Creation and validation of models to predict response to chemotherapy before treatment in serous ovarian cancer

This is a retrospective case-control study that integrates comprehensive clinical and genomic data from 88 patients with HGSC from a single institution. Responders were those patients with a progression-free survival of at least 6 months after treatment. Only patients with complete clinical information and frozen specimen at surgery were included. Gene, miRNA, exon, and long non-coding RNA (lncRNA) expression, gene copy number, somatic mutations, and fusion-gene determination were extracted from RNA-sequencing data. Initial models included only one variable. Variables were then combined to create complex models. Validation of all models was performed using TCGA HGSC database. By integrating clinical and genomic variables, we achieved prediction performances of over 95% in AUC. Most performances in the validation set did not differ from the training set. Models with DNA methylation or lncRNA underperformed in the validation set. A retrospective case-control study that integrates clinical and biological data to build prediction models of clinical outcomes.