An interactive resource to identify cancer genetic and lineage dependencies targeted by small molecules.

The high rate of clinical response to protein-kinase-targeting drugs matched to cancer patients with specific genomic alterations has prompted efforts to use cancer cell line (CCL) profiling to identify additional biomarkers of small-molecule sensitivities. We have quantitatively measured the sensitivity of 242 genomically characterized CCLs to an Informer Set of 354 small molecules that target many nodes in cell circuitry, uncovering protein dependencies that: (1) associate with specific cancer-genomic alterations and (2) can be targeted by small molecules. We have created the Cancer Therapeutics Response Portal (http://www.broadinstitute.org/ctrp) to enable users to correlate genetic features to sensitivity in individual lineages and control for confounding factors of CCL profiling. We report a candidate dependency, associating activating mutations in the oncogene ß-catenin with sensitivity to the Bcl-2 family antagonist, navitoclax. The resource can be used to develop novel therapeutic hypotheses and to accelerate discovery of drugs matched to patients by their cancer genotype and lineage.

Chromatin-targeting small molecules cause class-specific transcriptional changes in pancreatic endocrine cells.

Under the instruction of cell-fate-determining, DNA-binding transcription factors, chromatin-modifying enzymes mediate and maintain cell states throughout development in multicellular organisms. Currently, small molecules modulating the activity of several classes of chromatin-modifying enzymes are available, including clinically approved histone deacetylase (HDAC) and DNA methyltransferase (DNMT) inhibitors. We describe the genome-wide expression changes induced by 29 compounds targeting HDACs, DNMTs, histone lysine methyltransferases (HKMTs), and protein arginine methyltransferases (PRMTs) in pancreatic α- and ß-cell lines. HDAC inhibitors regulate several hundred transcripts irrespective of the cell type, with distinct clusters of dissimilar activity for hydroxamic acids and orthoamino anilides. In contrast, compounds targeting histone methyltransferases modulate the expression of restricted gene sets in distinct cell types. For example, we find that G9a/GLP methyltransferase inhibitors selectively up-regulate the cholesterol biosynthetic pathway in pancreatic but not liver cells. These data suggest that, despite their conservation across the entire genome and in different cell types, chromatin pathways can be targeted to modulate the expression of selected transcripts.

A flexible computational framework for detecting, characterizing, and interpreting statistical patterns of epistasis in genetic studies of human disease susceptibility.

Detecting, characterizing, and interpreting gene-gene interactions or epistasis in studies of human disease susceptibility is both a mathematical and a computational challenge. To address this problem, we have previously developed a multifactor dimensionality reduction (MDR) method for collapsing high-dimensional genetic data into a single dimension (i.e. constructive induction) thus permitting interactions to be detected in relatively small sample sizes. In this paper, we describe a comprehensive and flexible framework for detecting and interpreting gene-gene interactions that utilizes advances in information theory for selecting interesting single-nucleotide polymorphisms (SNPs), MDR for constructive induction, machine learning methods for classification, and finally graphical models for interpretation. We illustrate the usefulness of this strategy using artificial datasets simulated from several different two-locus and three-locus epistasis models. We show that the accuracy, sensitivity, specificity, and precision of a naïve Bayes classifier are significantly improved when SNPs are selected based on their information gain (i.e. class entropy removed) and reduced to a single attribute using MDR. We then apply this strategy to detecting, characterizing, and interpreting epistatic models in a genetic study (n = 500) of atrial fibrillation and show that both classification and model interpretation are significantly improved.