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A DNA Damage Response Gene Panel for Different Histologic Types of Epithelial Ovarian Carcinomas and Their Outcomes.

ABSTRACT: DNA damage response (DDR) is important for maintaining genomic integrity of the cell. Aberrant DDR pathways lead to accumulation of DNA damage, genomic instability and malignant transformations. Gene mutations have been proven to be associated with epithelial ovarian cancer, and the majority of the literature has focused on BRCA. In this study, we investigated the somatic mutation of DNA damage response genes in epithelial ovarian cancer patients using a multiple-gene panel with next-generation sequencing. In all, 69 serous, 39 endometrioid and 64 clear cell carcinoma patients were enrolled. Serous carcinoma patients (69.6%) had higher percentages of DDR gene mutations compared with patients with endometrioid (33.3%) and clear cell carcinoma (26.6%) (p < 0.001, chi-squared test). The percentages of DDR gene mutations in patients with recurrence (53.9 vs. 32.9% p = 0.006, chi-squared test) or cancer-related death (59.2 vs. 34.4% p = 0.001, chi-squared test) were higher than those without recurrence or living patients. In endometrioid carcinoma, patients with ≥2 DDR gene mutations had shorter PFS (p = 0.0035, log-rank test) and OS (p = 0.015, log-rank test) than those with one mutation or none. In clear cell carcinoma, patients with ≥2 DDR gene mutations had significantly shorter PFS (p = 0.0056, log-rank test) and OS (p = 0.0046, log-rank test) than those with 1 DDR mutation or none. In the EOC patients, somatic DDR gene mutations were associated with advanced-stage tumor recurrence and tumor-related death. Type I EOC patients with DDR mutations had an unfavorable prognosis, especially for clear cell carcinoma.

SUBMITTER: Chiang YC

PROVIDER: S-EPMC8533221 | biostudies-literature | 2021 Oct

REPOSITORIES: biostudies-literature

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A DNA Damage Response Gene Panel for Different Histologic Types of Epithelial Ovarian Carcinomas and Their Outcomes.

Chiang Ying-Cheng YC Lin Po-Han PH Lu Tzu-Pin TP Kuo Kuan-Ting KT Tai Yi-Jou YJ Hsu Heng-Cheng HC Wu Chia-Ying CY Lee Chia-Yi CY Shen Hung H Chen Chi-An CA Cheng Wen-Fang WF

Biomedicines 20211003 10

DNA damage response (DDR) is important for maintaining genomic integrity of the cell. Aberrant DDR pathways lead to accumulation of DNA damage, genomic instability and malignant transformations. Gene mutations have been proven to be associated with epithelial ovarian cancer, and the majority of the literature has focused on <i>BRCA</i>. In this study, we investigated the somatic mutation of DNA damage response genes in epithelial ovarian cancer patients using a multiple-gene panel with next-gene ...[more]

PMID: 34680501

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Project description:ImportanceRandomized clinical trials examining the effectiveness of neoadjuvant chemotherapy (NACT) for advanced ovarian cancer predominantly included patients with high-grade serous carcinomas. The use and outcomes of NACT in less common epithelial carcinomas are understudied.ObjectiveTo investigate the uptake and survival outcomes in treatment with NACT for less common histologic subtypes of epithelial ovarian cancer.Design, setting, and participantsA retrospective cohort study and systematic literature review with meta-analysis was conducted using the National Cancer Database from 2006 to 2017 and the National Cancer Institute's Surveillance, Epidemiology, and End Results Program from 2006 to 2019. Data analysis was performed from July 2022 to April 2023. The evaluation included patients with stage III to IV ovarian cancer with clear cell, mucinous, or low-grade serous histologic subtypes who received multimodal treatment with surgery and chemotherapy.ExposuresExposure assignment per the sequence of treatment: primary debulking surgery (PDS) followed by chemotherapy (PDS group) or NACT followed by interval surgery (NACT group).Main outcomes and measuresTemporal trends and characteristics of NACT use were assessed using multivariable analysis, and overall survival (OS) was assessed with the inverse probability of treatment weighting propensity score.ResultsA total of 3880 patients were examined in the National Cancer Database including 1829 women (median age, 56 [IQR, 49-63] years) with clear cell, 1156 women (median age, 53 [IQR, 42-64] years) with low-grade serous, and 895 women (median age, 57 [IQR, 48-66] years) with mucinous carcinomas. NACT use increased in patients with clear cell (from 10.2% to 16.2%, 58.8% relative increase; P < .001 for trend) or low-grade serous (from 7.7% to 14.2%, 84.4% relative increase; P = .007 for trend) carcinoma during the study period. This association remained consistent in multivariable analysis. NACT use also increased, but nonsignificantly, in mucinous carcinomas (from 8.6% to 13.9%, 61.6% relative increase; P = .07 for trend). Across the 3 histologic subtypes, older age and stage IV disease were independently associated with NACT use. In a propensity score-weighted model, the NACT and PDS groups had comparable OS for clear cell (4-year rates, 31.4% vs 37.7%; hazard ratio [HR], 1.12; 95% CI, 0.95-1.33) and mucinous (27.0% vs 26.7%; HR, 0.90; 95% CI, 0.68-1.19) carcinomas. For patients with low-grade serous carcinoma, NACT was associated with decreased OS compared with PDS (4-year rates, 56.4% vs 81.0%; HR, 2.12; 95% CI, 1.55-2.90). Increasing NACT use and histologic subtype-specific survival association were also found in the Surveillance, Epidemiology, and End Results Program cohort (n = 1447). A meta-analysis of 4 studies, including the current study, observed similar OS associations for clear cell (HR, 1.13; 95% CI, 0.96-1.34; 2 studies), mucinous (HR, 0.93; 95% CI, 0.71-1.21; 2 studies), and low-grade serous (HR, 2.11; 95% CI, 1.63-2.74; 3 studies) carcinomas.Conclusions and relevanceDespite the lack of data on outcomes of NACT among patients with less common carcinomas, this study noted that NACT use for advanced disease has gradually increased in the US. Primary chemotherapy for advanced-stage, low-grade serous ovarian cancer may be associated with worse survival compared with PDS.

Project description:ImportanceEpithelial ovarian carcinoma is heterogeneous and classified according to the World Health Organization Tumour Classification, which is based on histologic features and molecular alterations. Preoperative prediction of the histologic subtypes could aid in clinical management and disease prognostication.ObjectiveTo assess the value of radiomics based on contrast-enhanced computed tomography (CT) in differentiating histologic subtypes of epithelial ovarian carcinoma in multicenter data sets.Design, setting, and participantsIn this diagnostic study, 665 patients with histologically confirmed epithelial ovarian carcinoma were retrospectively recruited from 4 centers (Hong Kong, Guangdong Province of China, and Seoul, South Korea) between January 1, 2012, and February 28, 2022. The patients were randomly divided into a training cohort (n = 532) and a testing cohort (n = 133) with a ratio of 8:2. This process was repeated 100 times. Tumor segmentation was manually delineated on each section of contrast-enhanced CT images to encompass the entire tumor. The Mann-Whitney U test and voted least absolute shrinkage and selection operator were performed for feature reduction and selection. Selected features were used to build the logistic regression model for differentiating high-grade serous carcinoma and non-high-grade serous carcinoma.ExposuresContrast-enhanced CT-based radiomics.Main outcomes and measuresIntraobserver and interobserver reproducibility of tumor segmentation were measured by Dice similarity coefficients. The diagnostic efficiency of the model was assessed by receiver operating characteristic curve and area under the curve.ResultsIn this study, 665 female patients (mean [SD] age, 53.6 [10.9] years) with epithelial ovarian carcinoma were enrolled and analyzed. The Dice similarity coefficients of intraobserver and interobserver were all greater than 0.80. Twenty radiomic features were selected for modeling. The areas under the curve of the logistic regression model in differentiating high-grade serous carcinoma and non-high-grade serous carcinoma were 0.837 (95% CI, 0.835-0.838) for the training cohort and 0.836 (95% CI, 0.833-0.840) for the testing cohort.Conclusions and relevanceIn this diagnostic study, radiomic features extracted from contrast-enhanced CT were useful in the classification of histologic subtypes in epithelial ovarian carcinoma. Intraobserver and interobserver reproducibility of tumor segmentation was excellent. The proposed logistic regression model offered excellent discriminative ability among histologic subtypes.

Project description:IntroductionIntegrating additional factors into the International Federation of Gynecology and Obstetrics (FIGO) staging system is needed for accurate patient classification and survival prediction. In this study, we tested machine learning as a novel tool for incorporating additional prognostic parameters into the conventional FIGO staging system for stratifying patients with epithelial ovarian carcinomas and evaluating their survival.Material and methodsCancer-specific survival data for epithelial ovarian carcinomas were extracted from the Surveillance, Epidemiology, and End Results (SEER) program. Two datasets were constructed based upon the year of diagnosis. Dataset 1 (39 514 cases) was limited to primary tumor (T), regional lymph nodes (N) and distant metastasis (M). Dataset 2 (25 291 cases) included additional parameters of age at diagnosis (A) and histologic type and grade (H). The Ensemble Algorithm for Clustering Cancer Data (EACCD) was applied to generate prognostic groups with depiction in dendrograms. C-indices provided dendrogram cutoffs and comparisons of prediction accuracy.ResultsDataset 1 was stratified into nine epithelial ovarian carcinoma prognostic groups, contrasting with 10 groups from FIGO methodology. The EACCD grouping had a slightly higher accuracy in survival prediction than FIGO staging (C-index = 0.7391 vs 0.7371, increase in C-index = 0.0020, 95% confidence interval [CI] 0.0012-0.0027, p = 1.8 × 10-7 ). Nevertheless, there remained a strong inter-system association between EACCD and FIGO (rank correlation = 0.9480, p = 6.1 × 10-15 ). Analysis of Dataset 2 demonstrated that A and H could be smoothly integrated with the T, N and M criteria. Survival data were stratified into nine prognostic groups with an even higher prediction accuracy (C-index = 0.7605) than when using only T, N and M.ConclusionsEACCD was successfully applied to integrate A and H with T, N and M for stratification and survival prediction of epithelial ovarian carcinoma patients. Additional factors could be advantageously incorporated to test the prognostic impact of emerging diagnostic or therapeutic advances.

Dataset Information

A DNA Damage Response Gene Panel for Different Histologic Types of Epithelial Ovarian Carcinomas and Their Outcomes.

Publications

A DNA Damage Response Gene Panel for Different Histologic Types of Epithelial Ovarian Carcinomas and Their Outcomes.

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