Abstract
BackgroundHigh-grade serous ovarian cancer (HGSOC) has poor survival rates due to a combination of diagnosis at advanced stage and disease recurrence as a result of chemotherapy resistance. In BRCA1 (Breast Cancer gene 1) - or BRCA2-wild type (BRCAwt) HGSOC patients, resistance and progressive disease occur earlier and more often than in mutated BRCA. Identification of biomarkers helpful in predicting response to first-line chemotherapy is a challenge to improve BRCAwt HGSOC management.MethodsTo identify a gene signature that can predict response to first-line chemotherapy, pre-treatment tumor biopsies from a restricted cohort of BRCAwt HGSOC patients were profiled by RNA sequencing (RNA-Seq) technology. Patients were sub-grouped according to platinum-free interval (PFI), into sensitive (PFI > 12 months) and resistant (PFI < 6 months). The gene panel identified by RNA-seq analysis was then tested by high-throughput quantitative real-time PCR (HT RT-qPCR) in a validation cohort, and statistical/bioinformatic methods were used to identify eligible markers and to explore the relevant pathway/gene network enrichments of the identified gene set. Finally, a panel of primary HGSOC cell lines was exploited to uncover cell-autonomous mechanisms of resistance.ResultsRNA-seq identified a 42-gene panel discriminating sensitive and resistant BRCAwt HGSOC patients and pathway analysis pointed to the immune system as a possible driver of chemotherapy response. From the extended cohort analysis of the 42 DEGs (differentially expressed genes), a statistical approach combined with the random forest classifier model generated a ten-gene signature predictive of response to first-line chemotherapy. The ten-gene signature included: CKB (Creatine kinase B), CTNNBL1 (Catenin, beta like 1), GNG11 (G protein subunit gamma 11), IGFBP7 (Insulin-like growth factor-binding protein 7), PLCG2 (Phospholipase C, gamma 2), RNF24 (Ring finger protein 24), SLC15A3 (Solute carrier family 15 member 3), TSPAN31 (Tetraspanin 31), TTI1 (TELO2 interacting protein 1) and UQCC1 (Ubiquinol-cytochrome c reductase complex assembly factor). Cytotoxicity assays, combined with gene-expression analysis in primary HGSOC cell lines, allowed to define CTNNBL1, RNF24, and TTI1 as cell-autonomous contributors to tumor resistance.ConclusionsUsing machine-learning techniques we have identified a gene signature that could predict response to first-line chemotherapy in BRCAwt HGSOC patients, providing a useful tool towards personalized treatment modalities.
Highlights
High-grade serous ovarian cancer (HGSOC) has poor survival rates due to a combination of diagnosis at advanced stage and disease recurrence as a result of chemotherapy resistance
Overall results obtained from the present study suggest that both cell-autonomous and tumor microenvironment (TME)-driven noncell-autonomous mechanisms of drug resistance are critical in causing refractoriness of BRCA2-wild type (BRCAwt) HGSOC patients and antineoplastic treatment failure (Fig. 4)
Only three out of the ten Differentially expressed genes (DEGs) identified in tumor biopsies were found to be deregulated in primary HGSOC cells, i.e. Catenin beta like 1 (CTNNBL1), RNF24 and TTI1, this implicating a role for these genes in tumor cell exclusive pathways of chemotherapy resistance
Summary
High-grade serous ovarian cancer (HGSOC) has poor survival rates due to a combination of diagnosis at advanced stage and disease recurrence as a result of chemotherapy resistance. 314,000 women were estimated to have been diagnosed with ovarian cancer and 207,000 to have died from disease in 2020, with rates varying across the world [1]. Chemosensitivity to platinum-based chemotherapy is a prognostic factor for patient survival and a significant determinant for subsequent treatment, including maintenance therapy with PARP inhibitors (PARPI). PARPI approval has been granted for patients with ovarian cancer, both in the front line setting and as maintenance or treatment in recurrent platinum-sensitive disease regardless of HRD status [4, 5]
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