MuSiC: Identifying mutational significance in cancer genomes

Massively parallel sequencing technology and the associated rapidly decreasing sequencing costs have enabled systemic analyses of somatic mutations in large cohorts of cancer cases. Here we introduce a comprehensive mutational analysis pipeline that uses standardized sequence-based inputs along with multiple types of clinical data to establish correlations among mutation sites, affected genes and pathways, and to ultimately separate the commonly abundant passenger mutations from the truly significant events. In other words, we aim to determine the Mutational Significance in Cancer (MuSiC) for these large data sets. The integration of analytical operations in the MuSiC framework is widely applicable to a broad set of tumor types and offers the benefits of automation as well as standardization. Herein, we describe the computational structure and statistical underpinnings of the MuSiC pipeline and demonstrate its performance using 316 ovarian cancer samples from the TCGA ovarian cancer project. MuSiC correctly confirms many expected results, and identifies several potentially novel avenues for discovery

A framework for automated enrichment of functionally significant inverted repeats in whole genomes

<p>Abstract</p> <p>Background</p> <p>RNA transcripts from genomic sequences showing dyad symmetry typically adopt hairpin-like, cloverleaf, or similar structures that act as recognition sites for proteins. Such structures often are the precursors of non-coding RNA (ncRNA) sequences like microRNA (miRNA) and small-interfering RNA (siRNA) that have recently garnered more functional significance than in the past. Genomic DNA contains hundreds of thousands of such inverted repeats (IRs) with varying degrees of symmetry. But by collecting statistically significant information from a known set of ncRNA, we can sort these IRs into those that are likely to be functional.</p> <p>Results</p> <p>A novel method was developed to scan genomic DNA for partially symmetric inverted repeats and the resulting set was further refined to match miRNA precursors (pre-miRNA) with respect to their density of symmetry, statistical probability of the symmetry, length of stems in the predicted hairpin secondary structure, and the GC content of the stems. This method was applied on the <it>Arabidopsis thaliana</it> genome and validated against the set of 190 known Arabidopsis pre-miRNA in the miRBase database. A preliminary scan for IRs identified 186 of the known pre-miRNA but with 714700 pre-miRNA candidates. This large number of IRs was further refined to 483908 candidates with 183 pre-miRNA identified and further still to 165371 candidates with 171 pre-miRNA identified (i.e. with 90% of the known pre-miRNA retained).</p> <p>Conclusions</p> <p>165371 candidates for potentially functional miRNA is still too large a set to warrant wet lab analyses, such as northern blotting, on all of them. Hence additional filters are needed to further refine the number of candidates while still retaining most of the known miRNA. These include detection of promoters and terminators, homology analyses, location of candidate relative to coding regions, and better secondary structure prediction algorithms. The software developed is designed to easily accommodate such additional filters with a minimal experience in Perl.</p

Discovery and saturation analysis of cancer genes across 21 tumour types

Although a few cancer genes are mutated in a high proportion of tumours of a given type (>20%), most are mutated at intermediate frequencies (2–20%). To explore the feasibility of creating a comprehensive catalogue of cancer genes, we analysed somatic point mutations in exome sequences from 4,742 human cancers and their matched normal-tissue samples across 21 cancer types. We found that large-scale genomic analysis can identify nearly all known cancer genes in these tumour types. Our analysis also identified 33 genes that were not previously known to be significantly mutated in cancer, including genes related to proliferation, apoptosis, genome stability, chromatin regulation, immune evasion, RNA processing and protein homeostasis. Down-sampling analysis indicates that larger sample sizes will reveal many more genes mutated at clinically important frequencies. We estimate that near-saturation may be achieved with 600–5,000 samples per tumour type, depending on background mutation frequency. The results may help to guide the next stage of cancer genomics

Integrated analysis of germline and somatic variants in ovarian cancer

We report the first large-scale exome-wide analysis of the combined germline-somatic landscape in ovarian cancer. Here we analyze germline and somatic alterations in 429 ovarian carcinoma cases and 557 controls. We identify 3,635 high confidence, rare truncation and 22,953 missense variants with predicted functional impact. We find germline truncation variants and large deletions across Fanconi pathway genes in 20% of cases. Enrichment of rare truncations is shown in BRCA1, BRCA2, and PALB2. Additionally, we observe germline truncation variants in genes not previously associated with ovarian cancer susceptibility (NF1, MAP3K4, CDKN2B, and MLL3). Evidence for loss of heterozygosity was found in 100% and 76% of cases with germline BRCA1 and BRCA2 truncations respectively. Germline-somatic interaction analysis combined with extensive bioinformatics annotation identifies 237 candidate functional germline truncation and missense variants, including 2 pathogenic BRCA1 and 1 TP53 deleterious variants. Finally, integrated analyses of germline and somatic variants identify significantly altered pathways, including the Fanconi, MAPK, and MLL pathways

An Integrated TCGA Pan-Cancer Clinical Data Resource to Drive High-Quality Survival Outcome Analytics

For a decade, The Cancer Genome Atlas (TCGA) program collected clinicopathologic annotation data along with multi-platform molecular profiles of more than 11,000 human tumors across 33 different cancer types. TCGA clinical data contain key features representing the democratized nature of the data collection process. To ensure proper use of this large clinical dataset associated with genomic features, we developed a standardized dataset named the TCGA Pan-Cancer Clinical Data Resource (TCGA-CDR), which includes four major clinical outcome endpoints. In addition to detailing major challenges and statistical limitations encountered during the effort of integrating the acquired clinical data, we present a summary that includes endpoint usage recommendations for each cancer type. These TCGA-CDR findings appear to be consistent with cancer genomics studies independent of the TCGA effort and provide opportunities for investigating cancer biology using clinical correlates at an unprecedented scale. Analysis of clinicopathologic annotations for over 11,000 cancer patients in the TCGA program leads to the generation of TCGA Clinical Data Resource, which provides recommendations of clinical outcome endpoint usage for 33 cancer types

Pan-cancer Alterations of the MYC Oncogene and Its Proximal Network across the Cancer Genome Atlas

Although theMYConcogene has been implicated incancer, a systematic assessment of alterations ofMYC, related transcription factors, and co-regulatoryproteins, forming the proximal MYC network (PMN),across human cancers is lacking. Using computa-tional approaches, we define genomic and proteo-mic features associated with MYC and the PMNacross the 33 cancers of The Cancer Genome Atlas.Pan-cancer, 28% of all samples had at least one ofthe MYC paralogs amplified. In contrast, the MYCantagonists MGA and MNT were the most frequentlymutated or deleted members, proposing a roleas tumor suppressors.MYCalterations were mutu-ally exclusive withPIK3CA,PTEN,APC,orBRAFalterations, suggesting that MYC is a distinct onco-genic driver. Expression analysis revealed MYC-associated pathways in tumor subtypes, such asimmune response and growth factor signaling; chro-matin, translation, and DNA replication/repair wereconserved pan-cancer. This analysis reveals insightsinto MYC biology and is a reference for biomarkersand therapeutics for cancers with alterations ofMYC or the PMN

Pan-Cancer Analysis of lncRNA Regulation Supports Their Targeting of Cancer Genes in Each Tumor Context

Long noncoding RNAs (lncRNAs) are commonly dys-regulated in tumors, but only a handful are known toplay pathophysiological roles in cancer. We inferredlncRNAs that dysregulate cancer pathways, onco-genes, and tumor suppressors (cancer genes) bymodeling their effects on the activity of transcriptionfactors, RNA-binding proteins, and microRNAs in5,185 TCGA tumors and 1,019 ENCODE assays.Our predictions included hundreds of candidateonco- and tumor-suppressor lncRNAs (cancerlncRNAs) whose somatic alterations account for thedysregulation of dozens of cancer genes and path-ways in each of 14 tumor contexts. To demonstrateproof of concept, we showed that perturbations tar-geting OIP5-AS1 (an inferred tumor suppressor) andTUG1 and WT1-AS (inferred onco-lncRNAs) dysre-gulated cancer genes and altered proliferation ofbreast and gynecologic cancer cells. Our analysis in-dicates that, although most lncRNAs are dysregu-lated in a tumor-specific manner, some, includingOIP5-AS1, TUG1, NEAT1, MEG3, and TSIX, synergis-tically dysregulate cancer pathways in multiple tumorcontexts

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 smokin