We analyzed published tumor transcriptome data in conjunction with linked survival data to identify genes linked with breast cancer survival outcomes (1, 2). When comparing tumor transcriptomes based on 24-month survival, we discovered that the zinc-finger protein X-linked ZFX (3, 4) was among the most differentially expressed genes in both original and metastatic tumor tissues. ZFX expression was considerably enhanced in metastatic tumors of patients who survived more than 24 months, indicating that enhanced ZFX expression offers a survival advantage for patients with stage IV metastatic breast cancer.
Purpose/Objective(s): Genome sequencing efforts have identified millions of somatic mutations in cancer. A major challenge is, interpretation of cancer gene mutations and computational approaches have been devised to dichotomize mutations on this basis. However, the impact of gene mutations are not binary and methodologies that assess the pleiotropic effects of individual gene mutations on tumor phenotypes have yet to be developed. Herein, we develop a high-throughput phenotyping method to determine categories of clinically actionable BRAFmutants. Materials/Methods: We profiled 89 non-small cell lung cancer cell lines exposed to ionizing radiation using a high-throughput platform. Genomic correlates of radiosensitivity were explored by calculating the informationbased similarity metric and correlating genomic parameters by accessing Oncomap data from the Cancer Cell Line Encyclopedia, the COSMIC database of the Cancer Genome Project, and The Cancer Genome Atlas. We used site-directed mutagenesis to generate the BRAFmutant clones and transferred the ORFs into lentiviral vectors for stable expression in immortalized bronchial (Beas-2B) cells. Gene expression signatures were established for mutant alleles using L1000 profiling. Results: Correlation with cancer genomic data in 28 lung adenocarcinoma cell lines identified BRAF mutations as strongly correlated with radiation resistance. All mutations identified, except the missense mutation BRA-F E375D , were located in the highly conserved kinase domain. Most mutations were shown to enhance kinase activity analogous to the well-known BRAF V600E mutation. However, individual BRAF mutations had pleiotropic effects on several phenotypes including: growth rate, invasion, resistance to radiation, response to MEK and/or Braf inhibition and in vivo tumorigenesis. We used expression-based mutation fingerprinting and k means clustering to identify categories of mutations in BRAF. We identified associations between categories of mutations and the cellular phenotypes that indicate pleiotropic effects across these categories. We then established gene expression profiles from BRAF mutants of unclear significance and made predictions concerning their impact on cellular phenotypes using our classification method. We showed that this classification can accurately assess the phenotypic impact of these mutations. Conclusion: We established a methodology for determining the impact of individual BRAF mutations on therapeutic resistance and response to Mek/ Braf inhibitors. Our data indicates that categories of BRAF mutations are associated with radiation resistance and/or Mek/Braf inhibition in lung adenocarcinoma and that multiple rare BRAF mutations can be functionally important in cancer. These results can be translated clinically using diagnostic that incorporate BRAF sequencing and a gene signatures that are calibrated to measure individual phenotypes (i.e. radiation resistance, vulnerability to Mek/Braf inhibitors).
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