A genome-wide approach to link genotype to clinical outcome by utilizing next generation sequencing and gene chip data of 6,697 breast cancer patients

abstract: ...Background The use of somatic mutations for predicting clinical outcome is difficult because a mutation can indirectly influence the function of many genes, and also because clinical follow-up is sparse in the relatively young next generation sequencing (NGS) databanks. Here we approach this problem by linking sequence databanks to well annotated gene-chip datasets, using a multigene transcriptomic fingerprint as a link between gene mutations and gene expression in breast cancer patients. Methods The database consists of 763 NGS samples containing mutational status for 22,938 genes and RNA-seq data for 10,987 genes. The gene chip database contains 5,934 patients with 10,987 genes plus clinical characteristics. For the prediction, mutations present in a sample are first translated into a transcriptomic fingerprint by running ROC analysis on mutation and RNA-seq data. Then correlation to survival is assessed by computing Cox regression for both up- and downregulated signatures. Results According to this approach, the top driver oncogenes having a mutation prevalence over 5% included AKT1, TRANK1, TRAPPC10, RPGR, COL6A2, RAPGEF4, ATG2B, CNTRL, NAA38, OSBPL10, POTEF, SCLT1, SUN1, VWDE, MTUS2, and PIK3CA, and the top tumor suppressor genes included PHEX, TP53, GGA3, RGS22, PXDNL, ARFGEF1, BRCA2, CHD8, GCC2, and ARMC4. The system was validated by computing correlation between RNA-seq and microarray data (r 2 =0.73, P <1E-16). Cross-validation using 20 genes with a prevalence of approximately 5% confirmed analysis reproducibility. Conclusions We established a pipeline enabling rapid clinical validation of a discovered mutation in a large breast cancer cohort. An online interface is available for evaluating any human gene mutation or combinations of maximum three such genes ( http://www.g-2-o.com ). Electronic supplementary material The online version of this article (doi:10.1186/s13073-015-0228-1) contains supplementary material, which is available to authorized users.

...Genome Med Genome Med Genome Medicine 1756-994X BioMed Central London 26474971 4609150 228 10.1186/s13073-015-0228-1 Research A genome-wide approach to link genotype to clinical outcome by utilizing next generation sequencing and gene chip data of 6,697 breast cancer patients Pongor Lrinc Kormos Mt Hatzis Christos Pusztai Lajos Szab Andrs Gyrffy Balzs 0036-30-514-2822 gyorffy.balazs@ttk.mta.hu MTA TTK Lendlet Cancer Biomarker Research Group, Research Centre for Natural Sciences, Magyar tudsok krtja 2, Budapest, H-1117 Hungary 2nd Department of Pediatrics, Semmelweis University, Budapest, Hungary Yale Comprehensive Cancer Center, Yale School of Medicine, New Haven, CT USA MTA-SE Pediatrics and Nephrology Research Group, Budapest, Hungary 16 10 2015 16 10 2015 2015 7 104 20 7 2015 5 10 2015 Pongor et al. 2015 Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License ( http://creativecommons.org/licenses/by/4.0/ ), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/ ) applies to the data made available in this article, unless otherwise stated. Background The use of somatic mutations for predicting clinical outcome is difficult because a mutation can indirectly influence the function of many genes, and also because clinical follow-up is sparse in the relatively young next generation sequencing (NGS) databanks. Here we approach this problem by linking sequence databanks to well annotated gene-chip datasets, using a multigene transcriptomic fingerprint as a link between gene mutations and gene expression in breast cancer patients. Methods The database consists of 763 NGS samples containing mutational status for 22,938 genes and RNA-seq data for 10,987 genes. The gene chip database contains 5,934 patients with 10,987 genes plus clinical characteristics. For the prediction, mutations present in a sample are first translated into a transcriptomic fingerprint by running ROC analysis on mutation and RNA-seq data. Then correlation to survival is assessed by computing Cox regression for both up- and downregulated signatures. Results According to this approach, the top driver oncogenes having a mutation prevalence over 5% included AKT1, TRANK1, TRAPPC10, RPGR, COL6A2, RAPGEF4, ATG2B, CNTRL, NAA38, OSBPL10, POTEF, SCLT1, SUN1, VWDE, MTUS2, and PIK3CA, and the top tumor suppressor genes included PHEX, TP53, GGA3, RGS22, PXDNL, ARFGEF1, BRCA2, CHD8, GCC2, and ARMC4. The system was validated by computing correlation between RNA-seq and microarray data (r 2 =0.73, P <1E-16). Cross-validation using 20 genes with a prevalence of approximately 5% confirmed analysis reproducibility. Conclusions We established a pipeline enabling rapid

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Gene: TRANK1 - tetratricopeptide repeat and ankyrin repeat containing 1 - (NCBI)

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