TCGA RNA-seq Data Research Articles

Abstract 1934: Improved deconvolution of heterogeneous tumor data to reconstruct clonal evolution from bulk genomic samples

Abstract Intratumor heterogeneity has long been a confounding factor in interpreting cancer genomic data, but has also been useful in reconstructing progression processes based on variation between clonal populations in single tumors. We previously developed a strategy of applying computational deconvolution algorithms to gene expression or DNA copy number data to reconstruct models of progression of cell populations from bulk tumor samples. Many tools have since been proposed for similar deconvolution analysis, but all are limited by the computational difficulty of unambiguously distinguishing small cell populations and variants from noise in genomic assays. We present a novel approach to infer tumor progression pathways from deconvolved genomic data designed to leverage the fact that tumors that partition into subtypes with similar evolutionary trajectories would be expected to lead to a mathematical substructure in the genomic data, known as a simplicial complex, which can be modeled computationally to better deconvolve cell populations across tumor types. We have developed a computational pipeline to perform tumor deconvolution while taking into consideration this kind of mathematical structure. The pipeline clusters tumors to identify genetically distinct subgroups, fits mixture models to these subgroups, and uses overlap between them to infer a refined deconvolution of major cellular populations and possible pathways of progression between them. We apply our methods to a set of RNASeq data from the TCGA breast cancer data set and to synthetic data modeling distinct scenarios of tumor progression. We first compare our methods on the synthetic data to our earlier work and to a comparative Gaussian mixture model. All methods perform comparably on unstructured data but the new method substantially outperforms the others on data consistent with simple scenarios for tumor progression along multiple discrete subtypes. The novel method, however, shows lower tolerance for noisy data than a Gaussian mixture model. Application to the TCGA RNASeq data showed our method could partition tumors into discrete subcategories associated with HER2+, ER/PR+, and triple-negative status and could exploit the resulting substructure of the data to deconvolve tumor data and infer progression models reflecting partial sharing of progression states between subtypes. Gene enrichment analysis showed association of deconvolved cell populations with a variety of gene functional categories suggestive of distinct progression mechanisms of the subtypes. Citation Format: Theodore Roman, Russell Schwartz. Improved deconvolution of heterogeneous tumor data to reconstruct clonal evolution from bulk genomic samples. [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 1934. doi:10.1158/1538-7445.AM2015-1934

Loregic: a method to characterize the cooperative logic of regulatory factors.

The topology of the gene-regulatory network has been extensively analyzed. Now, given the large amount of available functional genomic data, it is possible to go beyond this and systematically study regulatory circuits in terms of logic elements. To this end, we present Loregic, a computational method integrating gene expression and regulatory network data, to characterize the cooperativity of regulatory factors. Loregic uses all 16 possible two-input-one-output logic gates (e.g. AND or XOR) to describe triplets of two factors regulating a common target. We attempt to find the gate that best matches each triplet’s observed gene expression pattern across many conditions. We make Loregic available as a general-purpose tool (github.com/gersteinlab/loregic). We validate it with known yeast transcription-factor knockout experiments. Next, using human ENCODE ChIP-Seq and TCGA RNA-Seq data, we are able to demonstrate how Loregic characterizes complex circuits involving both proximally and distally regulating transcription factors (TFs) and also miRNAs. Furthermore, we show that MYC, a well-known oncogenic driving TF, can be modeled as acting independently from other TFs (e.g., using OR gates) but antagonistically with repressing miRNAs. Finally, we inter-relate Loregic’s gate logic with other aspects of regulation, such as indirect binding via protein-protein interactions, feed-forward loop motifs and global regulatory hierarchy.

Abstract P4-04-16: Highly variably expressed exons (HVEE) of cancer genes and survival disparity in human breast cancer

Abstract Background: RNA-Seq is a powerful technology for accurately quantifying the transcriptome of human cancers. The human breast cancer section of the TCGA has generated RNA-Seq data for over 950 samples from primary tumors and adjacent normal tissues. Analysis of these data has provided insights into transcript isoforms and gene fusions related to the etiology of breast cancer. These rich data no doubt contain other insights into the expression of cancer genes and how it relates to cancer clinical outcomes. Methods: TCGA RNA-Seq data were downloaded from TCGA data portal. A total of 712 samples were available when this project started, including data from 607 primary breast tumors of which 108 were Basal-like, 316 Luminal A, 139 Luminal B, and 54 HER2-enriched. Data from 65 adjacent normal tissues were also included to be used as controls. We examined the exon-level expression for a group of 137 strongly cancer related genes that can be grouped into 12 pathways (Vogelstein et al. 2013). We focused on exons that display a significant (p <0.05) difference in their expression variation, compared to the other exons of the same gene (> 2 fold changes in terms of standard deviation based on bootstrap resampling). We refer to these exons as HVEEs (Highly variably expressed exons). Using both unsupervised and supervised algorithms (e.g. non-negative matrix factorization), we classified the samples from various subtypes based on the expression patterns of the HVEEs and looked for patterns that associate with clinical outcomes. Results: For many cancer genes, such as BRCA2 and PTEN, the distribution of within-gene exon expression is similar across all tumor samples and the control group. Subtype-specific amplification of oncogenes (e.g. ERBB2 in the HER2 subtype) and lower expression of cancer genes (e.g. AR in the basal subtype) have also been observed. We observed that exon expression across genes and across samples has in general small variations. Out of the 2,153 exons from the 137 cancer genes, we identified only 41 genes with HVEEs. Most of these genes have only one HVEE but a small number of them have multiple HVEEs (48 in total). Interestingly, HVEEs in 31 of these genes are consistent across the four breast cancer subtypes, as well as the normal group. However, the medians of such HVEE expression across the normal samples are in general significantly lower than those across the tumor samples in any tumor subtypes. Further, using the exon expression data of only the HVEEs, we identified distinct clusters for the basal-like subtype. The 108 basal-like subtype samples can be classified into two clusters with 41 and 67 samples, respectively, based on the expression level of the HVEEs from the above mentioned 31 genes. Overall survival for patients in these two clusters trends toward significance (log-rank test p = 0.058). Discussion: The consistency of HVEEs across breast cancer and the normal samples and the ability of the HVEE expression level to stratify basal-like breast cancer in a clinically meaningful way is intriguing. We are extending the study to a larger dataset for validation. The views expressed in this abstract are those of the authors and do not reflect the official policy of the Department of Defense, or US Government. Citation Information: Cancer Res 2013;73(24 Suppl): Abstract nr P4-04-16.

Abstract 1796: Analysis of TCGA RNASeq data using SpliceSeq provides a survey of alternative splicing in cancer.

Abstract The vast majority of human genes express more than one transcript isoform, and aberrant splicing patterns or changes in isoform expression ratios can lead to disease, notably cancer. Next generation sequencing platforms deliver the finest resolution yet of the transcriptome, and the wealth of sequencing data produced by TCGA provides an excellent basis for a broad study of alternative splicing in cancer. We have developed an interactive, exceptionally user-friendly tool, SpliceSeq, for investigation of alternative splicing in RNASeq data. The program is a follow-up to our SpliceCenter package (http://projects.insilico.us/SpliceCenter/SpliceOverview.jsp), which provides tools for use by the bench biologist in assessing experimental observations that may result from splice variation. Our results from SpliceSeq for TCGA RNASeq data from several cancer types will be presented. We identify pervasive changes in splicing patterns across different cancer types by comparing tumor vs. normal and clinical subgroups (e.g. tumor stage, hyper-mutator status, and MSI designation). Interesting observations include striking splice variations in APP, the amyloid beta precursor protein, and in PKM, which is centrally involved in the balance between oxidative and glycolytic metabolism in tumors. SpliceSeq is freely available at http://bioinformatics.mdanderson.org/main/SpliceSeq:Overview. 1. Ryan MC, Cleland J, Kim R, Wong WC, Weinstein JN. SpliceSeq: A Resource for Analysis and Visualization of RNA-Seq Data on Alternative Splicing and Its Functional Impacts. Bioinformatics, 10.1093, 2012. 2. Ryan MC, Zeeberg BR, Caplen NJ, Cleland JA, Kahn AB, Liu H, and Weinstein JN. SpliceCenter: a suite of web-based bioinformatic applications for evaluating the impact of alternative splicing on RT-PCR, RNAi, microarray, and peptide-based studies. BMC Bioinformatics 9:313, 2008. 3. Kahn AB, Ryan MC, Liu H, Zeeberg BR, Jamison DC, Weinstein JN. SpliceMiner: a high-throughput database implementation of the NCBI Evidence Viewer for microarray splice variant analysis. BMC Bioinformatics 8:752, 2006. Supported by NCI Grant No. U24CA143883. The content is solely the responsibility of the authors and does not necessarily represent official views of NCI or NIH. Also supported in part by a gift from the Mary K. Chapman Foundation and a grant from the Michael and Susan Dell Foundation honoring Lorraine Dell. Citation Format: Michael C. Ryan, John N. Weinstein. Analysis of TCGA RNASeq data using SpliceSeq provides a survey of alternative splicing in cancer. [abstract]. In: Proceedings of the 104th Annual Meeting of the American Association for Cancer Research; 2013 Apr 6-10; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2013;73(8 Suppl):Abstract nr 1796. doi:10.1158/1538-7445.AM2013-1796