A key mutational process in cancer is structural variation, in which rearrangements delete, amplify or reorder genomic segments that range in size from kilobases to whole chromosomes1–7. Here we develop methods to group, classify and describe somatic structural variants, using data from 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 tumour types8. Sixteen signatures of structural variation emerged. Deletions have a multimodal size distribution, assort unevenly across tumour types and patients, are enriched in late-replicating regions and correlate with inversions. Tandem duplications also have a multimodal size distribution, but are enriched in early-replicating regions—as are unbalanced translocations. Replication-based mechanisms of rearrangement generate varied chromosomal structures with low-level copy-number gains and frequent inverted rearrangements. One prominent structure consists of 2–7 templates copied from distinct regions of the genome strung together within one locus. Such cycles of templated insertions correlate with tandem duplications, and—in liver cancer—frequently activate the telomerase gene TERT. A wide variety of rearrangement processes are active in cancer, which generate complex configurations of the genome upon which selection can act. Whole-genome sequencing data from more than 2,500 cancers of 38 tumour types reveal 16 signatures that can be used to classify somatic structural variants, highlighting the diversity of genomic rearrangements in cancer.

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. Methods for reconstructing tumor evolution are benchmarked in the DREAM Somatic Mutation Calling Tumour Heterogeneity Challenge.

Objectives We sought to determine the feasibility and characterize the extinction kinetics of circulating cell-free tumor DNA (cfDNA) testing in endometrial and ovarian carcinomas (ECs, OCs) using a clinically-approved commercially-available assay. Methods Women with suspected EC/OC undergoing surgery were consented for tissue and plasma sampling including pre-operative and serial post-operative draws. Tumour tissue and patient-matched buffy coat was extracted for DNA and sequenced for somatic mutations using FINDIT™ panel assay. Plasma samples were extracted for cfDNA and sequenced using FOLLOWIT™, Illumina platform, and analyzed using Contextual Genomics’s QUALITY NEXUS analysis pipelines. Low-frequency variants were confirmed by digital droplet PCR. Results 44 individuals had sufficient tissue and follow-up for inclusion; 24 ECs (13 endometrioid, 10 high-grade serous (HGS), 1 clear cell(CC)), 18 OCs (17 HGS 1, CC), and 2 synchronous endometrial and ovarian carcinomas. Eight ECs and 15 OC cases were advanced stage (II-IV) with residual disease in 2 ECs and 5 OCs, 8 recurrence events and 3 deaths recorded. Compliance with plasma sampling was high(>95%) when requested in hospital or at routine surveillance visits but dropped to 68% for ‘extra’ study-associated visits. Analysis to date reveals cfDNA was detectable in pre-operative samples of 19 individuals (9 ECs, 10 OCs including 4 early stage) and 6/10 tested post-operatively. Normalization of conventional tumour markers post-operatively took a median of 3mo in contrast to rapid loss of detectable cfDNA. Conclusions cfDNA testing is feasible and may enhance surveillance of endometrial and ovarian carcinomas by reflecting i) volume of disease pre-/post-operatively, ii) response to therapy, and/or iii) recurrence.

We introduce a new dissimilarity measure between a pair of “clonal trees”, each representing the progression and mutational heterogeneity of a tumor sample, constructed by the use of single cell or bulk high throughput sequencing data. In a clonal tree, each vertex represents a specific tumor clone, and is labeled with one or more mutations in a way that each mutation is assigned to the oldest clone that harbors it. Given two clonal trees, our multi-labeled tree dissimilarity (MLTD) measure is defined as the minimum number of mutation/label deletions, (empty) leaf deletions, and vertex (clonal) expansions, applied in any order, to convert each of the two trees to the maximum common tree. We show that the MLTD measure can be computed efficiently in polynomial time and it captures the similarity between trees of different clonal granularity well.

Abstract Motivation Despite the remarkable advances in sequencing and computational techniques, noise in the data and complexity of the underlying biological mechanisms render deconvolution of the phylogenetic relationships between cancer mutations difficult. Besides that, the majority of the existing datasets consist of bulk sequencing data of single tumor sample of an individual. Accurate inference of the phylogenetic order of mutations is particularly challenging in these cases and the existing methods are faced with several theoretical limitations. To overcome these limitations, new methods are required for integrating and harnessing the full potential of the existing data. Results We introduce a method called Hintra for intra-tumor heterogeneity detection. Hintra integrates sequencing data for a cohort of tumors and infers tumor phylogeny for each individual based on the evolutionary information shared between different tumors. Through an iterative process, Hintra learns the repeating evolutionary patterns and uses this information for resolving the phylogenetic ambiguities of individual tumors. The results of synthetic experiments show an improved performance compared to two state-of-the-art methods. The experimental results with a recent Breast Cancer dataset are consistent with the existing knowledge and provide potentially interesting findings. Availability and implementation The source code for Hintra is available at https://github.com/sahandk/HINTRA.

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. There’s an emerging body of evidence to show how biological sex impacts cancer incidence, treatment and underlying biology. Here, using a large pan-cancer dataset, the authors further highlight how sex differences shape the cancer genome.

Recent technological advances in single cell sequencing (SCS) provide high resolution data for studying intra-tumor heterogeneity and tumor evolution. Available computational methods for tumor phylogeny inference via SCS typically aim to identify the most likely perfect phylogeny tree satisfying infinite sites assumption (ISA). However limitations of SCS technologies such as frequent allele dropout or highly variable sequence coverage, commonly result in mutational call errors and prohibit a perfect phylogeny. In addition, ISA violations are commonly observed in tumor phylogenies due to the loss of heterozygosity, deletions and convergent evolution. In order to address such limitations, we, for the first time, introduce a new combinatorial formulation that integrates single cell sequencing data with matching bulk sequencing data, with the objective of minimizing a linear combination of (i) potential false negatives (due to e.g. allele dropout or variance in sequence coverage) and (ii) potential false positives (due to e.g. read errors) among mutation calls, as well as (iii) the number of mutations that violate ISA - to define the optimal sub-perfect phylogeny. Our formulation ensures that several lineage constraints imposed by the use of variant allele frequencies (VAFs, derived from bulk sequence data) are satisfied. We express our formulation both in the form of an integer linear program (ILP) and - for the first time in the context of tumor phylogeny reconstruction - a boolean constraint satisfaction problem (CSP) and solve them by leveraging state-of-the-art ILP/CSP solvers. The resulting method, which we name PhISCS, is the first to integrate SCS and bulk sequencing data under the finite sites model. Using several simulated and real SCS data sets, we demonstrate that PhISCS is not only more general but also more accurate than the alternative tumor phylogeny inference tools. PhISCS is very fast especially when its CSP based variant is used returns the optimal solution, except in rare instances for which it provides an optimality gap. PhISCS is available at https://github.com/haghshenas/PhISCS.

Cancer develops through a continuous process of somatic evolution. Whole genome sequencing provides a snapshot of the tumor genome at the point of sampling, however, the data can contain information that permits the reconstruction of a tumor9s evolutionary past. Here, we apply such life history analyses on an unprecedented scale, to a set of 2,658 tumors spanning 39 cancer types. We estimated the timing of large chromosomal gains during tumor evolution, by comparing the rates of doubled to non-doubled point mutations within gained regions. Although we find that such events typically occur in the second half of clonal evolution, we also observe distinctive and early chromosomal gains in some cancer types, such as gains of chromosomes 7, 19 and 20 in glioblastoma, and isochromosome 17q in medulloblastoma. By integrating these results with the qualitative timing of individual driver mutations, we obtained an overall ranking, from early to late, of frequent somatic events per cancer type, which both identified novel patterns of tumor evolution, and incorporated additional detail into known models, such as the progression of APC-KRAS-TP53 in colorectal cancer proposed by Vogelstein and Fearon. To estimate how mutational processes acting on the tumor genome change over time, we classified mutations in each sample according to three broad time periods (early clonal, late clonal, and subclonal), and quantified the activity of mutational signatures in each period. Most mutational processes appear to remain remarkably constant, however, certain signatures show clear and consistent changes during clonal evolution. Particularly, mutational signatures associated with exposure to carcinogens, such as smoking and UV light, tend to decrease over time. In contrast, signatures associated with defective endogenous processes, such as APOBEC mutagenesis and defective double strand break repair, show an increase between early and late phases of tumor evolution. Making use of clock-like mutational signatures, we converted mutational time estimates for large events, such as whole genome duplication (WGD), and the emergence of the most recent common ancestor (MRCA), into real time estimates, which allowed us to combine our analyses into overall timelines of cancer evolution, per tumor type. For example, the typical timeline of ovarian adenocarcinoma development shows that early tumor evolution is characterized by mutations in TP53, and widespread genome instability, with WGD events taking place on average 8 years prior to diagnosis. In later stages of evolution, signatures of defective repair processes increase, and the MRCA emerges on average 1 year before diagnosis. Taken together, these data reveal the common and divergent evolutionary trajectories available to a cancer, which might be crucial in understanding specific tumor biology, and in providing new opportunities for early detection and cancer prevention. Citation Format: Clemency Jolly, Moritz Gerstung, Ignaty Leshchiner, Stefan C. Dentro, Santiago Gonzalez, Thomas J. Mitchell, Yulia Rubanova, Pavana Anur, Daniel Rosebrock, Kaixian Yu, Maxime Tarabichi, Amit Deshwar, Jeff Wintersinger, Kortine Kleinheinz, Ignacio Vasquez-Garcia, Kerstin Haase, Subhajit Sengupta, Geoff Macintyre, Salem Malikic, Nilgun Donmez, Dimitri G. Livitz, Mark Cmero, Jonas Demeulemeester, Steve Schumacher, Yu Fan, Xiaotong Yao, Juhee Lee, Matthias Schlesner, Paul C. Boutros, David D. Bowtell, Hongtu Zhu, Gad Getz, Marcin Imielinski, Rameen Beroukhim, S Cenk Sahinalp, Yuan Ji, Martin Peifer, Florian Markowetz, Ville Mustonen, Ke Juan, Wenyi Wang, Quaid D. Morris, Paul T. Spellman, David C. Wedge, Peter Van Loo, PCAWG Evolution and Heterogeneity Working Group. The evolutionary history of 2,658 cancers [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2018; 2018 Apr 14-18; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2018;78(13 Suppl):Abstract nr 218.

We have characterised intra-tumour heterogeneity (ITH) across 2,778 whole genome sequences of tumours in the International Cancer Genome Consortium Pan-Cancer Analysis of Whole Genomes project, representing 36 distinct cancer types. We applied 6 copy number (CNA) callers and 11 subclonal reconstruction algorithms and developed approaches to integrate the results in robust, high-confidence CNA calls and subclonal architectures. The analysis reveals widespread ITH. We find at least one subclone in nearly all (96.7%) tumours with sufficient sequencing depth. Analysis using dN/dS ratios yields clear signs of positive selection in clonal and subclonal mutations and we find subclonal driver mutations in known driver genes. However, only 24% of subclones contain a driver mutation in a known driver gene, suggesting that a multitude of undiscovered late drivers exist and that tumours continue to undergo selection after tumourigenesis, at least until diagnosis. Consistent with other studies, we find that in 9% of tumours all clinically actionable mutations are subclonal, while 20% of tumours contain at least one subclonal actionable driver. These findings emphasise the relevance of ITH in treatment decision making. Distinct patterns of ITH emerge; for example, prostate, uterus and esophageal adenocarcinomas show high proportions of both subclonal single nucleotide variants (SNVs) and CNAs. Kidney chromophobe and pancreatic endocrine tumours also contain high proportions of subclonal SNVs, but few subclonal CNAs. On the other hand, hepatocellular carcinomas and head-and-neck and lung SCCs contain low proportions of subclonal SNVs and high proportions of subclonal CNAs. Mutational signature analysis reveals changes in signature activity. Exposures to UV light in melanomas and acid reflux in stomach and oesophageal cancers contribute more clonal mutations. While APOBEC and DNA damage repair response related signatures show increased activity in subclones. These findings highlight distinct evolutionary narratives between and within histologically distinct tumour types. Citation Format: Stefan Dentro, Ignaty Leshchiner, Kerstin Haase, Jeff Wintersinger, Amit Deshwar, Maxime Tarabichi, Yulia Rubanova, Kaixian Yu, Ignacio Vazquez Garcia, Geoff Macintyre, Kortine Kleinheinz, Dimitri Livitz, Salem Malikic, Nilgun Donmez, Subhajit Sengupta, Yuan Ji, Jonas Demeulemeester, Pavana Anur, Clemency Jolly, Marek Cmero, Daniel Rosebrock, Steve Schumacher, Yu Fan, Matthew Fittall, Xiaotong Yao, Juhee Lee, Matthias Schlesner, Hongtu Zhu, David Adams, Gad Getz, Paul Boutros, Marcin Imielinski, Rameen Beroukhim, Cenk Sahinalp, Martin Peifer, Inigo Martincorena, Florian Markowetz, Ville Mustonen, Ke Yuan, Moritz Gerstung, Wenyi Wang, Paul Spellman, Quaid Morris, David Wedge, Peter Van Loo. Pervasive intra-tumour heterogeneity and subclonal selection across cancer types [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 3000.

Ongoing cancer evolution gives rise to intra-tumour heterogeneity (ITH), which is a major mechanism of therapeutic resistance and therefore an important clinical challenge. However, the extent, origin and drivers of ITH across cancer types are poorly understood. Here, we extensively characterise ITH across 2,778 cancer whole genome sequences from 36 cancer types. We demonstrate that nearly all tumours (94.7%) with sufficient sequencing depth contain evidence of recent subclonal expansions, and that most cancer types show clear signs of positive selection in both clonal and subclonal protein coding variants. We find distinctive subclonal patterns of driver gene mutations, fusions, structural variation and copy-number alterations across cancer types. Dynamic, tumour type-specific changes of mutational processes between subclonal expansions shape differences between clonal and subclonal events. Our results underline the importance of ITH and its drivers in tumour evolution, and provide an unprecedented pan-cancer resource of extensively annotated subclonal events, laying a foundation for future cancer genomic studies.

High-throughput sequencing provides the means to determine the allelic decomposition for any gene of interest—the number of copies and the exact sequence content of each copy of a gene. Although many clinically and functionally important genes are highly polymorphic and have undergone structural alterations, no high-throughput sequencing data analysis tool has yet been designed to effectively solve the full allelic decomposition problem. Here we introduce a combinatorial optimization framework that successfully resolves this challenging problem, including for genes with structural alterations. We provide an associated computational tool Aldy that performs allelic decomposition of highly polymorphic, multi-copy genes through using whole or targeted genome sequencing data. For a large diverse sequencing data set, Aldy identifies multiple rare and novel alleles for several important pharmacogenes, significantly improving upon the accuracy and utility of current genotyping assays. As more data sets become available, we expect Aldy to become an essential component of genotyping toolkits. Many genes of functional and clinical significance are highly polymorphic and experience structural alterations. Here, Numanagić et al. develop Aldy, a computational tool for resolving the copy number and the sequence content of each copy of a gene by analyzing whole or targeted genome sequencing data.

Understanding the clonal architecture and evolutionary history of a tumour poses one of the key challenges to overcome treatment failure due to resistant cell populations. Previously, studies on subclonal tumour evolution have been primarily based on bulk sequencing and in some recent cases on single-cell sequencing data. Either data type alone has shortcomings with regard to this task, but methods integrating both data types have been lacking. Here, we present B-SCITE, the first computational approach that infers tumour phylogenies from combined single-cell and bulk sequencing data. Using a comprehensive set of simulated data, we show that B-SCITE systematically outperforms existing methods with respect to tree reconstruction accuracy and subclone identification. B-SCITE provides high-fidelity reconstructions even with a modest number of single cells and in cases where bulk allele frequencies are affected by copy number changes. On real tumour data, B-SCITE generated mutation histories show high concordance with expert generated trees. Intra-tumour heterogeneity provides important information about subclonal tumour evolution. Here, the authors develop B-SCITE, a computational method for inferring tumour phylogenies from combined single-cell and bulk sequencing data.