Next-generation characterization of the Cancer Cell Line Encyclopedia.

Large panels of comprehensively characterized human cancer models, including the Cancer Cell Line Encyclopedia (CCLE), have provided a rigorous framework with which to study genetic variants, candidate targets, and small-molecule and biological therapeutics and to identify new marker-driven cancer dependencies. To improve our understanding of the molecular features that contribute to cancer phenotypes, including drug responses, here we have expanded the characterizations of cancer cell lines to include genetic, RNA splicing, DNA methylation, histone H3 modification, microRNA expression and reverse-phase protein array data for 1,072 cell lines from individuals of various lineages and ethnicities. Integration of these data with functional characterizations such as drug-sensitivity, short hairpin RNA knockdown and CRISPR-Cas9 knockout data reveals potential targets for cancer drugs and associated biomarkers. Together, this dataset and an accompanying public data portal provide a resource for the acceleration of cancer research using model cancer cell lines.

Allosteric inhibition of SHP2 phosphatase inhibits cancers driven by receptor tyrosine kinases.

The non-receptor protein tyrosine phosphatase SHP2, encoded by PTPN11, has an important role in signal transduction downstream of growth factor receptor signalling and was the first reported oncogenic tyrosine phosphatase. Activating mutations of SHP2 have been associated with developmental pathologies such as Noonan syndrome and are found in multiple cancer types, including leukaemia, lung and breast cancer and neuroblastoma. SHP2 is ubiquitously expressed and regulates cell survival and proliferation primarily through activation of the RAS­ERK signalling pathway. It is also a key mediator of the programmed cell death 1 (PD-1) and B- and T-lymphocyte attenuator (BTLA) immune checkpoint pathways. Reduction of SHP2 activity suppresses tumour cell growth and is a potential target of cancer therapy. Here we report the discovery of a highly potent (IC50 = 0.071 µM), selective and orally bioavailable small-molecule SHP2 inhibitor, SHP099, that stabilizes SHP2 in an auto-inhibited conformation. SHP099 concurrently binds to the interface of the N-terminal SH2, C-terminal SH2, and protein tyrosine phosphatase domains, thus inhibiting SHP2 activity through an allosteric mechanism. SHP099 suppresses RAS­ERK signalling to inhibit the proliferation of receptor-tyrosine-kinase-driven human cancer cells in vitro and is efficacious in mouse tumour xenograft models. Together, these data demonstrate that pharmacological inhibition of SHP2 is a valid therapeutic approach for the treatment of cancers.

Disordered methionine metabolism in MTAP/CDKN2A-deleted cancers leads to dependence on PRMT5.

5-Methylthioadenosine phosphorylase (MTAP) is a key enzyme in the methionine salvage pathway. The MTAP gene is frequently deleted in human cancers because of its chromosomal proximity to the tumor suppressor gene CDKN2A. By interrogating data from a large-scale short hairpin RNA-mediated screen across 390 cancer cell line models, we found that the viability of MTAP-deficient cancer cells is impaired by depletion of the protein arginine methyltransferase PRMT5. MTAP-deleted cells accumulate the metabolite methylthioadenosine (MTA), which we found to inhibit PRMT5 methyltransferase activity. Deletion of MTAP in MTAP-proficient cells rendered them sensitive to PRMT5 depletion. Conversely, reconstitution of MTAP in an MTAP-deficient cell line rescued PRMT5 dependence. Thus, MTA accumulation in MTAP-deleted cancers creates a hypomorphic PRMT5 state that is selectively sensitized toward further PRMT5 inhibition. Inhibitors of PRMT5 that leverage this dysregulated metabolic state merit further investigation as a potential therapy for MTAP/CDKN2A-deleted tumors.

High-throughput screening using patient-derived tumor xenografts to predict clinical trial drug response.

Profiling candidate therapeutics with limited cancer models during preclinical development hinders predictions of clinical efficacy and identifying factors that underlie heterogeneous patient responses for patient-selection strategies. We established ∼1,000 patient-derived tumor xenograft models (PDXs) with a diverse set of driver mutations. With these PDXs, we performed in vivo compound screens using a 1 × 1 × 1 experimental design (PDX clinical trial or PCT) to assess the population responses to 62 treatments across six indications. We demonstrate both the reproducibility and the clinical translatability of this approach by identifying associations between a genotype and drug response, and established mechanisms of resistance. In addition, our results suggest that PCTs may represent a more accurate approach than cell line models for assessing the clinical potential of some therapeutic modalities. We therefore propose that this experimental paradigm could potentially improve preclinical evaluation of treatment modalities and enhance our ability to predict clinical trial responses.

Metabolic Enzyme Sulfotransferase 1A1 Is the Trigger for N-Benzyl Indole Carbinol Tumor Growth Suppression.

In an attempt to identify novel therapeutics and mechanisms to differentially kill tumor cells using phenotypic screening, we identified N-benzyl indole carbinols (N-BICs), synthetic analogs of the natural product indole-3-carbinol (I3C). To understand the mode of action for the molecules we employed Cancer Cell Line Encyclopedia viability profiling and correlative informatics analysis to identify and ultimately confirm the phase II metabolic enzyme sulfotransferase 1A1 (SULT1A1) as the essential factor for compound selectivity. Further studies demonstrate that SULT1A1 activates the N-BICs by rendering the compounds strong electrophiles which can alkylate cellular proteins and thereby induce cell death. This study demonstrates that the selectivity profile for N-BICs is through conversion by SULT1A1 from an inactive prodrug to an active species that induces cell death and tumor suppression.

Global chromatin profiling reveals NSD2 mutations in pediatric acute lymphoblastic leukemia.

Epigenetic dysregulation is an emerging hallmark of cancers. We developed a high-information-content mass spectrometry approach to profile global histone modifications in human cancers. When applied to 115 lines from the Cancer Cell Line Encyclopedia, this approach identified distinct molecular chromatin signatures. One signature was characterized by increased histone 3 lysine 36 (H3K36) dimethylation, exhibited by several lines harboring translocations in NSD2, which encodes a methyltransferase. A previously unknown NSD2 p.Glu1099Lys (p.E1099K) variant was identified in nontranslocated acute lymphoblastic leukemia (ALL) cell lines sharing this signature. Ectopic expression of the variant induced a chromatin signature characteristic of NSD2 hyperactivation and promoted transformation. NSD2 knockdown selectively inhibited the proliferation of NSD2-mutant lines and impaired the in vivo growth of an NSD2-mutant ALL xenograft. Sequencing analysis of >1,000 pediatric cancer genomes identified the NSD2 p.E1099K alteration in 14% of t(12;21) ETV6-RUNX1-containing ALLs. These findings identify NSD2 as a potential therapeutic target for pediatric ALL and provide a general framework for the functional annotation of cancer epigenomes.

FGFR genetic alterations predict for sensitivity to NVP-BGJ398, a selective pan-FGFR inhibitor.

UNLABELLED: Patient stratification biomarkers that enable the translation of cancer genetic knowledge into clinical use are essential for the successful and rapid development of emerging targeted anticancer therapeutics. Here, we describe the identification of patient stratification biomarkers for NVP-BGJ398, a novel and selective fibroblast growth factor receptor (FGFR) inhibitor. By intersecting genome-wide gene expression and genomic alteration data with cell line-sensitivity data across an annotated collection of cancer cell lines called the Cancer Cell Line Encyclopedia, we show that genetic alterations for FGFR family members predict for sensitivity to NVP-BGJ398. For the first time, we report oncogenic FGFR1 amplification in osteosarcoma as a potential patient selection biomarker. Furthermore, we show that cancer cell lines harboring FGF19 copy number gain at the 11q13 amplicon are sensitive to NVP-BGJ398 only when concomitant expression of ß-klotho occurs. Thus, our findings provide the rationale for the clinical development of FGFR inhibitors in selected patients with cancer harboring tumors with the identified predictors of sensitivity. SIGNIFICANCE: The success of a personalized medicine approach using targeted therapies ultimately depends on being able to identify the patients who will benefit the most from any given drug. To this end, we have integrated the molecular profiles for more than 500 cancer cell lines with sensitivity data for the novel anticancer drug NVP-BGJ398 and showed that FGFR genetic alterations are the most significant predictors for sensitivity. This work has ultimately endorsed the incorporation of specific patient selection biomakers in the clinical trials for NVP-BGJ398.

The Cancer Cell Line Encyclopedia enables predictive modelling of anticancer drug sensitivity.

The systematic translation of cancer genomic data into knowledge of tumour biology and therapeutic possibilities 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 pharmacological annotation is available. 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 pharmacological profiles for 24 anticancer drugs across 479 of the cell 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. Together, our results indicate 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 regimens.

Quality control methodology for high-throughput protein-protein interaction screening.

Protein-protein interactions are key to many aspects of the cell, including its cytoskeletal structure, the signaling processes in which it is involved, or its metabolism. Failure to form protein complexes or signaling cascades may sometimes translate into pathologic conditions such as cancer or neurodegenerative diseases. The set of all protein interactions between the proteins encoded by an organism constitutes its protein interaction network, representing a scaffold for biological function. Knowing the protein interaction network of an organism, combined with other sources of biological information, can unravel fundamental biological circuits and may help better understand the molecular basics of human diseases. The protein interaction network of an organism can be mapped by combining data obtained from both low-throughput screens, i.e., "one gene at a time" experiments and high-throughput screens, i.e., screens designed to interrogate large sets of proteins at once. In either case, quality controls are required to deal with the inherent imperfect nature of experimental assays. In this chapter, we discuss experimental and statistical methodologies to quantify error rates in high-throughput protein-protein interactions screens.

A genome-wide gene function prediction resource for Drosophila melanogaster.

Predicting gene functions by integrating large-scale biological data remains a challenge for systems biology. Here we present a resource for Drosophila melanogaster gene function predictions. We trained function-specific classifiers to optimize the influence of different biological datasets for each functional category. Our model predicted GO terms and KEGG pathway memberships for Drosophila melanogaster genes with high accuracy, as affirmed by cross-validation, supporting literature evidence, and large-scale RNAi screens. The resulting resource of prioritized associations between Drosophila genes and their potential functions offers a guide for experimental investigations.

Edgetic perturbation models of human inherited disorders.

Cellular functions are mediated through complex systems of macromolecules and metabolites linked through biochemical and physical interactions, represented in interactome models as 'nodes' and 'edges', respectively. Better understanding of genotype-to-phenotype relationships in human disease will require modeling of how disease-causing mutations affect systems or interactome properties. Here we investigate how perturbations of interactome networks may differ between complete loss of gene products ('node removal') and interaction-specific or edge-specific ('edgetic') alterations. Global computational analyses of approximately 50,000 known causative mutations in human Mendelian disorders revealed clear separations of mutations probably corresponding to those of node removal versus edgetic perturbations. Experimental characterization of mutant alleles in various disorders identified diverse edgetic interaction profiles of mutant proteins, which correlated with distinct structural properties of disease proteins and disease mechanisms. Edgetic perturbations seem to confer distinct functional consequences from node removal because a large fraction of cases in which a single gene is linked to multiple disorders can be modeled by distinguishing edgetic network perturbations. Edgetic network perturbation models might improve both the understanding of dissemination of disease alleles in human populations and the development of molecular therapeutic strategies.

Literature-curated protein interaction datasets.

High-quality datasets are needed to understand how global and local properties of protein-protein interaction, or 'interactome', networks relate to biological mechanisms, and to guide research on individual proteins. In an evaluation of existing curation of protein interaction experiments reported in the literature, we found that curation can be error-prone and possibly of lower quality than commonly assumed.

Empirically controlled mapping of the Caenorhabditis elegans protein-protein interactome network.

To provide accurate biological hypotheses and elucidate global properties of cellular networks, systematic identification of protein-protein interactions must meet high quality standards.We present an expanded C. elegans protein-protein interaction network, or 'interactome' map, derived from testing a matrix of approximately 10,000 x approximately 10,000 proteins using a highly specific, high-throughput yeast two-hybrid system. Through a new empirical quality control framework, we show that the resulting data set (Worm Interactome 2007, or WI-2007) was similar in quality to low-throughput data curated from the literature. We filtered previous interaction data sets and integrated them with WI-2007 to generate a high-confidence consolidated map (Worm Interactome version 8, or WI8). This work allowed us to estimate the size of the worm interactome at approximately 116,000 interactions. Comparison with other types of functional genomic data shows the complementarity of distinct experimental approaches in predicting different functional relationships between genes or proteins

An experimentally derived confidence score for binary protein-protein interactions.

Information on protein-protein interactions is of central importance for many areas of biomedical research. At present no method exists to systematically and experimentally assess the quality of individual interactions reported in interaction mapping experiments. To provide a standardized confidence-scoring method that can be applied to tens of thousands of protein interactions, we have developed an interaction tool kit consisting of four complementary, high-throughput protein interaction assays. We benchmarked these assays against positive and random reference sets consisting of well documented pairs of interacting human proteins and randomly chosen protein pairs, respectively. A logistic regression model was trained using the data from these reference sets to combine the assay outputs and calculate the probability that any newly identified interaction pair is a true biophysical interaction once it has been tested in the tool kit. This general approach will allow a systematic and empirical assignment of confidence scores to all individual protein-protein interactions in interactome networks.

An empirical framework for binary interactome mapping.

Several attempts have been made to systematically map protein-protein interaction, or 'interactome', networks. However, it remains difficult to assess the quality and coverage of existing data sets. Here we describe a framework that uses an empirically-based approach to rigorously dissect quality parameters of currently available human interactome maps. Our results indicate that high-throughput yeast two-hybrid (HT-Y2H) interactions for human proteins are more precise than literature-curated interactions supported by a single publication, suggesting that HT-Y2H is suitable to map a significant portion of the human interactome. We estimate that the human interactome contains approximately 130,000 binary interactions, most of which remain to be mapped. Similar to estimates of DNA sequence data quality and genome size early in the Human Genome Project, estimates of protein interaction data quality and interactome size are crucial to establish the magnitude of the task of comprehensive human interactome mapping and to elucidate a path toward this goal.

High-quality binary protein interaction map of the yeast interactome network.

Current yeast interactome network maps contain several hundred molecular complexes with limited and somewhat controversial representation of direct binary interactions. We carried out a comparative quality assessment of current yeast interactome data sets, demonstrating that high-throughput yeast two-hybrid (Y2H) screening provides high-quality binary interaction information. Because a large fraction of the yeast binary interactome remains to be mapped, we developed an empirically controlled mapping framework to produce a "second-generation" high-quality, high-throughput Y2H data set covering approximately 20% of all yeast binary interactions. Both Y2H and affinity purification followed by mass spectrometry (AP/MS) data are of equally high quality but of a fundamentally different and complementary nature, resulting in networks with different topological and biological properties. Compared to co-complex interactome models, this binary map is enriched for transient signaling interactions and intercomplex connections with a highly significant clustering between essential proteins. Rather than correlating with essentiality, protein connectivity correlates with genetic pleiotropy.

Network modeling links breast cancer susceptibility and centrosome dysfunction.

Many cancer-associated genes remain to be identified to clarify the underlying molecular mechanisms of cancer susceptibility and progression. Better understanding is also required of how mutations in cancer genes affect their products in the context of complex cellular networks. Here we have used a network modeling strategy to identify genes potentially associated with higher risk of breast cancer. Starting with four known genes encoding tumor suppressors of breast cancer, we combined gene expression profiling with functional genomic and proteomic (or 'omic') data from various species to generate a network containing 118 genes linked by 866 potential functional associations. This network shows higher connectivity than expected by chance, suggesting that its components function in biologically related pathways. One of the components of the network is HMMR, encoding a centrosome subunit, for which we demonstrate previously unknown functional associations with the breast cancer-associated gene BRCA1. Two case-control studies of incident breast cancer indicate that the HMMR locus is associated with higher risk of breast cancer in humans. Our network modeling strategy should be useful for the discovery of additional cancer-associated genes.

Genome-scale analysis of in vivo spatiotemporal promoter activity in Caenorhabditis elegans.

Differential regulation of gene expression is essential for cell fate specification in metazoans. Characterizing the transcriptional activity of gene promoters, in time and in space, is therefore a critical step toward understanding complex biological systems. Here we present an in vivo spatiotemporal analysis for approximately 900 predicted C. elegans promoters (approximately 5% of the predicted protein-coding genes), each driving the expression of green fluorescent protein (GFP). Using a flow-cytometer adapted for nematode profiling, we generated 'chronograms', two-dimensional representations of fluorescence intensity along the body axis and throughout development from early larvae to adults. Automated comparison and clustering of the obtained in vivo expression patterns show that genes coexpressed in space and time tend to belong to common functional categories. Moreover, integration of this data set with C. elegans protein-protein interactome data sets enables prediction of anatomical and temporal interaction territories between protein partners.

Epstein-Barr virus and virus human protein interaction maps.

A comprehensive mapping of interactions among Epstein-Barr virus (EBV) proteins and interactions of EBV proteins with human proteins should provide specific hypotheses and a broad perspective on EBV strategies for replication and persistence. Interactions of EBV proteins with each other and with human proteins were assessed by using a stringent high-throughput yeast two-hybrid system. Overall, 43 interactions between EBV proteins and 173 interactions between EBV and human proteins were identified. EBV-EBV and EBV-human protein interaction, or "interactome" maps provided a framework for hypotheses of protein function. For example, LF2, an EBV protein of unknown function interacted with the EBV immediate early R transactivator (Rta) and was found to inhibit Rta transactivation. From a broader perspective, EBV genes can be divided into two evolutionary classes, "core" genes, which are conserved across all herpesviruses and subfamily specific, or "noncore" genes. Our EBV-EBV interactome map is enriched for interactions among proteins in the same evolutionary class. Furthermore, human proteins targeted by EBV proteins were enriched for highly connected or "hub" proteins and for proteins with relatively short paths to all other proteins in the human interactome network. Targeting of hubs might be an efficient mechanism for EBV reorganization of cellular processes.