Abstract
Heterogeneity in chemotherapeutic response is directly associated with prognosis and disease recurrence in patients with ovarian cancer (OvCa). Despite the significant clinical need, a credible gene signature for predicting response to platinum-based chemotherapy and for guiding the selection of personalized chemotherapy regimens has not yet been identified. The present study used an integrated approach involving both OvCa tumors and cell lines to identify an individualized gene expression signature, denoted as IndividCRS, consisting of 16 robust chemotherapy-responsive genes for predicting intrinsic or acquired chemotherapy response in the meta-discovery dataset. The robust performance of this signature was subsequently validated in 25 independent tumor datasets comprising 2215 patients and one independent cell line dataset, across different technical platforms. The IndividCRS was significantly correlated with the response to platinum therapy and predicted the improved outcome. Moreover, the IndividCRS correlated with homologous recombination deficiency (HRD) and was also capable of discriminating HR-deficient tumors with or without platinum-sensitivity for guiding HRD-targeted clinical trials. Our results reveal the universality and simplicity of the IndividCRS as a promising individualized genomic tool to rapidly monitor response to chemotherapy and predict the outcome of patients with OvCa.
Highlights
Ovarian cancer (OvCa) is one of the most common cancers of the female genital system and is the most lethal gynecological cancer, accounting for ~2.5 and 5% of female cancer occurrences and deaths, respectively[1]
Development of an individualized gene signature for predicting response to chemotherapy In clinical studies, response to chemotherapy is estimated based on the progression of disease during therapy
Cross-validation was used to prevent over-fitting by performing meta-discovery using 754 patients from three cohort studies and 171 transcriptomes from OvCa cell lines representing the largest datasets with detailed clinical information and the largest cisplatin-treated cell line dataset, respectively
Summary
Ovarian cancer (OvCa) is one of the most common cancers of the female genital system and is the most lethal gynecological cancer, accounting for ~2.5 and 5% of female cancer occurrences and deaths, respectively[1]. An additional study by Marchini et al.[11] analyzed 23 tumor biopsies from patients with stage III-IV disease and identified an epithelial-mesenchymal transition (EMT)related pathway signature associated with chemotherapy resistance. None of these existing gene signatures have been widely adopted in clinical practice, which may be due, in part, to the use of only one dataset for discovery that does not account for biological heterogeneity, technical biases across different datasets, the large number of genes in a signature or impractical linear scoring modeling methods[17,18]. With the current availability of large-scale transcriptional profiles of OvCa tumors and cell lines, there is an opportunity to identify and validate a robust and reproducible individualized gene signature for predicting response to platinum-based chemotherapy in OvCa
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