize and enable a crowdsourced benchmarking within the ICGC-TCGA DREAM Somatic Mutation Calling Challenge (SMC-DNA). We report here the results of structural variant benchmarking on three different tumors, comprising 204 submissions from 15 teams. In addition to ranking methods, we identify characteristic error profiles of individual algorithms and general trends across them. Surprisingly, we find that ensembles of analysis pipelines do not always outperform the best individual method, indicating a need for new ways to aggregate somatic structural variant detection approaches.

CONCLUSIONS

The synthetic tumors and somatic structural variant detection leaderboards remain available as a community benchmarking resource, and BAMSurgeon is available at https://github.com/adamewing/bamsurgeon .

Valection: design optimization for validation and verification studies

Background

Platform-specific error profiles necessitate confirmatory studies where predictions made on data generated using one technology are additionally verified by processing the same samples on an orthogonal technology. However, verifying all predictions can be costly and redundant, and testing a subset of findings is often used to estimate the true error profile.

Results

To determine how to create subsets of predictions for validation that maximize accuracy of global error profile inference, we developed Valection, a software program that implements multiple strategies for the selection of verification candidates. We evaluated these selection strategies on one simulated and two experimental datasets.

Conclusions

Valection is implemented in multiple programming languages, available at: http://labs.oicr.on.ca/boutros-lab/software/valection

Electronic supplementary material

The online version of this article (10.1186/s12859-018-2391-z) contains supplementary material, which is available to authorized users.

Genomic Hallmarks and Structural Variation in Metastatic Prostate Cancer

While mutations affecting protein-coding regions have been examined across many cancers, structural variants at the genome-wide level are still poorly defined. Through integrative deep whole-genome and -transcriptome analysis of 101 castration-resistant prostate cancer metastases (109X tumor/38X normal coverage), we identified structural variants altering critical regulators of tumorigenesis and progression not detectable by exome approaches. Notably, we observed amplification of an intergenic enhancer region 624 kb upstream of the androgen receptor (AR) in 81% of patients, correlating with increased AR expression. Tandem duplication hotspots also occur near MYC, in lncRNAs associated with post-translational MYC regulation. Classes of structural variations were linked to distinct DNA repair deficiencies, suggesting their etiology, including associations of CDK12 mutation with tandem duplications, TP53 inactivation with inverted rearrangements and chromothripsis, and BRCA2 inactivation with deletions. Together, these observations provide a comprehensive view of how structural variations affect critical regulators in metastatic prostate cancer.

Clinical and Genomic Characterization of Treatment-Emergent Small-Cell Neuroendocrine Prostate Cancer: A Multi-institutional Prospective Study

Purpose The prevalence and features of treatment-emergent small-cell neuroendocrine prostate cancer (t-SCNC) are not well characterized in the era of modern androgen receptor (AR)-targeting therapy. We sought to characterize the clinical and genomic features of t-SCNC in a multi-institutional prospective study. Methods Patients with progressive, metastatic castration-resistant prostate cancer (mCRPC) underwent metastatic tumor biopsy and were followed for survival. Metastatic biopsy specimens underwent independent, blinded pathology review along with RNA/DNA sequencing. Results A total of 202 consecutive patients were enrolled. One hundred forty-eight (73%) had prior disease progression on abiraterone and/or enzalutamide. The biopsy evaluable rate was 79%. The overall incidence of t-SCNC detection was 17%. AR amplification and protein expression were present in 67% and 75%, respectively, of t-SCNC biopsy specimens. t-SCNC was detected at similar proportions in bone, node, and visceral organ biopsy specimens. Genomic alterations in the DNA repair pathway were nearly mutually exclusive with t-SCNC differentiation ( P = .035). Detection of t-SCNC was associated with shortened overall survival among patients with prior AR-targeting therapy for mCRPC (hazard ratio, 2.02; 95% CI, 1.07 to 3.82). Unsupervised hierarchical clustering of the transcriptome identified a small-cell-like cluster that further enriched for adverse survival outcomes (hazard ratio, 3.00; 95% CI, 1.25 to 7.19). A t-SCNC transcriptional signature was developed and validated in multiple external data sets with > 90% accuracy. Multiple transcriptional regulators of t-SCNC were identified, including the pancreatic neuroendocrine marker PDX1. Conclusion t-SCNC is present in nearly one fifth of patients with mCRPC and is associated with shortened survival. The near-mutual exclusivity with DNA repair alterations suggests t-SCNC may be a distinct subset of mCRPC. Transcriptional profiling facilitates the identification of t-SCNC and novel therapeutic targets.

Abstract LB-373: Comprehensive analysis of cancer stemness

Cancer progression involves the gradual loss of a differentiated phenotype and acquisition of progenitor and stem cell-like features. Here, we provide new stemness indices for assessing the degree of oncogenic dedifferentiation. We took advantage of an innovative one-class logistic regression machine learning algorithm (OCLR) to extract transcriptomic and epigenetic feature sets derived from non-transformed pluripotent stem cells and their differentiated progenies. Using OCLR, we were able to sort TCGA tumor samples by stemness phenotype and identify previously undiscovered biological mechanisms associated with the dedifferentiated oncogenic state. Analyses of tumor microenvironment revealed the correlation of cancer stemness with immune checkpoint expression and infiltrating immune system cells not previously anticipated. We have shown the de-differentiated oncogenic phenotype increased in the metastatic tumor that further justify their more aggressive phenotype. Application of our stemness indices reveals features of intra-tumor heterogeneity in molecular profiles obtained from the single-cell analyses. Finally, the machine learning-based indices allowed for the identification of chemical compounds and novel targets for the cancer therapies aiming at tumor differentiation. Our findings provide new prognostic signatures that enable cancer biologists and oncologists to quantify the impact of tumor stemness on outcome across cancer types and may help to pave the way for progress in treatment strategies for cancer patients.

Citation Format: Tathiane M. Malta, Artem Sokolov, Andrew J. Gentles, Tomasz Burzykowski, Laila Poisson, John Weinstein, Bożena Kamińska, Joerg Huelsken, Larsson Omberg, Olivier Gevaert, Antonio Colaprico, Patrycja Czerwińska, Sylwia Mazurek, Lopa Mishra, Holger Heyn, Alex Krasnitz, Andrew K. Godwin, Alexander J. Lazar, The Cancer Genome Atlas Research Network, Joshua M. Stuart, Katherine Hoadley, Peter W. Laird, Houtan Noushmehr, Maciej Wiznerowicz. Comprehensive analysis of cancer stemness [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2018; 2018 Apr 14-18; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2018;78(13 Suppl):Abstract nr LB-373.

Comparative RNA-Sequencing Analysis Benefits a Pediatric Patient With Relapsed Cancer

Clinical detection of sequence and structural variants in known cancer genes points to viable treatment options for a minority of children with cancer.¹ To increase the number of children who benefit from genomic profiling, gene expression information must be considered alongside mutations.^2,3 Although high expression has been used to nominate drug targets for pediatric cancers,^4,5 its utility has not been evaluated in a systematic way.⁶ We describe a child with a rare sarcoma that was profiled with whole-genome and RNA sequencing (RNA-Seq) techniques. Although the tumor did not harbor DNA mutations targetable by available therapies, incorporation of gene expression information derived from RNA-Seq analysis led to a therapy that produced a significant clinical response. We use this case to describe a framework for inclusion of gene expression into the clinical genomic evaluation of pediatric tumors.

Perspective on Oncogenic Processes at the End of the Beginning of Cancer Genomics

The Cancer Genome Atlas (TCGA) has catalyzed systematic characterization of diverse genomic alterations underlying human cancers. At this historic junction marking the completion of genomic characterization of over 11,000 tumors from 33 cancer types, we present our current understanding of the molecular processes governing oncogenesis. We illustrate our insights into cancer through synthesis of the findings of the TCGA PanCancer Atlas project on three facets of oncogenesis: (1) somatic driver mutations, germline pathogenic variants, and their interactions in the tumor; (2) the influence of the tumor genome and epigenome on transcriptome and proteome; and (3) the relationship between tumor and the microenvironment, including implications for drugs targeting driver events and immunotherapies. These results will anchor future characterization of rare and common tumor types, primary and relapsed tumors, and cancers across ancestry groups and will guide the deployment of clinical genomic sequencing.

Cell-of-Origin Patterns Dominate the Molecular Classification of 10,000 Tumors from 33 Types of Cancer

We conducted comprehensive integrative molecular analyses of the complete set of tumors in The Cancer Genome Atlas (TCGA), consisting of approximately 10,000 specimens and representing 33 types of cancer. We performed molecular clustering using data on chromosome-arm-level aneuploidy, DNA hypermethylation, mRNA, and miRNA expression levels and reverse-phase protein arrays, of which all, except for aneuploidy, revealed clustering primarily organized by histology, tissue type, or anatomic origin. The influence of cell type was evident in DNA-methylation-based clustering, even after excluding sites with known preexisting tissue-type-specific methylation. Integrative clustering further emphasized the dominant role of cell-of-origin patterns. Molecular similarities among histologically or anatomically related cancer types provide a basis for focused pan-cancer analyses, such as pan-gastrointestinal, pan-gynecological, pan-kidney, and pan-squamous cancers, and those related by stemness features, which in turn may inform strategies for future therapeutic development.

Germline contamination and leakage in whole genome somatic single nucleotide variant detection

BACKGROUND

The clinical sequencing of cancer genomes to personalize therapy is becoming routine across the world. However, concerns over patient re-identification from these data lead to questions about how tightly access should be controlled. It is not thought to be possible to re-identify patients from somatic variant data. However, somatic variant detection pipelines can mistakenly identify germline variants as somatic ones, a process called "germline leakage". The rate of germline leakage across different somatic variant detection pipelines is not well-understood, and it is uncertain whether or not somatic variant calls should be considered re-identifiable. To fill this gap, we quantified germline leakage across 259 sets of whole-genome somatic single nucleotide variant (SNVs) predictions made by 21 teams as part of the ICGC-TCGA DREAM Somatic Mutation Calling Challenge.

RESULTS

The median somatic SNV prediction set contained 4325 somatic SNVs and leaked one germline polymorphism. The level of germline leakage was inversely correlated with somatic SNV prediction accuracy and positively correlated with the amount of infiltrating normal cells. The specific germline variants leaked differed by tumour and algorithm. To aid in quantitation and correction of leakage, we created a tool, called GermlineFilter, for use in public-facing somatic SNV databases.

CONCLUSIONS

The potential for patient re-identification from leaked germline variants in somatic SNV predictions has led to divergent open data access policies, based on different assessments of the risks. Indeed, a single, well-publicized re-identification event could reshape public perceptions of the values of genomic data sharing. We find that modern somatic SNV prediction pipelines have low germline-leakage rates, which can be further reduced, especially for cloud-sharing, using pre-filtering software.

Prophetic Granger Causality to infer gene regulatory networks

We introduce a novel method called Prophetic Granger Causality (PGC) for inferring gene regulatory networks (GRNs) from protein-level time series data. The method uses an L1-penalized regression adaptation of Granger Causality to model protein levels as a function of time, stimuli, and other perturbations. When combined with a data-independent network prior, the framework outperformed all other methods submitted to the HPN-DREAM 8 breast cancer network inference challenge. Our investigations reveal that PGC provides complementary information to other approaches, raising the performance of ensemble learners, while on its own achieves moderate performance. Thus, PGC serves as a valuable new tool in the bioinformatics toolkit for analyzing temporal datasets. We investigate the general and cell-specific interactions predicted by our method and find several novel interactions, demonstrating the utility of the approach in charting new tumor wiring.

TumorMap: Exploring the Molecular Similarities of Cancer Samples in an Interactive Portal

Vast amounts of molecular data are being collected on tumor samples, which provide unique opportunities for discovering trends within and between cancer subtypes. Such cross-cancer analyses require computational methods that enable intuitive and interactive browsing of thousands of samples based on their molecular similarity. We created a portal called TumorMap to assist in exploration and statistical interrogation of high-dimensional complex "omics" data in an interactive and easily interpretable way. In the TumorMap, samples are arranged on a hexagonal grid based on their similarity to one another in the original genomic space and are rendered with Google's Map technology. While the important feature of this public portal is the ability for the users to build maps from their own data, we pre-built genomic maps from several previously published projects. We demonstrate the utility of this portal by presenting results obtained from The Cancer Genome Atlas project data. Cancer Res; 77(21); e111-4. ©2017 AACR.

A Community Challenge for Inferring Genetic Predictors of Gene Essentialities through Analysis of a Functional Screen of Cancer Cell Lines

We report the results of a DREAM challenge designed to predict relative genetic essentialities based on a novel dataset testing 98,000 shRNAs against 149 molecularly characterized cancer cell lines. We analyzed the results of over 3,000 submissions over a period of 4 months. We found that algorithms combining essentiality data across multiple genes demonstrated increased accuracy; gene expression was the most informative molecular data type; the identity of the gene being predicted was far more important than the modeling strategy; well-predicted genes and selected molecular features showed enrichment in functional categories; and frequently selected expression features correlated with survival in primary tumors. This study establishes benchmarks for gene essentiality prediction, presents a community resource for future comparison with this benchmark, and provides insights into factors influencing the ability to predict gene essentiality from functional genetic screens. This study also demonstrates the value of releasing pre-publication data publicly to engage the community in an open research collaboration.

Revealing cancer subtypes with higher-order correlations applied to imaging and omics data

Background

Patient stratification to identify subtypes with different disease manifestations, severity, and expected survival time is a critical task in cancer diagnosis and treatment. While stratification approaches using various biomarkers (including high-throughput gene expression measurements) for patient-to-patient comparisons have been successful in elucidating previously unseen subtypes, there remains an untapped potential of incorporating various genotypic and phenotypic data to discover novel or improved groupings.

Methods

Here, we present HOCUS, a unified analytical framework for patient stratification that uses a community detection technique to extract subtypes out of sparse patient measurements. HOCUS constructs a patient-to-patient network from similarities in the data and iteratively groups and reconstructs the network into higher order clusters. We investigate the merits of using higher-order correlations to cluster samples of cancer patients in terms of their associations with survival outcomes.

Results

In an initial test of the method, the approach identifies cancer subtypes in mutation data of glioblastoma, ovarian, breast, prostate, and bladder cancers. In several cases, HOCUS provides an improvement over using the molecular features directly to compare samples. Application of HOCUS to glioblastoma images reveals a size and location classification of tumors that improves over human expert-based stratification.

Conclusions

Subtypes based on higher order features can reveal comparable or distinct groupings. The distinct solutions can provide biologically- and treatment-relevant solutions that are just as significant as solutions based on the original data.

Electronic supplementary material

The online version of this article (doi:10.1186/s12920-017-0256-3) contains supplementary material, which is available to authorized users.

TRACING CO-REGULATORY NETWORK DYNAMICS IN NOISY, SINGLE-CELL TRANSCRIPTOME TRAJECTORIES

The availability of gene expression data at the single cell level makes it possible to probe the molecular underpinnings of complex biological processes such as differentiation and oncogenesis. Promising new methods have emerged for reconstructing a progression 'trajectory' from static single-cell transcriptome measurements. However, it remains unclear how to adequately model the appreciable level of noise in these data to elucidate gene regulatory network rewiring. Here, we present a framework called Single Cell Inference of MorphIng Trajectories and their Associated Regulation (SCIMITAR) that infers progressions from static single-cell transcriptomes by employing a continuous parametrization of Gaussian mixtures in high-dimensional curves. SCIMITAR yields rich models from the data that highlight genes with expression and co-expression patterns that are associated with the inferred progression. Further, SCIMITAR extracts regulatory states from the implicated trajectory-evolvingco-expression networks. We benchmark the method on simulated data to show that it yields accurate cell ordering and gene network inferences. Applied to the interpretation of a single-cell human fetal neuron dataset, SCIMITAR finds progression-associated genes in cornerstone neural differentiation pathways missed by standard differential expression tests. Finally, by leveraging the rewiring of gene-gene co-expression relations across the progression, the method reveals the rise and fall of co-regulatory states and trajectory-dependent gene modules. These analyses implicate new transcription factors in neural differentiation including putative co-factors for the multi-functional NFAT pathway.

Targeting Adaptive Pathways in Metastatic Treatment-Resistant Prostate Cancer: Update on the Stand Up 2 Cancer/Prostate Cancer Foundation-Supported West Coast Prostate Cancer Dream Team

The Stand Up 2 Cancer/Prostate Cancer Foundation-funded West Coast Dream Team project is a prospective multi-institutional study focused on acquiring metastatic castration-resistant prostate cancer (mCRPC) biopsy tissue at the time of resistance to abiraterone or enzalutamide. It is the first large-scale study designed to analyze mCRPC tissue specifically in this patient population. Study accrual is on target, with 261 out of a planned 300 metastatic tumor biopsies performed by August 2016. Paired biopsies have been completed in 42 patients, with paired genomic data before and after therapy obtained in 26 cases. Accrual is expected to be complete by December 2016.

Phosphoproteome Integration Reveals Patient-Specific Networks in Prostate Cancer

We used clinical tissue from lethal metastatic castration-resistant prostate cancer (CRPC) patients obtained at rapid autopsy to evaluate diverse genomic, transcriptomic, and phosphoproteomic datasets for pathway analysis. Using Tied Diffusion through Interacting Events (TieDIE), we integrated differentially expressed master transcriptional regulators, functionally mutated genes, and differentially activated kinases in CRPC tissues to synthesize a robust signaling network consisting of druggable kinase pathways. Using MSigDB hallmark gene sets, six major signaling pathways with phosphorylation of several key residues were significantly enriched in CRPC tumors after incorporation of phosphoproteomic data. Individual autopsy profiles developed using these hallmarks revealed clinically relevant pathway information potentially suitable for patient stratification and targeted therapies in late stage prostate cancer. Here, we describe phosphorylation-based cancer hallmarks using integrated personalized signatures (pCHIPS) that shed light on the diversity of activated signaling pathways in metastatic CRPC while providing an integrative, pathway-based reference for drug prioritization in individual patients.

Identifying Aspects of the Post-Transcriptional Program Governing the Proteome of the Green Alga Micromonas pusilla

Micromonas is a unicellular motile alga within the Prasinophyceae, a green algal group that is related to land plants. This picoeukaryote (<2 μm diameter) is widespread in the marine environment but is not well understood at the cellular level. Here, we examine shifts in mRNA and protein expression over the course of the day-night cycle using triplicated mid-exponential, nutrient replete cultures of Micromonas pusilla CCMP1545. Samples were collected at key transition points during the diel cycle for evaluation using high-throughput LC-MS proteomics. In conjunction, matched mRNA samples from the same time points were sequenced using pair-ended directional Illumina RNA-Seq to investigate the dynamics and relationship between the mRNA and protein expression programs of M. pusilla. Similar to a prior study of the marine cyanobacterium Prochlorococcus, we found significant divergence in the mRNA and proteomics expression dynamics in response to the light:dark cycle. Additionally, expressional responses of genes and the proteins they encoded could also be variable within the same metabolic pathway, such as we observed in the oxygenic photosynthesis pathway. A regression framework was used to predict protein levels from both mRNA expression and gene-specific sequence-based features. Several features in the genome sequence were found to influence protein abundance including codon usage as well as 3’ UTR length and structure. Collectively, our studies provide insights into the regulation of the proteome over a diel cycle as well as the relationships between transcriptional and translational programs in the widespread marine green alga Micromonas.

Abstract 3882: Patient-specific druggable networks in lethal prostate cancer through proteome-guided multi-omic integration

Metastatic castration resistant prostate cancer (CRPC) remains incurable due to the lack of effective therapies. The need to identify new actionable targets in this disease is crucial as we begin to examine the resistance mechanisms related to androgen withdrawal. Pathway activation of signaling proteins, such as kinases, are hypothesized to drive the progression of lethal CRPC. We set out to define the global picture of signaling pathways in lethal prostate cancer through dataset integration. We used clinical tissue from lethal metastatic CRPC patients obtained at rapid autopsy to evaluate and integrate previously published genomic and transcriptomic datasets combined with a new complete and extensive dataset of the phosphoproteome in metastatic CRPC for pathway analysis. This included extending our analysis of the phosphoproteome to phosphoserine and phosphothreonine peptides with our published phosphotyrosine peptide data. Using Tied Diffusion through Interacting Events (TieDIE), we integrated differentially expressed master transcriptional regulators, functionally mutated genes, and differentially activated kinases in CRPC tissues relative to treatment naïve prostate cancer to synthesize a robust signaling network consisting of druggable kinase pathways for therapy. Using MSigDB hallmark gene sets, six major signaling pathways with phosphorylation of several key residues were significantly enriched in CRPC tumors after incorporation of phosphoproteomic data. Further, we were able to gather mRNA and phosphoproteome data from six individual patients where the integration of tissue samples from a single autopsy program allowed us to make inferences on the connections between the mRNA and phosphoproteome datasets. Individual patient profiles developed using these hallmarks revealed clinically relevant pathway information suitable for patient stratification and targeted therapies in lethal prostate cancer. Here we describe these pathways: personalized cancer hallmarks using an integrative phospho-signature (pCHIPs) that sheds light on the diversity of activated signaling pathways in metastatic CRPC while providing an integrative, pathway-based reference for drug prioritization in individual patients.

Citation Format: Justin M. Drake, Evan O. Paull, Nicholas A. Graham, John K. Lee, Bryan A. Smith, Tanya Stoyanova, Claire M. Faltermeier, Daniel E. Carlin, Ajay Vashisht, Jiaoti Huang, James A. Wohlschlegel, Thomas G. Graeber, Owen N. Witte, Joshua M. Stuart. Patient-specific druggable networks in lethal prostate cancer through proteome-guided multi-omic integration. [abstract]. In: Proceedings of the 107th Annual Meeting of the American Association for Cancer Research; 2016 Apr 16-20; New Orleans, LA. Philadelphia (PA): AACR; Cancer Res 2016;76(14 Suppl):Abstract nr 3882.

Abstract 128: Comprehensive molecular characterization of 412 muscle-invasive urothelial bladder carcinomas: final analysis of The Cancer Genome Atlas (TCGA) project

Introduction: In 2014, TCGA's Bladder Cancer Working Group presented a preliminary integrated molecular analysis of 131 muscle-invasive urothelial carcinomas (Nature 507:315, 2014). We now report on the entire cohort of 412 fresh-frozen, chemotherapy-naïve tumors. Included in the analysis were paired blood and/or tumor-adjacent tissue samples. This is the largest sequencing project on bladder cancer to date. After strict clinical and pathologic quality control, tumors were analyzed for DNA copy number variants, somatic mutations, DNA methylation, mRNA, microRNA and (phospho-) protein expression, transcript splicing, gene fusions, viral integration, APOBEC mutagenesis, pathway perturbation, clinical correlates, and histopathology.

Results: There was a high overall somatic mutation rate (8.0/Mb), with a median of 245 and mean of 348 coding-region mutations per sample. That is the third highest mutation rate among the cancer types profiled by TCGA (after cutaneous melanoma and non-small cell lung cancers). We identified 54 genes as significantly mutated, compared with 32 in the original report on 131 tumors. TP53 mutations were the most common (49%), and also quite common were mutations in a number of chromatin-modifying genes, including MLL2 (29%), KDM6A (26%), ARID1A (25%), MLL3 (19%), EP300 (15%), CREBBP (12%), and MLL (11%). Other cancer-related genes showing frequent mutations included PIK3CA (22%), RB1 (17%), FGFR3 (14%), STAG2 (14%), ATM (14%), ELF3 (12%), FAT1 (12%), SPTAN1 (12%), ERBB2 (12%), ERBB3 (11%), ASXL2 (10%), ERCC2 (9%), CDKN1A (9%), TSC1 (8%), CDKN2A (7%), RHOB (6%), NFE2L2 (6%), PARD3 (6%), FAM47C (5%), RBM10 (5%),HRAS (5%), KRAS (4%), and PTEN (3%). High mutation burden was associated with improved outcome (p = 0.0004). APOBEC mutagenesis explained 70% of the mutation burden and was associated with survival. Gene silencing by promoter hypermethylation was identified in 167 genes with at least 5% frequency in the cohort. The previously identified four mRNA expression subtypes were again found in the complete set of 412 tumors, and the proportions of samples in each subtype were similar to the previous proportions. Reverse-phase proteomic array analysis of 344 of the samples revealed clusters associated with diagnostic subtype, pathological stage, and grade but not with smoking history or non-muscle invasive status.

Conclusions: This integrated molecular analysis of 412 TCGA tumor samples largely validates and considerably extends observations from the initial cohort of 131 patients. The larger cohort significantly increased our power to detect lower-frequency aberrations that were not identified in the original cohort. The results provide a robust basis for further functional studies of bladder cancer biology and also provide additional incisive information for the identification of molecular targets for therapy.

Citation Format: John N. Weinstein, Seth P. Lerner, David J. Kwiatkowski, Gad Getz, Jaegil Kim, Hikmat A. Al-ahmadie, Andrew D. Cherniack, Guangwu Guo, Rehan Akbani, Katherine A. Hoadley, William Y. Kim, Gordon Robertson, Andrew J. Mungall, Toshinori Hinoue, Peter W. Laird, Jonathan E. Rosenberg, Joaquim Bellmunt, Dean F. Bajorin, Margaret B. Morgan, Chad J. Creighton, Dmitry Gordenin, Joshua M. Stuart, Xiaoping Su, Michael C. Ryan, Jeffrey S. Damrauer, Wei Zhang, Yuexin Liu, Yiling Lu, Nikolaus Schultz, Raju Kucherlapati, Gordon B. Mills, Donna E. Hansel, Brian D. Robinson, Bodgen A. Czerniak, Victor E. Reuter. Comprehensive molecular characterization of 412 muscle-invasive urothelial bladder carcinomas: final analysis of The Cancer Genome Atlas (TCGA) project. [abstract]. In: Proceedings of the 107th Annual Meeting of the American Association for Cancer Research; 2016 Apr 16-20; New Orleans, LA. Philadelphia (PA): AACR; Cancer Res 2016;76(14 Suppl):Abstract nr 128.

N-Myc Drives Neuroendocrine Prostate Cancer Initiated from Human Prostate Epithelial Cells

MYCN amplification and overexpression are common in neuroendocrine prostate cancer (NEPC). However, the impact of aberrant N-Myc expression in prostate tumorigenesis and the cellular origin of NEPC have not been established. We define N-Myc and activated AKT1 as oncogenic components sufficient to transform human prostate epithelial cells to prostate adenocarcinoma and NEPC with phenotypic and molecular features of aggressive, late-stage human disease. We directly show that prostate adenocarcinoma and NEPC can arise from a common epithelial clone. Further, N-Myc is required for tumor maintenance, and destabilization of N-Myc through Aurora A kinase inhibition reduces tumor burden. Our findings establish N-Myc as a driver of NEPC and a target for therapeutic intervention.

Computational Identification of Tumor Anatomic Location Associated with Survival in 2 Large Cohorts of Human Primary Glioblastomas

BACKGROUND AND PURPOSE

Tumor location has been shown to be a significant prognostic factor in patients with glioblastoma. The purpose of this study was to characterize glioblastoma lesions by identifying MR imaging voxel-based tumor location features that are associated with tumor molecular profiles, patient characteristics, and clinical outcomes.

MATERIALS AND METHODS

Preoperative T1 anatomic MR images of 384 patients with glioblastomas were obtained from 2 independent cohorts (n = 253 from the Stanford University Medical Center for training and n = 131 from The Cancer Genome Atlas for validation). An automated computational image-analysis pipeline was developed to determine the anatomic locations of tumor in each patient. Voxel-based differences in tumor location between good (overall survival of >17 months) and poor (overall survival of <11 months) survival groups identified in the training cohort were used to classify patients in The Cancer Genome Atlas cohort into 2 brain-location groups, for which clinical features, messenger RNA expression, and copy number changes were compared to elucidate the biologic basis of tumors located in different brain regions.

RESULTS

Tumors in the right occipitotemporal periventricular white matter were significantly associated with poor survival in both training and test cohorts (both, log-rank P < .05) and had larger tumor volume compared with tumors in other locations. Tumors in the right periatrial location were associated with hypoxia pathway enrichment and PDGFRA amplification, making them potential targets for subgroup-specific therapies.

CONCLUSIONS

Voxel-based location in glioblastoma is associated with patient outcome and may have a potential role for guiding personalized treatment.

Pathway-Based Genomics Prediction using Generalized Elastic Net

We present a novel regularization scheme called The Generalized Elastic Net (GELnet) that incorporates gene pathway information into feature selection. The proposed formulation is applicable to a wide variety of problems in which the interpretation of predictive features using known molecular interactions is desired. The method naturally steers solutions toward sets of mechanistically interlinked genes. Using experiments on synthetic data, we demonstrate that pathway-guided results maintain, and often improve, the accuracy of predictors even in cases where the full gene network is unknown. We apply the method to predict the drug response of breast cancer cell lines. GELnet is able to reveal genetic determinants of sensitivity and resistance for several compounds. In particular, for an EGFR/HER2 inhibitor, it finds a possible trans-differentiation resistance mechanism missed by the corresponding pathway agnostic approach.

Abstract B1-38: PARADIGM-SHIFT: Predicts the functional impact of ‘driver modules’ in multiple cancers using pathway impact analysis

The major mechanism by which cancer arises is through genomic alterations. These alterations can lead to changes in gene regulation, protein structure, and function. Individual tumors can contain hundreds to thousands of alterations. It is critical to distinguish alterations that have an important role defining the cancer – drivers – from alterations that are unimportant to the tumor – passengers. Driver genes can lead to significant changes in their pathways; however, alterations in a single gene may not be sufficient to explain all the pathway perturbations across patients. Additional alterations could range from DNA copy number changes, gene-fusions, or even lesser understood non-coding mutations. Identifying these ‘driver modules’ is essential for understanding cancer disease mechanisms, which can help guide treatment decisions as well as identify novel targets for treatment.

We have developed a functional impact prediction method called PARADIGM-SHIFT based on integrated pathway analysis to discriminate loss-of-function, neutral, and gain-of-function alterations. Utilizing the set of regulatory interactions annotated for a given gene, we can detect a shift in the downstream effects of an altered gene compared to what is expected from its upstream influences. Additionally, since these shifts in pathway signal can be detected for all samples, PARADIGM-SHIFT can be used to identify additional genomic alterations that lead to similar changes to the altered pathway to form ‘driver modules.’ Application of our method to the TCGA Pan-Cancer cohort identifies many genes with significant alterations that lead to loss- and gain- of function. PARADIGM-SHIFT then identifies several additional genomic alterations, including non-coding mutations variants, which are significantly implicated in these pathway changes. This analysis offers insight into the mechanism of non-coding mutations orthogonal to sequence-based methods by interpreting the pathway consequences of these variants.

Citation Format: Sam Ng, Christopher Benz, David Haussler, Joshua M. Stuart. PARADIGM-SHIFT: Predicts the functional impact of ‘driver modules’ in multiple cancers using pathway impact analysis. [abstract]. In: Proceedings of the AACR Special Conference on Computational and Systems Biology of Cancer; Feb 8-11 2015; San Francisco, CA. Philadelphia (PA): AACR; Cancer Res 2015;75(22 Suppl 2):Abstract nr B1-38.