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      Comprehensive genomic characterization of squamous cell lung cancers

      research-article
      The Cancer Genome Atlas Research Network
      Nature

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          Summary

          Lung squamous cell carcinoma (lung SqCC) is a common type of lung cancer, causing approximately 400,000 deaths per year worldwide. Genomic alterations in lung SqCC have not been comprehensively characterized and no molecularly targeted agents have been developed specifically for its treatment. As part of The Cancer Genome Atlas (TCGA), we profiled 178 lung SqCCs to provide a comprehensive landscape of genomic and epigenomic alterations. Lung SqCC is characterized by complex genomic alterations, with a mean of 360 exonic mutations, 165 genomic rearrangements, and 323 segments of copy number alteration per tumor. We found statistically recurrent mutations in 18 genes in including mutation of TP53 in nearly all specimens. Previously unreported loss-of-function mutations were seen in the HLA-A class I major histocompatibility gene. Significantly altered pathways included NFE2L2/KEAP1 in 34%, squamous differentiation genes in 44%, PI3K/AKT in 47%, and CDKN2A/RB1 in 72% of tumors. We identified a potential therapeutic target in the majority of tumors, offering new avenues of investigation for lung SqCC treatment.

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          EGF receptor gene mutations are common in lung cancers from "never smokers" and are associated with sensitivity of tumors to gefitinib and erlotinib.

          Somatic mutations in the tyrosine kinase (TK) domain of the epidermal growth factor receptor (EGFR) gene are reportedly associated with sensitivity of lung cancers to gefitinib (Iressa), kinase inhibitor. In-frame deletions occur in exon 19, whereas point mutations occur frequently in codon 858 (exon 21). We found from sequencing the EGFR TK domain that 7 of 10 gefitinib-sensitive tumors had similar types of alterations; no mutations were found in eight gefitinib-refractory tumors (P = 0.004). Five of seven tumors sensitive to erlotinib (Tarceva), a related kinase inhibitor for which the clinically relevant target is undocumented, had analogous somatic mutations, as opposed to none of 10 erlotinib-refractory tumors (P = 0.003). Because most mutation-positive tumors were adenocarcinomas from patients who smoked <100 cigarettes in a lifetime ("never smokers"), we screened EGFR exons 2-28 in 15 adenocarcinomas resected from untreated never smokers. Seven tumors had TK domain mutations, in contrast to 4 of 81 non-small cell lung cancers resected from untreated former or current smokers (P = 0.0001). Immunoblotting of lysates from cells transiently transfected with various EGFR constructs demonstrated that, compared to wild-type protein, an exon 19 deletion mutant induced diminished levels of phosphotyrosine, whereas the phosphorylation at tyrosine 1092 of an exon 21 point mutant was inhibited at 10-fold lower concentrations of drug. Collectively, these data show that adenocarcinomas from never smokers comprise a distinct subset of lung cancers, frequently containing mutations within the TK domain of EGFR that are associated with gefitinib and erlotinib sensitivity.
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            Exome sequencing of head and neck squamous cell carcinoma reveals inactivating mutations in NOTCH1.

            Head and neck squamous cell carcinoma (HNSCC) is the sixth most common cancer worldwide. To explore the genetic origins of this cancer, we used whole-exome sequencing and gene copy number analyses to study 32 primary tumors. Tumors from patients with a history of tobacco use had more mutations than did tumors from patients who did not use tobacco, and tumors that were negative for human papillomavirus (HPV) had more mutations than did HPV-positive tumors. Six of the genes that were mutated in multiple tumors were assessed in up to 88 additional HNSCCs. In addition to previously described mutations in TP53, CDKN2A, PIK3CA, and HRAS, we identified mutations in FBXW7 and NOTCH1. Nearly 40% of the 28 mutations identified in NOTCH1 were predicted to truncate the gene product, suggesting that NOTCH1 may function as a tumor suppressor gene rather than an oncogene in this tumor type.
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              Inference of patient-specific pathway activities from multi-dimensional cancer genomics data using PARADIGM

              Motivation: High-throughput data is providing a comprehensive view of the molecular changes in cancer tissues. New technologies allow for the simultaneous genome-wide assay of the state of genome copy number variation, gene expression, DNA methylation and epigenetics of tumor samples and cancer cell lines. Analyses of current data sets find that genetic alterations between patients can differ but often involve common pathways. It is therefore critical to identify relevant pathways involved in cancer progression and detect how they are altered in different patients. Results: We present a novel method for inferring patient-specific genetic activities incorporating curated pathway interactions among genes. A gene is modeled by a factor graph as a set of interconnected variables encoding the expression and known activity of a gene and its products, allowing the incorporation of many types of omic data as evidence. The method predicts the degree to which a pathway's activities (e.g. internal gene states, interactions or high-level ‘outputs’) are altered in the patient using probabilistic inference. Compared with a competing pathway activity inference approach called SPIA, our method identifies altered activities in cancer-related pathways with fewer false-positives in both a glioblastoma multiform (GBM) and a breast cancer dataset. PARADIGM identified consistent pathway-level activities for subsets of the GBM patients that are overlooked when genes are considered in isolation. Further, grouping GBM patients based on their significant pathway perturbations divides them into clinically-relevant subgroups having significantly different survival outcomes. These findings suggest that therapeutics might be chosen that target genes at critical points in the commonly perturbed pathway(s) of a group of patients. Availability:Source code available at http://sbenz.github.com/Paradigm Contact: jstuart@soe.ucsc.edu Supplementary information: Supplementary data are available at Bioinformatics online.
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                Author and article information

                Journal
                0410462
                6011
                Nature
                Nature
                Nature
                0028-0836
                1476-4687
                6 September 2012
                09 September 2012
                27 September 2012
                27 March 2013
                : 489
                : 7417
                : 519-525
                Author notes
                Correspondence to: Matthew Meyerson matthew_meyerson@ 123456dfci.harvard.edu
                Article
                NIHMS392594
                10.1038/nature11404
                3466113
                22960745
                594adf5a-dd0e-46cc-848e-3c1d678ef4fc

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                Funding
                Funded by: National Human Genome Research Institute : NHGRI
                Award ID: U54 HG003079 || HG
                Funded by: National Cancer Institute : NCI
                Award ID: U24 CA126546 || CA
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