Characterizing genetic intra-tumor heterogeneity across 2,658 human cancer genomes.

Intra-tumor heterogeneity (ITH) is a mechanism of therapeutic resistance and therefore an important clinical challenge. However, the extent, origin, and drivers of ITH across cancer types are poorly understood. To address this, we extensively characterize ITH across whole-genome sequences of 2,658 cancer samples spanning 38 cancer types. Nearly all informative samples (95.1%) contain evidence of distinct subclonal expansions with frequent branching relationships between subclones. We observe positive selection of subclonal driver mutations across most cancer types and identify cancer type-specific subclonal patterns of driver gene mutations, fusions, structural variants, and copy number alterations as well as dynamic changes in mutational processes between subclonal expansions. Our results underline the importance of ITH and its drivers in tumor evolution and provide a pan-cancer resource of comprehensively annotated subclonal events from whole-genome sequencing data.

Widespread and Functional RNA Circularization in Localized Prostate Cancer.

The cancer transcriptome is remarkably complex, including low-abundance transcripts, many not polyadenylated. To fully characterize the transcriptome of localized prostate cancer, we performed ultra-deep total RNA-seq on 144 tumors with rich clinical annotation. This revealed a linear transcriptomic subtype associated with the aggressive intraductal carcinoma sub-histology and a fusion profile that differentiates localized from metastatic disease. Analysis of back-splicing events showed widespread RNA circularization, with the average tumor expressing 7,232 circular RNAs (circRNAs). The degree of circRNA production was correlated to disease progression in multiple patient cohorts. Loss-of-function screening identified 11.3% of highly abundant circRNAs as essential for cell proliferation; for ∼90% of these, their parental linear transcripts were not essential. Individual circRNAs can have distinct functions, with circCSNK1G3 promoting cell growth by interacting with miR-181. These data advocate for adoption of ultra-deep RNA-seq without poly-A selection to interrogate both linear and circular transcriptomes.

The evolutionary history of 2,658 cancers.

Cancer develops through a process of somatic evolution1,2. Sequencing data from a single biopsy represent a snapshot of this process that can reveal the timing of specific genomic aberrations and the changing influence of mutational processes3. Here, by whole-genome sequencing analysis of 2,658 cancers 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)4, we reconstruct the life history and evolution of mutational processes and driver mutation sequences of 38 types of cancer. Early oncogenesis is characterized by mutations in a constrained set of driver genes, and specific copy number gains, such as trisomy 7 in glioblastoma and isochromosome 17q in medulloblastoma. The mutational spectrum changes significantly throughout tumour evolution in 40% of samples. A nearly fourfold diversification of driver genes and increased genomic instability are features of later stages. Copy number alterations often occur in mitotic crises, and lead to simultaneous gains of chromosomal segments. Timing analyses suggest that driver mutations often precede diagnosis by many years, if not decades. Together, these results determine the evolutionary trajectories of cancer, and highlight opportunities for early cancer detection.

Clonality inference in multiple tumor samples using phylogeny.

MOTIVATION: Intra-tumor heterogeneity presents itself through the evolution of subclones during cancer progression. Although recent research suggests that this heterogeneity has clinical implications, in silico determination of the clonal subpopulations remains a challenge. RESULTS: We address this problem through a novel combinatorial method, named clonality inference in tumors using phylogeny (CITUP), that infers clonal populations and their frequencies while satisfying phylogenetic constraints and is able to exploit data from multiple samples. Using simulated datasets and deep sequencing data from two cancer studies, we show that CITUP predicts clonal frequencies and the underlying phylogeny with high accuracy. AVAILABILITY AND IMPLEMENTATION: CITUP is freely available at: http://sourceforge.net/projects/citup/. CONTACT: cenk@sfu.ca SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

ORMAN: optimal resolution of ambiguous RNA-Seq multimappings in the presence of novel isoforms.

MOTIVATION: RNA-Seq technology is promising to uncover many novel alternative splicing events, gene fusions and other variations in RNA transcripts. For an accurate detection and quantification of transcripts, it is important to resolve the mapping ambiguity for those RNA-Seq reads that can be mapped to multiple loci: >17% of the reads from mouse RNA-Seq data and 50% of the reads from some plant RNA-Seq data have multiple mapping loci. In this study, we show how to resolve the mapping ambiguity in the presence of novel transcriptomic events such as exon skipping and novel indels towards accurate downstream analysis. We introduce ORMAN ( O ptimal R esolution of M ultimapping A mbiguity of R N A-Seq Reads), which aims to compute the minimum number of potential transcript products for each gene and to assign each multimapping read to one of these transcripts based on the estimated distribution of the region covering the read. ORMAN achieves this objective through a combinatorial optimization formulation, which is solved through well-known approximation algorithms, integer linear programs and heuristics. RESULTS: On a simulated RNA-Seq dataset including a random subset of transcripts from the UCSC database, the performance of several state-of-the-art methods for identifying and quantifying novel transcripts, such as Cufflinks, IsoLasso and CLIIQ, is significantly improved through the use of ORMAN. Furthermore, in an experiment using real RNA-Seq reads, we show that ORMAN is able to resolve multimapping to produce coverage values that are similar to the original distribution, even in genes with highly non-uniform coverage. AVAILABILITY: ORMAN is available at http://orman.sf.net

SCARPA: scaffolding reads with practical algorithms.

MOTIVATION: Scaffolding is the process of ordering and orienting contigs produced during genome assembly. Accurate scaffolding is essential for finishing draft assemblies, as it facilitates the costly and laborious procedures needed to fill in the gaps between contigs. Conventional formulations of the scaffolding problem are intractable, and most scaffolding programs rely on heuristic or approximate solutions, with potentially exponential running time. RESULTS: We present SCARPA, a novel scaffolder, which combines fixed-parameter tractable and bounded algorithms with Linear Programming to produce near-optimal scaffolds. We test SCARPA on real datasets in addition to a simulated diploid genome and compare its performance with several state-of-the-art scaffolders. We show that SCARPA produces longer or similar length scaffolds that are highly accurate compared with other scaffolders. SCARPA is also capable of detecting misassembled contigs and reports them during scaffolding. AVAILABILITY: SCARPA is open source and available from http://compbio.cs.toronto.edu/scarpa.

Polymorphism due to multiple amino acid substitutions at a codon site within Ciona savignyi.

We compared two haploid genotypes of one Ciona savignyi individual and identified codons at which these genotypes differ by two nonsynonymous substitutions. Using the C. intestinalis genome as an outgroup, we showed that both substitutions tend to occur in the same genotype. Only in 53 (34.4%) of 154 codons, one substitution occurred in each of the two genotypes, although 77 (50%) of such codons are to be expected if substitutions were independent. We considered two feasible evolutionary causes for the observed pattern: substitutions driven by positive selection and compensatory substitutions, as well as several potential biases. However, none of these explanations is fully compelling, and data on multiple genotypes of C. savignyi would help to elucidate the causes of this pattern.

The Proteogenomic Landscape of Curable Prostate Cancer.

DNA sequencing has identified recurrent mutations that drive the aggressiveness of prostate cancers. Surprisingly, the influence of genomic, epigenomic, and transcriptomic dysregulation on the tumor proteome remains poorly understood. We profiled the genomes, epigenomes, transcriptomes, and proteomes of 76 localized, intermediate-risk prostate cancers. We discovered that the genomic subtypes of prostate cancer converge on five proteomic subtypes, with distinct clinical trajectories. ETS fusions, the most common alteration in prostate tumors, affect different genes and pathways in the proteome and transcriptome. Globally, mRNA abundance changes explain only ∼10% of protein abundance variability. As a result, prognostic biomarkers combining genomic or epigenomic features with proteomic ones significantly outperform biomarkers comprised of a single data type.

Clonality Inference from Single Tumor Samples Using Low-Coverage Sequence Data.

Inference of intra-tumor heterogeneity can provide valuable insight into cancer evolution. Somatic mutations detected by sequencing can help estimate the purity of a tumor sample and reconstruct its subclonal composition. Although several methods have been developed to infer intra-tumor heterogeneity, the majority of these tools rely on variant allele frequencies as estimated via ultra-deep sequencing from multiple samples of the same tumor. In practice, obtaining sequencing data from a large number of samples per patient is only feasible in a few cancer types such as liquid tumors, or in rare cases involving solid tumors selected for research. We introduce CTPsingle, which aims at inferring the subclonal composition by using low-coverage sequencing data from a single tumor sample. We show that CTPsingle is able to infer the purity and the clonality of single-sample tumors with high accuracy, even restricted to a coverage depth of ∼30 × .

SRRM4 Drives Neuroendocrine Transdifferentiation of Prostate Adenocarcinoma Under Androgen Receptor Pathway Inhibition.

BACKGROUND: Neuroendocrine prostate cancer (NEPC) is an aggressive subtype of castration-resistant prostate cancer that typically does not respond to androgen receptor pathway inhibition (ARPI), and its diagnosis is increasing. OBJECTIVE: To understand how NEPC develops and to identify driver genes to inform therapy for NEPC prevention. DESIGN, SETTING, AND PARTICIPANTS: Whole-transcriptome sequencing data were extracted from prostate tumors from two independent cohorts: The Beltran cohort contained 27 adenocarcinoma and five NEPC patient samples, and the Vancouver Prostate Centre cohort contained three patient samples and nine patient-derived xenografts. INTERVENTION: A novel bioinformatics tool, comparative alternative splicing detection (COMPAS), was invented to analyze alternative RNA splicing on RNA-sequencing data. OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS: COMPAS identified potential driver genes for NEPC development. Biochemical and biological validations were performed in both prostate cell and tumor models. RESULTS AND LIMITATION: More than 66% of the splice events were predicted to be regulated by the RNA splicing factor serine/arginine repetitive matrix 4 (SRRM4). In vitro and in vivo evidence confirmed that one SRRM4 target gene was the RE1 silencing transcription factor (REST), a master regulator of neurogenesis. Moreover, SRRM4 strongly stimulated adenocarcinoma cells to express NEPC biomarkers, and this effect was exacerbated by ARPI. ARPI combined with a gain of SRRM4-induced adenocarcinoma cells to assume multicellular spheroid morphology and was essential in establishing progressive NEPC xenografts. These SRRM4 actions were further enhanced by loss of function of TP53. CONCLUSIONS: SRRM4 drives NEPC progression. This knowledge may guide the development of novel therapeutics aimed at NEPC. PATIENT SUMMARY: Using next-generation RNA sequencing and our newly developed bioinformatics tool, we identified a neuroendocrine prostate cancer (NEPC)-specific RNA splicing signature that is predominantly controlled by serine/arginine repetitive matrix 4 (SRRM4). We confirmed that SRRM4 drives NEPC progression, and we propose SRRM4 as a potential therapeutic target for NEPC.