Abstract P6-08-31: Linking genotype to clinical outcome in breast cancer by combining NGS and gene chip data

Abstract

Abstract Introduction: Next generation sequencing (NGS) provides the possibility to measure mutational status for any part of any gene. However, because of scarce data available to date, linking these mutations to relevant clinical outcome in a large number of patients is not possible. Aim: Our goal was to combine available genotype data generated by using NGS with gene expression data generated by gene chips to establish a framework to assess the effect of genotype on clinical outcome. Methods: NGS data generated by the TCGA consortia and publicly available gene chip data obtained from the GEO and EGA repositories were utilized. NGS data was processed using MuTect, SNPeff, GRCh37 and R. RNA-seq data was normalized using DEseq. Gene chip data was MAS5 normalized. Generation of the transcriptomic fingerprint for mutation status was computed by ROC utilizing the RNA-seq data. In the gene chip data, the average expression of significant genes identified was designated as a metagene for the given genotype. Correlation to survival for this metagene was assessed by computing Cox regression and plotting Kaplan-Meier survival plots. Finally, we have set up an online interface to enable running the analysis for any selected gene. Results: The database contains 332 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 detailed clinical characteristics and survival data. We evaluated correlation to outcome for previously identified genes harboring the ten most common somatic mutations in breast cancer. Of these, TP53 (n of mutations out of 332=93, hazard rate=0.51, p<1E-16), AKT1 (n=18, HR=1.6, p=1.6E-15), PIK3CA (n=119, HR=1.5, p=8.5E-12), MAP3K1 (n=20, HR=1.4, p=1.3E-08), CDH1 (n=34, HR=1.3, p=4.4E-07), and RB1 (n=21, HR=1.3, p=7E-06) reached statistical significance while PI3K, PTEN, CDKN1B and GATA3 were not significant or had insufficient number of mutated samples. Discussion: By connecting genotype to gene expression signature and employing this signature for survival analysis we have set up a pipeline enabling the functional validation of a discovered mutation for any gene in a large breast cancer cohort. Citation Format: Balazs Gyorffy, Lorinc Pongor, Mate Kormos. Linking genotype to clinical outcome in breast cancer by combining NGS and gene chip data [abstract]. In: Proceedings of the Thirty-Seventh Annual CTRC-AACR San Antonio Breast Cancer Symposium: 2014 Dec 9-13; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2015;75(9 Suppl):Abstract nr P6-08-31.