The systematic translation of cancer genomic data into knowledge of tumor biology and therapeutic avenues remains challenging. Such efforts should be greatly aided by robust preclinical model systems that reflect the genomic diversity of human cancers and for which detailed genetic and pharmacologic annotation is available1. Here we describe the Cancer Cell Line Encyclopedia (CCLE): a compilation of gene expression, chromosomal copy number, and massively parallel sequencing data from 947 human cancer cell lines. When coupled with pharmacologic profiles for 24 anticancer drugs across 479 of the lines, this collection allowed identification of genetic, lineage, and gene expression-based predictors of drug sensitivity. In addition to known predictors, we found that plasma cell lineage correlated with sensitivity to IGF1 receptor inhibitors; AHR expression was associated with MEK inhibitor efficacy in NRAS-mutant lines; and SLFN11 expression predicted sensitivity to topoisomerase inhibitors. Altogether, our results suggest that large, annotated cell line collections may help to enable preclinical stratification schemata for anticancer agents. The generation of genetic predictions of drug response in the preclinical setting and their incorporation into cancer clinical trial design could speed the emergence of “personalized” therapeutic regimens2.

Background: Elucidating gene regulatory networks is crucial for understanding normal cell physiology and complex pathologic phenotypes. Existing computational methods for the genomewide "reverse engineering" of such networks have been successful only for lower eukaryotes with simple genomes. Here we present ARACNE, a novel algorithm, using microarray expression profiles, specifically designed to scale up to the complexity of regulatory networks in mammalian cells, yet general enough to address a wider range of network deconvolution problems. This method uses an information theoretic approach to eliminate the majority of indirect interactions inferred by coexpression methods.

Cellular phenotypes are determined by the differential activity of networks linking coregulated genes. Available methods for the reverse engineering of such networks from genome-wide expression profiles have been successful only in the analysis of lower eukaryotes with simple genomes. Using a new method called ARACNe (algorithm for the reconstruction of accurate cellular networks), we report the reconstruction of regulatory networks from expression profiles of human B cells. The results are suggestive a hierarchical, scale-free network, where a few highly interconnected genes (hubs) account for most of the interactions. Validation of the network against available data led to the identification of MYC as a major hub, which controls a network comprising known target genes as well as new ones, which were biochemically validated. The newly identified MYC targets include some major hubs. This approach can be generally useful for the analysis of normal and pathologic networks in mammalian cells.

The NOTCH1 signaling pathway directly links extracellular signals with transcriptional responses in the cell nucleus and plays a critical role during T cell development and in the pathogenesis over 50% of human T cell lymphoblastic leukemia (T-ALL) cases. However, little is known about the transcriptional programs activated by NOTCH1. Using an integrative systems biology approach we show that NOTCH1 controls a feed-forward-loop transcriptional network that promotes cell growth. Inhibition of NOTCH1 signaling in T-ALL cells led to a reduction in cell size and elicited a gene expression signature dominated by down-regulated biosynthetic pathway genes. By integrating gene expression array and ChIP-on-chip data, we show that NOTCH1 directly activates multiple biosynthetic routes and induces c-MYC gene expression. Reverse engineering of regulatory networks from expression profiles showed that NOTCH1 and c-MYC govern two directly interconnected transcriptional programs containing common target genes that together regulate the growth of primary T-ALL cells. These results identify c-MYC as an essential mediator of NOTCH1 signaling and integrate NOTCH1 activation with oncogenic signaling pathways upstream of c-MYC.

CIViC is an expert-crowdsourced knowledgebase for Clinical Interpretation of Variants in Cancer describing the therapeutic, prognostic, diagnostic and predisposing relevance of inherited and somatic variants of all types. CIViC is committed to open-source code, open-access content, public application programming interfaces (APIs) and provenance of supporting evidence to allow for the transparent creation of current and accurate variant interpretations for use in cancer precision medicine.

We describe a computational protocol for the ARACNE algorithm, an information-theoretic method for identifying transcriptional interactions between gene products using microarray expression profile data. Similar to other algorithms, ARACNE predicts potential functional associations among genes, or novel functions for uncharacterized genes, by identifying statistical dependencies between gene products. However, based on biochemical validation, literature searches and DNA binding site enrichment analysis, ARACNE has also proven effective in identifying bona fide transcriptional targets, even in complex mammalian networks. Thus we envision that predictions made by ARACNE, especially when supplemented with prior knowledge or additional data sources, can provide appropriate hypotheses for the further investigation of cellular networks. While the examples in this protocol use only gene expression profile data, the algorithm's theoretical basis readily extends to a variety of other high-throughput measurements, such as pathway-specific or genome-wide proteomics, microRNA and metabolomics data. As these data become readily available, we expect that ARACNE might prove increasingly useful in elucidating the underlying interaction models. For a microarray data set containing approximately 10,000 probes, reconstructing the network around a single probe completes in several minutes using a desktop computer with a Pentium 4 processor. Reconstructing a genome-wide network generally requires a computational cluster, especially if the recommended bootstrapping procedure is used.

IntroductionBCL6 has emerged as a critical regulator of germinal centers (GCs), the sites where B cells undergo somatic hypermutation (SHM) and class switch recombination of their immunoglobulin genes (Ig) and are then selected on the basis of the production of antibodies with high affinity for the antigen. 1 BCL6 is also a frequently activated oncogene in the pathogenesis of human B-cell lymphomas, most of which derive from the GC B cells. The BCL6 gene encodes a 95-kDA nuclear phosphoprotein belonging to the BTB/POZ zinc-finger (ZF) family of transcription factors. 2-4 BCL6 functions as a transcriptional repressor via its C-terminal zinc-finger domain that binds to specific DNA sequences in the promoter region of target genes and 2 transcriptional repression domains 5 that interact with distinct corepressor complexes during the GC reaction. [6][7][8][9] Within the B-cell lineage, the BCL6 protein is expressed at high levels only in mature B cells within GCs. 10 GC formation and the development of normal T cell-dependent humoral immune responses require expression of BCL6 because BCL6-null mice do not form GCs and are unable to produce high-affinity antibodies. 2,4 BCL6 expression is regulated by several signals that are crucial for GC development. Activation of B-cell receptor (BCR) induces mitogen-activated protein kinase (MAPK)-mediated phosphorylation of the BCL6 protein, which targets BCL6 for rapid degradation by the ubiquitin proteasome pathway. 11 Stimulation of the CD40 receptor by CD40 ligands expressed by T cells leads to transcriptional down-regulation of BCL6 via a signaling pathway that involves nuclear factor (NF)-B-mediated transcriptional activation of interferon regulatory factor 4 (IRF4), which, in turn, directly represses BCL6 transcription. 12,13 BCL6 degradation is induced by DNA damage via a pathway that is distinct from the one induced by BCR, 14 whereas BCL6 function is also inactivated by acetylation, which triggers its dissociation from corepressor complexes. 15 These findings indicate that although BCL6 is required for GC formation, its downregulation may be critical for B cells to exit the GC and differentiate toward memory and plasma cells.A variety of structural alterations of the BCL6 gene are associated with its deregulated expression in B-cell lymphomas. Chromosomal translocations juxtaposing heterologous promoters to the BCL6 coding domain are found in approximately 40% of diffuse large B-cell lymphoma (DLBCL) and in a minority (5%-10%) of follicular lymphoma (FL). [16][17][18] The common denominator of these promoters is their constitutive activity in the B-cell lineage and in particular their persistent activity in post-GC cells such as immunoblasts and plasma cells, in contrast with the GC-specific activity of the BCL6 promoter. 19 In addition, although alterations of the 5Ј noncoding region of BCL6 by SHM is a feature of normal GC B cells, 20,21 specific mutations found only in DLBCL lead to the deregulated expression of BCL6 through disruption of the sequences mediating a...

Most small-molecule probes and drugs alter cell circuitry by interacting with 1 or more proteins. A complete understanding of the interacting proteins and their associated protein complexes, whether the compounds are discovered by cell-based phenotypic or targetbased screens, is extremely rare. Such a capability is expected to be highly illuminating-providing strong clues to the mechanisms used by small-molecules to achieve their recognized actions and suggesting potential unrecognized actions. We describe a powerful method combining quantitative proteomics (SILAC) with affinity enrichment to provide unbiased, robust and comprehensive identification of the proteins that bind to small-molecule probes and drugs. The method is scalable and general, requiring little optimization across different compound classes, and has already had a transformative effect on our studies of small-molecule probes. Here, we describe in full detail the application of the method to identify targets of kinase inhibitors and immunophilin binders.SILAC ͉ small molecules ͉ target identification