Pan-Cancer Analysis Of Whole Genomes Research Articles

Molecular medicine tumor board: whole-genome sequencing to inform on personalized medicine for a man with advanced prostate cancer

PurposeMolecular profiling of cancer is increasingly common as part of routine care in oncology, and germline and somatic profiling may provide insights and actionable targets for men with metastatic prostate cancer. However, all reported cases are of deidentified individuals without full medical and genomic data available in the public domain.Patient and methodsWe present a case of whole-genome tumor and germline sequencing in a patient with advanced prostate cancer, who has agreed to make his genomic and clinical data publicly available.ResultsWe describe an 84-year-old Caucasian male with a Gleason 10 oligometastastic hormone-sensitive prostate cancer. Whole-genome sequencing provided insights into his tumor’s underlying mutational processes and the development of an SPOP mutation. It also revealed an androgen-receptor dependency of his cancer which was reflected in his durable response to radiation and hormonal therapy. Potentially actionable genomic lesions in the tumor were identified through a personalized medicine approach for potential future therapy, but at the moment, he remains in remission, illustrating the hormonal sensitivity of his SPOP-driven prostate cancer. We also placed this patient in the context of a large prostate-cancer cohort from the PCAWG (Pan-cancer Analysis of Whole Genomes) group. In this comparison, the patient’s cancer appears typical in terms of the number and type of somatic mutations, but it has a somewhat larger contribution from the mutational process associated with aging.ConclusionWe combined the expertise of medical oncology and genomics approaches to develop a molecular tumor board to integrate the care and study of this patient, who continues to have an outstanding response to his combined modality treatment. This identifiable case potentially helps overcome barriers to clinical and genomic data sharing.

Systematic identification of non-coding somatic single nucleotide variants associated with altered transcription and DNA methylation in adult and pediatric cancers.

Whole-genome sequencing combined with transcriptomics can reveal impactful non-coding single nucleotide variants (SNVs) in cancer. Here, we developed an integrative analytical approach that, as a first step, identifies genes altered in expression or DNA methylation in association with nearby somatic SNVs, in contrast to alternative approaches that first identify mutational hotspots. Using genomic datasets from the Pan-Cancer Analysis of Whole Genomes (PCAWG) consortium and the Children's Brain Tumor Tissue Consortium (CBTTC), we identified hundreds of genes and associated CpG islands for which the nearby presence of a non-coding somatic SNV recurrently associated with altered expression or DNA methylation, respectively. Genomic regions upstream or downstream of genes, gene introns and gene untranslated regions were all involved. The PCAWG adult cancer cohort yielded different significant SNV-expression associations from the CBTTC pediatric brain tumor cohort. The SNV-expression associations involved a wide range of cancer types and histologies, as well as potential gain or loss of transcription factor binding sites. Notable genes with SNV-associated increased expression include TERT, COPS3, POLE2 and HDAC2—involving multiple cancer types—MYC, BCL2, PIM1 and IGLL5—involving lymphomas—and CYHR1—involving pediatric low-grade gliomas. Non-coding somatic SNVs show a major role in shaping the cancer transcriptome, not limited to mutational hotspots.

Identification and Validation of the N6-Methyladenosine RNA Methylation Regulator YTHDF1 as a Novel Prognostic Marker and Potential Target for Hepatocellular Carcinoma.

Purpose: N6-methyladenosine (m6A) RNA methylation has been implicated in various malignancies. This study aimed to identify the m6A methylation regulator-based prognostic signature for hepatocellular carcinoma (HCC) as well as provide candidate targets for HCC treatment.Methods: The least absolute shrinkage and selection operator (LASSO) analyses were performed to identify a risk signature in The Cancer Genome Atlas (TCGA) datasets. The risk signature was further validated in International Cancer Genome Consortium (ICGC) and Pan-Cancer Analysis of Whole Genomes (PCAWG) datasets. Following transfection of short hairpin RNA (shRNA) targeting YTHDF1, the biological activities of HCC cells were evaluated by Cell Counting Kit-8 (CCK-8), wound-healing, Transwell, flow cytometry, and xenograft tumor assays, respectively. The potential mechanisms mediated by YTHDF1 were predicted by overrepresentation enrichment analysis (ORA)/gene set enrichment analysis (GSEA) and validated by Western blotting.Results: Overexpression of m6A RNA methylation regulators was correlated with malignant clinicopathological characteristics of HCC patients. The Cox regression and LASSO analyses identified a risk signature with five m6A methylation regulators (KIAA1429, ZC3H13, YTHDF1, YTHDF2, and METTL3). In accordance with HCC cases in TCGA, the prognostic value of risk signature was also determined in ICGC and PCAWG datasets. Following analyzing the expression and clinical implications in TCGA and Gene Expression Omnibus (GEO), YTHDF1 was chosen for further experimental validation. Knockdown of YTHDF1 significantly inhibited the proliferation, migration, and invasion of HCC cells, as well as enhanced the apoptosis in vitro. Moreover, silencing YTHDF1 repressed the growth of xenograft tumors in vivo. Mechanism investigation indicated that YTHDF1 might promote the aggressive phenotypes by facilitating epithelial–mesenchymal transition (EMT) and activating AKT/glycogen synthase kinase (GSK)-3β/β-catenin signaling.Conclusion: The current study identified a robust risk signature consisting of m6A RNA methylation regulators for HCC prognosis. In addition, YTHDF1 was a potential molecular target for HCC treatment.

Abstract PO-029: CliP: A model-based method for subclonal architecture reconstruction using regularized maximum likelihood estimation

Abstract Background Subpopulations of tumor cells characterized by mutation profiles may confer differential fitness to treatment and prognosis across cancers. Understanding subclonal architecture has the potential to provide biological insight into tumor evolution and advance the precision treatment of cancers. Recent methods comprehensively integrate single nucleotide variants (SNVs) and copy number alterations (CNAs) to reconstruct subclonal architecture using whole-genome sequencing (WGS) data from bulk tumor samples. However, most methods follow a Bayesian framework and require extensive computational resources, a prior knowledge of the number of subclones, as well as ad hoc post-analysis data processing. Altogether this creates a bottleneck in processing time in large-scale studies. Objectives The primary objective of this study is to introduce a fast and accurate subclonal architecture reconstruction method, which utilizes a model-based clustering approach and addresses all the limitations above. Methods We introduce a novel model-based clustering method: Clonal structure identification through pairwise penalization (CliP). CliP assumes the number of reads observed with variant alleles follows a binomial model, which is a function of mutation cellular prevalence (CP), copy number aberrations, and tumor purity. We propose to minimize a penalized likelihood of this model with a SCAD penalty on CPs across pairs of mutations. The optimization problem is then efficiently solved via Alternating Direction Method of Multipliers (ADMM). As a subclonal reconstruction algorithm, CliP attempts to infer the population structure of heterogeneous tumors, and is the first method to utilize a regularized maximum likelihood framework in subclonal reconstruction, therefore benefiting from its computational efficiency in parameter estimation. Results Rigorous and extensive simulation results demonstrate that CliP is 100 times faster than MCMC-based algorithms without decreased performance. Unlike previous models, the CliP model is applicable to regions with or without CNAs. Furthermore, CliP generates subclonal structure without prior knowledge or post-processing. In an application to WGS data from Pan-Cancer Analysis of Whole Genomes (PCAWG), it only took 8 hours to process 2,500 tumor samples. Conclusion Since CliP executes quickly, it is ultimately suitable for 1) processing large datasets with thousands of samples and 2) participation in a group of methods to generate consensus calls. As the sizes of datasets continue to grow, CliP represents an important step towards fast and accurate subclonal reconstruction. Citation Format: Yujie Jiang, Kaixian Yu, Hongtu Zhu, Wenyi Wang. CliP: A model-based method for subclonal architecture reconstruction using regularized maximum likelihood estimation [abstract]. In: Proceedings of the AACR Virtual Special Conference on Tumor Heterogeneity: From Single Cells to Clinical Impact; 2020 Sep 17-18. Philadelphia (PA): AACR; Cancer Res 2020;80(21 Suppl):Abstract nr PO-029.

Parallelized Latent Dirichlet Allocation Provides a Novel Interpretability of Mutation Signatures in Cancer Genomes.

Mutation signatures are defined as the distribution of specific mutations such as activity of AID/APOBEC family proteins. Previous studies have reported numerous signatures, using matrix factorization methods for mutation catalogs. Different mutation signatures are active in different tumor types; hence, signature activity varies greatly among tumor types and becomes sparse. Because of this, many previous methods require dividing mutation catalogs for each tumor type. Here, we propose parallelized latent Dirichlet allocation (PLDA), a novel Bayesian model to simultaneously predict mutation signatures with all mutation catalogs. PLDA is an extended model of latent Dirichlet allocation (LDA), which is one of the methods used for signature prediction. It has parallelized hyperparameters of Dirichlet distributions for LDA, and they represent the sparsity of signature activities for each tumor type, thus facilitating simultaneous analyses. First, we conducted a simulation experiment to compare PLDA with previous methods (including SigProfiler and SignatureAnalyzer) using artificial data and confirmed that PLDA could predict signature structures as accurately as previous methods without searching for the optimal hyperparameters. Next, we applied PLDA to PCAWG (Pan-Cancer Analysis of Whole Genomes) mutation catalogs and obtained a signature set different from the one predicted by SigProfiler. Further, we have shown that the mutation spectrum represented by the predicted signature with PLDA provides a novel interpretability through post-analyses.

Retrospective evaluation of whole exome and genome mutation calls in 746 cancer samples

The Cancer Genome Atlas (TCGA) and International Cancer Genome Consortium (ICGC) curated consensus somatic mutation calls using whole exome sequencing (WES) and whole genome sequencing (WGS), respectively. Here, as part of the ICGC/TCGA Pan-Cancer Analysis of Whole Genomes (PCAWG) Consortium, which aggregated whole genome sequencing data from 2,658 cancers across 38 tumour types, we compare WES and WGS side-by-side from 746 TCGA samples, finding that ~80% of mutations overlap in covered exonic regions. We estimate that low variant allele fraction (VAF < 15%) and clonal heterogeneity contribute up to 68% of private WGS mutations and 71% of private WES mutations. We observe that ~30% of private WGS mutations trace to mutations identified by a single variant caller in WES consensus efforts. WGS captures both ~50% more variation in exonic regions and un-observed mutations in loci with variable GC-content. Together, our analysis highlights technological divergences between two reproducible somatic variant detection efforts.

Pathogenic impact of transcript isoform switching in 1,209 cancer samples covering 27 cancer types using an isoform-specific interaction network

Under normal conditions, cells of almost all tissue types express the same predominant canonical transcript isoform at each gene locus. In cancer, however, splicing regulation is often disturbed, leading to cancer-specific switches in the most dominant transcripts (MDT). To address the pathogenic impact of these switches, we have analyzed isoform-specific protein–protein interaction disruptions in 1,209 cancer samples covering 27 different cancer types from the Pan-Cancer Analysis of Whole Genomes (PCAWG) project of the International Cancer Genomics Consortium (ICGC). Our study revealed large variations in the number of cancer-specific MDT (cMDT) with the highest frequency in cancers of female reproductive organs. Interestingly, in contrast to the mutational load, cancers arising from the same primary tissue had a similar number of cMDT. Some cMDT were found in 100% of all samples in a cancer type, making them candidates for diagnostic biomarkers. cMDT tend to be located at densely populated network regions where they disrupted protein interactions in the proximity of pathogenic cancer genes. A gene ontology enrichment analysis showed that these disruptions occurred mostly in protein translation and RNA splicing pathways. Interestingly, samples with mutations in the spliceosomal complex tend to have higher number of cMDT, while other transcript expressions correlated with mutations in non-coding splice-site and promoter regions of their genes. This work demonstrates for the first time the large extent of cancer-specific alterations in alternative splicing for 27 different cancer types. It highlights distinct and common patterns of cMDT and suggests novel pathogenic transcripts and markers that induce large network disruptions in cancers.

Sex differences in oncogenic mutational processes

Sex differences have been observed in multiple facets of cancer epidemiology, treatment and biology, and in most cancers outside the sex organs. Efforts to link these clinical differences to specific molecular features have focused on somatic mutations within the coding regions of the genome. Here we report a pan-cancer analysis of sex differences in whole genomes of 1983 tumours of 28 subtypes as part of the ICGC/TCGA Pan-Cancer Analysis of Whole Genomes (PCAWG) Consortium. We both confirm the results of exome studies, and also uncover previously undescribed sex differences. These include sex-biases in coding and non-coding cancer drivers, mutation prevalence and strikingly, in mutational signatures related to underlying mutational processes. These results underline the pervasiveness of molecular sex differences and strengthen the call for increased consideration of sex in molecular cancer research.

Genomic and epigenetic aberrations of chromosome 1p36.13 have prognostic implications in malignancies.

Deletions of chromosome 1p36 are common in malignancies; however, there is limited information regarding the biological and prognostic implications of 1p36 in cancer. Steroid Receptor-Associated and Regulated Protein (SRARP) is a tumor suppressor on chromosome 1p36.13 that its inactivation predicts poor cancer outcome, indicating that the 1p36.13 segment requires further studies. Therefore, a comprehensive multi-omics analysis of The Cancer Genome Atlas (TCGA), the Pan-Cancer Analysis of Whole Genomes (PCAWD), the International Cancer Genome Consortium (ICGC), and the Genomic Data Commons (GDC) Pan-Cancer datasets was conducted to investigate the prognostic implications of 1p36.13 in malignancies. This study revealed that expression and DNA methylation of multiple genes on 1p36.13 are significantly associated with survival in primary tumors and normal adjacent tissues. In addition, copy-number loss in every gene on 1p36.13 predicts poor cancer outcome. Importantly, copy-number loss and somatic mutations of chromosome 1p36.13 segment are associated with worse survival in primary tumors, and DNA hypermethylation of 1p36.13 predicts poor outcome in normal adjacent tissues. Therefore, genomic and epigenetic aberrations of chromosome 1p36.13 have promising prognostic implications in cancer.

A user guide for the online exploration and visualization of PCAWG data

The Pan-Cancer Analysis of Whole Genomes (PCAWG) project generated a vast amount of whole-genome cancer sequencing resource data. Here, as part of the ICGC/TCGA Pan-Cancer Analysis of Whole Genomes (PCAWG) Consortium, which aggregated whole genome sequencing data from 2658 cancers across 38 tumor types, we provide a user’s guide to the five publicly available online data exploration and visualization tools introduced in the PCAWG marker paper. These tools are ICGC Data Portal, UCSC Xena, Chromothripsis Explorer, Expression Atlas, and PCAWG-Scout. We detail use cases and analyses for each tool, show how they incorporate outside resources from the larger genomics ecosystem, and demonstrate how the tools can be used together to understand the biology of cancers more deeply. Together, the tools enable researchers to query the complex genomic PCAWG data dynamically and integrate external information, enabling and enhancing interpretation.

Characterizations of Cancer Gene Mutations in Chinese Metastatic Breast Cancer Patients.

Background: Breast cancer (BC) is a type of disease with high heterogeneity. Molecular profiling, by revealing the intrinsic nature of its various subtypes, has extensively improved the therapeutic management of BC patients. However, the genomic mutation landscape of Chinese metastatic BC has not been fully explored.Methods: Matched plasma and mononuclear cells from 290 Chinese women with metastatic BC were sequenced using either of the two commercially-available panels consisting of 520 cancer-related and 108 BC-related genes. Both panels cover the same critical regions of 91 genes. The circulating tumor DNA mutation profile from our cohort was then compared with publicly-available metastatic BC datasets from Memorial Sloan Kettering Cancer Center (MSKCC) and Pan-cancer analysis of whole genomes (PCAWG).Results: A total of 1,201 mutations spanning 91 genes were detected from 234 patients, resulting in a mutation detection rate of 80.7%. TP53 (64.1%) was the gene with highest mutation frequency, followed by PIK3CA (31%), PTEN (11%), and RB1 (10%). Copy number amplifications (CNAs) in MYC (14.1%), FGFR1 (13.3%), CCND1 (6.6%), FGF3 (6.6%), FGF4 (6.2%) and FGF19 (6.2%) were also detected from our cohort. TP53 mutations were significantly more frequent among triple negative BC (TNBC), HR−/HER2+, and HR+/HER2+ BC, while less common in HR+/HER2– (P < 0.01). Meanwhile, PIK3CA mutations were significantly more frequent among HR+/HER2+, HR+/HER2–, and HR−/HER2+ BC, while less common in TNBC (P < 0.01). Pathogenic or likely pathogenic BRCA1/2 germline mutations were detected in 5.9% of the cohort and 4.4% in TNBC subgroup. Maximum allelic fraction (maxAF) of TP53, RB1, and PIK3CA mutations were associated with multiple organ metastasis. Patients with PIK3CA, PTEN, and RB1 mutation were more likely to have liver metastasis (P < 0.02). Compared with MSKCC and PCAWG dataset, Chinese patients had observably difference in genetic variation rates in different molecular subtypes (TNBC: TP53 73.0 vs. 91.5%, P < 0.001; PIK3CA 21.2 vs. 13.2%, P = 0.061; HR+/HER2−: FGFR1 3.3 vs. 0.7%, P = 0.035; TP 53 46.2 vs. 27.7%, P < 0.001; RB1 6.6 vs. 2.7%, P = 0.046; CDKN2A 7.7 vs. 1.0%, P < 0.001; PIK3CA 30.8 vs. 44.2%, P = 0.012; CDH1 1.1 vs. 18.2%, P < 0.001; GATA3 7.7 vs. 17.2%, P = 0.02).Conclusions: A distinct gene mutation profile was elucidated in Chinese women with metastatic BC, justifying further research. Liquid biopsy provides a quick, real-time, and minimally invasive tool for future clinical trial and routine practice.

The genome-wide landscape of C:G > T:A polymorphism at the CpG contexts in the human population

BackgroundThe C:G > T:A substitution at the CpG dinucleotide contexts is the most frequent substitution type in genome evolution. The mutational process is obviously ongoing in the human germline; however, its impact on common and rare genomic polymorphisms has not been comprehensively investigated yet. Here we observed the landscape and dynamics of C:G > T:A substitutions from population-scale human genome sequencing datasets including ~ 4300 whole-genomes from the 1000 Genomes and the pan-cancer analysis of whole genomes (PCAWG) Project and ~ 60,000 whole-exomes from the Exome Aggregation Consortium (ExAC) database.ResultsOf the 28,084,558 CpG sites in the human reference genome, 26.0% show C:G > T:A substitution in the dataset. Remarkably, CpGs in CpG islands (CGIs) have a much lower frequency of such mutations (5.6%). Interestingly, the mutation frequency of CGIs is not uniform with a significantly higher C:G > T:A substitution rate for intragenic CGIs compared to other types. For non-CGI CpGs, the mutation rate was positively correlated with the distance from the nearest CGI up to 2 kb. Finally, we found the impact of negative selection for coding CpG mutations resulting in amino acid change.ConclusionsThis study provides the first unbiased rate of C:G > T:A substitution at the CpG dinucleotide contexts, using population-scale human genome sequencing data. Our findings provide insights into the dynamics of the mutation acquisition in the human genome.

Prospecting whole cancer genomes.

A new suite of studies from the Pan Cancer Analysis of Whole Genomes (PCAWG) Consortium provides the most detailed resolution of cancer genomes to date, extending our knowledge of driver genes, mutational features, structural alterations and more. Kreisberg, Ideker, Mills and Meric-Bernstam discuss the foundational and translational insights gained from this project.

Cancer Gets a Global Genomic Map: The Pan-Cancer Analysis of Whole Genomes incorporated 2,658 whole genomes, 38 tumor types, 1,188 transcriptomes, and 1,300 scientists from 37 countries

Cancer Gets a Global Genomic Map: The Pan-Cancer Analysis of Whole Genomes incorporated 2,658 whole genomes, 38 tumor types, 1,188 transcriptomes, and 1,300 scientists from 37 countries

Passenger Mutations in More Than 2,500 Cancer Genomes: Overall Molecular Functional Impact and Consequences

The dichotomous model of "drivers" and "passengers" in cancer posits that only a few mutations in a tumor strongly affect its progression, with the remaining ones being inconsequential. Here, we leveraged the comprehensive variant dataset from the ICGC/TCGA Pan-Cancer Analysis of Whole Genomes (PCAWG) project to demonstrate that-in addition to the dichotomy of high- and low-impact variants-there is a third group of medium-impact putative passengers. Moreover, we also found that molecular impact correlates with subclonal architecture (i.e., early versus late mutations), and different signatures encode for mutations with divergent impact. Furthermore, we adapted an additive-effects model from complex-trait studies to show that the aggregated effect of putative passengers, including undetected weak drivers, provides significant additional power (∼12% additive variance) for predicting cancerous phenotypes, beyond PCAWG-identified driver mutations. Finally, this framework allowed us to estimate the frequency of potential weak-driver mutations in PCAWG samples lacking any well-characterized driver alterations.

Pan-cancer analysis of whole genomes identifies driver rearrangements promoted by LINE-1 retrotransposition

About half of all cancers have somatic integrations of retrotransposons. Here, to characterize their role in oncogenesis, we analyzed the patterns and mechanisms of somatic retrotransposition in 2,954 cancer genomes from 38 histological cancer subtypes within the framework of the Pan-Cancer Analysis of Whole Genomes (PCAWG) project. We identified 19,166 somatically acquired retrotransposition events, which affected 35% of samples and spanned a range of event types. Long interspersed nuclear element (LINE-1; L1 hereafter) insertions emerged as the first most frequent type of somatic structural variation in esophageal adenocarcinoma, and the second most frequent in head-and-neck and colorectal cancers. Aberrant L1 integrations can delete megabase-scale regions of a chromosome, which sometimes leads to the removal of tumor-suppressor genes, and can induce complex translocations and large-scale duplications. Somatic retrotranspositions can also initiate breakage–fusion–bridge cycles, leading to high-level amplification of oncogenes. These observations illuminate a relevant role of 22 L1 retrotransposition in remodeling the cancer genome, with potential implications for the development of human tumors.

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.

Genomic footprints of activated telomere maintenance mechanisms in cancer

Cancers require telomere maintenance mechanisms for unlimited replicative potential. They achieve this through TERT activation or alternative telomere lengthening associated with ATRX or DAXX loss. Here, as part of the ICGC/TCGA Pan-Cancer Analysis of Whole Genomes (PCAWG) Consortium, we dissect whole-genome sequencing data of over 2500 matched tumor-control samples from 36 different tumor types aggregated within the ICGC/TCGA Pan-Cancer Analysis of Whole Genomes (PCAWG) Consortium to characterize the genomic footprints of these mechanisms. While the telomere content of tumors with ATRX or DAXX mutations (ATRX/DAXXtrunc) is increased, tumors with TERT modifications show a moderate decrease of telomere content. One quarter of all tumor samples contain somatic integrations of telomeric sequences into non-telomeric DNA. This fraction is increased to 80% prevalence in ATRX/DAXXtrunc tumors, which carry an aberrant telomere variant repeat (TVR) distribution as another genomic marker. The latter feature includes enrichment or depletion of the previously undescribed singleton TVRs TTCGGG and TTTGGG, respectively. Our systematic analysis provides new insight into the recurrent genomic alterations associated with telomere maintenance mechanisms in cancer.

Combined burden and functional impact tests for cancer driver discovery using DriverPower

The discovery of driver mutations is one of the key motivations for cancer genome sequencing. Here, as part of the ICGC/TCGA Pan-Cancer Analysis of Whole Genomes (PCAWG) Consortium, which aggregated whole genome sequencing data from 2658 cancers across 38 tumour types, we describe DriverPower, a software package that uses mutational burden and functional impact evidence to identify driver mutations in coding and non-coding sites within cancer whole genomes. Using a total of 1373 genomic features derived from public sources, DriverPower’s background mutation model explains up to 93% of the regional variance in the mutation rate across multiple tumour types. By incorporating functional impact scores, we are able to further increase the accuracy of driver discovery. Testing across a collection of 2583 cancer genomes from the PCAWG project, DriverPower identifies 217 coding and 95 non-coding driver candidates. Comparing to six published methods used by the PCAWG Drivers and Functional Interpretation Working Group, DriverPower has the highest F1 score for both coding and non-coding driver discovery. This demonstrates that DriverPower is an effective framework for computational driver discovery.

Disruption of chromatin folding domains by somatic genomic rearrangements in human cancer

Chromatin is folded into successive layers to organize linear DNA. Genes within the same topologically associating domains (TADs) demonstrate similar expression and histone-modification profiles, and boundaries separating different domains have important roles in reinforcing the stability of these features. Indeed, domain disruptions in human cancers can lead to misregulation of gene expression. However, the frequency of domain disruptions in human cancers remains unclear. Here, as part of the Pan-Cancer Analysis of Whole Genomes (PCAWG) Consortium of the International Cancer Genome Consortium (ICGC) and The Cancer Genome Atlas (TCGA), which aggregated whole-genome sequencing data from 2,658 cancers across 38 tumor types, we analyzed 288,457 somatic structural variations (SVs) to understand the distributions and effects of SVs across TADs. Notably, SVs can lead to the fusion of discrete TADs, and complex rearrangements markedly change chromatin folding maps in the cancer genomes. Notably, only 14% of the boundary deletions resulted in a change in expression in nearby genes of more than twofold.