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.

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.

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

Cellular circuits sense the environment, process signals, and compute decisions using networks of interacting proteins. To model such a system, the abundance of each activated protein species can be described as a stochastic function of the abundance of other proteins. High-dimensional single-cell technologies, such as mass cytometry, offer an opportunity to characterize signaling circuit-wide. However, the challenge of developing and applying computational approaches to interpret such complex data remains. Here, we developed computational methods, based on established statistical concepts, to characterize signaling network relationships by quantifying the strengths of network edges and deriving signaling response functions. In comparing signaling between naïve and antigen-exposed CD4(+) T lymphocytes, we find that although these two cell subtypes had similarly wired networks, naïve cells transmitted more information along a key signaling cascade than did antigen-exposed cells. We validated our characterization on mice lacking the extracellular-regulated mitogen-activated protein kinase (MAPK) ERK2, which showed stronger influence of pERK on pS6 (phosphorylated-ribosomal protein S6), in naïve cells as compared with antigen-exposed cells, as predicted. We demonstrate that by using cell-to-cell variation inherent in single-cell data, we can derive response functions underlying molecular circuits and drive the understanding of how cells process signals.

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

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 gene's 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.

HNF-3A, a hepatocyte-enriched transcription factor of novel structure is regulated transcriptionally

Hepatocyte-specific gene expression requires the interaction of many proteins with multiple binding sites in the regulatory regions. HNF-3 is a site found to be important in the maximal hepatocyte-specific expression of several genes. We find that liver nuclear extracts contain three major binding activities for this site, which we call HNF-3A, HNF-3B, and HNF-3C. Purification from rat liver nuclear extracts of HNF-3A and HNF-3C reveals that each activity corresponds to a distinct polypeptide, as determined by SDS-PAGE. Peptide sequence derived from the most abundant species, HNF-3A, was used for synthesizing probes with which to isolate a cDNA clone of this protein. The encoded protein contains 466 amino acids (48.7 kD) and has binding properties identical to those of the purified protein. A 160-amino-acid region that does not resemble the binding domain of any known transcription factor is essential for DNA binding. The mRNA for HNF-3A is present in the rat liver but not in brain, kidney, intestine, or spleen, and the basis for this difference is cell-specific regulation of HNF-3A gene transcription.