TY - JOUR
T1 - Identification of a novel gene signature predicting response to first-line chemotherapy in BRCA wild-type high-grade serous ovarian cancer patients
AU - Buttarelli, Marianna
AU - Ciucci, Alessandra
AU - Palluzzi, Fernando
AU - Raspaglio, Giuseppina
AU - Marchetti, Claudia
AU - Perrone, Elisabetta
AU - Minucci, Angelo
AU - Giacò, Luciano
AU - Fagotti, Anna
AU - Scambia, Giovanni
AU - Gallo, Daniela
PY - 2022
Y1 - 2022
N2 - 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 < 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. 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.
AB - 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 < 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. 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.
KW - BRCA1 Protein
KW - Bioinformatics
KW - Biomarkers
KW - Drug-resistance
KW - Female
KW - Gene Expression Profiling
KW - HGSOC
KW - Humans
KW - Neoplasm Grading
KW - Ovarian Neoplasms
KW - Patient stratification
KW - Primary ovarian cancer cells
KW - Random forest classifier model
KW - Retrospective Studies
KW - Survival Analysis
KW - Transcriptomic
KW - BRCA1 Protein
KW - Bioinformatics
KW - Biomarkers
KW - Drug-resistance
KW - Female
KW - Gene Expression Profiling
KW - HGSOC
KW - Humans
KW - Neoplasm Grading
KW - Ovarian Neoplasms
KW - Patient stratification
KW - Primary ovarian cancer cells
KW - Random forest classifier model
KW - Retrospective Studies
KW - Survival Analysis
KW - Transcriptomic
UR - http://hdl.handle.net/10807/198361
U2 - 10.1186/s13046-022-02265-w
DO - 10.1186/s13046-022-02265-w
M3 - Article
SN - 0392-9078
VL - 41
SP - 1
EP - 17
JO - JOURNAL OF EXPERIMENTAL & CLINICAL CANCER RESEARCH
JF - JOURNAL OF EXPERIMENTAL & CLINICAL CANCER RESEARCH
ER -