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Background <p>Poly (ADP-ribose) polymerase inhibitors (PARPi) have emerged as a major therapeutic advance in ovarian cancer, particularly for patients with BRCA mutations or homologous recombination deficiency (HRD). However, a significant subset of BRCA wild-type patients also benefit from PARPi therapy, suggesting alternative mechanisms of action, including immune modulation, may be involved. Currently, no robust transcriptomic predictor exists to stratify such patients.</p> Methods <p>We developed a transcriptomic predictive model, termed the PARPi-sensor, using RNA-seq data from a patient-derived xenograft (PDX) model treated with niraparib. PARPi-responsive genes (PRGs) were identified and intersected with ovarian cancer–specific differentially expressed genes (DEGs) from TCGA and GTEx. Non-negative matrix factorization (NMF) was applied to define PARPi-responsive patterns (PRPs). A 23-gene PARPi-associated transcriptomic signature was constructed through Cox and LASSO regression analyses.es and validated in TCGA and ICGC cohorts.</p> Results <p>The PARPi-sensor model effectively stratified patients into high- and low-risk subgroups, with the low-risk group exhibiting significantly better overall survival in both cohorts. Functional enrichment revealed that low-risk patients showed upregulation of immune-related pathways, including antigen presentation and B/T cell activation. Immune subtype analysis, immune checkpoint gene expression, and deconvolution using TCIA data indicated that the low-risk group displayed enhanced immune activation and greater inferred responsiveness to immune checkpoint inhibitors. These findings suggest that PARPi may exert immunostimulatory effects, particularly in low-risk patients, independent of BRCA status.</p> Conclusion <p>We developed and validated a clinically applicable transcriptomic model for identifying transcriptomic features associated with PARPi sensitivity in ovarian cancer. The PARPi-sensor model offers a potential strategy for expanding the use of PARPi to BRCA-wild-type patients and reveals immunological features that may guide combination therapies with immune checkpoint inhibitors. Future research should focus on experimental validation of the proposed mechanisms and prospective clinical application of the model.</p>

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A transcriptomic-based model associated with PARP inhibitor sensitivity and immunogenic signatures in ovarian cancer

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年4月21日

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  • Xinxin Sun,
  • Cancan Li,
  • Zhihong Wang,
  • Jie Geng,
  • Wuliang Wang,
  • Kehong Deng,
  • Zhen Xu,
  • Hu Zhao,
  • Dongmei Huang

摘要

Background

Poly (ADP-ribose) polymerase inhibitors (PARPi) have emerged as a major therapeutic advance in ovarian cancer, particularly for patients with BRCA mutations or homologous recombination deficiency (HRD). However, a significant subset of BRCA wild-type patients also benefit from PARPi therapy, suggesting alternative mechanisms of action, including immune modulation, may be involved. Currently, no robust transcriptomic predictor exists to stratify such patients.

Methods

We developed a transcriptomic predictive model, termed the PARPi-sensor, using RNA-seq data from a patient-derived xenograft (PDX) model treated with niraparib. PARPi-responsive genes (PRGs) were identified and intersected with ovarian cancer–specific differentially expressed genes (DEGs) from TCGA and GTEx. Non-negative matrix factorization (NMF) was applied to define PARPi-responsive patterns (PRPs). A 23-gene PARPi-associated transcriptomic signature was constructed through Cox and LASSO regression analyses.es and validated in TCGA and ICGC cohorts.

Results

The PARPi-sensor model effectively stratified patients into high- and low-risk subgroups, with the low-risk group exhibiting significantly better overall survival in both cohorts. Functional enrichment revealed that low-risk patients showed upregulation of immune-related pathways, including antigen presentation and B/T cell activation. Immune subtype analysis, immune checkpoint gene expression, and deconvolution using TCIA data indicated that the low-risk group displayed enhanced immune activation and greater inferred responsiveness to immune checkpoint inhibitors. These findings suggest that PARPi may exert immunostimulatory effects, particularly in low-risk patients, independent of BRCA status.

Conclusion

We developed and validated a clinically applicable transcriptomic model for identifying transcriptomic features associated with PARPi sensitivity in ovarian cancer. The PARPi-sensor model offers a potential strategy for expanding the use of PARPi to BRCA-wild-type patients and reveals immunological features that may guide combination therapies with immune checkpoint inhibitors. Future research should focus on experimental validation of the proposed mechanisms and prospective clinical application of the model.