Stephen Charles Benz

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.

Subtype and pathway specific responses to anticancer compounds in breast cancer

Breast cancers are comprised of molecularly distinct subtypes that may respond differently to pathway-targeted therapies now under development. Collections of breast cancer cell lines mirror many of the molecular subtypes and pathways found in tumors, suggesting that treatment of cell lines with candidate therapeutic compounds can guide identification of associations between molecular subtypes, pathways, and drug response. In a test of 77 therapeutic compounds, nearly all drugs showed differential responses across these cell lines, and approximately one third showed subtype-, pathway-, and/or genomic aberration-specific responses. These observations suggest mechanisms of response and resistance and may inform efforts to develop molecular assays that predict clinical response.

Breast Cancer Growth Prevention by Statins

Statins are cholesterol-lowering drugs with pleiotropic activities including inhibition of isoprenylation reactions and reduction of signals driving cell proliferation and survival responses. The objectives of this study were to examine the effects of statins on breast cancer cells, both in vitro and in vivo, and to begin to determine their mechanism of action. We evaluated the effects of statins on breast cancer cell growth, phosphoprotein signaling intermediates, survival/apoptosis regulators, cell cycle regulators, and activated transcription factors. We also examined the in vivo effect of statin administration in a mouse ErbB2(+) breast cancer model. Only lipophilic statins had direct anticancer activity in vitro. Breast cancer cells with activated Ras or ErbB2 pathways seemed to be more sensitive than those overexpressing estrogen receptor, and this correlated with endogenous levels of activated nuclear factor kappaB (NF-kappaB). Key intermediates regulating cell survival by NF-kappaB activation, as well as cell proliferation by the mitogen activated protein kinase cascade, were among the earliest phosphoproteins influenced by statin treatment. These early effects were followed by declines in activator protein-1 and NF-kappaB activation and concordant changes in other mediators of proliferation and apoptosis. In vivo results showed that oral dosing of statins significantly inhibited the growth of a mouse mammary carcinoma. Lipophilic statins can exert direct anticancer activity in vitro by reducing proliferation and survival signals in susceptible breast cancer phenotypes. Tumor growth inhibition in vivo using a clinically relevant statin dose also seems to be associated with reduced tumor cell proliferation and survival. These findings provide supporting rationale for future statin trials in breast cancer patients.

Targeting RPL39 and MLF2 reduces tumor initiation and metastasis in breast cancer by inhibiting nitric oxide synthase signaling

Significance This manuscript describes the identification and characterization of two previously unidentified cancer genes, ribosomal protein L39 and myeloid leukemia factor 2, that play an important role in tumor initiation and metastasis. Knockdown of these genes in triple negative breast cancer (TNBC) models significantly reduces primary-tumor growth, as well as metastasis. Mutations in these genes are associated with worse survival in breast-cancer patients. Both genes are regulated by the nitric oxide signaling pathway. Identification of these two genes represents a significant breakthrough in our understanding of treatment resistance in TNBC. Targeting these genes could alter clinical practice for tumor metastasis in future and improve outcomes of patients with breast cancer. We previously described a gene signature for breast cancer stem cells (BCSCs) derived from patient biopsies. Selective shRNA knockdown identified ribosomal protein L39 (RPL39) and myeloid leukemia factor 2 (MLF2) as the top candidates that affect BCSC self-renewal. Knockdown of RPL39 and MLF2 by specific siRNA nanoparticles in patient-derived and human cancer xenografts reduced tumor volume and lung metastases with a concomitant decrease in BCSCs. RNA deep sequencing identified damaging mutations in both genes. These mutations were confirmed in patient lung metastases (n = 53) and were statistically associated with shorter median time to pulmonary metastasis. Both genes affect the nitric oxide synthase pathway and are altered by hypoxia. These findings support that extensive tumor heterogeneity exists within primary cancers; distinct subpopulations associated with stem-like properties have increased metastatic potential.

A gene signature for late distant metastasis in breast cancer identifies a potential mechanism of late recurrences

Breast cancer risk of recurrence is known to span 20 years, yet existing prognostic signatures are best at predicting early recurrences (≤5 years). There is a critical need to identify those patients at risk of late‐relapse (>5 years), in order to select potential candidates for further treatment and to identify molecular targets for such treatment.

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.

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.

S1-6: Characterization of Breast Cancer Distant Metastasis Based on Outcome over Time Using a Gene Expression Profiling Approach and Identification of Pathway Activities of Late Relapse.

Background Previous reports have described the use of microarrays to assess the molecular classification of human breast cancers and defined new subgroups based on gene expression that are relevant to patient management through their ability to predict metastatic relapse and survival relapse. However, different mechanisms may be associated with the development of early and late distant metastases. With the hypothesis that tumors may lead to early or distant metastases based on their intrinsic biological initial features, we aimed at defining molecular profiles for several subgroups of patients based on their outcome over time. Material and methods Breast primary tumors were selected from retrospective series of patients with frozen material available. These series include patients of all ages, LN- and LN+; Estrogen or Progesteron-receptor positive, Her2-negative, no adjuvant treatment, with a follow-up of more than 10 years (y) for the control group or distant metastatic relapse as first event (DM) for the study group (n=144). Patients tumors were classified in 4 groups: no relapse at 10 y (M0), DM before 3 y (M0-3, n=30), DM between 3 and 7 y (M3-7), DM after 7 y (M7+). Samples were collected in 2 different institutions (NKI series for identifying the signature and IGR series for validation). Gene expression analysis of breast tumor samples was performed using custom-made Agilent 44K high-density microarrays and hybridized against the Mammaprint® reference pool (MRP). Tumors were also assessed for their Mammaprint® status, wound-healing signature status and their intrinsic subtypes based on the Blueprint® signature. Moreover, we identified the pathway-level activities of the patient groups using PARADIGM. Results and Discussion For the NKI series, A subset of 144 samples was included based on the selection criteria: 57 M0, 31 M0-3, 25 M3-7, 31 M7+. None of the 3 previously mentioned signatures correctly identified M0 vs. M7+ patients. In order to identify a predictive signature of late relapse (after 7y) we considered M0 and M7+ MammaPrint-Low Risk patients and we split them in a training (n=41) and in a test (n=23) sets. A 73-gene signature was able to classify M7+ patients with 75% of sensitivity and 66% of specificity on the test set. DM after 7yr showed significant activation of pathway related to inflammatory response and angiogenesis. Detailed results and validation results on the independent IGR series will be presented at the meeting. Citation Information: Cancer Res 2011;71(24 Suppl):Abstract nr S1-6.