Construction and Validation of a Platinum Sensitivity Predictive Model With Multiple Genomic Variations for Epithelial Ovarian Cancer

Targeted DNA sequencing of high-grade serous ovarian carcinoma reveals association of TP53 mutations with platinum resistance when combined with gene expression

High-grade serous ovarian carcinoma (HGSC) is the most common subtype of ovarian cancer and is among the most fatal gynecological malignancies worldwide, due to late diagnosis at advanced stages and frequent therapy resistance. In 47 HGSC patients, we assessed somatic and germline genetic variability of a custom panel of 144 known or suspected HGSC-related genes by high-coverage targeted DNA sequencing to identify the genetic determinants associated with resistance to platinum-based therapy. In the germline, the most mutated genes were DNAH14 (17%), RAD51B (17%), CFTR (13%), BRCA1 (11%), and RAD51 (11%). Somatically, the most mutated gene was TP53 (98%), followed by CSMD1/2/3 (19/19/36%), and CFTR (23%). Results were compared with those from whole exome sequencing of a similar set of 35 HGSC patients. Somatic variants in TP53 were also validated using GENIE data of 1287 HGSC samples. Our approach showed increased prevalence of high impact somatic and germline mutations, especially those affecting splice sites of TP53, compared to validation datasets. Furthermore, nonsense TP53 somatic mutations were negatively associated with patient survival. Elevated TP53 transcript levels were associated with platinum resistance and presence of TP53 missense mutations, while decreased TP53 levels were found in tumors carrying mutations with predicted high impact, which was confirmed in The Cancer Genome Atlas data (n = 260). Targeted DNA sequencing of TP53 combined with transcript quantification may contribute to the concept of precision oncology of HGSC. Future studies should explore targeting the p53 pathway based on specific mutation types and co-analyze the expression and mutational profiles of other key cancer genes.

Predictive Value of Machine Learning for Platinum Chemotherapy Responses in Ovarian Cancer: Systematic Review and Meta-Analysis

BACKGROUND

Machine learning is a potentially effective method for predicting the response to platinum-based treatment for ovarian cancer. However, the predictive performance of various machine learning methods and variables is still a matter of controversy and debate.

OBJECTIVE

This study aims to systematically review relevant literature on the predictive value of machine learning for platinum-based chemotherapy responses in patients with ovarian cancer.

METHODS

Following the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines, we systematically searched the PubMed, Embase, Web of Science, and Cochrane databases for relevant studies on predictive models for platinum-based therapies for the treatment of ovarian cancer published before April 26, 2023. The Prediction Model Risk of Bias Assessment tool was used to evaluate the risk of bias in the included articles. Concordance index (C-index), sensitivity, and specificity were used to evaluate the performance of the prediction models to investigate the predictive value of machine learning for platinum chemotherapy responses in patients with ovarian cancer.

RESULTS

A total of 1749 articles were examined, and 19 of them involving 39 models were eligible for this study. The most commonly used modeling methods were logistic regression (16/39, 41%), Extreme Gradient Boosting (4/39, 10%), and support vector machine (4/39, 10%). The training cohort reported C-index in 39 predictive models, with a pooled value of 0.806; the validation cohort reported C-index in 12 predictive models, with a pooled value of 0.831. Support vector machine performed well in both the training and validation cohorts, with a C-index of 0.942 and 0.879, respectively. The pooled sensitivity was 0.890, and the pooled specificity was 0.790 in the training cohort.

CONCLUSIONS

Machine learning can effectively predict how patients with ovarian cancer respond to platinum-based chemotherapy and may provide a reference for the development or updating of subsequent scoring systems.

High-expressed ACAT2 predicted the poor prognosis of platinum-resistant epithelial ovarian cancer

BACKGROUND

Acetyl-CoA acetyltransferase 2 (ACAT2) is a lipid metabolism enzyme and rarely was researched in epithelial ovarian cancer (EOC).

METHODS

ACAT2 expressions were confirmed in two pairs of cell lines (A2780 and A2780/DDP, OVCAR8 and OVCAR8/DDP) from Gene Expression Omnibus database by bioinformatics analysis, and in A2780 and A2780/DDP cell lines by quantitative real-time polymerase chain reaction and western blotting. Tissue samples were stained by immunohistochemistry and scored for ACAT2 expression. The relationships between ACAT2 expression and clinicopathological characteristics were analyzed by χ2 test. The prognosis of ACAT2 was analyzed by the log-rank tests and Cox regression models.

RESULTS

ACAT2 was remarkably upregulated in the above drug-resistant cell lines by mRNA (all P < 0.05) and protein expression (P = 0.026) than those in sensitive ones. Patients were classified as ACAT2-high (n = 51) and ACAT2-low (n = 26) according to immunohistochemical score. ACAT2 expression had a significantly inverse correlation with FIGO stage (P = 0.030) and chemo-response (P = 0.041). A marginal statistical significance existed in ACAT2 expression and ascites volume (P = 0.092). Univariate analysis suggested that high-expressed ACAT2 was associated with decreased platinum-free interval (PFI) (8.57 vs. 14.13 months, P = 0.044), progression-free survival (PFS) (14.12 vs. 19.79 months, P = 0.039) and overall survival (OS) (36.89 vs. 52.40 months, P = 0.044). Multivariate analysis demonstrated that ACAT2 expression (hazard ratio = 2.18, 95% confidence interval: 1.15-4.11, P = 0.017) affected OS independently, rather than PFI and PFS.

CONCLUSION

The expression of ACAT2 in A2780/DDP and OVCAR8/DDP was higher than the corresponding A2780 and OVCAR8. High-expressed ACAT2 was associated with advanced FIGO stage, chemo-resistance, and decreased PFI, PFS and OS. It was an independent prognostic factor of OS in EOC.

Copy number variations and protein expression of Cyclin E1 and Epithelial cell transforming sequence 2 genes predict the chemotherapeutic response in patients with serous ovarian carcinoma

Background & Objectives

Serous ovarian carcinoma (SOC) is characterized by extreme genomic instability, chromosomal rearrangements and copy number variations (CNVs) leading to the development of early metastasis and chemo-resistance. The present study was designed to observe the role of CNVs of Cyclin E1 (CCNE1) and Epithelial cell transforming sequence- 2 (ECT2) genes and their encoded proteins in predicting the chemotherapeutic response in SOC patients.

Methods

This observational analytical study was conducted at University of Health Sciences, Lahore, Pakistan from December 2019 till June 2022.The study included twenty-five SOC patients with resectable ovarian tumors and twenty-five control subjects. The patients were followed-up for six months for their response to chemotherapy. The CNVs in CCNE1 and ECT-2 genes were determined by real time PCR while serum levels of encoded proteins were determined in controls and cases, before and after six months of treatment, through ELISA. The response to chemotherapy was categorized as sensitive or resistant based on serum CA-125 levels and radiological scans.

Results

The copy number variations in CCNE1 and ECT2 genes showed association with the clinic-pathological characteristics and chemotherapy response. Statistically significant difference was found between the mean pre-chemotherapy protein levels of CCNE1 in cases than controls (p-value <0.001) and between the mean pre and post-chemotherapy protein levels of CCNE1 and ECT2 (p-value <0.001) in SOC patients.

Conclusion

The copy number variations of CCNE1 and ECT2 genes and their protein expression are positively associated with chemotherapeutic response in SOC patients.

Whole-exome sequencing of epithelial ovarian carcinomas differing in resistance to platinum therapy

Exploration of the prognostic and predictive significance of exome variation in epithelial ovarian carcinoma patients, with TP53, Hippo, homologous recombination genes, and the SBS6 signature as the most interesting results.

Epithelial ovarian carcinoma (EOC) is highly fatal because of the risk of resistance to therapy and recurrence. We performed whole-exome sequencing of blood and tumor tissue pairs of 50 patients with surgically resected EOC. Compared with sensitive patients, platinum-resistant patients had a significantly higher somatic mutational rate in TP53 and lower in several genes from the Hippo pathway. We confirmed the pivotal role of somatic mutations in homologous recombination repair genes in platinum sensitivity and favorable prognosis of EOC patients. Implementing the germline homologous recombination repair profile significantly improved the prediction. In addition, distinct mutational signatures, for example, SBS6, and overall mutational load, somatic mutations in PABPC1, PABPC3, and TFAM co-segregated with the resistance status, high-grade serous carcinoma subtype, or overall survival of patients. We generated germline and somatic genetic landscapes of prognostically different subgroups of EOC patients for further follow-up studies focused on utilizing the observed associations in precision oncology.

Establish of an Initial Platinum-Resistance Predictor in High-Grade Serous Ovarian Cancer Patients Regardless of Homologous Recombination Deficiency Status

Backgrounds

Ovarian cancer (OC) is still the leading aggressive and lethal disease of gynecological cancers, and platinum-based regimes are the standard treatments. However, nearly 20%–30% of patients with OC are initial platinum resistant (IPR), and there is a lack of valid tools to predict whether they will be primary platinum resistant or not prior to chemotherapy.

Methods

Transcriptome data from The Cancer Genome Atlas (TCGA) was downloaded as the training data, and transcriptome data of GSE15622, GSE102073, GSE19829, and GSE26712 were retrieved from Gene Expression Omnibus (GEO) as the validation cohorts. Differentially expressed genes (DEGs) were selected between platinum-sensitive and platinum-resistant patients from the training cohort, and multiple machine-learning algorithms [including random forest, XGboost, and least absolute shrinkage and selection operator (LASSO) regression] were utilized to determine the candidate genes from DEGs. Then, we applied logistic regression to establish the IPR signature based on the expression. Finally, comprehensive clinical, genomic, and survival feature were analyzed to understand the application value of the established IPR signature.

Results

A total of 532 DEGs were identified between platinum-resistant and platinum-sensitive samples, and 11 of them were shared by these three-machine learning algorithms and utilized to construct an IPR prediction signature. The area under receiver operating characteristic curve (AUC) was 0.841 and 0.796 in the training and validation cohorts, respectively. Notably, the prediction capacity of this signature was stable and robust regardless of the patients’ homologous recombination deficiency (HRD) and mutation burden status. Meanwhile, the genomic feature was concordant between samples with high- or low-IPR signature, except a significantly higher prevalence of gain at Chr19q.12 (regions including CCNE1) in the high-IPR signature samples. The efficacy of prediction of platinum resistance of IPR signature successfully transferred to the precise survival prediction, with the AUC of 0.71, 0.72, and 0.66 to predict 1-, 3-, and 5-year survival, respectively. At last, we found a significantly different tumor-infiltrated lymphocytes feature, including lower abundance of CD4+ naive T cells in the samples with high-IPR signature. A relatively lower tumor immune dysfunction and exclusion (TIDE) value and more sensitivity to multiple therapies including Gefitinib may suggest the potency to transfer from platinum-based therapy to immunotherapy or target therapies in patients with high-IPR signature.

Conclusion

Our study established an IPR signature based on the expression of 11 genes that could stably and robustly distinguish OC patients with IPR and/or poor outcomes, which may guide therapeutic regimes tailoring.