Multimodal data integration with machine learning for predicting PARP inhibitor efficacy and prognosis in ovarian cancer

Xi'an Xiong; Li Cai; Zhen Yang; Zhongping Cao; Nayiyuan Wu; Qianxi Ni

doi:10.3389/fonc.2025.1571193

Multimodal data integration with machine learning for predicting PARP inhibitor efficacy and prognosis in ovarian cancer

Front Oncol. 2025 Jun 4:15:1571193. doi: 10.3389/fonc.2025.1571193. eCollection 2025.

Authors

Xi'an Xiong^#¹, Li Cai^#^{1

2}, Zhen Yang³, Zhongping Cao⁴, Nayiyuan Wu^{1

5}, Qianxi Ni¹

Affiliations

¹ The Affiliated Cancer Hospital of Xiangya School of Medicine, Central South University/Hunan Cancer Hospital, Changsha, China.
² College of Pharmacy, Dali University, Dali, China.
³ Oncology Department of Xiangya Hospital, Central South University, Changsha, China.
⁴ Department of Oncology of the Second XiangYa Hospital, Central of South University, Changsha, China.
⁵ Hunan Key Laboratory Of The Research and Development of Novel Pharmaceutical Preparations, Changsha Medical University, Changsha, China.

^# Contributed equally.

Abstract

Background: Poly(ADP)-ribose polymerase inhibitors (PARPi) have brought a significant breakthrough in the maintenance treatment of ovarian cancer. However, beyond BRCA mutation/HRD, the direct impact of other prognostic factors on PARPi response and prognosis remains inadequately characterized.

Methods: We assessed PARPi prognostic factors from clinical characteristics, pathological findings, and biochemical indicators from 251 ovarian cancer patients. Cox univariate and multivariate analyses were employed to identify the factors which influencing PARPi efficacy and patients prognosis. Feature screening was conducted using correlation analysis, significance analysis, Variance Inflation Factor (VIF), and Elastic Net stability analysis. Patient-specific efficacy and prognosis prediction models were then constructed using various machine learning algorithms.

Results: Total bile acids (TBAs) and CA-199 present as an independent risk factor in Cox multivariate analysis for primary and recurrent ovarian cancer patients respectively (P < 0.05). TBAs emerged as a risk factor, with each unit increase associated with a 10% rise in recurrence risk. The best-performing model has an AUC of 0.79 ± 0.09 and an AUC of 0.72 ± 0.03 for primary and recurrent ovarian cancer patients respectively. External validation(n=36) in multicenter cohorts maintained robust performance with AUC of 0.74 and an AUC of 0.70 for primary and recurrent ovarian cancer patients respectively.

Conclusions: We identified TBAs and CA-199 as a significant prognostic factor in primary and recurrent ovarian cancer patients respectively. The integration of multimodal data with machine learning holds significant potential for enhancing prognosis prediction in PARPi treatment for ovarian cancer.

Keywords: PARP inhibitors (PARPi); machine learning; ovarian cancer; prediction model; prognostic factor.