UNLABELLED: Hepatocellular carcinoma (HCC) is the most rapidly increasing cause of cancer-related mortality in the United States. Because of the lack of viable treatment options for HCC, prevention in high-risk patients has been proposed as an alternative strategy. The main risk factor for HCC is cirrhosis and several lines of evidence implicate epidermal growth factor (EGF) in the progression of cirrhosis and development of HCC. We therefore examined the effects of the EGF receptor (EGFR) inhibitor erlotinib on liver fibrogenesis and hepatocellular transformation in three different animal models of progressive cirrhosis: a rat model induced by repeated, low-dose injections of diethylnitrosamine (DEN), a mouse model induced by carbon tetrachloride (CCl4 ), and a rat model induced by bile duct ligation (BDL). Erlotinib reduced EGFR phosphorylation in hepatic stellate cells (HSC) and reduced the total number of activated HSC. Erlotinib also decreased hepatocyte proliferation and liver injury. Consistent with all these findings, pharmacological inhibition of EGFR signaling effectively prevented the progression of cirrhosis and regressed fibrosis in some animals. Moreover, by alleviating the underlying liver disease, erlotinib blocked the development of HCC and its therapeutic efficacy could be monitored with a previously reported gene expression signature predictive of HCC risk in human cirrhosis patients. CONCLUSION: These data suggest that EGFR inhibition using Food and Drug Administration-approved inhibitors provides a promising therapeutic approach for reduction of fibrogenesis and prevention of HCC in high-risk cirrhosis patients who can be identified and monitored by gene expression signatures.

Although a few cancer genes are mutated in a high proportion of tumours of a given type (>20%), most are mutated at intermediate frequencies (2-20%). To explore the feasibility of creating a comprehensive catalogue of cancer genes, we analysed somatic point mutations in exome sequences from 4,742 human cancers and their matched normal-tissue samples across 21 cancer types. We found that large-scale genomic analysis can identify nearly all known cancer genes in these tumour types. Our analysis also identified 33 genes that were not previously known to be significantly mutated in cancer, including genes related to proliferation, apoptosis, genome stability, chromatin regulation, immune evasion, RNA processing and protein homeostasis. Down-sampling analysis indicates that larger sample sizes will reveal many more genes mutated at clinically important frequencies. We estimate that near-saturation may be achieved with 600-5,000 samples per tumour type, depending on background mutation frequency. The results may help to guide the next stage of cancer genomics.

Comprehensive analyses of cancer genomes promise to inform prognoses and precise cancer treatments. A major barrier, however, is inaccessibility of metastatic tissue. A potential solution is to characterize circulating tumor cells (CTCs), but this requires overcoming the challenges of isolating rare cells and sequencing low-input material. Here we report an integrated process to isolate, qualify and sequence whole exomes of CTCs with high fidelity using a census-based sequencing strategy. Power calculations suggest that mapping of >99.995% of the standard exome is possible in CTCs. We validated our process in two patients with prostate cancer, including one for whom we sequenced CTCs, a lymph node metastasis and nine cores of the primary tumor. Fifty-one of 73 CTC mutations (70%) were present in matched tissue. Moreover, we identified 10 early trunk and 56 metastatic trunk mutations in the non-CTC tumor samples and found 90% and 73% of these mutations, respectively, in CTC exomes. This study establishes a foundation for CTC genomics in the clinic.

The selectivity of an enzyme inhibitor is a key determinant of its usefulness as a tool compound or its safety as a drug. Yet selectivity is never assessed comprehensively in the early stages of the drug discovery process, and only rarely in the later stages, because technical limitations prohibit doing otherwise. Here, we report EnPlex, an efficient, high-throughput method for simultaneously assessing inhibitor potency and specificity, and pilot its application to 96 serine hydrolases. EnPlex analysis of widely used serine hydrolase inhibitors revealed numerous previously unrecognized off-target interactions, some of which may help to explain previously confounding adverse effects. In addition, EnPlex screening of a hydrolase-directed library of boronic acid- and nitrile-containing compounds provided structure-activity relationships in both potency and selectivity dimensions from which lead candidates could be more effectively prioritized. Follow-up of a series of dipeptidyl peptidase 4 inhibitors showed that EnPlex indeed predicted efficacy and safety in animal models. These results demonstrate the feasibility and value of high-throughput, superfamily-wide selectivity profiling and suggest that such profiling can be incorporated into the earliest stages of drug discovery.

UNLABELLED: Pediatric Ewing sarcoma is characterized by the expression of chimeric fusions of EWS and ETS family transcription factors, representing a paradigm for studying cancers driven by transcription factor rearrangements. In this study, we describe the somatic landscape of pediatric Ewing sarcoma. These tumors are among the most genetically normal cancers characterized to date, with only EWS-ETS rearrangements identified in the majority of tumors. STAG2 loss, however, is present in more than 15% of Ewing sarcoma tumors; occurs by point mutation, rearrangement, and likely nongenetic mechanisms; and is associated with disease dissemination. Perhaps the most striking finding is the paucity of mutations in immediately targetable signal transduction pathways, highlighting the need for new therapeutic approaches to target EWS-ETS fusions in this disease. SIGNIFICANCE: We performed next-generation sequencing of Ewing sarcoma, a pediatric cancer involving bone, characterized by expression of EWS-ETS fusions. We found remarkably few mutations. However, we discovered that loss of STAG2 expression occurs in 15% of tumors and is associated with metastatic disease, suggesting a potential genetic vulnerability in Ewing sarcoma.

We performed massively parallel sequencing of paired tumor/normal samples from 203 multiple myeloma (MM) patients and identified significantly mutated genes and copy number alterations and discovered putative tumor suppressor genes by determining homozygous deletions and loss of heterozygosity. We observed frequent mutations in KRAS (particularly in previously treated patients), NRAS, BRAF, FAM46C, TP53, and DIS3 (particularly in nonhyperdiploid MM). Mutations were often present in subclonal populations, and multiple mutations within the same pathway (e.g., KRAS, NRAS, and BRAF) were observed in the same patient. In vitro modeling predicts only partial treatment efficacy of targeting subclonal mutations, and even growth promotion of nonmutated subclones in some cases. These results emphasize the importance of heterogeneity analysis for treatment decisions.

For the Library of Integrated Network-based Cellular Signatures (LINCS) project many gene expression signatures using the L1000 technology have been produced. The L1000 technology is a cost-effective method to profile gene expression in large scale. LINCS Canvas Browser (LCB) is an interactive HTML5 web-based software application that facilitates querying, browsing and interrogating many of the currently available LINCS L1000 data. LCB implements two compacted layered canvases, one to visualize clustered L1000 expression data, and the other to display enrichment analysis results using 30 different gene set libraries. Clicking on an experimental condition highlights gene-sets enriched for the differentially expressed genes from the selected experiment. A search interface allows users to input gene lists and query them against over 100 000 conditions to find the top matching experiments. The tool integrates many resources for an unprecedented potential for new discoveries in systems biology and systems pharmacology. The LCB application is available at http://www.maayanlab.net/LINCS/LCB. Customized versions will be made part of the http://lincscloud.org and http://lincs.hms.harvard.edu websites.

Lung adenocarcinomas harboring activating mutations in the epidermal growth factor receptor (EGFR) represent a common molecular subset of non-small cell lung cancer (NSCLC) cases. EGFR mutations predict sensitivity to EGFR tyrosine kinase inhibitors (TKIs) and thus represent a dependency in NSCLCs harboring these alterations, but the genetic basis of EGFR dependence is not fully understood. Here, we applied an unbiased, ORF-based screen to identify genetic modifiers of EGFR dependence in EGFR-mutant NSCLC cells. This approach identified 18 kinase and kinase-related genes whose overexpression can substitute for EGFR in EGFR-dependent PC9 cells, and these genes include seven of nine Src family kinase genes, FGFR1, FGFR2, ITK, NTRK1, NTRK2, MOS, MST1R, and RAF1. A subset of these genes can complement loss of EGFR activity across multiple EGFR-dependent models. Unbiased gene-expression profiling of cells overexpressing EGFR bypass genes, together with targeted validation studies, reveals EGFR-independent activation of the MEK-ERK and phosphoinositide 3-kinase (PI3K)-AKT pathways. Combined inhibition of PI3K-mTOR and MEK restores EGFR dependence in cells expressing each of the 18 EGFR bypass genes. Together, these data uncover a broad spectrum of kinases capable of overcoming dependence on EGFR and underscore their convergence on the PI3K-AKT and MEK-ERK signaling axes in sustaining EGFR-independent survival.

UNLABELLED: The efficacy of radiotherapy in many tumor types is limited by normal tissue toxicity and by intrinsic or acquired radioresistance. Therefore, it is essential to understand the molecular network responsible for regulating radiosensitivity/resistance. Here, an unbiased functional screen identified four microRNAs (miR1, miR125a, miR150, and miR425) that induce radioresistance. Considering the clinical importance of radiotherapy for patients with glioblastoma, the impact of these miRNAs on glioblastoma radioresistance was investigated. Overexpression of miR1, miR125a, miR150, and/or miR425 in glioblastoma promotes radioresistance through upregulation of the cell-cycle checkpoint response. Conversely, antagonizing with antagomiRs sensitizes glioblastoma cells to irradiation, suggesting their potential as targets for inhibiting therapeutic resistance. Analysis of glioblastoma datasets from The Cancer Genome Atlas (TCGA) revealed that these miRNAs are expressed in glioblastoma patient specimens and correlate with TGFbeta signaling. Finally, it is demonstrated that expression of miR1 and miR125a can be induced by TGFbeta and antagonized by a TGFbeta receptor inhibitor. Together, these results identify and characterize a new role for miR425, miR1, miR125, and miR150 in promoting radioresistance in glioblastomas and provide insight into the therapeutic application of TGFbeta inhibitors in radiotherapy. IMPLICATIONS: Systematic identification of miRs that cause radioresistance in gliomas is important for uncovering predictive markers for radiotherapy or targets for overcoming radioresistance.

Using a genome-scale, lentivirally delivered shRNA library, we performed massively parallel pooled shRNA screens in 216 cancer cell lines to identify genes that are required for cell proliferation and/or viability. Cell line dependencies on 11,000 genes were interrogated by 5 shRNAs per gene. The proliferation effect of each shRNA in each cell line was assessed by transducing a population of 11M cells with one shRNA-virus per cell and determining the relative enrichment or depletion of each of the 54,000 shRNAs after 16 population doublings using Next Generation Sequencing. All the cell lines were screened using standardized conditions to best assess differential genetic dependencies across cell lines. When combined with genomic characterization of these cell lines, this dataset facilitates the linkage of genetic dependencies with specific cellular contexts (e.g., gene mutations or cell lineage). To enable such comparisons, we developed and provided a bioinformatics tool to identify linear and nonlinear correlations between these features.