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Online Survival Analysis Software to Assess the Prognostic Value of Biomarkers Using Transcriptomic Data in Non-Small-Cell Lung Cancer

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      In the last decade, optimized treatment for non-small cell lung cancer had lead to improved prognosis, but the overall survival is still very short. To further understand the molecular basis of the disease we have to identify biomarkers related to survival. Here we present the development of an online tool suitable for the real-time meta-analysis of published lung cancer microarray datasets to identify biomarkers related to survival. We searched the caBIG, GEO and TCGA repositories to identify samples with published gene expression data and survival information. Univariate and multivariate Cox regression analysis, Kaplan-Meier survival plot with hazard ratio and logrank P value are calculated and plotted in R. The complete analysis tool can be accessed online at: All together 1,715 samples of ten independent datasets were integrated into the system. As a demonstration, we used the tool to validate 21 previously published survival associated biomarkers. Of these, survival was best predicted by CDK1 (p<1E-16), CD24 (p<1E-16) and CADM1 (p = 7E-12) in adenocarcinomas and by CCNE1 (p = 2.3E-09) and VEGF (p = 3.3E-10) in all NSCLC patients. Additional genes significantly correlated to survival include RAD51, CDKN2A, OPN, EZH2, ANXA3, ADAM28 and ERCC1. In summary, we established an integrated database and an online tool capable of uni- and multivariate analysis for in silico validation of new biomarker candidates in non-small cell lung cancer.

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      Cancer statistics, 2012.

      Each year, the American Cancer Society estimates the numbers of new cancer cases and deaths expected in the United States in the current year and compiles the most recent data on cancer incidence, mortality, and survival based on incidence data from the National Cancer Institute, the Centers for Disease Control and Prevention, and the North American Association of Central Cancer Registries and mortality data from the National Center for Health Statistics. A total of 1,638,910 new cancer cases and 577,190 deaths from cancer are projected to occur in the United States in 2012. During the most recent 5 years for which there are data (2004-2008), overall cancer incidence rates declined slightly in men (by 0.6% per year) and were stable in women, while cancer death rates decreased by 1.8% per year in men and by 1.6% per year in women. Over the past 10 years of available data (1999-2008), cancer death rates have declined by more than 1% per year in men and women of every racial/ethnic group with the exception of American Indians/Alaska Natives, among whom rates have remained stable. The most rapid declines in death rates occurred among African American and Hispanic men (2.4% and 2.3% per year, respectively). Death rates continue to decline for all 4 major cancer sites (lung, colorectum, breast, and prostate), with lung cancer accounting for almost 40% of the total decline in men and breast cancer accounting for 34% of the total decline in women. The reduction in overall cancer death rates since 1990 in men and 1991 in women translates to the avoidance of about 1,024,400 deaths from cancer. Further progress can be accelerated by applying existing cancer control knowledge across all segments of the population, with an emphasis on those groups in the lowest socioeconomic bracket. Copyright © 2012 American Cancer Society, Inc.
        • Record: found
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        Comprehensive genomic characterization of squamous cell lung cancers

        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.
          • Record: found
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          • Article: not found

          Oncogenic pathway signatures in human cancers as a guide to targeted therapies.

          The development of an oncogenic state is a complex process involving the accumulation of multiple independent mutations that lead to deregulation of cell signalling pathways central to the control of cell growth and cell fate. The ability to define cancer subtypes, recurrence of disease and response to specific therapies using DNA microarray-based gene expression signatures has been demonstrated in multiple studies. Various studies have also demonstrated the potential for using gene expression profiles for the analysis of oncogenic pathways. Here we show that gene expression signatures can be identified that reflect the activation status of several oncogenic pathways. When evaluated in several large collections of human cancers, these gene expression signatures identify patterns of pathway deregulation in tumours and clinically relevant associations with disease outcomes. Combining signature-based predictions across several pathways identifies coordinated patterns of pathway deregulation that distinguish between specific cancers and tumour subtypes. Clustering tumours based on pathway signatures further defines prognosis in respective patient subsets, demonstrating that patterns of oncogenic pathway deregulation underlie the development of the oncogenic phenotype and reflect the biology and outcome of specific cancers. Predictions of pathway deregulation in cancer cell lines are also shown to predict the sensitivity to therapeutic agents that target components of the pathway. Linking pathway deregulation with sensitivity to therapeutics that target components of the pathway provides an opportunity to make use of these oncogenic pathway signatures to guide the use of targeted therapeutics.

            Author and article information

            [1 ]Research Laboratory of Pediatrics and Nephrology, Hungarian Academy of Sciences, Budapest, Hungary
            [2 ]Department of Histology and Embryology, Wroclaw Medical University, Wrocław, Poland
            [3 ]Institut für Pathologie, Charité – Universitätsmedizin Berlin, Berlin, Germany
            H. Lee Moffitt Cancer Center & Research Institute, United States of America
            Author notes

            Competing Interests: The authors have declared that no competing interests exist.

            Conceived and designed the experiments: BG. Performed the experiments: BG PS JB AL. Analyzed the data: BG PS JB AL. Contributed reagents/materials/analysis tools: BG PS JB AL. Wrote the paper: BG.

            Role: Editor
            PLoS One
            PLoS ONE
            PLoS ONE
            Public Library of Science (San Francisco, USA )
            18 December 2013
            : 8
            : 12

            This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

            Pages: 8
            The authors work was supported by the OTKA PD 83154 grant, by the Predict project (grant no. 259303 of the EU Health.2010.2.4.1.-8 call) and by the KTIA U_BONUS_12-1-2013-0003 grant. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
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