Abstract 3418: Cross-comparison of targeted gene expression technologies for patient stratification
Authors: Raghavee Venkatramanan, Tuuli Saloranta, Inah Golez, Elliot Swanson, Kimberly Kruse, Vickie Satele, Saman Tahir, Sally Dow, Evan Anderson, Briana Hudson, Spencer Chee, Kerry Deutsch, Steve Anderson, Fang Yin Lo, Anup Madan
Journal: Cancer Research (2018)

Abstract Colorectal cancer (CRC) is one of the major causes of global cancer mortality. Until recently, KRAS has been the only predictive biomarker for anti-EGFR therapy for metastatic CRC, and yet predicting prognosis in clinical practice is still poor. Therefore, a more accurate method for prognosis of CRC patients is needed. Gene expression profiling has shown great promise in predicting prognosis of individual patients in diverse cancers. The development of RNA-sequencing has greatly facilitated identification of biomarkers that can be used to stratify patients for targeted therapies. Despite the decrease in cost of sequencing in last few years, the time and the resources needed for analysis limit its use in clinical trials for patient selection. Targeted gene expression technologies like qPCR and NanoString enable highly customizable assays that can be conveniently performed for patient recruitment. The aim of this study was to investigate potential alternatives for gene profiling using a novel NanoString Plex Set technology. The Plex Set system comes with prepackaged and custom code sets in identifying genetic markers. Up to 8 samples can be pooled to each nCounter cartridge lane, enabling a total of 96 samples per run, thus making the total cost relatively affordable. For this study, gene expression signature was developed using RNA-Seq data where we have profiled 74 CRC samples, 20 of which have matching normal samples. A RAS signature score based on expression profile was calculated for each sample. In order to look for potential gene signatures, differential gene expression analysis was performed between the following groups: (a) samples with high versus those with low RAS signature scores in the 54 CRC, (b) KRAS mutant versus wild-type samples, and (c) tumor versus normal samples in the clinical study. We hypothesized that our genes of interest are most likely significantly differentially expressed in one of these groups. The counts of significantly expressed gene for the groups (a-c) are 1560 and 34, respectively, and we are working on the third case. Therefore, significantly deferentially expressed genes between groups were selected and ranked based on frequency of occurrence. These genes of interest are being analyzed using NanoString Plex Set and qPCR to evaluate the potential of using NanoString Plex Set system for targeted gene expression profiling. Results of these analyses will be presented. Citation Format: Raghavee Venkatramanan, Tuuli Saloranta, Inah Golez, Elliot Swanson, Kimberly Kruse, Vickie Satele, Saman Tahir, Sally Dow, Evan Anderson, Briana Hudson, Spencer Chee, Kerry Deutsch, Steve Anderson, Fang Yin Lo, Anup Madan. Cross-comparison of targeted gene expression technologies for patient stratification [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2018; 2018 Apr 14-18; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2018;78(13 Suppl):Abstract nr 3418.