Abstract A2-47: Informatics framework for clustering and deriving gene signatures for prognostic stratification of cancer patients

Stratification of cancer patients into different molecular groups is essential for developing targeted therapies. High-throughput technologies, such as microarrays and next-generation sequencing, have been extensively used for generating multi-omics data. Indeed, The Cancer Genome Atlas (TCGA) consortium, one of the most comprehensive and popular databases of cancer, has been accumulating large volumes of invaluable data for more than 30 cancer types, offering unprecedented opportunity to attain new insights in cancer biology. Despite the technological advances, analyzing, integrating and translating the gene signatures across different platforms remains a computationally challenging task. Here, we developed a novel computational framework that integrates genomic and clinical data to stratify cancer patients into different molecular subgroups and predict clinically applicable phenotypes, such as survival. Application of this user-friendly framework derives platform-independent isoform-level gene signatures that can be translated from high-dimensional platforms (e.g., RNA-Seq) to clinically adaptable low-dimensional molecular assays (e.g., RT-PCR) for prognostic stratification. We applied the pipeline on two TCGA lung cancer datasets—Lung Adenocarcinoma (LUAD) and Lung Squamous Cell Carcinoma (LUSC). Using independent test data, we achieved about 93% (LUAD) and 98% (LUSC) classification accuracy using less than 70 isoform-level gene signature. The proposed informatics platform is applicable to other cancer types in TCGA data portal. Citation Format: Segun Jung, Yingtao Bi, Ramana V. Davuluri. Informatics framework for clustering and deriving gene signatures for prognostic stratification of cancer patients. [abstract]. In: Proceedings of the AACR Special Conference on Translation of the Cancer Genome; Feb 7-9, 2015; San Francisco, CA. Philadelphia (PA): AACR; Cancer Res 2015;75(22 Suppl 1):Abstract nr A2-47.