Oren Litvin

An Integrated Approach to Uncover Drivers of Cancer

Systematic characterization of cancer genomes has revealed a staggering number of diverse aberrations that differ among individuals, such that the functional importance and physiological impact of most tumor genetic alterations remain poorly defined. We developed a computational framework that integrates chromosomal copy number and gene expression data for detecting aberrations that promote cancer progression. We demonstrate the utility of this framework using a melanoma data set. Our analysis correctly identified known drivers of melanoma and predicted multiple tumor dependencies. Two dependencies, TBC1D16 and RAB27A, confirmed empirically, suggest that abnormal regulation of protein trafficking contributes to proliferation in melanoma. Together, these results demonstrate the ability of integrative Bayesian approaches to identify candidate drivers with biological, and possibly therapeutic, importance in cancer.

Data-driven phenotypic dissection of AML reveals progenitor-like cells that correlate with prognosis

Acute myeloid leukemia (AML) manifests as phenotypically and functionally diverse cells, often within the same patient. Intratumor phenotypic and functional heterogeneity have been linked primarily by physical sorting experiments, which assume that functionally distinct subpopulations can be prospectively isolated by surface phenotypes. This assumption has proven problematic, and we therefore developed a data-driven approach. Using mass cytometry, we profiled surface and intracellular signaling proteins simultaneously in millions of healthy and leukemic cells. We developed PhenoGraph, which algorithmically defines phenotypes in high-dimensional single-cell data. PhenoGraph revealed that the surface phenotypes of leukemic blasts do not necessarily reflect their intracellular state. Using hematopoietic progenitors, we defined a signaling-based measure of cellular phenotype, which led to isolation of a gene expression signature that was predictive of survival in independent cohorts. This study presents new methods for large-scale analysis of single-cell heterogeneity and demonstrates their utility, yielding insights into AML pathophysiology.

Conditional density-based analysis of T cell signaling in single-cell data

Introduction Cellular circuits sense the environment, process signals, and compute decisions using networks of interacting proteins. Emerging high-dimensional single-cell technologies such as mass cytometry can measure dozens of protein epitopes simultaneously in millions of individual cells. With thousands of individual cells, each providing a point of data on co-occurring protein states, it is possible to infer and quantify the functional forms of the relationships between proteins. However, in practice these underlying relationships are typically obscured by statistical limitations of the data, hence rendering the analysis and interpretation of single-cell data challenging. We developed computational methods, tailored to single-cell data, to more completely define the function and strength of signaling relationships. Quantitative characterization of T cell signaling. (A) The pCD3ζ-pSLP76 signaling interaction shown as (I) a scatterplot, (II) a kernel density estimate, and (III) by using a conditional DREVI method. (IV) Shape features are extracted and quantified. (B) DREVI plots of a signaling cascade downstream of TCR show the time-varying nature of edge shapes and strengths

Modularity and interactions in the genetics of gene expression

Understanding the effect of genetic sequence variation on phenotype is a major challenge that lies at the heart of genetics. We developed GOLPH (GenOmic Linkage to PHenotype), a statistical method to identify genetic interactions, and used it to characterize the landscape of genetic interactions between gene expression quantitative trait loci. Our results reveal that allele-specific interactions, in which a gene only exerts an influence on the phenotype in the presence of a particular allele at the primary locus, are widespread and that genetic interactions are predominantly nonadditive. The data portray a complex picture in which interacting loci influence the expression of modules of coexpressed genes involved in coherent biological processes and pathways. We show that genetic variation at a single gene can have a major impact on the global transcriptional response, altering interactions between genes through shutdown or activation of pathways. Thus, different cellular states occur not only in response to the external environment but also result from intrinsic genetic variation.

Context Sensitive Modeling of Cancer Drug Sensitivity

Recent screening of drug sensitivity in large panels of cancer cell lines provides a valuable resource towards developing algorithms that predict drug response. Since more samples provide increased statistical power, most approaches to prediction of drug sensitivity pool multiple cancer types together without distinction. However, pan-cancer results can be misleading due to the confounding effects of tissues or cancer subtypes. On the other hand, independent analysis for each cancer-type is hampered by small sample size. To balance this trade-off, we present CHER (Contextual Heterogeneity Enabled Regression), an algorithm that builds predictive models for drug sensitivity by selecting predictive genomic features and deciding which ones should—and should not—be shared across different cancers, tissues and drugs. CHER provides significantly more accurate models of drug sensitivity than comparable elastic-net-based models. Moreover, CHER provides better insight into the underlying biological processes by finding a sparse set of shared and type-specific genomic features.

Interferon α/β Enhances the Cytotoxic Response of MEK Inhibition in Melanoma

Drugs that inhibit the MAPK pathway have therapeutic benefit in melanoma, but responses vary between patients, for reasons that are still largely unknown. Here we aim at explaining this variability using pre- and post-MEK inhibition transcriptional profiles in a panel of melanoma cell lines. We found that most targets are context specific, under the influence of the pathway in only a subset of cell lines. We developed a computational method to identify context-specific targets, and found differences in the activity levels of the interferon pathway, driven by a deletion of the interferon locus. We also discovered that IFNα/β treatment strongly enhances the cytotoxic effect of MEK inhibition, but only in cell lines with low activity of interferon pathway. Taken together, our results suggest that the interferon pathway plays an important role in, and predicts, the response to MAPK inhibition in melanoma. Our analysis demonstrates the value of system-wide perturbation data in predicting drug response.

Inference of modules associated to eQTLs

Cataloging the association of transcripts to genetic variants in recent years holds the promise for functional dissection of regulatory structure of human transcription. Here, we present a novel approach, which aims at elucidating the joint relationships between transcripts and single-nucleotide polymorphisms (SNPs). This entails detection and analysis of modules of transcripts, each weakly associated to a single genetic variant, together exposing a high-confidence association signal between the module and this ‘main’ SNP. To explore how transcripts in a module are related to causative loci for that module, we represent such dependencies by a graphical model. We applied our method to the existing data on genetics of gene expression in the liver. The modules are significantly more, larger and denser than found in permuted data. Quantification of the confidence in a module as a likelihood score, allows us to detect transcripts that do not reach genome-wide significance level. Topological analysis of each module identifies novel insights regarding the flow of causality between the main SNP and transcripts. We observe similar annotations of modules from two sources of information: the enrichment of a module in gene subsets and locus annotation of the genetic variants. This and further phenotypic analysis provide a validation for our methodology.

Abstract SY17-02: A systems approach to understanding tumor specific drug response

Proceedings: AACR 102nd Annual Meeting 2011‐‐ Apr 2‐6, 2011; Orlando, FL Systematic characterization of cancer genomes has revealed a staggering complexity of aberrations among individuals, such that the functional importance and physiological impact of most tumor genetic alterations remains poorly defined. A major challenge involves the development of analysis methods to uncover biological insights from the data, including the identification of the key mutations that drive cancer and how these events alter cellular regulation. We have developed Conexic, a novel Bayesian Network-based framework to integrate chromosomal copy number and gene expression data to detect to detect driver genes located in regions that are aberrant in tumors. The underlying assumption is that a driving mutation might be associated with a characteristic gene expression signature representing genes whose expression is modulated by the driver. Thus our score guided approach searches for genes that are both recurrently aberrant and associated with variance of expression patterns across tumor samples. This method not only pinpoints specific regulators within a large aberrant region, but also by associating drivers with gene modules whose expression vary with the driver, provides insight into the physiological roles of drivers and associated genes. We demonstrated the utility of the CONEXIC framework using a melanoma dataset, our analysis correctly identified known drivers in melanoma (such as MITF) and connected these to many of their known targets, as well as the biological processes they regulate. In addition, it predicted multiple tumor dependencies TBC1D16 and RAB27A in melanoma and showed that tumors highly expressing these genes are dependent on the same gene for growth. Additionally, gene expression in the associated modules is altered following knockdown as predicted by our model. The identity of these drivers suggests that abnormal regulation of protein trafficking is important for cell survival in melanoma and highlights the importance of protein trafficking in this malignancy. We also present more recent results of applying CONEXIC to additional cancers, including glioblastoma and ovarian cancers, as well as additional phenotypes including invasion and drug resistance. Together, these results demonstrate the ability of integrative Bayesian approaches to identify novel drivers with biological, and possibly therapeutic, importance in cancer. 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 SY17-02. doi:10.1158/1538-7445.AM2011-SY17-02

Abstract B70: Conexic: A Bayesian framework to detect drivers and their function uncovers an endosomal signature in melanoma

Genomics is revolutionizing our understanding of cancer biology. Tumor samples assayed for comprehensive chromosomal and gene expression data are accumulating at a staggering rate. A major challenge involves the development of analysis methods to uncover biological insights from these data, including the identification of the key mutations that drive cancer and how these events alter cellular regulation. We have developed Conexic, a novel computational framework to integrate chromosomal copy number and gene expression data to detect genetic alterations in tumors that drive proliferation, and to model how these alterations perturb normal cell growth/survival. The underlying assumption to our approach is that significantly recurring copy number change, coinciding with its ability to predict the expression patterns varying across tumors, strengthens the evidence of a gene9s causative role in cancer. This method not only pinpoints specific regulators within an a large region of copy number variation, but can shed light on the way in which gene regulation is altered We applyed our Conexic framework to a melanoma dataset (Lin et al, Cancer Research, 2007) comprising 65 paired measurements of gene expression and copy number, with interesting results. Our analysis correctly identified many known ‘driver’ events, while also connecting these to many of their known targets (e.g. MITF). Our global integrative analysis reveals insight into how the drivers alter transcriptional programs. An interesting recurring characteristic is that there are a number of different ways by which drivers can be altered; e.g., the expression of a driver may be altered through copy number variation or other mechanisms, but its influence downstream remains the same. In addition to confirming the role of known drivers in melanoma, our analysis suggests a number of novel drivers. Most strikingly, these point to a major role in regulation of protein trafficking and endosome biology in this malignancy. These results have linked endosomal processing and sorting to adhesion and survival. Preliminary experimental validation supports three novel drivers including TBC1D16, RAB7A and RAB27A. Together, these results affirm the potential of Conexic to elaborate novel driver modules with biological and possibly therapeutic importance in melanoma and other cancers. Citation Information: Cancer Res 2009;69(23 Suppl):B70.