Cancer Genomic Datasets Research Articles

Abstract 2584: The UCSC Xena system for cancer genomics data visualization and interpretation

Abstract The UCSC Xena platform (http://xena.ucsc.edu/) allows biologists and bioinformaticians to securely analyze and visualize their private functional genomics data in the context of public genomic and clinical data sets. The Xena platform consists of a set of federated data hubs and the Xena browser, which integrates across hubs, providing one location to analyze and visualize all data. Our expanding public Xena Data Hubs currently hosts 1400+ data sets from more than 35 cancer types, as well as Pan-Cancer data sets. Our public data hubs serve seminal cancer genomics and functional genomics data set to the scientific community, including the latest TCGA, TARGET, ICGC, and GTEx data sets. We support most data types including somatic and germline SNPs, INDELs, large structural variants, CNV, gene-, transcript-, exon- protein-, miRNA-expression, DNA methylation, phenotypes, clinical data, subtype classifications and genomic biomarkers. Additionally, investigators’ own functional genomics data can be hosted on private hubs running on their laptop or behind the firewall. Data is integrated on the UCSC Xena Browser, allowing biologists to view and interpretation of their genomic data in the context of a large collection of cancer genomics data sets such as TCGA. The lightweight Xena data hubs are straightforward to install on Windows, Mac and Linux operating systems and loading data is easy using either our application or command line interface. This system of the browser and hubs helps researchers combine new or preliminary results from their laptops or internal servers, or even data from a new paper, securely with vetted data from the public sphere. Visualizations and analyses include dynamic Kaplan-Meier survival analysis to assess survival stratification by any information in addition to our visual spreadsheet, scatter plots and bar graphs. We seek feedback at our poster on new visualizations and functionalities. Citation Format: Mary Goldman, Brian Craft, Jingchun Zhu, David Haussler. The UCSC Xena system for cancer genomics data visualization and interpretation [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2017; 2017 Apr 1-5; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2017;77(13 Suppl):Abstract nr 2584. doi:10.1158/1538-7445.AM2017-2584

Abstract 385: Network-driven discovery of cancer drivers and pathways using 2,500 whole cancer genomes

Abstract Cancer driver genes exhibit unexpectedly high mutation rates in large cancer genomic datasets. We hypothesize that driver mutations specifically alter molecular interaction networks by disrupting “active sites” - interaction interfaces in proteins and DNA. We present ActiveDriverWGS, a novel computational method to discover cancer drivers in whole-genome sequencing (WGS) data. ActiveDriverWGS finds genome regions that are significantly enriched in somatic single nucleotide variants (SNVs) and indels and ascertains whether these associate to known active sites. Analysis of active sites allows us to predict the mechanisms of mutation on three layers of the central dogma: regulatory DNA with transcription factor (TF) binding sites (TFBS), mRNA with microRNA binding sites in untranslated regions (UTRs), and post-translational modification (PTM) sites in proteins. To discover cancer driver genes and pathways, we analysed the WGS dataset of >2,500 samples from the International Cancer Genome Consortium (ICGC) Pan-Cancer Analysis Working Group (PCAWG). We found 61 protein-coding candidates with 34 known drivers (P=10-40), validating the high accuracy of our method. 40 genes have significant mutations of PTM sites, suggesting that rewiring of PTM signalling networks is a common oncogenic mechanism. For example, the BRAF V600E SNV flanks two phosphorylation sites and one ubiquitination site (FDR P=10-44), a novel interpretation and potential avenue for precision therapies targeting the kinase and ubiquitin network of BRAF. In the non-coding genome, we detected known lncRNAs (NEAT1, MALAT1), promoters (TERT, WDR74) and novel candidates with mutation enrichment. For example, an enhancer on chr6 has a mutation hotspot in 33 patients (FDR P=10-19), with 20 SNVs affecting binding motifs of cancer-associated TFs FOXO3, SOX2, HMGA2 (FDR P=10-10). Thus our method discovers non-coding drivers and their candidate mechanisms in a single analysis. Our ActiveDriverPW method extends coding and non-coding mutations to biological pathways. We found >600 mutation-enriched pathways in the PCAWG pan-cancer dataset. Of these ~200 are also significant when only non-coding mutations are analysed, showing that the non-coding genome includes previously unstudied mutations in pathways. The DNA double-strand break response pathway (FDR p=10-10) includes non-coding SNVs in ~20 histones and chromatin modifiers, such as the demethylase KDM4B with 46 SNVs in its promoter and enhancers. ActiveDriverPW maps mutations of the long tail that affect genes in hallmark cancer processes yet remain undiscovered in gene-focused analyses. Our methods accurately capture known drivers in the ICGC-PCAWG dataset and suggest specific mechanistic details. Our benchmarks also emphasize the robust performance of our methods. ActiveDriverWGS and ActiveDriverPW are valuable additions to the toolbox for cancer genome analysis. Citation Format: Jüri Reimand. Network-driven discovery of cancer drivers and pathways using 2,500 whole cancer genomes [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2017; 2017 Apr 1-5; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2017;77(13 Suppl):Abstract nr 385. doi:10.1158/1538-7445.AM2017-385

Abstract LB-338: A critical evaluation of genomic data sharing: Barriers to accessing pediatric cancer genomic datasets: a Treehouse Childhood Cancer Initiative experience

Abstract Genomic data sharing is increasingly recognized as critical to genomic research. The need is acute in pediatric cancer research due to the rarity of pediatric tumor types and paucity of pediatric cancer data, and in translational research to assess the impact of genomic research on human health. However, genomic data sharing is hindered by an absence of standards regarding timing, patient privacy, use agreement standards, and data characterization and quality. At UC Santa Cruz Treehouse Childhood Cancer Initiative (treehousegenomics.soe.ucsc.edu), we examine individual pediatric cancer tumor RNA sequencing profiles against a database of over 11,000 tumor RNA sequencing profiles from public genomic datasets such as The Cancer Genome Atlas, Therapeutically Applicable Research To Generate Effective Treatments, International Cancer Genome Consortium, and Medulloblastoma Advanced Genomics International, and pediatric cancer clinical trials with which we partner, such as those at Stanford University, UC San Francisco, Children’s Hospital of Orange County, and British Columbia Children’s Hospital. For over 18 months, we have worked systematically to enhance the Treehouse dataset by adding pediatric cancer data and presently underrepresented tumor types. The NIH and other leading funding agencies now regularly require grantees to make genomic data generated available to the research community, either post-publication or after an embargo period. We have combed websites and public repositories, searched PubMed, and contacted researchers directly. Finding data requires a mining of literature, often with limited information, and initiating the many different processes for requesting permission for these datasets, with different and often cumbersome data use obligations. The combination of cryptically named datasets, multiple data types and the practice of grouping datasets from multiple papers under a single study accession makes zeroing in on the correct dataset challenging. Downloading the genomic data is time-consuming, such that a dataset of under a 100 files can take up to a week to download under optimal conditions. Matching metadata is inconsistently available, often vague, sparse or error ridden. Only after months of identifying, permissioning for use, committing to use- and sharing-restricting terms, and downloading the genomic and metadata, is it possible to assess the quality, often discovering that data quality is low. We evaluate the barriers to data sharing based on the Treehouse experience and offer guidelines for timing, use agreement standards, and data characterization and quality, to enhance data sharing and outcomes for all pediatric cancer patients. Citation Format: Katrina Learned, Ann Durbin, Robert Currie, Holly Beale, Du Linh Lam, Theodore Goldstein, Sofie R. Salama, David Haussler, Olena Morozova, Isabel Bjork. A critical evaluation of genomic data sharing: Barriers to accessing pediatric cancer genomic datasets: a Treehouse Childhood Cancer Initiative experience [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2017; 2017 Apr 1-5; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2017;77(13 Suppl):Abstract nr LB-338. doi:10.1158/1538-7445.AM2017-LB-338

Landscape of Combination Immunotherapy and Targeted Therapy to Improve Cancer Management.

Cancer treatments composed of immune checkpoint inhibitors and oncogene-targeted drugs might improve cancer management, but there has been little investigation of their combined potential as yet. To estimate the fraction of cancer cases that might benefit from such combination therapy, we conducted an exploratory study of cancer genomic datasets to determine the proportion with somatic mutation profiles amenable to either immunotherapy or targeted therapy. We surveyed 13,349 genomic profiles from public databases for cases with specific mutations targeted by current agents or a burden of exome-wide nonsynonymous mutations (NsM) that exceed a proposed threshold for response to checkpoint inhibitors. Overall, 8.9% of cases displayed profiles that could benefit from combination therapy, which corresponded to approximately 11.2% of U.S. annual incident cancer cases. Frequently targetable mutations were in PIK3CA, BRAF, NF1, NRAS, and PTEN We also noted a high burden of NsM in cases with targetable mutations in SMO, DDR2, FGFR1, PTCH1, FGFR2, and MET Our results indicate that a significant proportion of solid tumor patients are eligible for immuno-targeted combination therapy, and they suggest prioritizing specific cancers for trials of certain targeted and checkpoint inhibitor drugs. Cancer Res; 77(13); 3666-71. ©2017 AACR.

Abstract MIP-058: YAP, VIA INTERACTIONS WITH THE FGF SIGNALING PATHWAY, REGULATES TUMORIGENESIS OF FALLOPIAN TUBE SECRETORY EPITHELIAL CELLS

Abstract INTRODUCTION: Accumulating evidence indicates that ovarian high-grade serous carcinoma (HGSC) originates from Fallopian tube secretory epithelial cells (FTSECs). Our recent studies indicated that the Hippo/YAP signaling pathway contributes to transformation of ovarian surface epithelial cells. However, whether this pathway is also involved in the initiation and progression of FTSEC-derived ovarian HGSC has not been examined. AIMS: The present study aims to determine whether the Hippo/YAP signaling pathway plays a role in initiation and progression of Fallopian tube-derived HGSC and the potential mechanism(s) by which YAP regulates FTSEC tumorigenesis. RESULTS: Immunohistochemical analysis showed that YAP expression was very low in normal human fallopian tube and ovarian tissues. However, YAP immunosignals significantly increased in ovarian HGSCs, cancerous and inflammatory fallopian tube tissues. Data extracted from multidimensional human cancer genomics datasets revealed that the YAP and TEADs (the major target transcription factors of YAP) genes are frequently amplified and/or up-regulated, while the upstream tumor suppressor genes such as NF1 and LATS1, are frequently deleted and/or mutated in ovarian HGSC tissues. Overexpression of YAP in immortalized non-tumorigenic FTSECs promoted cell proliferation in both 2D and 3D culture systems, induced colony formation in soft agar, and drove tumorigenesis in athymic nude mice. Pathohistological and biochemical analyses clearly indicated that tumors derived from the FTSECs resembled ovarian HGSC. In addition, our mechanistic studies showed that activation of YAP in human FTSECs upregulated the expression of fibroblast growth factors (FGF1, FGF2) and their receptors (FGFR1, FGFR2, FGFR3 and FGFR4). Intriguingly, we found that the activated FGF signaling in turn suppressed the Hippo pathway and activated YAP protein. Consistent with this result, treatment of FTSECs with YAP antagonist (Verteporfin) and/or FGFR inhibitor (BGI398) not only blocked YAP-induced FTSECs transformation, but also suppressed tumor cell growth and induced tumor cell death in a human HGSC xenograft model. CONCLUSION: Our results demonstrate that the disruption of the Hippo/YAP signaling pathway contributes to the carcinogenesis of the fallopian tube secretory epithelial cells. The Hippo/YAP and FGF signaling pathways formed an autocrine/paracrine positive feedback loop to drive the progression of the FTSECs-derived HGSC. Evidence in this study strongly suggests that combined targeting of YAP and FGFRs represents a novel therapeutic strategy for ovarian HGSCs. Citation Format: Chunbo He, Guohua Hua, Xiangmin Lv, Zhengfeng Wang, Ronny I. Drapkin, John S. Davis, Cheng Wang. YAP, VIA INTERACTIONS WITH THE FGF SIGNALING PATHWAY, REGULATES TUMORIGENESIS OF FALLOPIAN TUBE SECRETORY EPITHELIAL CELLS [abstract]. In: Proceedings of the 11th Biennial Ovarian Cancer Research Symposium; Sep 12-13, 2016; Seattle, WA. Philadelphia (PA): AACR; Clin Cancer Res 2017;23(11 Suppl):Abstract nr MIP-058.

Inferring network structure in non-normal and mixed discrete-continuous genomic data.

Inferring dependence structure through undirected graphs is crucial for uncovering the major modes of multivariate interaction among high-dimensional genomic markers that are potentially associated with cancer. Traditionally, conditional independence has been studied using sparse Gaussian graphical models for continuous data and sparse Ising models for discrete data. However, there are two clear situations when these approaches are inadequate. The first occurs when the data are continuous but display non-normal marginal behavior such as heavy tails or skewness, rendering an assumption of normality inappropriate. The second occurs when a part of the data is ordinal or discrete (e.g., presence or absence of a mutation) and the other part is continuous (e.g., expression levels of genes or proteins). In this case, the existing Bayesian approaches typically employ a latent variable framework for the discrete part that precludes inferring conditional independence among the data that are actually observed. The current article overcomes these two challenges in a unified framework using Gaussian scale mixtures. Our framework is able to handle continuous data that are not normal and data that are of mixed continuous and discrete nature, while still being able to infer a sparse conditional sign independence structure among the observed data. Extensive performance comparison in simulations with alternative techniques and an analysis of a real cancer genomics data set demonstrate the effectiveness of the proposed approach.

Sequencing Errors Rife in Genome Databases.

Low-frequency genetic variants in cancer genome datasets are often simply artifacts of DNA damage introduced by routine sample preparation, not tumor-driving mutations. A new algorithm found that around three quarters of all the samples in The Cancer Genome Atlas contain large numbers of these sequencing errors.

Cancer therapeutic target genes identified on chromosome 20q

Cancer therapeutic target genes identified on chromosome 20q

Abstract 847: Molecular characterization of breast tumor T-cell infiltration in exome datasets

Abstract Tumors are heterogeneous, containing tumor-infiltrating lymphocytes (TILs) which presence has been associated with favorable prognosis in multiple tumor types. Importantly, most of the T-cell specificity for tumor neo-antigen lies in a unique sequence of DNA (CDR3) resulting from the recombination of the V, D, and J segments of the T-cell receptor beta (TCRB clonotype). The Cancer Genome Atlas (TCGA) is the most comprehensive molecular dataset of tumors but does not include detailed information about TILs or tumor immunity in general. In order to characterize the T-cell infiltration in public cancer genomic datasets, we hypothesize that such a unique CDR3 DNA fragment can be recovered from bulk tumor genome-wide DNA or RNA sequencing and that the information can be used for clinical association studies. We first establish the feasibility of the approach by comparing the deep CDR3 sequencing (ImmunoSeq) and CDR3 reads recovered in tumor exome data. The clonotypes were detected using three CDR3 read detection tools and results were compared to the ImmunoSeq results. Each tool identifies between 10 and 38 reads. As expected, most of the CDR3 reads are misaligned to the naïve reference genome (60% clipped). Across all three tools, we identify 26 unique clonotypes, 15 of which are detected in the ImmunoSeq experiment. IMSEQ shows the strongest overlap with the other tools (13/14 clonotypes) and with ImmunoSeq (9/14 clonotypes) showing that our strategy identifies bona fide CDR3 reads from exome datasets. We applied the approach to a cohort of 1,078 TCGA samples. We identified CDR3 reads in 473 samples which are more likely to have a higher fraction of TILs as detected by histological review (p = 1.14×10-8). Using the RNA-seq datasets, 906 samples had CDR3 reads and also associated with higher TIL content (p = 4.41×10-6). Tumors with CDR3 reads in both DNA and RNA datasets tend have a higher TIL fraction. A set of 66 tumors shares a unique TCRâ clonotype with however no association with tumor subtypes or HLA haplotype. When restricted to Her2+ tumors, high CDR3 read counts, but not high fraction of TILs, was associated with better overall survival (p = 0.016), suggesting that CDR3 reads are predictive of anti-Her2 response. In contrast, the presence of CDR3 reads in Her2- tumors is not prognostic. A detailed analysis of 22 immune meta-gene expression signatures in these samples complements CDR3 reads information and suggests mechanisms of immune-tolerance. Our results demonstrate the feasibility of identifying genetic evidence of T-cells in bulk tumor samples using deep sequencing, providing information about the T-cell abundance and clonal identity. This novel type of tumor immune biomarker enriches the comprehensive dataset collected through TCGA, and reveals correlations with other molecular features and clinical outcomes. Citation Format: Olivier Harismendy, Eric D. Levy, Valentine Garate-Calderon, Brian Woo, Ricardo Armisen. Molecular characterization of breast tumor T-cell infiltration in exome datasets. [abstract]. In: Proceedings of the 107th Annual Meeting of the American Association for Cancer Research; 2016 Apr 16-20; New Orleans, LA. Philadelphia (PA): AACR; Cancer Res 2016;76(14 Suppl):Abstract nr 847.

NIH creates unified data-sharing system

Vice President Joe Biden last week announced the launch of the Genomic Data Commons (GDC). The National Cancer Institute project will create a unified system that will allow scientists to share genomic and clinical trial data. GDC is part of both the Precision Medicine Initiative and the National Cancer Moonshot Initiative, which Biden is leading. “Increasing the pool of researchers who can access data and decreasing the time it takes for them to review and find new patterns in that data is critical to speeding up development of lifesaving treatments for patients,” says Biden, whose son died from brain cancer. Operated by the University of Chicago, GDC will standardize and centralize data from large-scale cancer research programs such as the Cancer Genome Atlas and the Therapeutically Applicable Research to Generate Effective Treatments programs, which together comprise cancer genomic data sets from 12,000 patients. Other researchers will be able to submit

Glutamate E15 and E171 are Hotspots in p60TRP-Related Cancer

ABSTRACTRAS protein is a small G protein linked to multiple G protein-coupled receptor (GPCR) signaling cascades and is responsible for various types of cancer, but to this day, Ras is considered “undruggable.” Multiple alternative regulators of G protein signaling (RGS) pathways have become the focus of ongoing efforts to identify new cancer therapeutics. We analyzed human cancer genome datasets and describe p60TRP, a recently identified GPCR-associated sorting protein (GPRASP), and its role in various types of cancer. We found that some regions of p60TRP were more prone to specific mutations, with two hotspots for mutations at E15 and E171.

Cascade: an RNA-seq visualization tool for cancer genomics.

BackgroundCancer genomics projects are producing ever-increasing amounts of rich and diverse data from patient samples. The ability to easily visualize this data in an integrated an intuitive way is currently limited by the current software available. As a result, users typically must use several different tools to view the different data types for their cohort, making it difficult to have a simple unified view of their data.ResultsHere we present Cascade, a novel web based tool for the intuitive 3D visualization of RNA-seq data from cancer genomics experiments. The Cascade viewer allows multiple data types (e.g. mutation, gene expression, alternative splicing frequency) to be simultaneously displayed, allowing a simplified view of the data in a way that is tuneable based on user specified parameters. The main webpage of Cascade provides a primary view of user data which is overlaid onto known biological pathways that are either predefined or added by users. A space-saving menu for data selection and parameter adjustment allows users to access an underlying MySQL database and customize the features presented in the main view.ConclusionsThere is currently a pressing need for new software tools to allow researchers to easily explore large cancer genomics datasets and generate hypotheses. Cascade represents a simple yet intuitive interface for data visualization that is both scalable and customizable.Electronic supplementary materialThe online version of this article (doi:10.1186/s12864-016-2389-8) contains supplementary material, which is available to authorized users.

Abstract B101: Molecular pathology of patient derived xenografts

Abstract Background. The Cancer Genome Atlas (TCGA) and many other genomic profiling of patient samples generated abundant human cancer genomic datasets of various histological types and individual patients, enabling development of molecular pathology methodology per big data fashion1-4. Patient derived xenograft (PDX) is believed to mimic patient tumors with anecdotal data, and assumed to be predictive experimental models (avatar) for human trial. There is also a need to investigate tumor pathology among different histopathology types, either patient or xenograft diseases, per “big data” approach. Methods. We first systematically compared transcriptome expression similarity within and between specific histological types using either global or specific subsets of genes using the datasets from TCGA. We next investigated PDXs (also cell lines) using the same systemic approaches. Results. We demonstrated the presence of highly consistent correlation in transcriptome expression within a given histology types, but more distinct between the types, establishing molecular specificity alternative to histological type based on morphology, or an alternative diagnostic approach. We performed the same systematic examination of PDXs of different histological types corresponding to the TCGA types. The similar general pattern of similarity, also somewhat reduced in degree, have been observed. These suggest the high relevance of PDX to human cancer tissues in general from “big data” perspective. In contrast, this seems generally untrue for cancer cell lines: quite similar among themselves even for different histological types, but less correlation to either TCGA or PDXs per disease type. The similarity among cell lines of all types may suggest a “new type of cancer, or tissue culture type”. Our studies also revealed certain specific correlations, with several examples: 1) colon and rectal adenocarcinoma are highly correlated, without apparent difference; 2) NSCLC SCC are quite different from NSCLC ADC, as seen morphologically; 3) NSCLC SCC are also quite closely related to HNSCC. Conclusion. There are generally good correlation between transcriptome expression based molecular pathology and histopathology; the molecular pathology of PDXs are generally similar to that of human's, not so to human cell lines.

Abstract B1-07: Using the UCSC Xena Platform to integrate, visualize, and analyze your own data in the context of large external genomic datasets

Abstract With the advent of cancer genome analysis, there is an enormous need for an integrative computational approach to understand the functional impact of the genomic aberrations that drive and characterize cancers. This requires mechanisms to aggregate and visualize both public and investigator-generated data on cancer genomes, transcriptomes, epigenomes and more. Extending the UCSC Cancer Genomics Browser, we are developing the UCSC Xena platform to achieve this. UCSC's Xena is a data server-based platform that stores functional genomics data and serves them in response to data requests in real-time and with minimal informatics overhead. Examples of these data requests include data visualization, integration and further downstream analysis. Xena can easily be installed on a laptop, or on servers behind a firewall. The UCSC Xena server provides access to TCGA open access data, with 526 datasets from 31 different TCGA cancer types. Types of hosted datasets include copy number, somatic mutation, DNA methylation, gene and exon expression, protein expression, PARADIGM pathway inference, and phenotype data. Our automated pipeline updates TCGA data periodically, ensuring we are visualizing the most recent data available. Additionally, our pipeline ingests TCGA phenotype data and attempts to assign more readable feature names and values. We further derive overall and recurrence free survival from TCGA phenotype data, allowing users to perform survival analysis. We are extending the UCSC Cancer Genomics Browser to access and visualize data hosted across multiple Xena servers while maintaining data privacy. This functionality allows viewing and interpretation of one's own data (e.g. stored on a private Xena) in the context of a large collection of cancer genomics datasets (e.g. TCGA data stored at UCSC). The outcome is a platform for researchers to store and analyze their datasets in an interoperable manner. We are integrating Xena with other tools such as MuPIT (enables visualization of somatic mutations on three-dimensional protein structures), and with Galaxy to allow integration with other bioinformatics tools such as Trinity (via Galaxy, RNA-seq data analysis to identify coding and non-coding transcripts, score them for aberrations, and quantify their expression). Integrating these tools provides researchers with a workflow with strong analysis and visualization capabilities, and brings sophisticated computational analyses within the reach of non-computational scientists. Citation Format: Jingchun Zhu, Brian Craft, Mary Goldman, Melissa Cline, Mark Diekhans, David Haussler. Using the UCSC Xena Platform to integrate, visualize, and analyze your own data in the context of large external genomic datasets. [abstract]. In: Proceedings of the AACR Special Conference on Computational and Systems Biology of Cancer; Feb 8-11 2015; San Francisco, CA. Philadelphia (PA): AACR; Cancer Res 2015;75(22 Suppl 2):Abstract nr B1-07.

Increasing Nucleosome Occupancy Is Correlated with an Increasing Mutation Rate so Long as DNA Repair Machinery Is Intact.

Deciphering the multitude of epigenomic and genomic factors that influence the mutation rate is an area of great interest in modern biology. Recently, chromatin has been shown to play a part in this process. To elucidate this relationship further, we integrated our own ultra-deep sequenced human nucleosomal DNA data set with a host of published human genomic and cancer genomic data sets. Our results revealed, that differences in nucleosome occupancy are associated with changes in base-specific mutation rates. Increasing nucleosome occupancy is associated with an increasing transition to transversion ratio and an increased germline mutation rate within the human genome. Additionally, cancer single nucleotide variants and microindels are enriched within nucleosomes and both the coding and non-coding cancer mutation rate increases with increasing nucleosome occupancy. There is an enrichment of cancer indels at the theoretical start (74 bp) and end (115 bp) of linker DNA between two nucleosomes. We then hypothesized that increasing nucleosome occupancy decreases access to DNA by DNA repair machinery and could account for the increasing mutation rate. Such a relationship should not exist in DNA repair knockouts, and we thus repeated our analysis in DNA repair machinery knockouts to test our hypothesis. Indeed, our results revealed no correlation between increasing nucleosome occupancy and increasing mutation rate in DNA repair knockouts. Our findings emphasize the linkage of the genome and epigenome through the nucleosome whose properties can affect genome evolution and genetic aberrations such as cancer.

ZBTB7A Suppresses Melanoma Metastasis by Transcriptionally Repressing MCAM.

The excessive metastatic propensity of melanoma makes it the most deadly form of skin cancer, yet the underlying mechanism of metastasis remains elusive. Here, mining of cancer genome datasets discovered a frequent loss of chromosome 19p13.3 and associated downregulation of the zinc finger transcription factor ZBTB7A in metastatic melanoma. Functional assessment of ZBTB7A-regulated genes identified MCAM, which encodes an adhesion protein key to melanoma metastasis. Using an integrated approach, it is demonstrated that ZBTB7A directly binds to the promoter and transcriptionally represses the expression of MCAM, establishing ZBTB7A as a bona fide transcriptional repressor of MCAM. Consistently, downregulation of ZBTB7A results in marked upregulation of MCAM and enhanced melanoma cell invasion and metastasis. An inverse correlation of ZBTB7A and MCAM expression in association with melanoma metastasis is further validated with data from analysis of human melanoma specimens. Together, these results uncover a previously unrecognized role of ZBTB7A in negative regulation of melanoma metastasis and have important clinical implications.

MAP2K1 (MEK1) Mutations Define a Distinct Subset of Lung Adenocarcinoma Associated with Smoking.

Genetic alterations affecting the MAPK/ERK pathway are common in lung adenocarcinoma (LAD). Early steps of the signaling pathway are most often affected with EGFR, KRAS, and BRAF mutations encompassing more than 70% of all alterations. Somatic mutations in MEK1, located downstream of BRAF, are rare and remain poorly defined as a distinct molecular subset. Tumors harboring MEK1 mutations were identified through targeted screening of a large LAD cohort concurrently interrogated for recurrent mutations in MEK1, EGFR, KRAS, BRAF, ERBB2/HER2, NRAS, PIK3CA, and AKT. Additional cases were identified through a search of publically available cancer genomic datasets. Mutations were correlated with patient characteristics and treatment outcomes. Overall survival was compared with stage-matched patients with KRAS- and EGFR-mutant LADs. We identified 36 MEK1-mutated cases among 6,024 LAD (0.6%; 95% confidence interval, 0.42-0.85). The majority of patients were smokers (97%, n = 35/36). There was no association with age, sex, race, or stage. The most common mutations were K57N (64%, 23/36) followed by Q56P (19%, 7/36), all mutually exclusive with other driver mutations in the targeted panel. Transversions G:C>T:A were predominant (89%, 31/35), in keeping with smoking-associated DNA damage. Additional less common somatic mutations were identified in the kinase domain, all of which are predicted to converge into a single interaction area based on in silico 3D modeling. MEK1 mutations define a distinct subset of lung cancers (∼1%) with potential sensitivity to MEK inhibitors. Mutations are predominantly transversions, in keeping with a strong association with smoking.

Systematic identification of cancer driving signaling pathways based on mutual exclusivity of genomic alterations.

We present a novel method for the identification of sets of mutually exclusive gene alterations in a given set of genomic profiles. We scan the groups of genes with a common downstream effect on the signaling network, using a mutual exclusivity criterion that ensures that each gene in the group significantly contributes to the mutual exclusivity pattern. We test the method on all available TCGA cancer genomics datasets, and detect multiple previously unreported alterations that show significant mutual exclusivity and are likely to be driver events.Electronic supplementary materialThe online version of this article (doi:10.1186/s13059-015-0612-6) contains supplementary material, which is available to authorized users.

Cooperation between Noncanonical Ras Network Mutations

Cancer develops after the acquisition of a collection of mutations that together create the cancer phenotype. How collections of mutations work together within a cell and whether there is selection for certain combinations of mutations are not well understood. We investigated this problem with a mathematical model of the Ras signaling network, including a computational random mutagenesis. Modeling and subsequent experiments revealed that mutations of the tumor suppressor gene NF1 can amplify the effects of other Ras pathway mutations, including weakly activating, noncanonical Ras mutants. Furthermore, analyzing recently available, large, cancer genomic data sets uncovered increased co-occurrence of NF1 mutations with mutations in other Ras network genes. Overall, these data suggest that combinations of Ras pathway mutations could serve the role of cancer "driver." More generally, this work suggests that mutations that result in network instability may promote cancer in a manner analogous to genomic instability.

Abstract PR04: Cooperation between Ras network mutations in cancer

Abstract Cancer genome sequencing often identifies mutations within the Ras signaling network other than the well-established KRAS, NRAS, and BRAF oncogene mutations and NF1 tumor suppressor gene mutations. The extent to which a novel mutation in a commonly mutated pathway can be assumed to have cancer promoting potential, and whether or not certain contextual conditions are required for the mutation to have cancer promoting effects, is not well understood. We investigated this problem with a computational model of the Ras signaling network, with focused cell-based experiments, and with statistical analysis of large cancer genomic data sets. We first applied our previously developed computational model of the Ras signaling network to identify potential synergistic combinations within the Ras network. For example, our modeling suggested that mutations to the tumor suppressor gene NF1 can amplify the effects of other Ras pathway mutations, including the effects of weakly-activating, noncanonical, Ras mutants. Experiments comparing the strength of signal produced by noncanonical Ras mutants expressed in neurofibromin deficient and wild-type cells confirmed this prediction. If such synergistic effects contribute to the development of cancer, we reasoned that there should be an increased rate of co-occurrence between the mutations that the model predicted to have cooperative potential. Analysis of large cancer genomic data sets from the Cancer Cell Line Encyclopedia and The Cancer Genome Atlas identified increased rates of co-occurrence between mutations predicted to display synergy. Overall, our study suggests that there may be combinations of Ras pathway mutations that serve the role of cancer “driver”. This abstract is also presented as Poster B33. Citation Format: Edward C. Stites, Paul C. Trampont, Lisa B. Haney, Scott F. Walk, Kodi S. Ravichandran. Cooperation between Ras network mutations in cancer. [abstract]. In: Proceedings of the AACR Special Conference on RAS Oncogenes: From Biology to Therapy; Feb 24-27, 2014; Lake Buena Vista, FL. Philadelphia (PA): AACR; Mol Cancer Res 2014;12(12 Suppl):Abstract nr PR04. doi: 10.1158/1557-3125.RASONC14-PR04