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Enrichment of Immune signaling in the integrated pan-cancer cluster. Different level of evidence support the association of the integrated pan-cancer cluster with an immune phenotype. (A) Enrichment of T- and B-cell signaling shown on the integrated map, yellow gradient (see Supplemental Methods). (B) Enrichment of high ESTIMATE scores in the pan-cancer samples, waterfall plot with pan-cancer cluster samples in red. (C) Enrichment of the immune-related pathways identified by differential expression analysis. (D) Unsupervised analysis of master regulator activities inferred by the MARINa method. Gene clusters enriched for T- and B-cell signaling, interferon signaling, and TNFA via NFKB signaling, red font. (E) The top enriched pathways based on the output of master regulator scores derived with MARINa are immune-related. (F) Copy number events in the integrated pan-cancer cluster compared to other samples in the full cohort. The pan-cancer cluster shows a lower number of copy number events in both arm-level events and focal events. Arm-level events (pan-cancer group on the right, background cohort on the left). (G) Same as F but focal events (pan-cancer group on the right, background cohort on the left). (H) Purity estimates in the integrated pan-cancer cluster compared to all other samples in the cohort. The pan-cancer cluster (green box) shows lower purity when compared to the whole cohort as a background (yellow box). This finding is consistent with other analyses indicating high immune signaling in the pan-cancer cluster.

Supplemental Figure 1: RSS transformed correlations correct for platform-specific inflation biases. Maps produced from different molecular data types. Samples in the maps are colored by tissue of origin. (A) Similarity score distributions for every pair of samples for each individual data platform. Spearman Rho was computed for mRNA, miRNA, RPPA, SCNV, and methylation platforms. HOCUS score was used for mutation data. (B) RSS-standardized similarity scores for every pair of samples for each individual data platform. The density curves demonstrate that RSS transformation corrects for platform-specific inflation biases. (C) (i)-(vi) Maps produced from each of the six molecular data types using OpenOrd layout. (vii) mRNA expression map produced using Principal Component Analysis method. Principal components 1 and 2 are used to represent the mRNA expression space. (viii) mRNA expression map produced using tSNE method. Dimensions 1 and 2 are used to represent the mRNA expression space. (D) Maps produced from inferred gene activities using the PARADIGM and SPIA methods. (E) Maps integrating more than one molecular data type.

Molecular subtype visualization. Known biology recapitulated by the mRNA expression map. (A) BRCA molecular subtypes and their layout in the map. The map shows that tumors of the same molecular subtype tend to cluster together, with very little mixing of those subtypes, indicating that those subtypes are indeed molecularly different. (B) Estrogen signaling (yellow) and basal signaling (blue) are the top differentiating programs between the basal and non-basal BRCA tumors. Very few tumors exhibit signals of both programs (green). The group of samples labeled as Basal subtype in part A predominantly exhibits the basal signaling program and the group of samples consisting of LumA (for luminal A) and LumB (for luminal B) in part A predominantly exhibits the estrogen signaling program. (C) Association of TP53 mutations (blue) with basal breast tumors and PIK3CA mutations (yellow) with luminal tumors demonstrates at-a-glance view of genomic event association with molecular subtypes in the map. Samples that exhibit both mutations are shown in green. (D) COAD and READ tumors cluster together (left) and the map separates genomically stable and unstable tumors (right). (E) BLCA tumors separate into three previously discovered molecular subtypes. These subtypes are BLCA-core, BLCA-lung-like, and BLCA-squamous-like. (F) KIRC tumors are deficient in MSH2 (activity level indicated by the intensity of yellow), a component of the DNA mismatch repair pathway. This is a known characterization of KIRC tumors. (G) Co-occurrence of VHL mutations (green) and high HIF1A activity (indicated by the intensity of yellow) in KIRC tumors. Samples colored in yellow indicate an absence of VHL mutation but high HIF1A activity. Samples colored in green indicate both the presence of VHL mutation and high HIF1A activity. No samples show a presence of VHL mutation and low HIF1A activity (such samples would be colored in blue). (H) Spatial correlation analysis (SCA). Mutual exclusivity of RB1 mutations and CDKN2A deletions. These two events do not co-occur in the same patients even when they occur in patients clustering in the same regions of the map. (I) SCA - positive spatial correlation of RB1 mutations and PTEN mutations. In regions of the map where both of these events occur, they tend to occur in the same patients. (J) The overlap in gene signatures distinctive of the Tumor Map's relative positioning of BRCA molecular subtypes. HER2+ tumors share more differentially expressed genes in common between luminal than basal tumors. The subtype groupings are defined by BRCA sample annotations. Differential expression analysis resulted in 150-gene signatures for each subtype. Venn-diagram of these gene signatures shows the overlap between gene sets for each of the subtype.

More detail into the methodology behind the resource are provided as well as proof-of-concept benchmarks.

Vast amounts of molecular data are being collected on tumor samples, which provide unique opportunities for discovering trends within and between cancer subtypes. Such cross-cancer analyses require computational methods that enable intuitive and interactive browsing of thousands of samples based on their molecular similarity. We created a portal called TumorMap to assist in exploration and statistical interrogation of high-dimensional complex “omics” data in an interactive and easily interpretable way. In the TumorMap, samples are arranged on a hexagonal grid based on their similarity to one another in the original genomic space and are rendered with Google's Map technology. While the important feature of this public portal is the ability for the users to build maps from their own data, we pre-built genomic maps from several previously published projects. We demonstrate the utility of this portal by presenting results obtained from The Cancer Genome Atlas project data. Cancer Res; 77(21); e111–4. ©2017 AACR.

Video showing basic functionality of the TumorMap for the first-time user.

Supplemental Table 1A: Differential expression scores used in the pan-cancer cluster GSEA analysis. These scores represent integrated t-statistic scores from the differential analysis within each tissue (see Supplemental Methods). Supplemental Table 1B: Full results of the GSEA analysis of the pan-cancer cluster, based on differential expression scores. Supplemental Table 1C: Per-sample ESTIMATE scores used in analysis of the pan-cancer cluster. Supplemental Table 1D: Per-sample surrogate purity scores used in analysis of the pan-cancer cluster. Supplemental Table 1E: List of genes located on sex chromosomes that were excluded from the methylation dataset in order to analyze only autosomal gene features. Supplemental Table 1F: Attributes available in the TumorMap that annotate metadata of the samples, along with descriptions of those attributes. Supplemental Table 1G: Statistical tests computed by different attribute enrichment analysis (AEA) tools available in the TumorMap. Supplemental Table 1H: List of 82 samples in the pan-cancer cluster in the integrated map as well as tissue composition, along with the number of samples, in the integrated pan-cancer cluster. Supplemental Table 1I: Input data for the LAML survival analysis.

Supplemental Figure 5: (A) Overview of the method to compute differential expression for samples in the integrated pan-cancer group vs. other samples in the TCGA cohort. Tissue composition imbalance was corrected for by performing t-tests within each tissue. For each gene, t-statistics were computed within each tumor type separately and then summarized per-gene t-statistics were calculated as an arithmetic mean, weighted by the inverse variance of the all the tissue-specific t-statistics values. (B) The distribution of log-transformed TPM values estimated from the mRNA-Seq data showing a normal-like distribution. (C) Comparing the log-transformed TPM values of the mRNA-Seq data to a normal distribution shows a reasonable agreement with the tumor data having slightly heavier tails. (D) Venn diagram representing the innate and adaptive immune systems and different levels of evidence supporting higher activity of each of the components of those systems in the pan-cancer cluster when compared to the rest of the TCGA cohort. We found evidence of both innate and adaptive immune system signaling with a number of different analyses. (E) Detailed version of the immune-related pathway characterizing the integrated pan-cancer cluster. This version of the pathway shows additional genes and their connections that are not shown in the summarized version of the immune signaling pathway of Figure 1D of the main text.

Tissue and molecular subtype distribution among the samples in the entire cohort (A-B), and in the pan-cancer cluster (C-D). The pie charts represent the number of samples from each tissue of origin in the entire cohort (A) and the integrated pan-cancer cluster (C). Black and white matrices illustrate the presence of molecular features of each platform (x-axis) across samples (y-axis), in the entire cohort (B) or in the integrated pan-cancer cluster (D). Data available for this sample for a given platform is marked black, otherwise the entry is white.

Salivary duct carcinoma (SDC) is one of the most aggressive subtypes of salivary gland cancers. Conventional chemotherapy and/or radiation have shown only limited clinical efficacy in the treatment of recurrent or metastatic SDC. Currently, clinically approved targeted-therapeutics are not generally applicable except in very limited cases, and there exists a strong need for the development of treatment against this unique tumor type. To further interrogate genomic features of SDC, we have conducted multi-omic profiling of the SDC to describe the genomic alterations prevalent in this disease. Whole-genome sequencing, whole exome-sequencing and transcriptome sequencing were performed on a discovery cohort of 10 SDC samples. Targeted genomic profiling was performed in additional 32 SDC samples to support the findings obtained from the original discovery cohort. The cancer cohort was characterized by an average mutation burden of 85 somatic exonic mutations per tumor sample. The cohort harbored a mutational signature of BRCA and APOBEC/AID. Several genes, including TP53, RB1, SMAD4, HRAS, APC, PIK3CA and GNAQ were recurrently somatically altered in SDC. A novel fusion gene, generated by genomic rearrangement, MYB-NHSL1, was also noted. Our findings represent a significant layer in the systematic understanding of potentially clinically useful genomic and molecular targets for a subset of recurrent/metastatic SDC.

Vast amounts of molecular data are being collected on tumor samples, which provide unique opportunities for discovering trends within and between cancer subtypes. Such cross-cancer analyses require computational methods that enable intuitive and interactive browsing of thousands of samples based on their molecular similarity. We created a portal called TumorMap to assist in exploration and statistical interrogation of high-dimensional complex “omics” data in an interactive and easily interpretable way. In the TumorMap, samples are arranged on a hexagonal grid based on their similarity to one another in the original genomic space and are rendered with Google's Map technology. While the important feature of this public portal is the ability for the users to build maps from their own data, we pre-built genomic maps from several previously published projects. We demonstrate the utility of this portal by presenting results obtained from The Cancer Genome Atlas project data. Cancer Res; 77(21); e111–4. ©2017 AACR.

This cohort study evaluates the feasibility and utility of incorporating comparative gene expression information into the precision medicine framework for difficult-to-treat pediatric and young adult patients with cancer., Key Points Question Is it feasible and useful to compare the tumor RNA sequencing data of a child or young adult with the tumor RNA sequencing data of thousands of other patients (of all ages) in a research setting? Findings Among 144 tumor samples from children and young adults, comparative RNA sequencing analysis, conducted across 4 precision medicine studies in the United States and Canada, was feasible and potentially useful for 99 of 144 pediatric and young adult cancer samples. In contrast, DNA mutation information was potentially useful for only 34 of 74 samples. Meaning This study’s findings suggest that open sharing and combined analysis of tumor RNA sequencing data from pediatric and young adult patients treated on different clinical trials may represent a feasible approach and may produce useful clinical and biological information for individual patients., Importance Pediatric cancers are epigenetic diseases; therefore, considering tumor gene expression information is necessary for a complete understanding of the tumorigenic processes. Objective To evaluate the feasibility and utility of incorporating comparative gene expression information into the precision medicine framework for difficult-to-treat pediatric and young adult patients with cancer. Design, Setting, and Participants This cohort study was conducted as a consortium between the University of California, Santa Cruz (UCSC) Treehouse Childhood Cancer Initiative and clinical genomic trials. RNA sequencing (RNA-Seq) data were obtained from the following 4 clinical sites and analyzed at UCSC: British Columbia Children’s Hospital (n = 31), Lucile Packard Children’s Hospital at Stanford University (n = 80), CHOC Children’s Hospital and Hyundai Cancer Institute (n = 46), and the Pacific Pediatric Neuro-Oncology Consortium (n = 24). The study dates were January 1, 2016, to March 22, 2017. Exposures Participants underwent tumor RNA-Seq profiling as part of 4 separate clinical trials at partner hospitals. The UCSC either downloaded RNA-Seq data from a partner institution for analysis in the cloud or provided a Docker pipeline that performed the same analysis at a partner institution. The UCSC then compared each participant’s tumor RNA-Seq profile with more than 11 000 uniformly analyzed tumor profiles from pediatric and young adult patients with cancer, downloaded from public data repositories. These comparisons were used to identify genes and pathways that are significantly overexpressed in each patient’s tumor. Results of the UCSC analysis were presented to clinical partners. Main Outcomes and Measures Feasibility of a third-party institution (UCSC Treehouse Childhood Cancer Initiative) to obtain tumor RNA-Seq data from patients, conduct comparative analysis, and present analysis results to clinicians; and proportion of patients for whom comparative tumor gene expression analysis provided useful clinical and biological information. Results Among 144 samples from children and young adults (median age at diagnosis, 9 years; range, 0-26 years; 72 of 118 [61.0%] male [26 patients sex unknown]) with a relapsed, refractory, or rare cancer treated on precision medicine protocols, RNA-Seq–derived gene expression was potentially useful for 99 of 144 samples (68.8%) compared with DNA mutation information that was potentially useful for only 34 of 74 samples (45.9%). Conclusions and Relevance This study’s findings suggest that tumor RNA-Seq comparisons may be feasible and highlight the potential clinical utility of incorporating such comparisons into the clinical genomic interpretation framework for difficult-to-treat pediatric and young adult patients with cancer. The study also highlights for the first time to date the potential clinical utility of harmonized publicly available genomic data sets.

Clinical detection of sequence and structural variants in known cancer genes points to viable treatment options for a minority of children with cancer.1 To increase the number of children who benefit from genomic profiling, gene expression information must be considered alongside mutations.2,3 Although high expression has been used to nominate drug targets for pediatric cancers,4,5 its utility has not been evaluated in a systematic way.6 We describe a child with a rare sarcoma that was profiled with whole-genome and RNA sequencing (RNA-Seq) techniques. Although the tumor did not harbor DNA mutations targetable by available therapies, incorporation of gene expression information derived from RNA-Seq analysis led to a therapy that produced a significant clinical response. We use this case to describe a framework for inclusion of gene expression into the clinical genomic evaluation of pediatric tumors.

We report a comprehensive molecular characterization of pheochromocytomas and paragangliomas (PCCs/PGLs), a rare tumor type. Multi-platform integration revealed that PCCs/PGLs are driven by diverse alterations affecting multiple genes and pathways. Pathogenic germline mutations occurred in eight PCC/PGL susceptibility genes. We identified CSDE1 as a somatically mutated driver gene, complementing four known drivers (HRAS, RET, EPAS1, and NF1). We also discovered fusion genes in PCCs/PGLs, involving MAML3, BRAF, NGFR, and NF1. Integrated analysis classified PCCs/PGLs into four molecularly defined groups: a kinase signaling subtype, a pseudohypoxia subtype, a Wnt-altered subtype, driven by MAML3 and CSDE1, and a cortical admixture subtype. Correlates of metastatic PCCs/PGLs included the MAML3 fusion gene. This integrated molecular characterization provides a comprehensive foundation for developing PCC/PGL precision medicine., National Institutes of Health (U.S.) (Grant U54HG003273), National Institutes of Health (U.S.) (Grant U54HG003067), National Institutes of Health (U.S.) (Grant U54HG003079), National Institutes of Health (U.S.) (Grant U24CA143799), National Institutes of Health (U.S.) (Grant U24CA143835), National Institutes of Health (U.S.) (Grant U24CA143840), National Institutes of Health (U.S.) (Grant U24CA143843), National Institutes of Health (U.S.) (Grant U24CA143845), National Institutes of Health (U.S.) (Grant U24CA143848), National Institutes of Health (U.S.) (Grant U24CA143858), National Institutes of Health (U.S.) (Grant U24CA143866), National Institutes of Health (U.S.) (Grant U24CA143867), National Institutes of Health (U.S.) (Grant U24CA143882), National Institutes of Health (U.S.) (Grant U24CA143883), National Institutes of Health (U.S.) (Grant U24CA144025), National Institutes of Health (U.S.) (Grant P30CA016672)

The UCSC Cancer Genomics Browser (https://genome-cancer.ucsc.edu/) is a web-based application that integrates relevant data, analysis and visualization, allowing users to easily discover and share their research observations. Users can explore the relationship between genomic alterations and phenotypes by visualizing various -omic data alongside clinical and phenotypic features, such as age, subtype classifications and genomic biomarkers. The Cancer Genomics Browser currently hosts 575 public datasets from genome-wide analyses of over 227 000 samples, including datasets from TCGA, CCLE, Connectivity Map and TARGET. Users can download and upload clinical data, generate Kaplan–Meier plots dynamically, export data directly to Galaxy for analysis, plus generate URL bookmarks of specific views of the data to share with others.

SummaryWe describe the landscape of somatic genomic alterations based on multidimensional and comprehensive characterization of more than 500 glioblastoma tumors (GBMs). We identify several novel mutated genes as well as complex rearrangements of signature receptors, including EGFR and PDGFRA. TERT promoter mutations are shown to correlate with elevated mRNA expression, supporting a role in telomerase reactivation. Correlative analyses confirm that the survival advantage of the proneural subtype is conferred by the G-CIMP phenotype, and MGMT DNA methylation may be a predictive biomarker for treatment response only in classical subtype GBM. Integrative analysis of genomic and proteomic profiles challenges the notion of therapeutic inhibition of a pathway as an alternative to inhibition of the target itself. These data will facilitate the discovery of therapeutic and diagnostic target candidates, the validation of research and clinical observations and the generation of unanticipated hypotheses that can advance our molecular understanding of this lethal cancer.

The UCSC Cancer Genomics Browser (https://genome-cancer.ucsc.edu/) is a set of web-based tools to display, investigate and analyse cancer genomics data and its associated clinical information. The browser provides whole-genome to base-pair level views of several different types of genomics data, including some next-generation sequencing platforms. The ability to view multiple datasets together allows users to make comparisons across different data and cancer types. Biological pathways, collections of genes, genomic or clinical information can be used to sort, aggregate and zoom into a group of samples. We currently display an expanding set of data from various sources, including 201 datasets from 22 TCGA (The Cancer Genome Atlas) cancers as well as data from Cancer Cell Line Encyclopedia and Stand Up To Cancer. New features include a completely redesigned user interface with an interactive tutorial and updated documentation. We have also added data downloads, additional clinical heatmap features, and an updated Tumor Image Browser based on Google Maps. New security features allow authenticated users access to private datasets hosted by several different consortia through the public website.

6083Background: Salivary duct carcinoma (SDC) is one of the most aggressive histological subtypes of salivary gland cancers. Conventional chemotherapy and radiation have shown only limited efficacy...

Summary Recent genomic analyses of pathologically defined tumor types identify "within-a-tissue" disease subtypes. However, the extent to which genomic signatures are shared across tissues is still unclear. We performed an integrative analysis using five genome-wide platforms and one proteomic platform on 3,527 specimens from 12 cancer types, revealing a unified classification into 11 major subtypes. Five subtypes were nearly identical to their tissue-of-origin counterparts, but several distinct cancer types were found to converge into common subtypes. Lung squamous, head and neck, and a subset of bladder cancers coalesced into one subtype typified by TP53 alterations, TP63 amplifications, and high expression of immune and proliferation pathway genes. Of note, bladder cancers split into three pan-cancer subtypes. The multiplatform classification, while correlated with tissue-of-origin, provides independent information for predicting clinical outcomes. All data sets are available for data-mining from a unified resource to support further biological discoveries and insights into novel therapeutic strategies.

The UCSC Cancer Genomics Browser (https://genome-cancer.ucsc.edu) offers interactive visualization and exploration of TCGA genomic, phenotypic, and clinical data, as produced by the Cancer Genome Atlas Research Network. Researchers can explore the impact of genomic alterations on phenotypes by visualizing gene and protein expression, copy number, DNA methylation, somatic mutation and pathway inference data alongside clinical features, Pan-Cancer subtype classifications and genomic biomarkers. Integrated Kaplan-Meier survival analysis helps investigators to assess survival stratification by any of the information.

The Cancer Genome Atlas (TCGA) Research Network has profiled and analyzed large numbers of human tumors to discover molecular aberrations at the DNA, RNA, protein and epigenetic levels. The resulting rich data provide a major opportunity to develop an integrated picture of commonalities, differences and emergent themes across tumor lineages. The Pan-Cancer initiative compares the first 12 tumor types profiled by TCGA. Analysis of the molecular aberrations and their functional roles across tumor types will teach us how to extend therapies effective in one cancer type to others with a similar genomic profile., National Cancer Institute (U.S.), National Human Genome Research Institute (U.S.)

UCSC Xena (http://xena.ucsc.edu/) is a bioinformatics tool to visualize functional genomics data from multiple sources simultaneously, including both public and private data. The Xena system 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. The lightweight Xena data hubs are straightforward to install on Windows, Mac and Linux operating systems and easily allow hub administrators to authenticate users, ensuring that only authorized users have access to secure data. Loading data into a Xena hub is easy using either our application or command line interface. Hosting public data from major projects, such as TCGA, on public Xena hubs gives users access without having to download these large datasets. The Xena system makes it easy to aggregate across many hubs, allowing users to integrate public datasets and private secure data together or view them separately. 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. The largest public Xena hub, based at UCSC, currently hosts an expanding set of searchable data, including 806 public datasets from several large consortiums including TCGA (The Cancer Genome Atlas), ICGC (International Cancer Genome Consortium), Treehouse Childhood Cancer Project, CCLE (Cancer Cell Line Encyclopedia), and more. Xena hubs are flexible enough to handle most data types, including gene, exon, miRNA and protein expression, copy number, DNA methylation and somatic mutation data along with phenotypes, subtype classifications and genomic biomarkers. Dynamic Kaplan-Meier survival analysis helps investigators to assess survival stratification by any information while scatter plots and bar graphs offer new insights into the data. Integration with Galaxy gives users access to a myriad of bioinformatics tools for further analysis and hypothesis testing. Citation Format: Mary Goldman, Brian Craft, Jingchun Zhu, Teresa Swatloski, Melissa Cline, David Haussler. The UCSC Xena system for integrating and visualizing functional genomics. [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 5270.

The UCSC Cancer Genomics Browser (https://genome-cancer.ucsc.edu) is a set of web-based tools to display, investigate and analyze cancer genomics data and associated clinical data. Experimental quantities such as gene expression levels, copy number variation and somatic mutations are displayed next to clinical features such as age of onset and cancer subtype. Users can interactively group or sort data by clinical features to dynamically explore how genomic aberrations relate to clinical outcomes. Integrated Kaplan–Meier plots help investigators assess how clinical or genomic values impact long-term survival. The browser currently hosts data from 144 cancer studies including TCGA, CCLE and LINCS. It can display data from multiple studies at once, facilitating cross-cancer comparisons. We are currently interested in hosting new pediatric cancer datasets, and can offer either public or protected access as appropriate. Figure 1 illustrates the power of this approach. It compares patterns of somatic mutations in pediatric high-risk neuroblastoma, a cancer of the peripheral nervous system (Pugh, Morozova et al, Nature Genetics 2013) to those in two adult cancers of the central nervous system: lower grade glioma (LGG, TCGA) and glioblastoma multiforme (GBM, TCGA), and contrasts the age of the patient with frequency of mutations in the Alpha Thalassemia/Mental Retardation Syndrome X-linked (ATRX) gene. The data for all three cancers is sorted by the age of the patient, with yellow being the oldest. The age ranges overlap: the oldest neuroblastoma patients are 16, while the youngest LGG and GBM patients are 14 and 21 respectively. While the three cancers show distinct patterns of somatic mutations, all three show frequent mutations in the ATRX gene in older teens or young adults. This suggests that these cancers may share age-related subtypes with similar genomic signatures. These subtypes may indicate the age-dependent importance of ATRX in the development of both central and peripheral nervous systems, and may ultimately highlight common therapeutic avenues for the two groups of diseases. This data set can be explored at the UCSC Cancer Genomics Browser at https://genome-cancer.ucsc.edu/proj/site/hgHeatmap/#?bookmark=pc Citation Format: Melissa Cline, Olena Morozova, Teresa Swatloski, Brian Craft, Mary Goldman, David Haussler, Jingchun Zhu. Exploring pediatric cancer genomics with the UCSC Cancer Genomics Browser. [abstract]. In: Proceedings of the AACR Special Conference on Pediatric Cancer at the Crossroads: Translating Discovery into Improved Outcomes; Nov 3-6, 2013; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2013;74(20 Suppl):Abstract nr A33.

As the rate of cancer genomic information production has increased so has the task of managing the computational infrastructure required to host, visualize and analyze that data. The UCSC Cancer Genomics Browser (https://genome-cancer.ucsc.edu) provides a variety of tools to help researchers analyze their data. It includes sortable genomic heatmaps linked to patient clinical attributes that can be explored on demand. A user can run statistical tests between user defined groupings of samples, genomic signature evaluations, and toggle between chromosomal and custom gene sets views. The browser is linked to the UCSC Genome Browser; thus inheriting and integrating the Genome Browser's rich set of human biological and genetic data that enhances the interpretability of the cancer genomics information. UCSC Cancer Genomics Browser 2.0 has been released with drastically enhanced user. Other recent improvements in the Cancer Genomics Browser have been in response to the growing number of collaborative cancer analysis projects. Designed with the privacy issues that come along with patient data in mind, cancer browser accounts provide user-specific sessions, gene sets, genomic profiles, and user-specific data tracks controls. In addition to the public portal, the Cancer Genomics Browser also supports projects that require secured access control. The system hosts results from TCGA, SU2C (Stand Up To Cancer), LINCS (Library of Integrated Network-based Cellular Signatures), GEO (Gene Expression Omnibus) and the ISPY trials along with information from individual labs. Researchers that have been authenticated to multiple projects can see the results from all of their projects in a single page. The other aspect of working with multiple collaborative cancer projects is the issue of properly defining data interchange formats. For the Cancer Genome Browser we have created a specification called cgData, which will be used by UCSC to consolidate various types of information from TCGA. cgData is a designed to provide the variety of file formats that are required to describe the heterogeneous data required for the browser. Formats for defining expression matrices, genomic probe definitions, clinical feature data, mutation information and sample mapping have all been specified to enable rapid integration of new data into the Cancer Genome Browser. This format is being developed in collaboration with partners from SU2C and LINCS, and utilities to convert The Broad Institutes TCGA Firehose analysis and MSKCC cBio portal information have already been developed. The publicly available data is organized into downloadable releases on a monthly basis. 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 5087. doi:1538-7445.AM2012-5087