Abstract 753: Profiling of multiple RNA-based biomarkers for cancer identification
Authors: Song Tian, Hong Xu, Mohammad Nezami Ranjbar, Frank Reinecke, Xiujing Gu, Raghavendra Padmanabhan, Jixin Deng, John DiCarlo, Scott Winter, James Keller, George Quellhorst, Young Woo Kim, Dan Heard, Raed Samara, Eric Lader
Journal: Cancer Research (2020)

Abstract With the adoption of NGS as a powerful tool for molecular biology research, researchers seek to efficiently utilize genetic variation information for cancer identification which otherwise would be difficult with traditional method. For example, targeted DNA and RNA sequencing are being used in an attempt to identify relatively rare cancers in popular thyroid nodules as the overtreatment in a majority of patients at low risk for disease-specific mortality and morbidity will be a significant burden with potential harmful side effects. Detection of fusions or SNP/InDels offers some limited differentiation power but can be insufficient to identify pathologic changes from benign when approaching an individual sample. As cancer is a multi-factorial disease and carcinogenesis results from many genetic alterations especially at the RNA level, RNA sequencing has become a crucial modality for cancer research. Here we explored the possibility of using a targeted RNA sequencing approach (QIAseq Fusion XP panel focused on solid tumor) to detect not only fusions, but also SNP/InDel and alteration in gene expression, and combine these key factors together as a new form of biomarker to differentiate tumor from benign modules. The presence of these variants were surveyed in RNA samples from 6 thyroid cancer (PTC and FVPTC) samples and 6 non-cancer control samples (NH and FA) using this targeted panel targeting selected solid tumor related gene fusions, gene expression and SNP/InDel detection. With in-house bioinformatics pipeline analysis we can identify the trend of gene expression, SNP/InDel alterations and fusions in different groups. For example we observe a decrease in the expression of the cellular differentiation related gene c-KIT, the increased expression of the oncogene LAMP3, the presence of the translocation fusion SQSTM1-NTRK3 and increased SNP/InDel levels in the tumor group. However none of the individual alterations can fully separate the tumor samples from benign ones. Here we explored the utility of a multi-marker scoring system from targeted RNA sequencing to enhance the tumor differentiation power. We normalized and scored gene expression, fusions, and SNP/InDel count separately and then combined them together to get a ‘tumor possibility score'. Our results suggest that this type of combined score system can separate tumor from benign well (44 vs -1 in average with p&amp;lt;0.001, no overlap for individual score between groups). These results demonstrate a new approach to use targeted RNA sequencing to characterize changes in gene structure, sequence, and expression as a potential combinatorial biomarker for tumor identification and differentiation. RNA Fusion XP panel provides a useful tool for biomarker analysis. Citation Format: Song Tian, Hong Xu, Mohammad Nezami Ranjbar, Frank Reinecke, Xiujing Gu, Raghavendra Padmanabhan, Jixin Deng, John DiCarlo, Scott Winter, James Keller, George Quellhorst, Young Woo Kim, Dan Heard, Raed Samara, Eric Lader. Profiling of multiple RNA-based biomarkers for cancer identification [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr 753.