Practical biomarkers and robust multiplex models for the prediction of response to the promising first-line chemotherapy: A theranostic study in metastatic ovarian cancer patients with residual peritoneal tumors

Abstract Background: In advanced or metastatic ovarian cancer patients, the therapeutic impact of molecular targeted agents and immunotherapy is limited, and current chemotherapeutic algorithm is still far from personalized medicine. We recently demonstrated that intraperitoneal carboplatin with dose-dense paclitaxel (ddTCip) therapy is a promising front-line chemotherapy even in the patients with residual peritoneal tumors, which led us to this theranostic study for biomarker discovery to realize the precision medicine (ID: UMIN000001713 on Feb 16 th , 2009). Methods: We first validated previously suggested markers (41 genes and 3 predictive models for the therapeutic efficacy and 31 polymorphisms for the toxicity), sought out more active effective biomarkers through genome-wide transcriptome and genotyping analyses, and then developed multiplex statistical prediction models for progression free-survival (PFS) and toxicity. Multiple regression analysis following forward stepwise method and Classification and Regression Trees (CART) algorithm were mainly employed to develop multiplex prediction models. Results: The association analyses with PFS in 76 patients followed by the validation study using data sets in 189 patients published in The Cancer Genome Atlas revealed that SPINK1 expression could be a possible predictive biomarker of ddTCip efficacy even when used alone, and multiple regression analyses provided a potent efficacy prediction model using expression data of 5 genes. SPINK1 appeared to be a critical resistant determinant of ddTCip therapy, which indicates the potential of SPINK1 also to be a novel therapeutic target. As for the toxicity prediction, ABCB1rs1045642 and ERCC1rs11615 polymorphisms appeared to closely associate with grade2-4 hematologic toxicity and peripheral neuropathy, respectively. We further successfully composed robust multiplex prediction models for the adverse events-CART models using a total of 4 genotype combinations and further powerful multiple regression models using 15 polymorphisms on 12 genes-. Conclusions: We newly proposed SPINK1 expression as a powerful predictive biomarker of the efficacy for ddTCip therapy and confirmed the predictive values of ABCB1 and/or ERCC1 polymorphisms for the toxicity. Multiplex prediction models composed herein were also found to work well for the prediction of therapeutic response. These may raise the potential to realize a precision medicine in the essential treatment for metastatic ovarian cancer patients.