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Clear cell sarcoma (CCS) is a deadly malignancy affecting adolescents and young adults. It is characterized by reciprocal translocations resulting in expression of the chimeric EWSR1-ATF1 or EWSR1-CREB1 fusion proteins, driving sarcomagenesis. Besides these characteristics, CCS has remained genomically uncharacterized. Copy number analysis of human CCSs showed frequent amplifications of the MITF locus and chromosomes 7 and 8. Few alterations were shared with Ewing sarcoma or desmoplastic, small round cell tumors, which are other EWSR1-rearranged tumors. Exome sequencing in mouse tumors generated by expression of EWSR1-ATF1 from the Rosa26 locus demonstrated no other repeated pathogenic variants. Additionally, we generated a new CCS mouse by Cre-loxP-induced chromosomal translocation between Ewsr1 and Atf1, resulting in copy number loss of chromosome 6 and chromosome 15 instability, including amplification of a portion syntenic to human chromosome 8, surrounding Myc. Additional experiments in the Rosa26 conditional model demonstrated that Mitf or Myc can contribute to sarcomagenesis. Copy number observations in human tumors and genetic experiments in mice rendered, for the first time to our knowledge, a functional landscape of the CCS genome. These data advance efforts to understand the biology of CCS using innovative models that will eventually allow us to validate preclinical therapies necessary to achieve longer and better survival for young patients with this disease.

Purpose: Tumor genomic features have been of particular interest because of their potential impact on the tumor immune microenvironment and response to immunotherapy. Due to the substantial heterogeneity, an integrative approach incorporating diverse molecular features is needed to characterize immunologic features underlying primary resistance to immunotherapy and for the establishment of novel predictive biomarkers. Experimental Design: We developed a pan-cancer deep machine learning model integrating tumor mutation burden, microsatellite instability, and somatic copy-number alterations to classify tumors of different types into different genomic clusters, and assessed the immune microenvironment in each genomic cluster and the association of each genomic cluster with response to immunotherapy. Results: Our model grouped 8,646 tumors of 29 cancer types from The Cancer Genome Atlas into four genomic clusters. Analysis of RNA-sequencing data revealed distinct immune microenvironment in tumors of each genomic class. Furthermore, applying this model to tumors from two melanoma immunotherapy clinical cohorts demonstrated that patients with melanoma of different genomic classes achieved different benefit from immunotherapy. Interestingly, tumors in cluster 4 demonstrated a cold immune microenvironment and lack of benefit from immunotherapy despite high microsatellite instability burden. Conclusions: Our study provides a proof for principle that deep learning modeling may have the potential to discover intrinsic statistical cross-modality correlations of multifactorial input data to dissect the molecular mechanisms underlying primary resistance to immunotherapy, which likely involves multiple factors from both the tumor and host at different molecular levels.

Brain tumor segmentation plays an important role in assisting diagnosis, planning treatment and surgical navigation. In this paper, we propose a convolutional neural network-based learning transfer to support vector machine classifier method for brain tumor segmentation. Our algorithm is composed of two cascaded stages. In the first stage, we trained CNN to learn the mapping from the image space to the tumor label space. During the testing phase, we used the predicted label output from CNN and sent it along with the testing image to an SVM classifier for accurate segmentation. Then we iterate our deep CNN-SVM classifier. Experiments and comparisons demonstrate that the proposed framework is better than the separate SVM-based segmentation or CNN-based segmentation.

Synovial sarcomas are aggressive soft-tissue malignancies that express chromosomal translocation-generated fusion genes, SS18-SSX1 or SS18-SSX2 in most cases. Here, we report a mouse sarcoma model expressing SS18-SSX1, complementing our prior model expressing SS18-SSX2. Exome sequencing identified no recurrent secondary mutations in tumors of either genotype. Most of the few mutations identified in single tumors were present in genes that were minimally or not expressed in any of the tumors. Chromosome 6, either entirely or around the fusion gene expression locus, demonstrated a copy number gain in a majority of tumors of both genotypes. Thus, by fusion oncogene coding sequence alone, SS18-SSX1 and SS18-SSX2 can each drive comparable synovial sarcomagenesis, independent from other genetic drivers. SS18-SSX1 and SS18-SSX2 tumor transcriptomes demonstrated very few consistent differences overall. In direct tumorigenesis comparisons, SS18-SSX2 was slightly more sarcomagenic than SS18-SSX1, but equivalent in its generation of biphasic histologic features. Meta-analysis of human synovial sarcoma patient series identified two tumor-gentoype-phenotype correlations that were not modeled by the mice, namely a scarcity of male hosts and biphasic histologic features among SS18-SSX2 tumors. Re-analysis of human SS18-SSX1 and SS18-SSX2 tumor transcriptomes demonstrated very few consistent differences, but highlighted increased native SSX2 expression in SS18-SSX1 tumors. This suggests that the translocated locus may drive genotype-phenotype differences more than the coding sequence of the fusion gene created. Two possible roles for native SSX2 in synovial sarcomagenesis are explored. Thus, even specific partial failures of mouse genetic modeling can be instructive to human tumor biology.

Summary MIRMMR predicts microsatellite instability status in cancer samples using methylation and mutation information, in contrast to existing methods that rely on observed microsatellites. Additionally, MIRMMR highlights those genetic alterations contributing to microsatellite instability. Availability and implementation Source code is freely available at https://github.com/ding-lab/MIRMMR under the MIT license, implemented in R and supported on Unix/OS X operating systems. Supplementary information Supplementary data are available at Bioinformatics online.

Several genetic alterations characteristic of leukemia and lymphoma have been detected in the blood of individuals without apparent hematological malignancies. The Cancer Genome Atlas (TCGA) provides a unique resource for comprehensive discovery of mutations and genes in blood that may contribute to the clonal expansion of hematopoietic stem/progenitor cells. Here, we analyzed blood-derived sequence data from 2,728 individuals from TCGA and discovered 77 blood-specific mutations in cancer-associated genes, the majority being associated with advanced age. Remarkably, 83% of these mutations were from 19 leukemia and/or lymphoma-associated genes, and nine were recurrently mutated (DNMT3A, TET2, JAK2, ASXL1, TP53, GNAS, PPM1D, BCORL1 and SF3B1). We identified 14 additional mutations in a very small fraction of blood cells, possibly representing the earliest stages of clonal expansion in hematopoietic stem cells. Comparison of these findings to mutations in hematological malignancies identified several recurrently mutated genes that may be disease initiators. Our analyses show that the blood cells of more than 2% of individuals (5-6% of people older than 70 years) contain mutations that may represent premalignant events that cause clonal hematopoietic expansion.

We applied a newly developed bioinformatics system called VirusScan to investigate the viral basis of 6,813 human tumors and 559 adjacent normal samples across 23 cancer types and identified 505 virus positive samples with distinctive, organ system- and cancer type-specific distributions. We found that herpes viruses (e.g., subtypes HHV4, HHV5 and HHV6) that are highly prevalent across cancers of the digestive tract showed significantly higher abundances in tumor versus adjacent normal samples, supporting their association with these cancers. We also found three HPV16-positive samples in brain lower grade glioma (LGG). Further, recurrent HBV integration at the KMT2B locus is present in three liver tumors, but absent in their matched adjacent normal samples, indicating that viral integration induced host driver genetic alterations are required on top of viral oncogene expression for initiation and progression of liver hepatocellular carcinoma. Notably, viral integrations were found in many genes, including novel recurrent HPV integrations at PTPN13 in cervical cancer. Finally, we observed a set of HHV4 and HBV variants strongly associated with ethnic groups, likely due to viral sequence evolution under environmental influences. These findings provide important new insights into viral roles of tumor initiation and progression and potential new therapeutic targets.

Complex insertions and deletions (indels) are formed by simultaneously deleting and inserting DNA fragments of different sizes at a common genomic location. Here we present a systematic analysis of somatic complex indels in the coding sequences of samples from over 8,000 cancer cases using Pindel-C. We discovered 285 complex indels in cancer-associated genes (such as PIK3R1, TP53, ARID1A, GATA3 and KMT2D) in approximately 3.5% of cases analyzed; nearly all instances of complex indels were overlooked (81.1%) or misannotated (17.6%) in previous reports of 2,199 samples. In-frame complex indels are enriched in PIK3R1 and EGFR, whereas frameshifts are prevalent in VHL, GATA3, TP53, ARID1A, PTEN and ATRX. Furthermore, complex indels display strong tissue specificity (such as VHL in kidney cancer samples and GATA3 in breast cancer samples). Finally, structural analyses support findings of previously missed, but potentially druggable, mutations in the EGFR, MET and KIT oncogenes. This study indicates the critical importance of improving complex indel discovery and interpretation in medical research.

Large-scale cancer sequencing data enable discovery of rare germline cancer susceptibility variants. Here we systematically analyse 4,034 cases from The Cancer Genome Atlas cancer cases representing 12 cancer types. We find that the frequency of rare germline truncations in 114 cancer-susceptibility-associated genes varies widely, from 4% (acute myeloid leukaemia (AML)) to 19% (ovarian cancer), with a notably high frequency of 11% in stomach cancer. Burden testing identifies 13 cancer genes with significant enrichment of rare truncations, some associated with specific cancers (for example, RAD51C, PALB2 and MSH6 in AML, stomach and endometrial cancers, respectively). Significant, tumour-specific loss of heterozygosity occurs in nine genes (ATM, BAP1, BRCA1/2, BRIP1, FANCM, PALB2 and RAD51C/D). Moreover, our homology-directed repair assay of 68 BRCA1 rare missense variants supports the utility of allelic enrichment analysis for characterizing variants of unknown significance. The scale of this analysis and the somatic-germline integration enable the detection of rare variants that may affect individual susceptibility to tumour development, a critical step toward precision medicine., Published sequencing data sets of cancer samples could be used to identify genetic variants associated with the risk of developing cancer. Here, Lu et al. analyse over 4,000 tumour-normal pairs to reveal variable frequencies of inherited susceptibilities across 12 cancer types and find enrichment of functionally validated missense variants of unknown significance.

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.

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