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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. Data is kept on the researcher9s computer (we support Mac, Windows, and Linux) and is never uploaded to public servers. Questions Xena can help you answer: * Is overexpression of this gene associated with lower/higher survival? * Do my two subgroups have differential survival? * Is this gene differentially expressed in tumor vs normal 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 phenotypes, subtype classifications and biomarkers. All of our data is available for download via python or R APIs, or URL links. We show multiple data types side-by-side enabling discovery of correlations across and within genes and genomic regions. Other visualizations and analyses include dynamic Kaplan-Meier survival analysis, powerful filtering and subgrouping, 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: * Data from PCAWG, latest data from the GDC, ATAC-seq from TCGA, and other studies like MET500 * New visualizations for ATAC-seq and DNA methylation data * Multiple survival endpoints for Kaplan-Meier analyses from the PanCan Atlas * Export PDFs from Chart View * Genomic signatures now supported for all datasets, including data from the GDC * Updated navigation to make it easier to dive into any genomic region * Better support for probes (e.g. methylation probes like "cg16203911") We are now published in Nature Biotechnology! If you use us, cite us here: https://www.nature.com/articles/s41587-020-0546-8 We have also started to visualize scRNA-seq data including data from the HCA and the literature. Our beta prototype site delivers million-cell-scale multi-omics data for interactive visualization in a web browser. Contact us for access to our beta prototype site. Citation Format: Mary J. Goldman, Brian Craft, Jing-chun Zhu, David Haussler. UCSC Xena for the visualization and analysis of cancer genomics data [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2021; 2021 Apr 10-15 and May 17-21. Philadelphia (PA): AACR; Cancer Res 2021;81(13_Suppl):Abstract nr 250.

Funder: U.S. Department of Health & Human Services | NIH | National Cancer Institute (NCI), Funder: Ontario Institute for Cancer Research (Institut Ontarien de Recherche sur le Cancer); doi: https://doi.org/10.13039/100012118, Funder: EMBL Member States EU FP7 Programme projects EurocanPlatform (260791) CAGEKID (241669), Funder: European Union’s Framework Programme For Research and Innovation Horizon 2020 under the Marie Sklodowska-Curie grant agreement no. 703543, Funder: Michael & Susan Dell Foundation; Mary K. Chapman Foundation; CCSG Grant P30 CA016672 (Bioinformatics Shared Resource); ITCR U24 CA199461; GDAN U24 CA210949; GDAN U24 CA210950, Funder: European Commission's H2020 Programme, project SOUND, Grant Agreement no 633974, Funder: Spanish Government (SEV 2015-0493) BSC-Lenovo Master Collaboration Agreement (2015), The Pan-Cancer Analysis of Whole Genomes (PCAWG) project generated a vast amount of whole-genome cancer sequencing resource data. Here, as part of the ICGC/TCGA Pan-Cancer Analysis of Whole Genomes (PCAWG) Consortium, which aggregated whole genome sequencing data from 2658 cancers across 38 tumor types, we provide a user’s guide to the five publicly available online data exploration and visualization tools introduced in the PCAWG marker paper. These tools are ICGC Data Portal, UCSC Xena, Chromothripsis Explorer, Expression Atlas, and PCAWG-Scout. We detail use cases and analyses for each tool, show how they incorporate outside resources from the larger genomics ecosystem, and demonstrate how the tools can be used together to understand the biology of cancers more deeply. Together, the tools enable researchers to query the complex genomic PCAWG data dynamically and integrate external information, enabling and enhancing interpretation.

We describe a comprehensive genomic characterization of adrenocortical carcinoma (ACC). Using this dataset, we expand the catalogue of known ACC driver genes to include PRKAR1A, RPL22, TERF2, CCNE1, and NF1. Genome wide DNA copy-number analysis revealed frequent occurrence of massive DNA loss followed by whole-genome doubling (WGD), which was associated with aggressive clinical course, suggesting WGD is a hallmark of disease progression. Corroborating this hypothesis were increased TERT expression, decreased telomere length, and activation of cell-cycle programs. Integrated subtype analysis identified three ACC subtypes with distinct clinical outcome and molecular alterations which could be captured by a 68-CpG probe DNA-methylation signature, proposing a strategy for clinical stratification of patients based on molecular markers.

UCSC Xena is a visual exploration resource for both public and private omics data, supported through the web-based Xena Browser and multiple turn-key Xena Hubs. This unique archecture allows researchers to view their own data securely, using private Xena Hubs, simultaneously visualizing large public cancer genomics datasets, including TCGA and the GDC. Data integration occurs only within the Xena Browser, keeping private data private. Xena supports virtually any functional genomics data, including SNVs, INDELs, large structural variants, CNV, expression, DNA methylation, ATAC-seq signals, and phenotypic annotations. Browser features include the Visual Spreadsheet, survival analyses, powerful filtering and subgrouping, statistical analyses, genomic signatures, and bookmarks. Xena differentiates itself from other genomics tools, including its predecessor, the UCSC Cancer Genomics Browser, by its ability to easily and securely view public and private data, its high performance, its broad data type support, and many unique features.

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.

Gene co-expression networks capture biologically important patterns in gene expression data, enabling functional analyses of genes, discovery of biomarkers, and interpretation of regulatory genetic variants. Most network analyses to date have been limited to assessing correlation between total gene expression levels in a single or small sets of tissues. Here, we have reconstructed networks that capture a much more complete set of regulatory relationships, specifically including regulation of relative isoform abundance and splicing, and tissue-specific connections unique to each of a diverse set of tissues. Using the Genotype-Tissue Expression (GTEx) project v6 RNA-sequencing data across 44 tissues in 449 individuals, we evaluated shared and tissue-specific network relationships. First, we developed a framework called Transcriptome Wide Networks (TWNs) for combining total expression and relative isoform levels into a single sparse network, capturing the complex interplay between the regulation of splicing and transcription. We built TWNs for sixteen tissues, and found that hubs with isoform node neighbors in these networks were strongly enriched for splicing and RNA binding genes, demonstrating their utility in unraveling regulation of splicing in the human transcriptome, and providing a set of candidate shared and tissue-specific regulatory hub genes. Next, we used a Bayesian biclustering model that identifies network edges between genes with co-expression in a single tissue to reconstruct tissue-specific networks (TSNs) for 27 distinct GTEx tissues and for four subsets of related tissues. Using both TWNs and TSNs, we characterized gene co-expression patterns shared across tissues. Finally, we found genetic variants associated with multiple neighboring nodes in our networks, supporting the estimated network structures and identifying 33 genetic variants with distant regulatory impact on transcription and splicing. Our networks provide an improved understanding of the complex relationships between genes in the human transcriptome, including tissue-specificity of gene co-expression, regulation of splicing, and the coordinated impact of genetic variation on transcription.

The Pan-Cancer Analysis of Whole Genomes (PCAWG) project has generated, to our knowledge, the largest whole-genome cancer sequencing resource to date. Here we provide a user’s guide to the five publicly available online data exploration and visualization tools introduced in the PCAWG marker paper: The ICGC Data Portal, UCSC Xena, Expression Atlas, PCAWG-Scout, and Chromothripsis Explorer. We detail use cases and analyses for each tool, show how they incorporate outside resources from the larger genomics ecosystem, as well as demonstrate how the tools can be used together to more deeply understand tumor biology. Together, these tools enable researchers to dynamically query complex genomics data and integrate external information, enabling and enhancing PCAWG data interpretation. More information on these tools and their capabilities is available from The PCAWG Data Portals and Visualizations Page (http://docs.icgc.org/pcawg).

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.

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.

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

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.

Adenocarcinoma of the lung is the leading cause of cancer death worldwide. Here we report molecular profiling of 230 resected lung adenocarcinomas using messenger RNA, microRNA and DNA sequencing integrated with copy number, methylation and proteomic analyses. High rates of somatic mutation were seen (mean 8.9 mutations per megabase). Eighteen genes were statistically significantly mutated, including RIT1 activating mutations and newly described loss-of-function MGA mutations which are mutually exclusive with focal MYC amplification. EGFR mutations were more frequent in female patients, whereas mutations in RBM10 were more common in males. Aberrations in NF1, MET, ERBB2 and RIT1 occurred in 13% of cases and were enriched in samples otherwise lacking an activated oncogene, suggesting a driver role for these events in certain tumours. DNA and mRNA sequence from the same tumour highlighted splicing alterations driven by somatic genomic changes, including exon 14 skipping in MET mRNA in 4% of cases. MAPK and PI(3)K pathway activity, when measured at the protein level, was explained by known mutations in only a fraction of cases, suggesting additional, unexplained mechanisms of pathway activation. These data establish a foundation for classification and further investigations of lung adenocarcinoma molecular pathogenesis.

Lung squamous cell carcinoma is a common type of lung cancer, causing approximately 400,000 deaths per year worldwide. Genomic alterations in squamous cell lung cancers have not been comprehensively characterized, and no molecularly targeted agents have been specifically developed for its treatment. As part of The Cancer Genome Atlas, here we profile 178 lung squamous cell carcinomas to provide a comprehensive landscape of genomic and epigenomic alterations. We show that the tumour type is characterized by complex genomic alterations, with a mean of 360 exonic mutations, 165 genomic rearrangements, and 323 segments of copy number alteration per tumour. We find statistically recurrent mutations in 11 genes, including mutation of TP53 in nearly all specimens. Previously unreported loss-of-function mutations are seen in the HLA-A class I major histocompatibility gene. Significantly altered pathways included NFE2L2 and KEAP1 in 34%, squamous differentiation genes in 44%, phosphatidylinositol-3-OH kinase pathway genes in 47%, and CDKN2A and RB1 in 72% of tumours. We identified a potential therapeutic target in most tumours, offering new avenues of investigation for the treatment of squamous cell lung cancers., National Institutes of Health (U.S.) (Grant U24 CA126561), National Institutes of Health (U.S.) (Grant U24 CA126551), National Institutes of Health (U.S.) (Grant U24 CA126554), National Institutes of Health (U.S.) (Grant U24 CA126543), National Institutes of Health (U.S.) (Grant U24 CA126546), National Institutes of Health (U.S.) (Grant U24 CA126563), National Institutes of Health (U.S.) (Grant U24 CA126544), National Institutes of Health (U.S.) (Grant U24 CA143845), National Institutes of Health (U.S.) (Grant U24 CA143858), National Institutes of Health (U.S.) (Grant U24 CA144025), National Institutes of Health (U.S.) (Grant U24 CA143882), National Institutes of Health (U.S.) (Grant U24 CA143866), National Institutes of Health (U.S.) (Grant U24 CA143867), National Institutes of Health (U.S.) (Grant U24 CA143848), National Institutes of Health (U.S.) (Grant U24 CA143840), National Institutes of Health (U.S.) (Grant U24 CA143835), National Institutes of Health (U.S.) (Grant U24 CA143799), National Institutes of Health (U.S.) (Grant U24 CA143883), National Institutes of Health (U.S.) (Grant U24 CA143843), National Institutes of Health (U.S.) (Grant U54 HG003067), National Institutes of Health (U.S.) (Grant U54 HG003079), National Institutes of Health (U.S.) (Grant U54 HG003273)

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 data sets 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 data sets. 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.

Motivation: High-throughput data is providing a comprehensive view of the molecular changes in cancer tissues. New technologies allow for the simultaneous genome-wide assay of the state of genome copy number variation, gene expression, DNA methylation and epigenetics of tumor samples and cancer cell lines. Analyses of current data sets find that genetic alterations between patients can differ but often involve common pathways. It is therefore critical to identify relevant pathways involved in cancer progression and detect how they are altered in different patients. Results: We present a novel method for inferring patient-specific genetic activities incorporating curated pathway interactions among genes. A gene is modeled by a factor graph as a set of interconnected variables encoding the expression and known activity of a gene and its products, allowing the incorporation of many types of omic data as evidence. The method predicts the degree to which a pathway's activities (e.g. internal gene states, interactions or high-level ‘outputs’) are altered in the patient using probabilistic inference. Compared with a competing pathway activity inference approach called SPIA, our method identifies altered activities in cancer-related pathways with fewer false-positives in both a glioblastoma multiform (GBM) and a breast cancer dataset. PARADIGM identified consistent pathway-level activities for subsets of the GBM patients that are overlooked when genes are considered in isolation. Further, grouping GBM patients based on their significant pathway perturbations divides them into clinically-relevant subgroups having significantly different survival outcomes. These findings suggest that therapeutics might be chosen that target genes at critical points in the commonly perturbed pathway(s) of a group of patients. Availability:Source code available at http://sbenz.github.com/Paradigm Contact: jstuart@soe.ucsc.edu Supplementary information: Supplementary data are available at Bioinformatics online.

Taking advantage of the complete genome sequences of several mammals, we developed a novel method to detect losses of well-established genes in the human genome through syntenic mapping of gene structures between the human, mouse, and dog genomes. Unlike most previous genomic methods for pseudogene identification, this analysis is able to differentiate losses of well-established genes from pseudogenes formed shortly after segmental duplication or generated via retrotransposition. Therefore, it enables us to find genes that were inactivated long after their birth, which were likely to have evolved nonredundant biological functions before being inactivated. The method was used to look for gene losses along the human lineage during the approximately 75 million years (My) since the common ancestor of primates and rodents (the euarchontoglire crown group). We identified 26 losses of well-established genes in the human genome that were all lost at least 50 My after their birth. Many of them were previously characterized pseudogenes in the human genome, such as GULO and UOX. Our methodology is highly effective at identifying losses of single-copy genes of ancient origin, allowing us to find a few well-known pseudogenes in the human genome missed by previous high-throughput genome-wide studies. In addition to confirming previously known gene losses, we identified 16 previously uncharacterized human pseudogenes that are definitive losses of long-established genes. Among them is ACYL3, an ancient enzyme present in archaea, bacteria, and eukaryotes, but lost approximately 6 to 8 Mya in the ancestor of humans and chimps. Although losses of well-established genes do not equate to adaptive gene losses, they are a useful proxy to use when searching for such genetic changes. This is especially true for adaptive losses that occurred more than 250,000 years ago, since any genetic evidence of the selective sweep indicative of such an event has been erased., Author Summary One of the most important questions in biology is to identify the genetic changes underlying evolution, especially those along the lineage leading to the modern human. Although counterintuitive, losing a gene might actually bring a selective advantage to the organism. This type of gene loss is called adaptive gene loss. Although a few cases have been characterized in the literature, this is the first study to address adaptive gene losses on a scale of the whole human genome and a time period of up to 75 million years. The difficulty of identifying adaptive gene losses is in part the large number of pseudogenes in the human genome. To circumvent this problem, we used two methods to enrich the process for the adaptive candidates. The first is a novel approach for pseudogene detection that is highly sensitive in identifying single-copy pseudogenes that bear no apparent sequence homology to any functional human genes. Second, we used the length of time a gene is functional before loss as a proxy for biological importance, which allows us to differentiate losses of long-established genes from mere losses due to functional redundancy after gene duplication.

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

Plasmodium falciparum is the causative agent of the most burdensome form of human malaria, affecting 200–300 million individuals per year worldwide. The recently sequenced genome of P. falciparum revealed over 5,400 genes, of which 60% encode proteins of unknown function. Insights into the biochemical function and regulation of these genes will provide the foundation for future drug and vaccine development efforts toward eradication of this disease. By analyzing the complete asexual intraerythrocytic developmental cycle (IDC) transcriptome of the HB3 strain of P. falciparum, we demonstrate that at least 60% of the genome is transcriptionally active during this stage. Our data demonstrate that this parasite has evolved an extremely specialized mode of transcriptional regulation that produces a continuous cascade of gene expression, beginning with genes corresponding to general cellular processes, such as protein synthesis, and ending with Plasmodium-specific functionalities, such as genes involved in erythrocyte invasion. The data reveal that genes contiguous along the chromosomes are rarely coregulated, while transcription from the plastid genome is highly coregulated and likely polycistronic. Comparative genomic hybridization between HB3 and the reference genome strain (3D7) was used to distinguish between genes not expressed during the IDC and genes not detected because of possible sequence variations. Genomic differences between these strains were found almost exclusively in the highly antigenic subtelomeric regions of chromosomes. The simple cascade of gene regulation that directs the asexual development of P. falciparum is unprecedented in eukaryotic biology. The transcriptome of the IDC resembles a “just-in-time” manufacturing process whereby induction of any given gene occurs once per cycle and only at a time when it is required. These data provide to our knowledge the first comprehensive view of the timing of transcription throughout the intraerythrocytic development of P. falciparum and provide a resource for the identification of new chemotherapeutic and vaccine candidates., A tight cascade of gene regulation during the lifecycle of the malaria parasite in human blood cells suggests new functions for many Plasmodium genes

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. 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

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.

We analysed primary breast cancers by genomic DNA copy number arrays, DNA methylation, exome sequencing, messenger RNA arrays, microRNA sequencing and reverse-phase protein arrays. Our ability to integrate information across platforms provided key insights into previously defined gene expression subtypes and demonstrated the existence of four main breast cancer classes when combining data from five platforms, each of which shows significant molecular heterogeneity. Somatic mutations in only three genes (TP53, PIK3CA and GATA3) occurred at >10% incidence across all breast cancers; however, there were numerous subtype-associated and novel gene mutations including the enrichment of specific mutations in GATA3, PIK3CA and MAP3K1 with the luminal A subtype. We identified two novel protein-expression-defined subgroups, possibly produced by stromal/microenvironmental elements, and integrated analyses identified specific signalling pathways dominant in each molecular subtype including a HER2/phosphorylated HER2/EGFR/phosphorylated EGFR signature within the HER2-enriched expression subtype. Comparison of basal-like breast tumours with high-grade serous ovarian tumours showed many molecular commonalities, indicating a related aetiology and similar therapeutic opportunities. The biological finding of the four main breast cancer subtypes caused by different subsets of genetic and epigenetic abnormalities raises the hypothesis that much of the clinically observable plasticity and heterogeneity occurs within, and not across, these major biological subtypes of breast cancer.