Gene expression-based personalized prescription of targeted drugs in colorectal cancer.

Abstract

151 Background: Colorectal cancer (CRC) is the fourth most common cancer worldwide with relatively poor patient survival. Transcriptome assay could be used to personalize CRC treatment thus complementing standard mutation analysis. Methods: We performed retrospective hybrid experimental and meta-analysis of CRC patient gene expression data with available progression-free survival (PFS) information and/or targeted drug response status. In total we analyzed 243 gene expression profiles from four publicly available (TCGA and three datasets from Gene Expression Omnibus GSE19860, GSE19862, GSE104645), and one experimental (PRJNA663280) patient cohorts. Each gene expression profile was analyzed using bioinformatic second-opinion platform Oncobox to calculate balanced drug efficiency scores (BES) to build personalized ratings of potentially effective targeted drugs. Area under the ROC curve (AUC) metric and Cox regression analysis were used to assess Oncobox capacity to predict tumor response and PFS, respectively. Results: Patients from GSE19860 (n = 12), GSE19862 (n = 14), GSE104645 (n = 81) received bevacizumab as monotherapy or in combination with chemotherapy as the nearest line of treatment after biopsy collection. Oncobox correctly classified treatment responders vs non-responders with AUC 0.94, 0.90 and 0.84, respectively. BES value was strongly associated with PFS (HR = 0.53, CI 0.33-0.84, log-rank test p-value 0.0057) in the GSE104645 cohort. However, BES was ineffective for predicting response and PFS after second-line (after biopsy collection) treatment with cetuximab. BES also predicted treatment response with AUC 0.74 in the TCGA cohort (n = 17) treated with 4 different targeted drugs. Thirty clinical outcomes were collected for 14 patients from our experimental cohort PRJNA663280. Patients were treated with 10 different targeted drugs. BES was an effective biomarker that could predict treatment outcomes with AUC 0.74 for all lines of therapy and 0.94 for the first line therapy (after biopsy), and could predict PFS after first-line treatment (HR 0.14, CI 0.027-0.73, log-rank test p-value 0.0091). Conclusions: Our results suggest that RNA profiling in tumor samples may be helpful for personalizing prescriptions of targeted therapeutics in CRC. Using recent biopsies is essential to obtain robust estimates of targeted drugs efficacy.