Interplay of replication stress response and immune microenvironment in high-grade serous ovarian cancer
Bibliographic record
Abstract
High-grade serous ovarian cancer (HGSOC) is the most lethal gynecological malignancy. Therapeutic options remain limited for patients lacking predictive biomarkers, particularly those with BRCA wild-type tumors or those who have acquired resistance to both PARP inhibitors and platinum-based chemotherapy. Replication stress, TP53 mutations, and genomic instability characterize HGSOC. The cellular response to replication stress is primarily mediated by checkpoint kinases; however, this mechanism is frequently impaired in tumor cells. Consequently, cancer cells become increasingly dependent on the replication stress response (RSR) pathway for survival, and susceptible to therapies targeting the ATR-CHK1-WEE1 axis-a key regulator of genomic integrity. Inhibition of these checkpoint kinases can disrupt cell cycle control, inducing mitotic catastrophe and subsequent cancer cell death. Another defining feature of HGSOC is its immunosuppressive tumor microenvironment (TME), which has limited the efficacy of immune checkpoint inhibitors. Emerging evidence suggests that inhibition of the RSR pathway may not only exploit intrinsic tumor vulnerabilities but also modulate the TME to enhance anti-tumor immune responses. This provides rationale for combination approaches integrating RSR pathway inhibitors with innovative immune checkpoint blockade (ICB). This review examines the mechanistic rationale and therapeutic potential of such combinations, drawing on both preclinical and clinical data.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".