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.