A bioinformatic approach to define transcriptome alterations in platinum resistance ovarian cancers

Epithelial ovarian cancer (EOC) is the most lethal gynecological malignancy due to its diagnosis at advanced stages, when the disease has already spread beyond the ovaries. EOC is generally sensitive to first line chemotherapy, and the vast majority of patients respond to platinum (Pt)-based therapy after debulking surgery. Unfortunately, more than 80% of Pt-responsive patients relapse with a disease that progressively becomes Pt-resistant. Based mainly on clinical evidence, the process by which disease relapses is still poorly understood. The aim is to identify biomarkers of sensitivity to chemotherapy and therapeutic targets in HGS-EOC by integrating transcriptomic data, coding and non-coding RNAs. The bioinformatic analysis was applied on microarray data and RNA-seq data, embracing different classes of patients (resistant, sensitive, partially sensitive and normal). Two complementary approaches have been adopted to identify biomarkers of therapy response in microarray data: i) a classic approach and ii) a network-based approach using micrographite. The results obtained with both procedures have then been used to reconstruct a regulatory circuit involved in therapy response. The final outcome is a regulatory cell signal pathway composed of genes and miRNAs mainly involved in the therapy response. Circuit has been validated using two external and independent cohorts by quantitative real-time PCR (qRT-PCR). However, in order to complete the characterization of network as prognostic factor we decided to consider in survival analysis defect of the Homologous Recombination (HR). Approaching in survival analysis, a signature of three genes (SDF2L1, PPP1R12A and PRKG1) found to be independent prognostic biomarkers, was able to predict, at the time of diagnosis, resistance to Pt-based chemotherapy. Also, a new approach has been evaluated in order to characterize new mechanisms of chemotherapy resistance in ovarian cancers. On microarray data, we tried to stratify patients for the immunotherapy, with recent improved understanding of the immune recognition and regulation of cancer cells. In addition, using RNA-seq data and somatic DNA mutations, we went deeper in immunogenicity of ovarian cancer trying to find new elements as therapy targets, neoantigens, not associated to this tumor till now. At last, in addition, the small amount of molecular differences between Pt-r and Pt-s patients suggested the presence of potential new transcripts involved in therapy response maybe due to aberrant splicing events. To investigate this hypothesis, we used a set of RNA-seq experiments, to identify new aberrant splicing such as circular RNAs. We reported 5 circRNAs differentially expressed between tumour resistance types, and a large number of class-specific circRNAs. In particular, circ_BARD1 showed a character as prognostic factor significative in OS and PFS, in multivariate analysis with residual tumour and age as covariates. The consistency of circular RNA expression, in conjunction with the regulatory circuit, may offer new candidates for cancer treatment and prognosis, revealing that the integration of coding and non-coding RNAs data may shed light on chemotherapy resistance mechanisms in ovarian cancer.