Such mutations are responsible not only for the development of the cancer in the first instance but also for maintaining the proliferation status and evasion of cell death that are the hallmarks of cancer [2]. To date approximately 500 genes have been identified for which mutations (including somatic coding changes and structural rearrangements) have been causally implicated in cancer (http://www.sanger.ac.uk/genetics/CGP/Census/) [3•]. Moreover, next-generation sequencing of large numbers of tumours across many GDC-0941 manufacturer tissue types is currently underway as part of the International Cancer Genome Consortium (ICGC) and The Cancer Genome Atlas (TCGA), and we can expect to have within

a decade complete catalogues of somatic mutations for many of the most prevalent cancer types

(www.icgc.org;http://cancergenome.nih.gov/). There is an expectation that these studies will reveal genetic dependencies in cancer that can be targeted therapeutically to improve patient survival. Indeed they have begun to reveal pathways and 3Methyladenine cellular processes that are subverted in cancer and that may be promising drug targets. However, it is also clear that cross-talk between such pathways and compensatory signalling following drug treatment are also present and as such can only be captured by the examination of how cancer cells respond to treatment over time. Such ‘dynamic’ experiments by their nature require biological models, and here we discuss how large-scale cancer cell line models can be used to associate mutated pathways and processes with the likelihood of drug response in cancer patients. ioxilan While most of the current treatment regimens for cancer are based on the tissue of origin, the clinical response of cancer patients to treatment with a particular drug is often highly variable. There is a compelling

body of evidence, both clinical and experimental, that for an increasing number of drugs used in the clinic the likelihood of a patient’s cancer responding to treatment is strongly influenced by alterations in the cancer genome (Table 1) [4, 5, 6, 7, 8, 9, 10, 11, 12, 13 and 14]. Critically, these genomic changes can be used as molecular biomarkers to identify patients most likely to benefit from a particular treatment. Arguably the most celebrated example of this has been the use of imatinib, a small molecule inhibitor of the ABL1 tyrosine kinase, to target the fusion protein product of the BCR-ABL translocation seen in chronic myeloid leukaemia [15]. More recently, the use of EGFR and ALK inhibitors in lung cancer patients whose tumours harbour EGFR mutations and EML4-ALK rearrangements, respectively, as well as BRAF inhibitors in melanoma has resulted in significantly improved response rates compared to conventional therapies in those subsets of patients [5, 6 and 9].