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

Breast cancer is a complex and heterogeneous disease. Gene expression profiling has contributed significantly to our understanding of this heterogeneity at a molecular level, refining taxonomy based on simple measures such as histological type, tumour grade, lymph node status and the presence of predictive markers like oestrogen receptor and human epidermal growth factor receptor 2 (HER2) to a more sophisticated classification comprising luminal A, luminal B, basal-like, HER2-positive and normal subgroups. In the laboratory, breast cancer is often modelled using established cell lines. In the present review we discuss some of the issues surrounding the use of breast cancer cell lines as experimental models, in light of these revised clinical classifications, and put forward suggestions for improving their use in translational breast cancer research.

/pdf/choosing-the-right-cell-line-for-breast-cancer-research-2vywvl4eet.pdf

Choosing the right cell line for breast cancer research

https://www.wistar.org/sites/default/files/content/Villanueva_CancerCell_2010.pdf

Acquired Resistance to BRAF Inhibitors Mediated by a RAF Kinase Switch in Melanoma Can Be Overcome by Cotargeting MEK and IGF-1R/PI3K

�Colorectal cancer (CRC) is a major cause of cancer mortality. Whereas some patients respond well to therapy, others do not, and thus more precise, individualized treatment strategies are needed. To that end, we analyzed gene expression profiles from 1,290 CRC tumors using consensus-based unsupervised clustering. The resultant clusters were then associated with therapeutic response data to the epidermal growth factor receptor–targeted drug cetuximab in 80 patients. The results of these studies define six clinically relevant CRC subtypes. Each subtype shares similarities to distinct cell types within the normal colon crypt and shows differing degrees of ‘stemness’ and Wnt signaling. Subtype-specific gene signatures are proposed to identify these subtypes. Three subtypes have markedly better disease-free survival (DFS) after surgical resection, suggesting these patients might be spared from the adverse effects of chemotherapy when they have localized disease. One of these three subtypes, identified by filamin A expression, does not respond to cetuximab but may respond to cMET receptor tyrosine kinase inhibitors in the metastatic setting. Two other subtypes, with poor and intermediate DFS, associate with improved response to the chemotherapy regimen FOLFIRI 1 in adjuvant or metastatic settings. Development of clinically deployable assays for these subtypes and of subtype-specific therapies may contribute to more effective management of this challenging disease. Previous studies have identified molecular subtypes of various human cancers by gene expression profiling 2–8 , including CRC subtypes 9,10 . However, these subtypes have not been associated with outcomes in patients treated with specific therapeutic interventions. Therefore, we sought to refine the approach of molecular classification of CRC by associating gene expression profiles of CRC tumors with corresponding clinical response to cetuximab. We first used consensusbased non-negative matrix factorization (NMF) 11 to cluster two published gene expression data sets (GSE13294 (ref. 12) and GSE14333 (ref. 13)) derived from resected primary CRCs (core data sets, n = 445). These data were corrected for batch effects and merged using the distance-weighted discrimination method 5,14 before clustering. This analysis defined five distinct high-consensus molecular subtypes of CRC (Supplementary Fig. 1a–e and Supplementary Results and

/pdf/a-colorectal-cancer-classification-system-that-associates-1u2ps2l7mt.pdf

A colorectal cancer classification system that associates cellular phenotype and responses to therapy

Two large-scale pharmacogenomic studies were published recently in this journal. Genomic data are well correlated between studies; however, the measured drug response data are highly discordant. Although the source of inconsistencies remains uncertain, it has potential implications for using these outcome measures to assess gene-drug associations or select potential anticancer drugs on the basis of their reported results.

Inconsistency in large pharmacogenomic studies

Phosphoinositide 3-kinase α (PI3Kα) is a critical regulator of cell growth and transformation, and its signaling pathway is the most commonly mutated pathway in human cancers. The mammalian target of rapamycin (mTOR), a class IV PI3K protein kinase, is also a central regulator of cell growth, and mTOR inhibitors are believed to augment the antiproliferative efficacy of PI3K/AKT pathway inhibition. 2,4-Difluoro-N-{2-(methyloxy)-5-[4-(4-pyridazinyl)-6-quinolinyl]-3-pyridinyl}benzenesulfonamide (GSK2126458, 1) has been identified as a highly potent, orally bioavailable inhibitor of PI3Kα and mTOR with in vivo activity in both pharmacodynamic and tumor growth efficacy models. Compound 1 is currently being evaluated in human clinical trials for the treatment of cancer.

Discovery of GSK2126458, a Highly Potent Inhibitor of PI3K and the Mammalian Target of Rapamycin

Preclinical cellular response profiling of tumor models has become a cornerstone in the development of novel cancer therapeutics. As efforts to predict clinical efficacy using cohorts of in vitro tumor models have been successful, expansive panels of tumor-derived cell lines can recapitulate an “all comers” efficacy trial, thereby identifying which tumors are most likely to benefit from treatment. The response profile of a therapy is most often studied in isolation; however, drug treatment effect patterns in tumor models across a diverse panel of compounds can help determine the value of unique molecular target classes in specific tumor cohorts. To this end, a panel of 19 compounds was evaluated against a diverse group of cancer cell lines (n = 311). The primary oncogenic targets were a key determinant of concentration-dependent proliferation response, as a total of five of six, four of four, and five of five phosphatidylinositol 3-kinase (PI3K)/AKT/mammalian target of rapamycin (mTOR) pathway, insulin-like growth factor-I receptor (IGF-IR), and mitotic inhibitors, respectively, clustered with others of that common target class. In addition, molecular target class was correlated with increased responsiveness in certain histologies. A cohort of PI3K/AKT/mTOR inhibitors was more efficacious in breast cancers compared with other tumor types, whereas IGF-IR inhibitors more selectively inhibited growth in colon cancer lines. Finally, specific phenotypes play an important role in cellular response profiles. For example, luminal breast cancer cells (nine of nine; 100%) segregated from basal cells (six of seven; 86%). The convergence of a common cellular response profile for different molecules targeting the same oncogenic pathway substantiates a rational clinical path for patient populations most likely to benefit from treatment. Cancer Res; 70(9); 3677–86. ©2010 AACR.

https://cancerres.aacrjournals.org/content/canres/70/9/3677.full.pdf

Ronald Wegrzyn

Papers

Discovery of GSK2126458, a Highly Potent Inhibitor of PI3K and the Mammalian Target of Rapamycin

Molecular Target Class Is Predictive of In vitro Response Profile