Identification of a novel gene signature predicting response to first-line chemotherapy in BRCA wild-type high-grade serous ovarian cancer patients

Autor: Marianna Buttarelli, Alessandra Ciucci, Fernando Palluzzi, Giuseppina Raspaglio, Claudia Marchetti, Emanuele Perrone, Angelo Minucci, Luciano Giacò, Anna Fagotti, Giovanni Scambia, Daniela Gallo
Rok vydání: 2022
Předmět:
Zdroj: Journal of Experimental & Clinical Cancer Research, Vol 41, Iss 1, Pp 1-17 (2022)
Journal of Experimental & Clinical Cancer Research : CR
ISSN: 1756-9966
DOI: 10.1186/s13046-022-02265-w
Popis: Background 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. 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. Methods To 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 Results RNA-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. Conclusions Using 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.
Databáze: OpenAIRE
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