PhD Scientific Days

Poster Presentation: Oncology

P34: Performing survival analysis of gene expression signatures associated to genetic alterations in lung cancer patients

Előadó neve

Pongor, Lőrinc Sándor

Előadó munkahelye

2nd Department of Pediatrics, Semmelweis University

Előadó telefonszáma

+36-70-517-0113

Előadó e-mail címe

pongorlorinc@gmail.com

Az előadás címe

Performing survival analysis of gene expression signatures associated to genetic alterations in lung cancer patients

Szerző(k) neve és munkahelye

Lőrinc S. Pongor1,2, Ádám Nagy1,2, Boglárka Weltz2, Balázs Győrffy1,2
1 2nd Department of Pediatrics, Semmelweis University
2 Research Centre for Natural Sciences, Hungarian Academy of Sciences

Szekció

Poster Presentation: Oncology

Data of the presenter

Doctoral School: Pathological Sciences
Program: Experimental Oncology
Supervisor: Balázs Győrffy
E-mail: pongorlorinc@gmail.com

Text of the abstract

Introduction. Identifying biomarkers using cancer somatic mutations is difficult since these can have direct and indirect effects on the expression of multiple genes. Current methods used to identify biomarkers utilize mutation- and gene expression data separately. A major drawback of this approach is that mutation data has no information on the downstream effects (e.g. gene expression change), while expression data lacks the genomic status of the samples. Another difficulty is the fact that clinical follow-up in next generation sequencing (NGS) databanks is exceptionally short.
Aim. Our aim was to predict the effect of a genetic alteration on gene expression followed by survival analysis using the identified gene signature as a surrogate of mutation status in lung cancer patients.
Methods. NGS data generated by the TCGA and gene chip data obtained from GEO, CaArray and TCGA were utilized. Transcriptomic fingerprint for mutation status was identified by running Wilcoxon analysis on mutation and RNA-seq data across all genes. Differentially expressed genes were designated as a genotype‘s transcriptomic fingerprint. Correlation to survivalwas assessed by computing Cox regression. A new on-line interface enables running the analysis for any selected gene (http://www.g-2-o.com).
Results. The database contains 555 lung cancer samples containing both mutational status and RNA-seq data for 10,987 genes. The gene chip database contains 2,437 patients with expression data for 10,987 genes plus clinical characteristics. A set of established oncogenes and tumor suppressor genes known to influence survival were used as a validation set (KRAS: p=3.6E-10, BRAF=3.6E-06, PIK3CA: p=1.7E-14, PDGFRA: p=1.6E-06, and TP53: p=2.6-14).
Conclusion. We have set up an online tool that captures indirect effects of mutations by connecting genotype to gene expression changes followed by survival analysis using these genes on an independent dataset of lung cancer patients. The tool is freely available at www.g-2-o.com.

Azonosító

P34

Kind

Szabad

Előadás fájl jóváhagyás

nem rendelkezett róla

Előadó

1115

Authors (legacy)