123 research outputs found

    Nuclear Factor I/B is an Oncogene in Small Cell Lung Cancer

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    Small cell lung cancer (SCLC) is an aggressive cancer often diagnosed after it has metastasized. Despite the need to better understand this disease, SCLC remains poorly characterized at the molecular and genomic levels. Using a genetically engineered mouse model of SCLC driven by conditional deletion of Trp53 and Rb1 in the lung, we identified several frequent, high-magnitude focal DNA copy number alterations in SCLC. We uncovered amplification of a novel, oncogenic transcription factor, Nuclear factor I/B (Nfib), in the mouse SCLC model and in human SCLC. Functional studies indicate that NFIB regulates cell viability and proliferation during transformation.National Cancer Institute (U.S.) (grant P30-CA14051)David H. Koch Institute for Integrative Cancer Research at MIT (Ludwig Center for Molecular Oncology)Howard Hughes Medical InstituteAlfred P. Sloan Foundation (Research Fellowship)International Association for the Study of Lung Cance

    High-throughput identification of genotype-specific cancer vulnerabilities in mixtures of barcoded tumor cell lines.

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    Hundreds of genetically characterized cell lines are available for the discovery of genotype-specific cancer vulnerabilities. However, screening large numbers of compounds against large numbers of cell lines is currently impractical, and such experiments are often difficult to control. Here we report a method called PRISM that allows pooled screening of mixtures of cancer cell lines by labeling each cell line with 24-nucleotide barcodes. PRISM revealed the expected patterns of cell killing seen in conventional (unpooled) assays. In a screen of 102 cell lines across 8,400 compounds, PRISM led to the identification of BRD-7880 as a potent and highly specific inhibitor of aurora kinases B and C. Cell line pools also efficiently formed tumors as xenografts, and PRISM recapitulated the expected pattern of erlotinib sensitivity in vivo

    Activating mTOR Mutations in a Patient with an Extraordinary Response on a Phase I Trial of Everolimus and Pazopanib

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    Understanding the genetic mechanisms of sensitivity to targeted anticancer therapies may improve patient selection, response to therapy, and rational treatment designs. One approach to increase this understanding involves detailed studies of exceptional responders: rare patients with unexpected exquisite sensitivity or durable responses to therapy. We identified an exceptional responder in a phase I study of pazopanib and everolimus in advanced solid tumors. Whole-exome sequencing of a patient with a 14-month complete response on this trial revealed two concurrent mutations in mTOR, the target of everolimus. In vitro experiments demonstrate that both mutations are activating, suggesting a biologic mechanism for exquisite sensitivity to everolimus in this patient. The use of precision (or “personalized”) medicine approaches to screen patients with cancer for alterations in the mTOR pathway may help to identify subsets of patients who may benefit from targeted therapies directed against mTOR.National Human Genome Research Institute (U.S.) (5U54HG003067-11

    Characterizing genomic alterations in cancer by complementary functional associations.

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    Systematic efforts to sequence the cancer genome have identified large numbers of mutations and copy number alterations in human cancers. However, elucidating the functional consequences of these variants, and their interactions to drive or maintain oncogenic states, remains a challenge in cancer research. We developed REVEALER, a computational method that identifies combinations of mutually exclusive genomic alterations correlated with functional phenotypes, such as the activation or gene dependency of oncogenic pathways or sensitivity to a drug treatment. We used REVEALER to uncover complementary genomic alterations associated with the transcriptional activation of β-catenin and NRF2, MEK-inhibitor sensitivity, and KRAS dependency. REVEALER successfully identified both known and new associations, demonstrating the power of combining functional profiles with extensive characterization of genomic alterations in cancer genomes

    Assessing the clinical utility of cancer genomic and proteomic data across tumor types

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    Molecular profiling of tumors promises to advance the clinical management of cancer, but the benefits of integrating molecular data with traditional clinical variables have not been systematically studied. Here we retrospectively predict patient survival using diverse molecular data (somatic copy-number alteration, DNA methylation and mRNA, miRNA and protein expression) from 953 samples of four cancer types from The Cancer Genome Atlas project. We found that incorporating molecular data with clinical variables yielded statistically significantly improved predictions (FDR < 0.05) for three cancers but those quantitative gains were limited (2.2–23.9%). Additional analyses revealed little predictive power across tumor types except for one case. In clinically relevant genes, we identified 10,281 somatic alterations across 12 cancer types in 2,928 of 3,277 patients (89.4%), many of which would not be revealed in single-tumor analyses. Our study provides a starting point and resources, including an open-access model evaluation platform, for building reliable prognostic and therapeutic strategies that incorporate molecular data

    Profiling Critical Cancer Gene Mutations in Clinical Tumor Samples

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    BACKGROUND: Detection of critical cancer gene mutations in clinical tumor specimens may predict patient outcomes and inform treatment options; however, high-throughput mutation profiling remains underdeveloped as a diagnostic approach. We report the implementation of a genotyping and validation algorithm that enables robust tumor mutation profiling in the clinical setting. METHODOLOGY: We developed and implemented an optimized mutation profiling platform ("OncoMap") to interrogate approximately 400 mutations in 33 known oncogenes and tumor suppressors, many of which are known to predict response or resistance to targeted therapies. The performance of OncoMap was analyzed using DNA derived from both frozen and FFPE clinical material in a diverse set of cancer types. A subsequent in-depth analysis was conducted on histologically and clinically annotated pediatric gliomas. The sensitivity and specificity of OncoMap were 93.8% and 100% in fresh frozen tissue; and 89.3% and 99.4% in FFPE-derived DNA. We detected known mutations at the expected frequencies in common cancers, as well as novel mutations in adult and pediatric cancers that are likely to predict heightened response or resistance to existing or developmental cancer therapies. OncoMap profiles also support a new molecular stratification of pediatric low-grade gliomas based on BRAF mutations that may have immediate clinical impact. CONCLUSIONS: Our results demonstrate the clinical feasibility of high-throughput mutation profiling to query a large panel of "actionable" cancer gene mutations. In the future, this type of approach may be incorporated into both cancer epidemiologic studies and clinical decision making to specify the use of many targeted anticancer agents
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