PRJNA663280
Personalized targeted therapy prescription in colorectal cancer using algorithmic analysis of RNA sequencing data
Colorectal cancer (CRC) is fourth most common cancer and the second most common cause of cancer mortality. Overall survival of advanced CRC patients remains poor, and gene expression analysis could potentially complement detection of clinically relevant mutations to personalize CRC treatments. In this study we performed RNA sequencing of formalin-fixed, paraffin-embedded (FFPE) cancer tissue samples of 14 CRC patients and interpreted the data obtained using bioinformatic method Oncobox for expression-based rating of targeted therapeutics. This method calculates drug efficiency score and ranks cancer drugs accordingly using data on the expression of target genes and activation of molecular pathways. The patients had primary and metastatic CRC with metastases in liver, peritoneum, lymph nodes and ovary. One patient had mutation in NRAS, four others had mutated KRAS gene. Patients were treated by aflibercept, bevacizumab, bortezomib, cabozantinib, cetuximab, crizotinib, denosumab, panitumumab and regorafenib as monotherapy or in combination with chemotherapy, and information on the success of totally 30 lines of therapy was collected. Oncobox drug efficiency score was effective biomarker that could predict treatment outcomes in the experimental (AUC 0.74 for all lines of therapy and 0.94 for the first line after tumor sampling) and in independent literature CRC datasets. It also predicted progression-free survival in univariate (Hazard ratio 0.14) and multivariate (Hazard ratio 0.011) analyses. Difference in AUC scores evidences importance of using recent biosamples for the prediction quality. Our results suggest that RNA sequencing analysis of tumor FFPE materials may be helpful for personalizing prescriptions of targeted therapeutics in CRC.