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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.

Tumors with defective mismatch repair acquire large numbers of strand slippage mutations including frameshifts in coding sequence repeats. We identified a mutational hotspot, p.T204fs, in the insulator-binding protein (CTCF) in MSI-positive endometrial cancers. Although CTCF was described as a significantly mutated gene by the endometrial cancer TCGA, the A7 track variants leading to T204 frameshifts were not reported. Reanalysis of TCGA data using Pindel revealed frequent T204fs mutations, confirming CTCF is an MSI target gene and revealed the same frameshifts in tumors with intact mismatch repair. We show that T204fs transcripts are subject to nonsense-mediated decay and as such, T204fs mutations are unlikely to act as dominant negatives. The spectrum and pattern of mutations observed is consistent with CTCF acting as a haploinsufficient tumor suppressor.

Motivation: Microsatellite instability (MSI) is an important indicator of larger genome instability and has been linked to many genetic diseases, including Lynch syndrome. MSI status is also an independent prognostic factor for favorable survival in multiple cancer types, such as colorectal and endometrial. It also informs the choice of chemotherapeutic agents. However, the current PCR–electrophoresis-based detection procedure is laborious and time-consuming, often requiring visual inspection to categorize samples. We developed MSIsensor, a C++ program for automatically detecting somatic microsatellite changes. It computes length distributions of microsatellites per site in paired tumor and normal sequence data, subsequently using these to statistically compare observed distributions in both samples. Comprehensive testing indicates MSIsensor is an efficient and effective tool for deriving MSI status from standard tumor-normal paired sequence data. Availability and implementation: https://github.com/ding-lab/msisensor Contact: kye@genome.wustl.edu or lding@genome.wustl.edu Supplementary information: Supplementary data are available at Bioinformatics online.

Recent advancements in sequencing-based DNA methylation profiling methods provide an unprecedented opportunity to map complete DNA methylomes. These include whole-genome bisulfite sequencing (WGBS, MethylC-seq, or BS-seq), reduced-representation bisulfite sequencing (RRBS), and enrichment-based methods such as MeDIP-seq, MBD-seq, and MRE-seq. These methods yield largely comparable results but differ significantly in extent of genomic CpG coverage, resolution, quantitative accuracy, and cost, at least while using current algorithms to interrogate the data. None of these existing methods provides single-CpG resolution, comprehensive genome-wide coverage, and cost feasibility for a typical laboratory. We introduce methylCRF, a novel conditional random fields–based algorithm that integrates methylated DNA immunoprecipitation (MeDIP-seq) and methylation-sensitive restriction enzyme (MRE-seq) sequencing data to predict DNA methylation levels at single-CpG resolution. Our method is a combined computational and experimental strategy to produce DNA methylomes of all 28 million CpGs in the human genome for a fraction (

Sequences surrounding splice junctions function to recruit splicing elements to promote and suppress the inclusion of an adjacent exon. Mutations disrupting motifs around splice regions and the creation of de novo splice sites have not been functionally characterized genome-wide. Furthermore, many variants that alter the mRNA isoform have been shown to introduce splicing defects, such as premature termination codons (PTCs), which are degraded via nonsense-mediated decay (NMD). Using cancer samples across multiple cancer types, we have the unique opportunity to study the effects of somatic and germline events introduced throughout the genome and with the large dataset provided by The Cancer Genome Atlas (TCGA), we have the power to identify significant events that are influencing mRNA splicing and stability. In this study we aim to characterize splice altering variants (SAVs) and nonsense mutations that directly effect exon inclusion and mRNA degradation, respectively, utilizing the TCGA dataset consisting of both DNA-Seq (whole genome and/or exome sequencing) and RNA-seq from approximately 8,000 tumors representing 24 major cancer types. We have identified 23,615 somatic splice site mutations and 71,009 somatic nonsense mutations in 24 cancer types. For each variant, we have collected the following expression data derived from the RNA-seq: exon, splice junction, isoform, and gene; defining the expression signature of each variant. Expression signatures of known SAVs will facilitate the identification of novel and mis-classified missense and silent mutations affecting splicing. Exploring the distribution of mutations and their correlation with the aforementioned expression data will allow us to establish commonalities and differences that may be indicative of cancer type, molecular subtype, or clinical characteristics. Furthermore our position dependent analysis of nonsense mutations and their correlation with degradation will enhance our ability to determine significant sites influencing mRNA isoform presence. We hypothesize that some intronic/exonic variants outside the canonical splice site will influence exon inclusion and alter the ratio of mRNA isoforms present. Our analysis has identified a number of SAVs, including variants that were mis-classified as missense mutations such as c.190 in BRCA1. The germline coding mutation was found to strengthen a cryptic splice site and discovered to have a higher variant allele fraction in the tumor relative to the normal tissue in two ovarian samples. In conclusion our findings and the continued development of our project will contribute to improving current annotation methods and broaden our understanding of variants that affect splicing and their biological contribution to mRNA isoforms selection in the cell. Citation Format: Reyka G. Jayasinghe, Kuan-lin Huang, Jie Ning, Matthew Wyczalkowski, Charles Lu, Mingchao Xie, Michael Wendl, Michael McLellan, Kai Ye, Li Ding. Pan-Cancer analysis of the effects of splice-altering variants on mRNA splicing and stability. [abstract]. In: Proceedings of the 106th Annual Meeting of the American Association for Cancer Research; 2015 Apr 18-22; Philadelphia, PA. Philadelphia (PA): AACR; Cancer Res 2015;75(15 Suppl):Abstract nr 1929. doi:10.1158/1538-7445.AM2015-1929

A significant fraction of cancers have a heritable component, and require an interplay between somatic and germline variants. Common and rare germline variants have been investigated by previous GWAS and family based studies. However, a comprehensive analysis of both somatic and germline variants in cancer using high throughput sequencing data to discover genetic variants of functional relevance is lacking. Herein, we investigated the potential role of somatic and germline variants from over 20 major cancer types from large-scale studies such as TCGA and ICGC and discovered thousands of somatic and germline variants in cancer genomes. To link these genetic variants to cancer phenotypes, we analyzed the proteomics data in breast, ovarian and colorectal cancers generated by the Clinical Proteomic Tumour Analysis Consortium (CPTAC) using a PepScan pipeline that can detect whether a genomic variant is observed at the peptide level. The pipeline incorporated QUILTS to construct patient-specific protein database and MS-GF+ to identify peptide sequences in the database from MS spectra. Our analysis validated roughly 2% of non-synonymous genetic variants in peptides with matched spectra. We correlated the effect of genetic mutations on proteomic subtypes based on global protein expression levels. Additionally, we assessed the role of genetic variations in kinase genes using phosphoproteome profiles, and identified downstream markers that may be candidate targets for diagnosis or treatment. This analysis also helped us prioritize kinase variants that are likely functional candidates for experimental validation. In conclusion, the comprehensive study of genetic variants utilizing an integrated proteogenomic approach revealed genetic variants with potential functional impacts in cancer. Citation Format: Kuan-lin Huang, Jaiyin Wang, Song Cao, Mingchao Xie, Reyka Jayasinghe, Jie Ning, Michael McLellan, Michael Wendl, Adam Scott, Kimberly Johnson, Sherri Davies, David Fenyö, Reid Townsend, Feng Chen, Jeffrey Parvin, Matthew Ellis, Li Ding. Discovery and proteogenomic investigation of genetic variants in human cancers. [abstract]. In: Proceedings of the 106th Annual Meeting of the American Association for Cancer Research; 2015 Apr 18-22; Philadelphia, PA. Philadelphia (PA): AACR; Cancer Res 2015;75(15 Suppl):Abstract nr 1939. doi:10.1158/1538-7445.AM2015-1939

Each year in the United States, there are approximately 54,000 new cases of kidney and upper urinary tract cancers and 13,000 deaths. Renal clear cell carcinoma (RCC) is the most common type of kidney cancer comprising more than 75% of cases. It is known that certain genetic syndromes strongly increase risk for renal cell carcinoma including Von Hippel Lindau disease (VHL) that is caused by germline mutations in the VHL tumor suppressor gene. However, there are a large number of cases with familial RCC that are not explained by germline VHL mutations. Publically available data generated through large scale genomic sequencing projects such as The Cancer Genome Atlas (TCGA) provides new opportunities for identifying both known and novel germline cancer susceptibility variants. Moreover, the availability of matched tumor and normal genomic sequencing data from cancer patients (including RCCC) through TCGA allows for integrated analyses of germline and somatic interactions. Toward this end, we analyzed the TCGA exome sequencing data from 499 RCC cases. We employed GATK, VarScan, and Pindel tools to identify germline single nucleotide variants and indels and identified 10,492 candidate germline rare truncation variants (X truncation variants were in cancer genes). All the truncation candidates in commonly mutated cancer driver genes were manually reviewed, resulting in a total of 119 high confidence germline truncation variants in cancer genes. To date, we have identified one nonsense mutation in the VHL gene. In addition, there was one nonsense mutation and a frame-shift deletion in WRN and a nonsense and a splice-site mutation in BAP1, a recently identified RCC predisposition gene. Moreover, we also identified 6,079 candidate missense variants in known cancer driver genes, among a total of 179,066 rare missense variants. Future directions for this project will be to use the burden analysis to determine genes with significant enrichment for deleterious variants. We will also examine loss of heterozygosity patterns to evaluate germline somatic interactions. Citation Format: Jiayin Wang, Charles Lu, Mingchao Xie, Piyush Tripathi, Michael McLellan, Feng Chen, Kimberly J. Johnson, Li Ding. Integrated analysis of germline and somatic variants in renal clear cell carcinoma. [abstract]. In: Proceedings of the 105th Annual Meeting of the American Association for Cancer Research; 2014 Apr 5-9; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2014;74(19 Suppl):Abstract nr 3438. doi:10.1158/1538-7445.AM2014-3438

Carcinogenesis results from aberrations in gene regulatory networks at the genetic and epigenetic levels. Epigenetic changes such as global hypomethylation along with hypermethylated tumor-suppressor gene promoters are hallmarks of many cancers. Transposable elements (TEs) constitute the majority of non-coding sequence in the human genome and 50% of the total human genome sequence; they are thought to be suppressed by DNA methylation in somatic tissues. TEs often carry transcription factor binding sites and can function as transcriptional enhancers. Mis-regulation of DNA methylation can lead to abnormal gene activity from TE-based enhancers. Although many studies have profiled cancer DNA methylomes, there has not been a focused study to investigate the changes in TE DNA methylation in cancer and its effect on cancer gene expression. We hypothesize that mis-expression of genes from TE-based enhancers contributes to carcinogenesis. To test this hypothesis, we generated complete DNA methylomes of endometrial cancer samples and the matched normal tissue. We then used computational algorithms to identify differentially methylated transposon subfamilies. Our approach consists of using two complementary sequencing-based methods to map the DNA methylomes (MeDIP-seq and MRE-seq). To map unmethylated sites at single CpG resolution, we sequence DNA, digested by three methylation-sensitive restriction enzymes (MRE). We use the HpaII (C^CGG), Hin6I (G^CGC) and AciI (C^CGC) enzymes to access a very large fraction of potentially non-methylated CpG sites. In addition, to map methylated regions we use an anti-methylcytosine antibody to immunoprecipitate the methylated fraction of DNA fragments and then sequence them. Analyses of sequencing data, generally discards reads that map to repetitive regions in the genome, thus significantly reducing our ability to investigate TEs. Our computational approach, harnesses reads that map to repetitive regions by re-aligning them to TE subfamilies. We then measure the enrichment of reads for each TE subfamily from both the sequencing methods, in the cancer tissue compared to the matched normal tissue. Our findings reveal that, as expected, TEs in normal endometrial tissues are mostly methylated. However, contrary to the common belief, we found that majority of TEs remain methylated in endometrial cancer samples. A few dozen TE subfamilies, including several ERV/LTR subfamilies (endogeneous retrovirus) are demethylated. More interestingly, when we compare our three type I cancer samples with the three type II cancer samples, we detect a clear cancer type-specific, TE-subfamily-specific DNA demethylation pattern. This observation, coupled with the idea that different TE subfamilies may encode different enhancer elements, suggests that demethylated TEs may rewire cancer type-specific gene regulatory networks and contribute to cancer-specific phenotypes. Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 103rd Annual Meeting of the American Association for Cancer Research; 2012 Mar 31-Apr 4; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2012;72(8 Suppl):Abstract nr LB-379. doi:1538-7445.AM2012-LB-379