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UCSC Xena (http://xena.ucsc.edu/) is a web-based visual integration and exploration tool for multi-omic data and associated clinical and phenotypic annotations. Researchers can easily view and explore public data, their own private data, or both using the Xena Browser. Private data are kept on the researcher's computer and are never uploaded to our public servers. We support Mac, Windows, and Linux. Questions Xena can help you answer: * Is overexpression of this gene associated with lower/higher survival? * What genes are differentially expressed between these two groups of samples? * What is the relationship between mutation, copy number, expression, etc for this gene? Xena showcases seminal cancer genomics datasets from TCGA, the Pan-Cancer Atlas, GDC, PCAWG, ICGC, and more; a total of more than 1500 datasets across 50 cancer types. We support virtually any type of functional genomics data: SNPs, INDELs, copy number variation, gene expression, ATAC-seq, DNA methylation, exon-, transcript-, miRNA-, lncRNA-expression and structural variants. We also support clinical data such as phenotype information, subtype classifications and biomarkers. All of our data is available for download via python or R APIs, or using our URL links. Our signature Visual Spreadsheet view shows multiple data types side-by-side enabling discovery of correlations across and within genes and genomic regions. We also have dynamic Kaplan-Meier survival analysis, powerful filtering and subgrouping, differential gene expression analysis, charts, statistical analyses, genomic signatures, and the ability to generate URLs to live views. We link out to the UCSC Genome Browser as well as MuPIT/CRAVAT and TumorMap. New features include: * Genome-wide differential gene expression analysis * Select samples directly from the screen for filtering and creating subgroups * Violin plots on any numerical data * Loading of Microsoft Excel files Our beta prototype site for visualizing single-cell data delivers million-cell-scale multi-omics data for interactive visualization in a web browser. Contact us for access to our beta prototype site. If you use us please cite our publication in Nature Biotechnology: https://www.nature.com/articles/s41587-020-0546-8 Citation Format: Mary Goldman, Brian Craft, Jingchun Zhu, David Haussler. Visualization and analysis of cancer genomics data using UCSC Xena [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 2066.

UCSC Xena (http://xena.ucsc.edu/) is a web-based visual integration and exploration tool for multi-omic data and associated clinical and phenotypic annotations. Researchers can easily view and explore public data, their own private data, or both using the Xena Browser. Private data are kept on the researcher's computer and are never uploaded to our public servers. The toll supports Mac, Windows, and Linux. Questions Xena can help you answer: 1) Is overexpression of this gene associated with lower/higher survival? 2) What genes are differentially expressed between these two groups of samples? 3) What is the relationship between mutation, copy number, expression, etc for this gene? Xena showcases seminal cancer genomics datasets from TCGA, the Pan-Cancer Atlas, GDC, PCAWG, ICGC, and more; a total of more than 1500 datasets across 50 cancer types. We support virtually any type of functional genomics data: SNPs, INDELs, copy number variation, gene expression, ATAC-seq, DNA methylation, exon-, transcript-, miRNA-, lncRNA-expression, and structural variants. We also support clinical data such as phenotype information, subtype classifications and biomarkers. All of our data is available for download via python or R APIs, or using our URL links. Our signature Visual Spreadsheet view shows multiple data types side-by-side enabling discovery of correlations across and within genes and genomic regions. We also have dynamic Kaplan-Meier survival analysis, powerful filtering and subgrouping, differential gene expression analysis, charts, statistical analyses, genomic signatures, and the ability to generate URLs to live views. We link out to the UCSC Genome Browser as well as MuPIT/CRAVAT and TumorMap. New features include: - Genome-wide differential gene expression analysis - New interface for filtering samples and creating subgroups - New interface to create charts and graphs - Violin plots on any numerical data - Loading of Microsoft Excel files - A new Publication Page showcasing publications and authors that use UCSC Xena Our beta prototype for visualizing single-cell data delivers million-cell-scale multi-omics data for interactive visualization in a web browser. Citation Format: Mary Goldman, Brian Craft, Jingchun Zhu, David Haussler. Visualization and analysis of cancer genomics data using UCSC Xena [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 5039.

Whole-exome sequencing studies have transformed our understanding of recurrent somatic mutations that contribute to cancer pathogenesis; however, these studies limit our ability to identify cancer-associated mutations to those that cause protein-coding changes. To more comprehensively catalogue cancer-associated gene alterations, we have extensively characterized tumor transcriptomes from 1,220 donors with matched whole-genome sequence data to identify recurrent RNA-level aberrations. Specifically, we created a unified RNA-Seq analysis pipeline including sequence alignment and quality control and subsequently identified gene alterations through outlier detection from estimated gene expression levels, alternative splicing, alternative transcription starts, and allele-specific expression and through identified RNA-edited sites and gene fusions. Our data represent an extensive catalog of RNA aberrations for each gene across 27 cancer types. We have also tested for genetic associations with these RNA phenotypes. Using an integrative analysis approach, we have mapped genome-wide cis and trans effects on individual RNA phenotypes, considering both common germline variants as well as somatic SNVs in gene promoters, enhancers, and intronic and other regions. Many of the regulatory associations we identify are not accessible by exome sequencing, underlining the importance of whole-genome sequence data. Utilizing this RNA-centric view, we have identified genes that are recurrently altered, yet have not been previously characterized as cancer genes or identified through DNA-level driver gene analysis. To identify further supporting evidence that these recurrent alterations are potential drivers, we identified genes with mutually exclusive RNA-level alterations. Our findings reveal new insights into selective advantages of somatic changes and molecular mechanisms of cancer. This work is by the Transcriptome Working Group of the Pan-Cancer Analysis of Whole Genomes (PCAWG) consortium and authors are listed in alphabetical order. Citation Format: Samirkumar Amin, Philip Awadalla, Andrew Biankin, Paul Boutros, Alvis Brazma, Angela Norie Brooks, Claudia Calabrese, David Chang, Aurélien Chateigner, Ken Chen, Zechen Chong, Brian Craft, Chad Creighton, Deniz Demircioğlu, Nuno Fonseca, Milana Frenkel-Morgenstern, Gad Getz, Jonathan Göke, Mary Goldman, Liliana Greger, Syed Haider, Yao He, Katherine Hoadley, Yuan Ji, Andre Kahles, Ekta Khurana, Jan Korbel, Kjong Lehmann, Han Liang, Fenglin Liu, Maximillian Marin, Matthew Meyerson, Akinyemi Ojesina, Francis Ouellette, Chandra Pedamallu, Marc Perry, Gunnar Rätsch, Roland Schwarz, Yuichi Shiraishi, Cameron Soulette, Oliver Stegle, Patrick Tan, Alfonso Valencia, Linda Xiang, Christina Yung, Junjun Zhang, Fan Zhang, Zemin Zhang, Jingchun Zhu. Pan-cancer study of recurrent and heterogeneous RNA aberrations and association with whole-genome variants [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 SY10-02. doi:10.1158/1538-7445.AM2017-SY10-02

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.

We hypothesize that new insights into how cancers progress and respond to treatment will come from clinical trials that i) extensively characterize the molecular features of a patient’s cancer; ii) use results to predict drug susceptibilities; iii) treat in accordance with these predictions; and iv) learn from individual patient outcomes to iterate and improve over time. To investigate the feasibility of this type of clinical study, we launched the "Intensive Trial of OMics in Cancer" (ITOMIC) for patients with metastatic triple negative breast cancer (TNBC) (Clinicaltrials.gov ID: NCT01957514). Eligible patients have metastatic TNBC, are platinum-naive, and are scheduled to receive Cisplatin. Biopsies are performed under carefully controlled conditions prior to Cisplatin – starting all subjects on a common treatment path, and uncoupling the time needed for specimen analysis from immediate therapy. Biopsies are repeated upon completion of Cisplatin and following subsequent therapies. A subset of specimens is chosen for whole Exome Sequencing, deep sequencing of a panel of cancer associated genes, and RNA-sequencing. De-identified results are placed on a web-based server for analysis and discussed at a meeting of the ITOMIC tumor board. A report describing results and potential therapies is provided to the subject’s oncologist. Treatment decisions are left to the discretion of the oncologist. If a decision is taken to pursue treatments identified in our report we offer assistance in accessing those treatments. Ten patients have been screened and seven have enrolled. Subjects range in age from 40 to 77 years and all but one has received extensive prior treatment for metastatic TNBC. All seven underwent an initial set of biopsies, targeting between two and five metastatic sites. For most metastatic sites, multiple core needle passes are performed. All subjects tolerated the biopsies well without significant adverse events, and all started treatment with Cisplatin. Three subjects completed Cisplatin and underwent a second round of biopsies. Potential targets for therapy were identified in 5 of the first 6 subjects, and three subjects have received four predicted therapies: 1) a patient with somatic loss of BRCA1 and two linked FGFR2 activating mutations, who was treated first with Veliparib through a single-patient IND and then switched to Ponatinib which produced a partial response; 2) a patient with a novel missense ROS1 mutation treated with crizotinib; and 3) a patient with CYP3A4 copy gain treated with cyclophosphamide. Conclusion: Our early experience indicates that this approach is feasible and may increase the efficiency of learning from patients with advanced cancer. Citation Format: C Anthony Blau, Colin Pritchard, Michael O Dorschner, Sibel Blau, Brigham Mecham, Elisabeth Mahen, VK Gadi, Wayne Monsky, Kimberly Burton, Arturo Ramirez, Jackie Stilwell, Eric Kladjian, Carol Collins, Jeannine S McCune, William S Noble, Julie Gralow, Frank Senecal, Linda Dhaene, Nicole Kuderer, Jennifer Specht, Chaozhong Song, Carla Grandori, Nathan Price, Mary Goldman, Aime Radenbaugh, David Haussler, Jingchun Zhu. Assessing the safety and feasibility of efficient hypothesis testing in patients with metastatic triple negative breast cancer [abstract]. In: Proceedings of the Thirty-Seventh Annual CTRC-AACR San Antonio Breast Cancer Symposium: 2014 Dec 9-13; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2015;75(9 Suppl):Abstract nr P4-08-01.

UCSC Xena (http://xena.ucsc.edu/) is a web-based visual integration and exploration tool for multi-omic data and associated clinical and phenotypic annotations. Our unique Visual Spreadsheet shows multiple data types side-by-side enabling discovery of correlations across and within genes and genomic regions. We offer dynamic Kaplan-Meier survival analysis, powerful filtering and subgrouping, charts, statistical analyses, genomic signatures, comparative transcript views, and bookmarks. We link out to the UCSC Genome Browser, giving users additional genomic context for any gene or coordinate, as well as MuPIT/CRAVAT and TumorMap, to give users complementary views of the same data. Xena showcases seminal cancer genomics datasets from TCGA, the Pan-Cancer Atlas, PCAWG, GDC, GTEx, ICGC, and more; a total of more than 1500 datasets across 50 cancer types. We support virtually any type of functional genomics data. In addition to the commonly available SNPs, INDELs, copy number variation, and gene expression datasets, we support DNA methylation, exon-, transcript-, miRNA-, lncRNA-expression and structural variants. We also support clinical data such as phenotypes, subtype classifications and biomarkers. A recompute of TCGA, TARGET and GTEx datasets through the same bioinformatics pipeline allows users to compare expression between tumor and normal tissues. All of our data is available for download via our python API or through AWS S3 buckets. A researcher can host their own data securely via private hubs running on a laptop or behind a firewall, with visual and analytical integration occurring only within the Xena Browser. The lightweight Xena Data Hubs are straightforward to install on Windows, Mac and Linux. Loading data is easy using either our application or command line interface. Our newest features include: * a new, more intuitive wizard to load your data into a local Hub * URL bookmarks to save interactive views for yourself or to share with collaborators * genomic signatures: dynamically build as a weighted sum over a set of genes * hierarchically cluster a list of genes, regulons or probes * upper vs lower quartile in a KM plot Citation Format: Mary Goldman, Brian Craft, Jingchun Zhu, David Haussler. UCSC Xena for cancer genomics visualization and interpretation [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 911.

Objective: Genomic interrogation of tumors has changed the way we care for cancer patients. Monogenic, single mutation tests clearly and accurately define patient populations whose tumors will or will not likely respond to specific therapies. Next generation sequencing has enabled diagnostics derived from broader footprints, such as signatures of mutational processes. Currently, no universal guidelines exist on translating complex predictive signatures into the clinic, and their interpretability is a barrier to uptake by clinicians. Visually displaying genomic signatures may help clinicians interpret them. We designed a user-directed approach to developing understandable displays of an individual’s BRCAness signature through testing at clinical cancer conferences. Materials and Methods: We adopted a two-stage approach to optimizing user-testing methods. First, 44 attendees at the 2018 Gastrointestinal Cancer Symposium participated in a tablet-based test of a visualization of a patient’s BRCAness signature. We refined our testing methods and continued tabled-based testing of 51 attendees at the 2018 American Society of Clinical Oncology. Participants viewed slides explaining the signature and visualization. They were asked to interpret a patient’s BRCAness signature when provided information both as text and as a visualization. Participants received one of three similar versions of the visualization. Results: Participants interpreted patients’ BRCAness signatures faster when given a visual. When shown a visual indicating BRCAness scores of germline BRCA mutated tumors, participants interpreted the information fastest without sacrificing accuracy or confidence in their answer. Participants’ interpretation of an individual’s BRCAness signature was associated with their self-reported likelihood to recruit the individual to a clinical trial. Conclusion: Visualizing distributions of BRCAness signatures with germline BRCA mutations displayed helped participants respond faster without sacrificing accuracy or confidence in their answer. Testing attendees at cancer conferences presents a potential platform to include clinicians and researchers in the development of genomics visualization tools. Citation Format: Hannah R. Allegakoen, Mary Goldman, Bennett Caughey, Martin Consunji, Christopher C. Benz, David Haussler, Jingchun Zhu, Eric A. Collisson. Designing an intuitive visualization of BRCAness scores for clinicians [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 2466.

Background: Her-2/neu overexpression, by immunohistochemistry (IHC) or fluorescence in-situ hybridization (FISH), is highly correlated with response to trastuzumab and these are currently the gold-standard, FDA-cleared testing methods for assigning treatment to Her-2-directed therapies. However, substantial variability has been documented between community and central laboratory IHC and FISH testing. Biologically, Her-2 overexpression may reflect increased gene copy number, gene expression and/or protein production, and these can be measured by other platforms, including comparative genomic hybridization (CGH), expression arrays and quantitative protein assays, respectively. We sought to determine the degree to which community IHC/FISH results differed from centrally-assessed IHC, FISH, and other assessment platforms within the I-SPY Trial and whether response to neoadjuvant chemotherapy (NAC) differed by platform. Methods: The I-SPY Trial enrolled 237 women 2002–06 with invasive breast tumors at least 3 cm in clinical/radiographic size who subsequently underwent anthracycline/taxane NAC, serial core biopsies and imaging. Pathologic complete response (pCR) was determined at time of surgery and 3-year follow up has been reached. Trastuzumab was given to Her2+ patients at physician discretion, based upon community IHC/FISH results, and became more widespread after 2005. Central I-SPY laboratories determined Her2 copy number by MIP array, gene expression by Affymetrix and Agilent arrays, and Her2 protein by reverse-phase protein array (RPMA). Unsupervised clustering algorithms were used to evaluate expression patterns. Composite variables were constructed for DNA, RNA and protein positivity as well as for community and central IHC/FISH. Platforms were compared and Kaplan-Meier curves were constructed to compare outcomes by platform. Results: 222 women were evaluable, though not all patients had results for all platforms. Community composite IHC/FISH was positive in 64/214 (30%) but only 41 of these (64%) were confirmed by central IHC/FISH and 4 additional cases were centrally positive despite negative community testing. Concordance was high among centrally-assessed Her2 platforms, but was lower between community IHC/protein and central RNA (90%), DNA (91%) and protein (91%). Among patients receiving trastuzumab (n=36), the pCR rate was ∼50% regardless of Her2-assessment platform; in contrast, those not receiving trastuzumab had pCR rates below 30%. Among the 64 patients deemed Her2+ by community IHC/FISH, 30 (48%) had pCR and 15 (25%) have had distant relapse. Five distant relapses have occurred despite pCR; all received trastuzumab, all were Her2 positive by multiple central platforms and 3/5 were ER-positive. Sites of distant relapse included brain, bone and viscera; only 1 of 5 had isolated brain relapse. Conclusions: Community IHC/FISH testing for Her2 expression in the I-SPY Trial overcalled Her2 positivity compared to central testing while central results were highly concordant among DNA, RNA and protein platforms. Despite the high rate of community “false positives”, relapse after pCR occurred only in central Her2 “true positives,” exclusively among those receiving trastuzumab, and was rarely isolated to CNS sanctuary sites. Citation Information: Cancer Res 2011;71(24 Suppl):Abstract nr P1-06-11.

The UCSC Xena platform (http://xena.ucsc.edu/) allows biologists and bioinformaticians to securely analyze and visualize functional genomics data. Our unique Visual Spreadsheet shows multiple data types side by side enabling discovery of correlations across and within genes and genomic regions. Dynamic Kaplan-Meier survival analysis assesses survival stratification in addition to scatter plots, bar graphs, and boxplots all shown with statistical tests. In addition to the commonly available SNPs, INDELs, CNV, and gene expression datasets, we support DNA methylation, exon-, transcript-, miRNA-, lncRNA-expression and structural variants. We also support clinical data such as phenotypes, subtype classifications and biomarkers. Our new whole genome views allow users to easily visualize non-coding regions for both copy number variation and somatic mutations. All of our data is available for download via our python API or through AWS S3 buckets. Our expanding public Xena Data Hubs currently host 1500+ datasets from more than 35 cancer types, as well as Pan-Cancer datasets. In addition to serving seminal cancer genomic datasets to the scientific community, including the latest from the GDC, TCGA, TARGET, and ICGC, we also host 'normal tissue' datasets from GTEx. A recompute of TCGA, TARGET and GTEx datasets through the same bioinformatics pipeline allows users to compare expression between tumor and normal tissues. In addition to exploring these public datasets, the UCSC Xena Browser can easily display an investigator's genomic and clinical data on their own Xena Hub. By empowering users to install and load data into their own hub, our architecture ensures that the investigator's data remains private. 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. Citation Format: Mary Goldman, Brian Craft, Jingchun Zhu, David Haussler. Cancer genomics visualization and interpretation using UCSC Xena [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2018; 2018 Apr 14-18; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2018;78(13 Suppl):Abstract nr 2274.

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

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.

Deep sequencing of adult and pediatric tumors revealed that different cancers share common genetic mutations. Aside from sequence mutation, gene expression, copy number, and epigenetic mechanisms contribute to tumorigenesis, and integrating this information may reveal more aberrant signaling pathways than analysis of mutations alone. Significantly, agents targeting specific pathways may be effective against multiple malignancies, regardless of the mechanisms of pathway deregulation. These observations suggest that pediatric cancer patients may benefit from targeted therapies developed for adults. Since the development of pediatric-cancer-specific therapies is hindered by the limited involvement of pharmaceutical companies and small patient cohorts, repositioning drugs designed for adult tumors remains the fastest and most effective way to bring new treatment options to pediatric cancer patients While pediatric tumors have been characterized by genome-wide technologies, the data from these studies are typically under-utilized beyond the initial single cohort, single data type analyses. Consequently, we still lack a comprehensive picture of the molecular pathways that contribute to pediatric cancer in each patient, especially those that can be targeted in the clinic. Integrating multiple datasets is essential for assembling large enough patient cohorts to achieve an understanding of cancer-driving molecular aberrations in individual patients. The Treehouse Childhood Cancer Project consolidates gene expression, mutation and copy number datasets under the UCSC Cancer Genomics Browser (https://genome-cancer.ucsc.edu), and currently contains data from over 1000 pediatric tumors from TARGET and other studies. Treehouse enables mining these data alongside the data from adult cancers studied by The Cancer Genome Atlas consortium (TCGA). This is accomplished using bioinformatics tools developed for the TCGA Pan-Cancer Analysis Working Group and aimed at identifying situations where a subset of pediatric tumors may be driven by similar molecular pathways as adult tumors. We have assembled a consortium of researchers who plan to both contribute data to the Treehouse platform and apply Treehouse data in their analyses. These include John Maris (Children's Hospital of Philadelphia), Michael Taylor (Hospital for Sick Children, Toronto), Poul Sorensen (University of British Columbia), Timothy Triche (Children's Hospital Los Angeles), Soheil Meshinchi (Fred Hutchinson Cancer Research Center), Doug Hawkins (Seattle Children's Hospital), Javed Khan (NIH Center for Cancer Research), Ching Lao (Texas Children's Hospital), Leonard Sender (UC Irvine, Children's Hospital of Orange County), Alejandro Sweet-Cordero (Stanford School of Medicine), and D.W. Parsons (Baylor College of Medicine). In this submission, we demonstrate the utility of the Treehouse resource by analyzing the neuroblastoma TARGET cohort in the context of adult TCGA cancers. This work presents a proof of concept that cross-cancer multiple cohort analysis can lead to new insights into pediatric malignancies. Citation Format: Olena Morozova, Yulia Newton, Melissa Cline, Jingchun Zhu, Katrina Learned, Josh Stuart, Sofie Salama, Robert Arceci, David Haussler. Treehouse Childhood Cancer Project: a resource for sharing and multiple cohort analysis of pediatric cancer genomics data. [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 LB-212. doi:10.1158/1538-7445.AM2015-LB-212

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

The UCSC Cancer Genomics Browser (https://genome-cancer.ucsc.edu) comprises a suite of web-based tools to integrate, visualize and analyze cancer genomics and clinical data. The browser displays whole-genome views of genome-wide experimental measurements for multiple samples alongside their associated clinical information. Multiple datasets can be viewed simultaneously as coordinated “heatmap tracks” to compare across studies or different data modalities. Clinical information is encoded in color and shown as “clinical heatmap.” The browser provides interactive and dynamic views of the data from whole-genome scale to base-pair resolution, as well as interactive zooming onto a subset of the sample population. Users can order, filter, aggregate, classify, and display data interactively based on any given feature set including clinical features, annotated biological pathways, and user-contributed collections of genes. Integrated standard statistical tools provide dynamic quantitative analysis within each dataset. In June 2011, we deployed the current new site that supports a public portal, controlled-access portal for the TCGA consortium, Stand Up To Cancer breast cancer project, l-SPY clinical trial, and UCSF pancreas cancer project. User can view multiple project data, which she/he is authorized to access, under a single web page. In order to achieve this, we re-architected our system for a major data access security upgrade as well as deployed UCSC cancer browser user account. Although our public portal does not require an account to obtain access, user can control and store their favorite genesets, signatures, and manage their passwords if an account is established. An account and project-specific authorization are required for access to non-public tier data. Since the initial launch in November 2008, the cancer browser now contains large number of publicly accessible cancer genomic data, including 50 TCGA public-tier datasets and 19 other published studies (gene expression, copy number, DNA methylation, mutation and clinical information), corresponding to genome-wide experiments from 24488 samples across 15 cancers. Many new features have been added, including a tumor image viewer called hgMicroscope and a genomic signature evaluator, called hgSignature. User can define a genomic signature as any arithmetic formula calculated on a set of genes, such as a gene expression profile that correlates with better survival. User can also apply several gene expression signatures we derived from published studies on any browser track. The browser is integrated with the UCSC Genome Browser; thus inheriting and integrating the Genome Browser's rich set of human biology and genetics data that enhances the interpretability of the cancer genomics data. Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the Second AACR International Conference on Frontiers in Basic Cancer Research; 2011 Sep 14-18; San Francisco, CA. Philadelphia (PA): AACR; Cancer Res 2011;71(18 Suppl):Abstract nr A39.

The UCSC Cancer Genomics Browser (https://genome-cancer.ucsc.edu) comprises a suite of web-based tools to integrate, visualize and analyze cancer genomics and clinical data. The browser displays whole-genome views of genome-wide experimental measurements for multiple samples alongside their associated clinical information. Multiple datasets can be viewed simultaneously as coordinated “heatmap tracks” to compare across studies or different data modalities. Users can order, filter, aggregate, classify, and display data interactively based on any given feature set including clinical features, annotated biological pathways, and user-contributed collections of genes. Integrated standard statistical tools provide dynamic quantitative analysis within all available datasets. The browser hosts a growing body of publicly available cancer genomics data from a variety of cancer types, including data generated from the Cancer Genome Atlas project. Multiple consortiums use the browser on confidential prepublication data enabled by private installations. Many new features have been added, including the hgMicroscope tumor image viewer, hgSignature for real-time genomic signature evaluation on any browser track, and PARADIGM pathway tracks to display integrative pathway activities. The browser is integrated with the UCSC Genome Browser; thus inheriting and integrating the Genome Browser's rich set of human biology and genetics data that enhances the interpretability of the cancer genomics data. Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 102nd Annual Meeting of the American Association for Cancer Research; 2011 Apr 2-6; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2011;71(8 Suppl):Abstract nr 4985. doi:10.1158/1538-7445.AM2011-4985

With increasing ubiquity of genome-wide assays it is now common to molecularly subtype cancers to predict patient therapy response. However identifying high-performing, robust molecular signatures for predictions remains difficult. Presented here is work towards a novel machine-learning algorithm that discovers intuitively understood and clinically relevant stratifying molecular signatures. This classification method, as well as many competing methods (SVM, random forests, Bayes nets, etc.), were applied by predicting drug-sensitivity to hundreds of compounds tested on the NCI60 cell lines. This new drug-sensitivity prediction method competes with and in many cases outperforms leading classifiers. The prediction results, as well as the molecular signatures that they are derived from, are publicly available for web-browsing through a new extension to the UCSC Cancer Genomics Browser, hgClassifications (http://genome-cancer.soe.ucsc.edu/hgClassifications). Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 102nd Annual Meeting of the American Association for Cancer Research; 2011 Apr 2-6; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2011;71(8 Suppl):Abstract nr 43. doi:10.1158/1538-7445.AM2011-43

Background: The efficacy of cancer chemotherapy is generally assessed by the ability to directly kill or inhibit the growth of cancer cells. However, there is evidence suggesting that anticancer immune responses also contribute to the efficacy of conventional chemotherapy. Recent studies have shown that chemotherapeutic agents (anthracyclines in particular) can elicit innate immune responses via the release of proteins from dying tumor cells that interact with Toll-like receptors (TLR) on macrophages (mΦ) and dendritic cells (DC). The subsequent induction of cytotoxic T cell responses leads to the elimination of residual cancer cells not killed by the chemotherapy. To test this idea of chemotherapy-induced immunogenic cell death in breast cancer, we examined gene expression data for women with breast cancer treated neoadjuvantly, starting with adriamycin, on the I-SPY TRIAL (2003-2006), to see if genes that predict this type of immune response would also predict complete pathologic response (pCR) Methods: A panel of 47 genes were selected, including those expressed by the cancer cells as well as infiltrating immune cells: TLR/mΦ/DC activating proteins, TLR signaling genes, chemokines & chemokine receptors, cytokines & cytokine receptors, and mO, DC, & T cells markers. This gene set was then evaluated using expression array data from the I-SPY TRIAL, available on both Affymetrix (n=) and Agilent (n= 153) platforms. Initial analyses were performed using the UCSC Cancer Genomics Browser, a suite of web-based tools to integrate, visualize and analyze cancer genomics/expression data and clinical data. Differentially expressed genes were compared between patients who achieved a pathological complete response (pCR) following neoadjuvant chemotherapy vs. those who did not. A second independent data set available on the Cancer Genomics browser served as a validation set. These data, from MD Anderson, included 133 patients treated with neoadjuvant therapy (TFAC) with available Affymetrix array data. Results: Of the 47 selected genes, 1 was not present on the Affymetrix array and 7 were absent from the Agilent array. Twelve of the 46 genes (26%) on the Affymetrix array and 20 of the 40 genes (50%) on the Agilent array were significantly differentially expressed comparing responders vs. non-responders. Overall, 25 of the 47 genes (53%) were differentially expressed on one or both arrays. Gene set enrichment and Paradigm pathway inference analyses confirmed that these genes are significantly enriched among responders. In the validation set, 20 of 46 genes (43%) were significantly differentially expressed. Conclusion: We identified a panel of genes associated with immunogenic cell death and demonstrated their differential expression in patients who responded to neoadjuvant chemotherapy vs. non-responders. These results suggest a role for the host immune system in the response to chemotherapy. If the success of currently used chemotherapies depends on synergistic interactions with the immune system, then a better understanding of the mechanisms involved in chemotherapy-induced immunogenic cell death will provide a foundation for the design of novel chemo-immunotherapeutic strategies for breast cancer treatment. Citation Information: Cancer Res 2010;70(24 Suppl):Abstract nr P5-04-01.

Abstract #2022 As experimental techniques for a comprehensive survey of the cancer landscape mature, there is a great demand in the cancer research field to develop advanced analysis and visualization tools for the characterization and integrative analysis of the large, complex genomic datasets arising from different technology platforms. The UCSC Cancer Genomics Browser is a suite of web-based tools designed to integrate, visualize and analyze genomic and clinical data. The secured-access browser, available at https://cancer.cse.ucsc.edu/, consists of three major components: hgHeatmap, hgFeatureSorter, and hgPathSorter. The main panel, hgHeatmap, displays a whole-genome-oriented view of genome-wide experimental measurements for individual and sets of samples/patients alongside their clinical information. hgFeatureSorter and hgPathSorter together enable investigators to order, filter, aggregate and display data interactively based on any given feature set ranging from clinical features to annotated biological pathways to user-edited collections of genes. Standard and advanced statistical tools are available to provide quantitative analysis of whole genomic data or any of its subsets. The UCSC Cancer Genomics Browser is an extension of the UCSC Genome Browser; thus it inherits and integrates the Genome Browser's existing rich set of human biology and genetics data to enhance the interpretability of cancer genomics data. We demonstrate the UCSC Cancer Genomics Browser by integrating several independent studies on breast cancer including the I-SPY chemotherapy clinical trial and other studies focused on chemotherapeutic response or long-term survival. The types of data that are visualized and analyzed by the browser include microarray measurements of gene expression, copy number variation and phosphoprotein expression, MRI imaging measurements, and clinical parameters. Collectively, these tools facilitate a synergistic interaction among clinicians, experimental biologists, and bioinformaticians. They enable cancer researchers to better explore the breadth and depth of the cancer genomics data resources, and to further characterize molecular pathways that influence cellular dynamics and stability in cancer. Ultimately, insights gained by applying these tools may advance our knowledge of human cancer biology and stimulate the discovery of new prognostic and diagnostic markers, as well as the development of therapeutic and prevention strategies. Funding sources: CALGB CA31964 and CA33601, ACRIN U01 CA079778 and CA080098, NCI SPORE CA58207, California Institute for Quantitative Biosciences, NHGRI. Citation Information: Cancer Res 2009;69(2 Suppl):Abstract nr 2022.