Charles J. Vaske

Inference of patient-specific pathway activities from multi-dimensional cancer genomics data using PARADIGM

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 pathways 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.

Voltage-Gated Na + Channel SCN5A Is a Key Regulator of a Gene Transcriptional Network That Controls Colon Cancer Invasion

Voltage-gated Na(+) channels (VGSC) have been implicated in the metastatic potential of human breast, prostate, and lung cancer cells. Specifically, the SCN5A gene encoding the VGSC isotype Na(v)1.5 has been defined as a key driver of human cancer cell invasion. In this study, we examined the expression and function of VGSCs in a panel of colon cancer cell lines by electrophysiologic recordings. Na(+) channel activity and invasive potential were inhibited pharmacologically by tetrodotoxin or genetically by small interfering RNAs (siRNA) specifically targeting SCN5A. Clinical relevance was established by immunohistochemistry of patient biopsies, with strong Na(v)1.5 protein staining found in colon cancer specimens but little to no staining in matched-paired normal colon tissues. We explored the mechanism of VGSC-mediated invasive potential on the basis of reported links between VGSC activity and gene expression in excitable cells. Probabilistic modeling of loss-of-function screens and microarray data established an unequivocal role of VGSC SCN5A as a high level regulator of a colon cancer invasion network, involving genes that encompass Wnt signaling, cell migration, ectoderm development, response to biotic stimulus, steroid metabolic process, and cell cycle control. siRNA-mediated knockdown of predicted downstream network components caused a loss of invasive behavior, demonstrating network connectivity and its function in driving colon cancer invasion.

Integrated molecular profiles of invasive breast tumors and ductal carcinoma in situ (DCIS) reveal differential vascular and interleukin signaling

We use an integrated approach to understand breast cancer heterogeneity by modeling mRNA, copy number alterations, microRNAs, and methylation in a pathway context utilizing the pathway recognition algorithm using data integration on genomic models (PARADIGM). We demonstrate that combining mRNA expression and DNA copy number classified the patients in groups that provide the best predictive value with respect to prognosis and identified key molecular and stromal signatures. A chronic inflammatory signature, which promotes the development and/or progression of various epithelial tumors, is uniformly present in all breast cancers. We further demonstrate that within the adaptive immune lineage, the strongest predictor of good outcome is the acquisition of a gene signature that favors a high T-helper 1 (Th1)/cytotoxic T-lymphocyte response at the expense of Th2-driven humoral immunity. Patients who have breast cancer with a basal HER2-negative molecular profile (PDGM2) are characterized by high expression of protumorigenic Th2/humoral-related genes (24–38%) and a low Th1/Th2 ratio. The luminal molecular subtypes are again differentiated by low or high FOXM1 and ERBB4 signaling. We show that the interleukin signaling profiles observed in invasive cancers are absent or weakly expressed in healthy tissue but already prominent in ductal carcinoma in situ, together with ECM and cell-cell adhesion regulating pathways. The most prominent difference between low and high mammographic density in healthy breast tissue by PARADIGM was that of STAT4 signaling. In conclusion, by means of a pathway-based modeling methodology (PARADIGM) integrating different layers of molecular data from whole-tumor samples, we demonstrate that we can stratify immune signatures that predict patient survival.

JARID1B Is a Luminal Lineage-Driving Oncogene in Breast Cancer

Recurrent mutations in histone-modifying enzymes imply key roles in tumorigenesis, yet their functional relevance is largely unknown. Here, we show that JARID1B, encoding a histone H3 lysine 4 (H3K4) demethylase, is frequently amplified and overexpressed in luminal breast tumors and a somatic mutation in a basal-like breast cancer results in the gain of unique chromatin binding and luminal expression and splicing patterns. Downregulation of JARID1B in luminal cells induces basal genes expression and growth arrest, which is rescued by TGFβ pathway inhibitors. Integrated JARID1B chromatin binding, H3K4 methylation, and expression profiles suggest a key function for JARID1B in luminal cell-specific expression programs. High luminal JARID1B activity is associated with poor outcome in patients with hormone receptor-positive breast tumors.

Single-cell analyses of transcriptional heterogeneity during drug tolerance transition in cancer cells by RNA sequencing.

Significance Tumor cells are heterogeneous, and much variation occurs at the single-cell level, which may contribute to therapeutic response. Here, we studied drug resistance dynamics in a model of tolerance with a metastatic breast cancer cell line by leveraging the power of single-cell RNA-Seq technology. Drug-tolerant cells within a single clone rapidly express high cell-to-cell transcript variability, with a gene expression profile similar to untreated cells, and the population reacquires paclitaxel sensitivity. Our gene expression and single nucleotide variants analyses suggest that equivalent phenotypes are achieved without relying on a unique molecular event or fixed transcriptional programs. Thus, transcriptional heterogeneity might ensure survival of cancer cells with equivalent combinations of gene expression programs and/or single nucleotide variants. The acute cellular response to stress generates a subpopulation of reversibly stress-tolerant cells under conditions that are lethal to the majority of the population. Stress tolerance is attributed to heterogeneity of gene expression within the population to ensure survival of a minority. We performed whole transcriptome sequencing analyses of metastatic human breast cancer cells subjected to the chemotherapeutic agent paclitaxel at the single-cell and population levels. Here we show that specific transcriptional programs are enacted within untreated, stressed, and drug-tolerant cell groups while generating high heterogeneity between single cells within and between groups. We further demonstrate that drug-tolerant cells contain specific RNA variants residing in genes involved in microtubule organization and stabilization, as well as cell adhesion and cell surface signaling. In addition, the gene expression profile of drug-tolerant cells is similar to that of untreated cells within a few doublings. Thus, single-cell analyses reveal the dynamics of the stress response in terms of cell-specific RNA variants driving heterogeneity, the survival of a minority population through generation of specific RNA variants, and the efficient reconversion of stress-tolerant cells back to normalcy.

A factor graph nested effects model to identify networks from genetic perturbations.

Complex phenotypes such as the transformation of a normal population of cells into cancerous tissue result from a series of molecular triggers gone awry. We describe a method that searches for a genetic network consistent with expression changes observed under the knock-down of a set of genes that share a common role in the cell, such as a disease phenotype. The method extends the Nested Effects Model of Markowetz et al. (2005) by using a probabilistic factor graph to search for a network representing interactions among these silenced genes. The method also expands the network by attaching new genes at specific downstream points, providing candidates for subsequent perturbations to further characterize the pathway. We investigated an extension provided by the factor graph approach in which the model distinguishes between inhibitory and stimulatory interactions. We found that the extension yielded significant improvements in recovering the structure of simulated and Saccharomyces cerevisae networks. We applied the approach to discover a signaling network among genes involved in a human colon cancer cell invasiveness pathway. The method predicts several genes with new roles in the invasiveness process. We knocked down two genes identified by our approach and found that both knock-downs produce loss of invasive potential in a colon cancer cell line. Nested effects models may be a powerful tool for inferring regulatory connections and genes that operate in normal and disease-related processes.

Lymphocyte Invasion in IC10/Basal-Like Breast Tumors Is Associated with Wild-Type TP53

Lymphocytic infiltration is associated with better prognosis in several epithelial malignancies including breast cancer. The tumor suppressor TP53 is mutated in approximately 30% of breast adenocarcinomas, with varying frequency across molecular subtypes. In this study of 1,420 breast tumors, we tested for interaction between TP53 mutation status and tumor subtype determined by PAM50 and integrative cluster analysis. In integrative cluster 10 (IC10)/basal-like breast cancer, we identify an association between lymphocytic infiltration, determined by an expression score, and retention of wild-type TP53. The expression-derived score agreed with the degree of lymphocytic infiltration assessed by pathologic review, and application of the Nanodissect algorithm was suggestive of this infiltration being primarily of cytotoxic T lymphocytes (CTL). Elevated expression of this CTL signature was associated with longer survival in IC10/Basal-like tumors. These findings identify a new link between the TP53 pathway and the adaptive immune response in estrogen receptor (ER)–negative breast tumors, suggesting a connection between TP53 inactivation and failure of tumor immunosurveillance. Implications: The association of lymphocytic invasion of ER-negative breast tumors with the retention of wild-type TP53 implies a novel protective connection between TP53 function and tumor immunosurveillance. Mol Cancer Res; 13(3); 493–501. ©2014 AACR.

The UCSC Interaction Browser: multidimensional data views in pathway context.

High-throughput data sets such as genome-wide protein–protein interactions, protein–DNA interactions and gene expression data have been published for several model systems, especially for human cancer samples. The University of California, Santa Cruz (UCSC) Interaction Browser (http://sysbio.soe.ucsc.edu/nets) is an online tool for biologists to view high-throughput data sets simultaneously for the analysis of functional relationships between biological entities. Users can access several public interaction networks and functional genomics data sets through the portal as well as upload their own networks and data sets for analysis. Users can navigate through correlative relationships for focused sets of genes belonging to biological pathways using a standard web browser. Using a new visual modality called the CircleMap, multiple ‘omics’ data sets can be viewed simultaneously within the context of curated, predicted, directed and undirected regulatory interactions. The Interaction Browser provides an integrative viewing of biological networks based on the consensus of many observations about genes and their products, which may provide new insights about normal and disease processes not obvious from any isolated data set.

Learning subgroup-specific regulatory interactions and regulator independence with PARADIGM

High-dimensional ‘-omics’ profiling provides a detailed molecular view of individual cancers; however, understanding the mechanisms by which tumors evade cellular defenses requires deep knowledge of the underlying cellular pathways within each cancer sample. We extended the PARADIGM algorithm (Vaske et al., 2010, Bioinformatics, 26, i237–i245), a pathway analysis method for combining multiple ‘-omics’ data types, to learn the strength and direction of 9139 gene and protein interactions curated from the literature. Using genomic and mRNA expression data from 1936 samples in The Cancer Genome Atlas (TCGA) cohort, we learned interactions that provided support for and relative strength of 7138 (78%) of the curated links. Gene set enrichment found that genes involved in the strongest interactions were significantly enriched for transcriptional regulation, apoptosis, cell cycle regulation and response to tumor cells. Within the TCGA breast cancer cohort, we assessed different interaction strengths between breast cancer subtypes, and found interactions associated with the MYC pathway and the ER alpha network to be among the most differential between basal and luminal A subtypes. PARADIGM with the Naive Bayesian assumption produced gene activity predictions that, when clustered, found groups of patients with better separation in survival than both the original version of PARADIGM and a version without the assumption. We found that this Naive Bayes assumption was valid for the vast majority of co-regulators, indicating that most co-regulators act independently on their shared target. Availability: http://paradigm.five3genomics.com Contact: charlie@five3genomics.com Supplementary information: Supplementary data are available at Bioinformatics online.

The Integration of Biological Pathway Knowledge in Cancer Genomics: A review of existing computational approaches

We review existing computational approaches for the integration of cancer genomic data with regulatory mechanisms represented in biological pathway databases and suggest opportunities for the signal processing community to contribute to this exciting field.