Mathew J. Garnett

Mutations of the BRAF gene in human cancer

Cancers arise owing to the accumulation of mutations in critical genes that alter normal programmes of cell proliferation, differentiation and death. As the first stage of a systematic genome-wide screen for these genes, we have prioritized for analysis signalling pathways in which at least one gene is mutated in human cancer. The RAS–RAF–MEK–ERK–MAP kinase pathway mediates cellular responses to growth signals. RAS is mutated to an oncogenic form in about 15% of human cancer. The three RAF genes code for cytoplasmic serine/threonine kinases that are regulated by binding RAS. Here we report BRAF somatic missense mutations in 66% of malignant melanomas and at lower frequency in a wide range of human cancers. All mutations are within the kinase domain, with a single substitution (V599E) accounting for 80%. Mutated BRAF proteins have elevated kinase activity and are transforming in NIH3T3 cells. Furthermore, RAS function is not required for the growth of cancer cell lines with the V599E mutation. As BRAF is a serine/threonine kinase that is commonly activated by somatic point mutation in human cancer, it may provide new therapeutic opportunities in malignant melanoma.

Mechanism of Activation of the Raf-Erk Signaling Pathway by Oncogenic Mutations of B-Raf

Over 30 mutations of the B-RAF gene associated with human cancers have been identified, the majority of which are located within the kinase domain. Here we show that of 22 B-RAF mutants analyzed, 18 have elevated kinase activity and signal to ERK in vivo. Surprisingly, three mutants have reduced kinase activity towards MEK in vitro but, by activating C-RAF in vivo, signal to ERK in cells. The structures of wild type and oncogenic V599EB-RAF kinase domains in complex with the RAF inhibitor BAY43-9006 show that the activation segment is held in an inactive conformation by association with the P loop. The clustering of most mutations to these two regions suggests that disruption of this interaction converts B-RAF into its active conformation. The high activity mutants signal to ERK by directly phosphorylating MEK, whereas the impaired activity mutants stimulate MEK by activating endogenous C-RAF, possibly via an allosteric or transphosphorylation mechanism.

Systematic identification of genomic markers of drug sensitivity 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.

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

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.

A Landscape of Pharmacogenomic Interactions in Cancer

Summary Systematic studies of cancer genomes have provided unprecedented insights into the molecular nature of cancer. Using this information to guide the development and application of therapies in the clinic is challenging. Here, we report how cancer-driven alterations identified in 11,289 tumors from 29 tissues (integrating somatic mutations, copy number alterations, DNA methylation, and gene expression) can be mapped onto 1,001 molecularly annotated human cancer cell lines and correlated with sensitivity to 265 drugs. We find that cell lines faithfully recapitulate oncogenic alterations identified in tumors, find that many of these associate with drug sensitivity/resistance, and highlight the importance of tissue lineage in mediating drug response. Logic-based modeling uncovers combinations of alterations that sensitize to drugs, while machine learning demonstrates the relative importance of different data types in predicting drug response. Our analysis and datasets are rich resources to link genotypes with cellular phenotypes and to identify therapeutic options for selected cancer sub-populations.

MED12 Controls the Response to Multiple Cancer Drugs through Regulation of TGF-β Receptor Signaling

Inhibitors of the ALK and EGF receptor tyrosine kinases provoke dramatic but short-lived responses in lung cancers harboring EML4-ALK translocations or activating mutations of EGFR, respectively. We used a large-scale RNAi screen to identify MED12, a component of the transcriptional MEDIATOR complex that is mutated in cancers, as a determinant of response to ALK and EGFR inhibitors. MED12 is in part cytoplasmic where it negatively regulates TGF-βR2 through physical interaction. MED12 suppression therefore results in activation of TGF-βR signaling, which is both necessary and sufficient for drug resistance. TGF-β signaling causes MEK/ERK activation, and consequently MED12 suppression also confers resistance to MEK and BRAF inhibitors in other cancers. MED12 loss induces an EMT-like phenotype, which is associated with chemotherapy resistance in colon cancer patients and to gefitinib in lung cancer. Inhibition of TGF-βR signaling restores drug responsiveness in MED12(KD) cells, suggesting a strategy to treat drug-resistant tumors that have lost MED12.

UBE2S elongates ubiquitin chains on APC/C substrates to promote mitotic exit

The anaphase-promoting complex (APC/C), a ubiquitin ligase, is the target of the spindle-assembly checkpoint (SAC), and it ubiquitylates protein substrates whose degradation regulates progress through mitosis. The identity of the ubiquitin-conjugating (E2) enzymes that work with the APC/C is unclear. In an RNA interference (RNAi) screen for factors that modify release from drug-induced SAC activation, we identified the E2 enzyme UBE2S as an APC/C auxiliary factor that promotes mitotic exit. UBE2S is dispensable in a normal mitosis, but its depletion prolongs drug-induced mitotic arrest and suppresses mitotic slippage. In vitro, UBE2S elongates ubiquitin chains initiated by the E2 enzymes UBCH10 and UBCH5, enhancing the degradation of APC/C substrates by the proteasome. Indeed, following release from SAC-induced mitotic arrest, UBE2S-depleted cells neither degrade crucial APC/C substrates, nor silence this checkpoint, whereas bypassing the SAC through BUBR1 depletion or Aurora-B inhibition negates the requirement for UBE2S. Thus, UBE2S functions with the APC/C in a two-step mechanism to control substrate ubiquitylation that is essential for mitotic exit after prolonged SAC activation, providing a new model for APC/C function in human cells.

Mutations of C-RAF are rare in human cancer because C-RAF has a low basal kinase activity compared with B-RAF

The protein kinase B-RAF is mutated in approximately 8% of human cancers. Here we show that presumptive mutants of the closely related kinase, C-RAF, were detected in only 4 of 545 (0.7%) cancer cell lines. The activity of two of the mutated proteins is not significantly different from that of wild-type C-RAF and these variants may represent rare human polymorphisms. The basal and B-RAF-stimulated kinase activities of a third variant are unaltered but its activation by RAS is significantly reduced, suggesting that it may act in a dominant-negative manner to modulate pathway signaling. The fourth variant has elevated basal kinase activity and is hypersensitive to activation by RAS but does not transform mammalian cells. Furthermore, when we introduce the equivalent of the most common cancer mutation in B-RAF (V600E) into C-RAF, it only has a weak effect on kinase activity and does not convert C-RAF into an oncogene. This lack of activation occurs because C-RAF lacks a constitutive charge within a motif in the kinase domain called the N-region. This fundamental difference in RAF isoform regulation explains why B-RAF is frequently mutated in cancer whereas C-RAF mutations are rare.

Machine Learning Prediction of Cancer Cell Sensitivity to Drugs Based on Genomic and Chemical Properties

Predicting the response of a specific cancer to a therapy is a major goal in modern oncology that should ultimately lead to a personalised treatment. High-throughput screenings of potentially active compounds against a panel of genomically heterogeneous cancer cell lines have unveiled multiple relationships between genomic alterations and drug responses. Various computational approaches have been proposed to predict sensitivity based on genomic features, while others have used the chemical properties of the drugs to ascertain their effect. In an effort to integrate these complementary approaches, we developed machine learning models to predict the response of cancer cell lines to drug treatment, quantified through IC50 values, based on both the genomic features of the cell lines and the chemical properties of the considered drugs. Models predicted IC50 values in a 8-fold cross-validation and an independent blind test with coefficient of determination R2 of 0.72 and 0.64 respectively. Furthermore, models were able to predict with comparable accuracy (R2 of 0.61) IC50s of cell lines from a tissue not used in the training stage. Our in silico models can be used to optimise the experimental design of drug-cell screenings by estimating a large proportion of missing IC50 values rather than experimentally measuring them. The implications of our results go beyond virtual drug screening design: potentially thousands of drugs could be probed in silico to systematically test their potential efficacy as anti-tumour agents based on their structure, thus providing a computational framework to identify new drug repositioning opportunities as well as ultimately be useful for personalized medicine by linking the genomic traits of patients to drug sensitivity.

A Biobank of Breast Cancer Explants with Preserved Intra-tumor Heterogeneity to Screen Anticancer Compounds

Summary The inter- and intra-tumor heterogeneity of breast cancer needs to be adequately captured in pre-clinical models. We have created a large collection of breast cancer patient-derived tumor xenografts (PDTXs), in which the morphological and molecular characteristics of the originating tumor are preserved through passaging in the mouse. An integrated platform combining in vivo maintenance of these PDTXs along with short-term cultures of PDTX-derived tumor cells (PDTCs) was optimized. Remarkably, the intra-tumor genomic clonal architecture present in the originating breast cancers was mostly preserved upon serial passaging in xenografts and in short-term cultured PDTCs. We assessed drug responses in PDTCs on a high-throughput platform and validated several ex vivo responses in vivo. The biobank represents a powerful resource for pre-clinical breast cancer pharmacogenomic studies (http://caldaslab.cruk.cam.ac.uk/bcape), including identification of biomarkers of response or resistance.