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

Abstract

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.

The interaction of β-arrestin1 with talin1 driven by endothelin A receptor as a feature of α5β1 integrin activation in high-grade serous ovarian cancer

“…The experimental procedures related to primary cancer cell culture and characterization have been described previously [ 49 ]. Briefly, samples were collected at surgery from “leftover tissues” by using sterile scalpels.…”

Preclinical models of epithelial ovarian cancer: practical considerations and challenges for a meaningful application

“…Therefore, appropriate choice of cell lines for the different histotypes of EOC along with routine analysis of cell line authenticity are the first steps to increase the translational value of cell line studies in preclinical research on EOC. Besides, our and literature data have also demonstrated that established EOC cell lines, although easier to work with than primary cells, may not be recommended to understand determinants of sensitivity to current therapies or to translate novel therapies into the clinics [ 6 , 175 ]. These models, however, under the right conditions and with the appropriate controls, retain their utility in mechanistic work at the protein and pathway level (Fig.…”

“…On the other hand, primary tumour cell cultures more closely resemble the patient situation, and therefore, they represent a more experimentally accurate model for the reproduction of cancer in in vitro systems. Due to these unique features, they are a valuable experimental tool during preclinical studies of drug resistance mechanisms, as we also recently demonstrated [ 175 ]. However, the need of characterization for determining origin and purity, the limited lifespan of days or weeks, and the slow growth, constitute important limitations to their use (Fig.…”

Integrated single-cell and transcriptome sequencing analyses determines a chromatin regulator-based signature for evaluating prognosis in lung adenocarcinoma

“…Recent studies have suggested that patients with the same histology and TNM stages may have very distinct clinical outcomes, mainly due to their genetic heterogeneities ( 20 ). With the rapid development of the next-generation sequencing, a growing number of prognostic signatures based on transcriptome data were established to depict the individual differences, and to forecast the prognosis in various cancers ( 21 – 23 ). Therefore, a more reliable prognostic model based on genetic alterations is urgently needed to provide early detection and personalized treatment for LUAD patients.…”