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.

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

The Cancer Genome Atlas (TCGA) Research Network has profiled and analyzed large numbers of human tumors to discover molecular aberrations at the DNA, RNA, protein and epigenetic levels. The resulting rich data provide a major opportunity to develop an integrated picture of commonalities, differences and emergent themes across tumor lineages. The Pan-Cancer initiative compares the first 12 tumor types profiled by TCGA. Analysis of the molecular aberrations and their functional roles across tumor types will teach us how to extend therapies effective in one cancer type to others with a similar genomic profile.

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The cancer genome atlas pan-cancer analysis project

The Cancer Genome Atlas Pan-Cancer analysis project

Cancer epigenetics: from mechanism to therapy.

Alterations in cancer genomes strongly influence clinical responses to treatment and in many instances are potent biomarkers for response to drugs. The Genomics of Drug Sensitivity in Cancer (GDSC) database (www.cancerRxgene.org) is the largest public resource for information on drug sensitivity in cancer cells and molecular markers of drug response. Data are freely available without restriction. GDSC currently contains drug sensitivity data for almost 75 000 experiments, describing response to 138 anticancer drugs across almost 700 cancer cell lines. To identify molecular markers of drug response, cell line drug sensitivity data are integrated with large genomic datasets obtained from the Catalogue of Somatic Mutations in Cancer database, including information on somatic mutations in cancer genes, gene amplification and deletion, tissue type and transcriptional data. Analysis of GDSC data is through a web portal focused on identifying molecular biomarkers of drug sensitivity based on queries of specific anticancer drugs or cancer genes. Graphical representations of the data are used throughout with links to related resources and all datasets are fully downloadable. GDSC provides a unique resource incorporating large drug sensitivity and genomic datasets to facilitate the discovery of new therapeutic biomarkers for cancer therapies.

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Genomics of Drug Sensitivity in Cancer (GDSC): a resource for therapeutic biomarker discovery in cancer cells

Clinical responses to anticancer therapies are often restricted to a subset of patients. In some cases, mutated cancer genes are potent biomarkers for responses to targeted agents. Here, to uncover new biomarkers of sensitivity and resistance to cancer therapeutics, we screened a panel of several hundred cancer cell lines--which represent much of the tissue-type and genetic diversity of human cancers--with 130 drugs under clinical and preclinical investigation. In aggregate, we found that mutated cancer genes were associated with cellular response to most currently available cancer drugs. Classic oncogene addiction paradigms were modified by additional tissue-specific or expression biomarkers, and some frequently mutated genes were associated with sensitivity to a broad range of therapeutic agents. Unexpected relationships were revealed, including the marked sensitivity of Ewing's sarcoma cells harbouring the EWS (also known as EWSR1)-FLI1 gene translocation to poly(ADP-ribose) polymerase (PARP) inhibitors. By linking drug activity to the functional complexity of cancer genomes, systematic pharmacogenomic profiling in cancer cell lines provides a powerful biomarker discovery platform to guide rational cancer therapeutic strategies.

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Systematic identification of genomic markers of drug sensitivity in cancer cells

Loss of Function of ATXN1 Increases Amyloid β-Protein Levels by Potentiating β-Secretase Processing of β-Amyloid Precursor Protein

Mounting evidence suggests that Alzheimer's disease (AD) is caused by the accumulation of the small peptide, amyloid-β (Aβ), a proteolytic cleavage product of amyloid-β protein precursor (AβPP). Aβ is generated through a serial cleavage of AβPP by β- and γ-secretase. Aβ40 and Aβ42 are the two main components of amyloid plaques in AD brains, with Aβ42 being more prone to aggregation. AβPP can also be processed by α-secretase, which cleaves AβPP within the Aβ sequence, thereby preventing the generation of Aβ. Little is currently known regarding the effects of cell density on AβPP processing and Aβ generation. Here we assessed the effects of cell density on AβPP processing in neuronal and non-neuronal cell lines, as well as mouse primary cortical neurons. We found that decreased cell density significantly increases levels of Aβ40, Aβ42, total Aβ, and the ratio of Aβ42: Aβ40. These results also indicate that cell density is a significant modulator of AβPP processing. Overall, these findings carry profound implications for both previous and forthcoming studies aiming to assess the effects of various conditions and genetic/chemical factors, e.g., novel drugs on AβPP processing and Aβ generation in cell-based systems. Moreover, it is interesting to speculate whether cell density changes in vivo may also affect AβPP processing and Aβ levels in the AD brain.

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Jesse A. Stevenson

Papers

Systematic identification of genomic markers of drug sensitivity in cancer cells

Loss of Function of ATXN1 Increases Amyloid β-Protein Levels by Potentiating β-Secretase Processing of β-Amyloid Precursor Protein

Amyloid-β production via cleavage of amyloid-β protein precursor is modulated by cell density.