John Zachary Sanborn

UCSC cancer genomics browser.

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 Browser9s 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.

Comprehensive genomic transcriptomic tumor-normal gene panel analysis for enhanced precision in patients with lung cancer.

A CMS approved test for lung cancer is based on tumor-only analysis of a targeted 35 gene panel, specifically excluding the use of the patient’s normal germline tissue. However, this tumor-only approach increases the risk of mistakenly identifying germline single nucleotide polymorphisms (SNPs) as somatically-derived cancer driver mutations (false positives). 621 patients with 30 different cancer types, including lung cancer, were studied to compare the precision of tumor somatic variant calling in 35 genes using tumor-only DNA sequencing versus tumor-normal DNA plus RNA sequencing. When sequencing of lung cancer was performed using tumor genomes alone without normal germline controls, 94% of variants identified were SNPs and thus false positives. Filtering for common SNPs still resulted in as high as 48% false positive variant calling. With tumor-only sequencing, 29% of lung cancer patients had a false positive variant call in at least one of twelve genes with directly targetable drugs. RNA analysis showed 18% of true somatic variants were not expressed. Thus, sequencing and analysis of both normal germline and tumor genomes is necessary for accurate identification of molecular targets. Treatment decisions based on tumor-only analysis may result in the administration of ineffective therapies while also increasing the risk of negative drug-related side effects.

Abstract 3982: Cancer genome sequencing analysis, storage, discovery and delivery

Genome instability and structural rearrangement is a distinctive hallmark of the cancer genome. With next-gen sequencing technologies, our ability to measure structural rearrangements that occur throughout tumorigenesis and progression has improved significantly, while creating an urgent need for rearrangement discovery, analysis, and visualization methods to aid in our understanding of these events. We have developed a sequencing analysis pipeline that streamlines the discovery of an individual tumor9s mutations, small indels, copy number alterations, allele-specific amplifications and deletions, and genomic rearrangements. Rearrangements are refined to base-pair precision using unmapped, putative split reads found in the vicinity of the breakpoint. Results are presented in an interactive, web-based genome browser that provides analysis and visualization of both high-level, processed results as well as the raw data from which they were derived. From the web-based interface, results from multiple samples can be aggregated into user-defined sample cohorts to help identify features that are shared among a significant number of the samples. Using this sequencing analysis pipeline, we discovered high-confident, small- and large-scale somatic events in 17 whole genome glioblastoma multiforme (GBM) tumor samples from The Cancer Genome Atlas (TCGA) project, using their matched normal sequences to filter out germline variants. Among many interesting structural aberrations identified in these samples, we find four samples exhibiting EGFR amplifications and the presence of the EGFRvIII mutant, characterized by the in-frame deletion of exons 2-7 to produce a constitutively active form of the receptor. Comparing the read support of the EGFRvIII-associated breakpoints to the number of normally-mapped reads in the neighborhood suggest that, in all cases, the EGFRvIII mutant emerges after the amplification of wild-type EGFR, existing as a small fraction of total number of EGFR copies. In other samples, evidence of high copy number amplicons containing both MDM2 and EGFR are discovered at detectable levels in the blood sequencing data, raising the possibility that patient-specific PCR-based assays could be developed to quantitate the presence of somatic rearrangements as a proxy to monitor the progression of brain tumors. 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 3982. doi:1538-7445.AM2012-3982

PD03-04: SuperPathway Analyses of Luminal and Basaloid Breast Cancers from the Cancer Genome Atlas (TCGA) Program.

The biological and clinical heterogeneity of breast cancer is clearly evident by its different intrinsic transcriptome subtypes. With exception of the HER2 subtype, pathways and signaling networks driving and distinguishing the other major breast cancer subtypes (basaloid, luminal-A, luminal-B) remain largely undefined. As of April 2011, >300 TCGA breast cancer samples have been characterized by both genomic copy number analysis and mRNA expression profiling (44% luminal-A, 26% luminal-B, 16% basaloid). As well, overall survival (OS) data are presently available on 104 luminal-A and 52 luminal-B cases, showing significantly poorer outcome for the latter of these hormonally driven subtypes (log rank p TP53 gene mutations (∼34%), we used a gene expression signature reported for TP53 mutated estrogen receptor-positive (ER+) breast cancers (Coutant et al., 2011), and confirmed that the TCGA luminal-B cases significantly overexpress this signature relative to luminal-A breast cases in which TP53 mutations are uncommon (6%). Curiously, within each luminal subtype, while this signature correlated with TP53 mutations it did not correlate with TP53 loss of heterozygosity (LOH), which was also higher in luminal-B (∼30%) relative to luminal-A (∼10%) cases. We used the network analysis tool PARADIGM (Vaske et al., Bioinformatics 26, i247-245) to integrate both DNA copy number and transcriptome data and infer pathway activity and interaction differences between luminal-A and luminal-B cases, and between the luminal and basaloid subtypes. This tool merges features derived from curated signal transduction, transcriptional and metabolic pathways into a Superimposed Pathway (SuperPathway) containing ∼3.1K unique activities, 1820 of which revealed significant differences among the breast cancer subtypes (Kruskal-Wallis test, Benjamin Hochberg FDR-corrected p 10 edges), higher FOXA1 (ER signaling) and lower HIF1A/ARNT transcription factor hub activities emerged as shared luminal differences relative to basaloid breast cancers. Of the 433 significant activities differentiating luminal-A from luminal-B subtypes, MYC/MAX, FOXM1, and MYB emerged as more active hubs in luminal-B, with normal TP53 function as a more active hub in luminal-A breast cancers. In sum, these TCGA analyses offer further insights into the signaling pathways differentiating basaloid from luminal breast cancers, and reveal major transcription factor hubs distinguishing the two common hormone dependent subtypes, luminal-A and luminal-B breast cancers. These findings suggest that SuperPathway analyses may inform therapeutic target opportunities and promote clinical development of breast cancer subtype specific treatment regimens. Citation Information: Cancer Res 2011;71(24 Suppl):Abstract nr PD03-04.

Abstract 59: Identification of RNA editing events in cancer using high-throughput sequencing data

RNA editing is a post-transcriptional modification of pre-mRNA that has recently been identified as an additional epigenetic mechanism relevant to cancer development and progression. With projects like The Cancer Genome Atlas (TCGA) providing high-throughput sequencing datasets measuring both DNA and RNA from the same patients across multiple cancers, it is now possible to search for RNA editing events at a genome-wide scale. Using fully-sequenced tumor and matched-normal genomes and RNA-Seq data from TCGA project, we will identify RNA editing events in acute myeloid leukemia (AML) patients. We have analyzed the tumor and matched normal genomes to identify SNPs, mutations (both germline and somatic), and heterozygosity across the entire genome. By comparing the patient9s genomic data to the RNA transcripts assembled by the UCSC RNA-Seq pipeline, we can identify any bases that were transcribed abnormally. All putative RNA editing events will be assessed according to the most common types of RNA editing, such as the deamination of adenosine into inosine (A-to-I) or the conversion of cytosine into uracil (C-to-U). Local phasing information inferred from the genomic sequence will be used to disambiguate potential RNA editing events found at heterozygous locations. As a positive control, we will confirm RNA-editing events previously discovered experimentally in AML patients, such as an A-to-I conversion in the protein tyrosine phosphatase PTPN6 gene. The PTPN6 gene is recognized as a tumor suppressor gene and is important for the down-regulation of growth-promoting receptors. The A-to-I conversion of adenosine 7866 causes the splicing mechanism to ignore a splicing junction, leading to a non-functional PTPN6 protein via the inclusion of an intron in the mature RNA transcript. Using the TCGA sequencing data, we will identify all AML samples that exhibit this particular A-to-I conversion as well as the inclusion of the intron with the RNA-Seq data. In addition, we will report novel RNA editing events in AML and other cancer types and look for patterns that may be cancer specific or globally relevant to cancer development. 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 59. doi:10.1158/1538-7445.AM2011-59

P3-06-07: Integrated Genomic and Pathway Analysis Reveals Key Pathways across Breast Subtypes.

Cancer is a disease of genomic perturbations that lead to dysregulation of multiple pathways within the cellular system. While common pathways are believed to be shared within specific cancer types, the mechanisms behind why particular patients respond differently to treatment is not well understood. Genomics studies such as The Cancer Genome Atlas (TCGA) and Stand Up To Cancer (SU2C) attempt to address this issue by collecting large-scale whole-genome measurements of mRNA expression, DNA copy number, and epigenetic features. Common analysis of these measurements integrates data across multiple samples to distinguish signal from noise. However, serious challenges remain in identifying genomic features and pathways significant for prognosis and clinical treatment classifications. We have created the Five3 Analysis Pipeline to streamline discovery of individual samples’ mutations, small indels, copy number alterations, genome rearrangements, expression changes, and resulting pathway activities. This pipeline is capable of processing and integrating data from both next generation sequencing and microarray platforms in the analysis of single or multiple tumor samples. Our sequence analysis corrects for both tumor sample impurity and germline variation to accurately identify somatic mutations present in the tumor. Our pathway analysis incorporates gene copy number, mutations, expression, and promoter methylation on a superimposed pathway constructed from several curated pathway databases in a sample-specific manner. By applying this pipeline to the TCGA breast cancer datasets, we recapitulate established breast subtypes at a pathway-dependent level. For example, basal tumors appear enriched for proliferation pathways compared to luminal samples within this cohort. Expanding the pathway analysis to include TCGA lung cancer samples, we find similar subnetworks activated between basal and squamous lung and between luminal and lung adenocarcinomas. This hints at similar genomic mechanisms for these subtypes independent of tissue of origin. Finally, by analyzing genomic alterations across all breast cancers we see mutational clusters in PIK3CA that correspond with publicly-available hotspots [1]. As suggested by previous reports [2], we find that samples with mutations clustered in exon 10 exhibit differential pathway activities relative to those samples with mutations clustered in exon 21, independent of subtype and TP53 mutation status. These results show the power of this integrated genomic platform in elucidating pathway signatures and the need to consider cross cancer analyses to identify shared tumorigenic mechanisms that may suggest common therapeutic targets. [1] Forbes, S.A et al. COSMIC: mining complete cancer genomes in the Catalogue of Somatic Mutations in Cancer. Nucl. Acids Res. (2011) 39: D945-D950 [2] Vasudevan KM et al. AKT-independent signaling downstream of oncogenic PIK3CA mutations in human cancer. Cancer Cell 2009 Jul.;16(1):21–32. Citation Information: Cancer Res 2011;71(24 Suppl):Abstract nr P3-06-07.

Abstract 4905: Patient-specific pathway analysis using PARADIGM identifies key activities in multiple cancers

Cancer is a disease of genomic perturbations that lead to dysregulation of multiple pathways within the cellular system. While common pathways are believed to be shared within specific cancer types, the mechanisms of why particular patients respond differently to treatment is not fully understood. Current -omics studies such as The Cancer Genome Atlas (TCGA) and Stand Up To Cancer (SU2C) have attempted to address this issue by using large-scale whole-genome measurements of mRNA expression, DNA copy number, and epigenetic features. Typical analysis of these measurements relies on integrating data from multiple samples to distinguish signal from noise. However, few analytical methods allow for sample-specific differences to identify features and pathways that are significant for prognosis and clinical treatment classifications. We developed a pathway inference method called PARADIGM (PAthway Recognition Algorithm using Data Integration on Genomic Models) (Bioinformatics (2010) vol. 26 (12) pp. i237) to identify patient- and sample-specific pathway activities. Previously we have shown that PARADIGM is capable of stratifying patients into clinically relevant subgroups in both TCGA ovarian serous carcinoma and glioblastoma multiforme using gene expression and copy number alteration values. We have since enhanced PARADIGM in multiple ways allowing more in-depth analysis and discovery across multiple cancer types. We have expanded the underlying pathway database previously consisting of NCI9s Pathway Interaction Database to include pathways from BioCarta and Reactome, increasing the number of pathways by ten-fold and increasing the total number of features to approximately 22,000. We have refined the central dogma framework underlying all features in each pathway to support a more accurate regulatory model, allowing us to efficiently learn the strength of each interaction. Finally, PARADIGM now supports additional input data sources in the form of methylation and mutational interventions. By adding gene-level mutational information on the new regulatory model we show it is possible to distinguish between gain-of-function and loss-of-function mutations in each patient. In addition, through these enhancements we show increased ability to identify key pathway activities that effectively stratify patient cohorts. 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 4905. doi:10.1158/1538-7445.AM2011-4905