Unità di Medicina Traslazionale per la Salute della Donna e del Bambino, Dipartimento Scienze della Salute della Donna, del Bambino e di Sanità Pubblica, Fondazione Policlinico Universitario A. Gemelli, IRCCS, Largo A. Gemelli 8, 00168, Rome, Italy.
Dipartimento Universitario Scienze della Vita e Sanità pubblica - Sezione di Ginecologia ed Ostetricia - Università Cattolica del Sacro Cuore, Largo A. Gemelli 8, 00168, Rome, Italy.
J Exp Clin Cancer Res. 2022 Feb 4;41(1):50. doi: 10.1186/s13046-022-02265-w.
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.
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.
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.
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.
由于诊断时已处于晚期以及化疗耐药导致疾病复发，高级别浆液性卵巢癌（HGSOC）患者的生存率较差。在 BRCA1（乳腺癌基因 1）或 BRCA2 野生型（BRCAwt）HGSOC 患者中，耐药和进行性疾病的发生早于且多于 BRCA 突变患者。鉴定有助于预测一线化疗反应的生物标志物是改善 BRCAwt HGSOC 管理的一项挑战。
为了鉴定可预测一线化疗反应的基因特征，我们对一组有限的 BRCAwt HGSOC 患者的预处理肿瘤活检组织进行了 RNA 测序（RNA-Seq）技术分析。根据无铂间隔（PFI）将患者分为敏感组（PFI＞12 个月）和耐药组（PFI＜6 个月）。然后，通过高通量实时定量 PCR（HT RT-qPCR）在验证队列中对 RNA-Seq 分析鉴定的基因面板进行测试，并使用统计/生物信息学方法鉴定合格标志物并探索鉴定基因集的相关途径/基因网络富集。最后，利用一组原发性 HGSOC 细胞系揭示耐药的细胞自主性机制。
RNA-Seq 鉴定出了一组 42 个基因可区分敏感和耐药的 BRCAwt HGSOC 患者，通路分析表明免疫系统可能是化疗反应的驱动因素。从 42 个差异表达基因（DEGs）的扩展队列分析中，一种统计学方法与随机森林分类器模型相结合，生成了一个预测一线化疗反应的十个基因特征。该十个基因特征包括：CKB（肌酸激酶 B）、CTNNBL1（连环蛋白β样 1）、GNG11（G 蛋白亚单位 gamma 11）、IGFBP7（胰岛素样生长因子结合蛋白 7）、PLCγ2（磷脂酶 Cγ2）、RNF24（环指蛋白 24）、SLC15A3（溶质载体家族 15 成员 3）、TSPAN31（四旋蛋白 31）、TTI1（TELO2 相互作用蛋白 1）和 UQCC1（泛醌-细胞色素 c 还原酶复合物组装因子）。细胞毒性测定与原发性 HGSOC 细胞系中的基因表达分析相结合，可定义 CTNNBL1、RNF24 和 TTI1 为肿瘤耐药的细胞自主性贡献者。
我们使用机器学习技术鉴定了一个基因特征，该特征可预测 BRCAwt HGSOC 患者对一线化疗的反应，为个性化治疗方法提供了有用的工具。
