Targeting receptor tyrosine kinases in ovarian cancer: Genomic dysregulation, clinical evaluation of inhibitors, and potential for combinatorial therapies
Bibliographic record
Abstract
Epithelial ovarian cancer (EOC) remains one of the leading causes of cancer-related deaths among women worldwide. Receptor tyrosine kinases (RTKs) have long been sought as therapeutic targets for EOC, as they are frequently hyperactivated in primary tumors and drive disease relapse, progression, and metastasis. More recently, these oncogenic drivers have been implicated in EOC response to poly(ADP-ribose) polymerase (PARP) inhibitors and epigenome-interfering agents. This evidence revives RTKs as promising targets for therapeutic intervention of EOC. This review summarizes recent studies on the role of RTKs in EOC malignancy and the use of their inhibitors for clinical treatment. Our focus is on the ERBB family, c-Met, and VEGFR, as they are linked to drug resistance and targetable using commercially available drugs. The importance of these RTKs and their inhibitors is highlighted by their impact on signal transduction and intratumoral heterogeneity in EOC and successful use as maintenance therapy in the clinic through suppression of the VEGF/VEGFR axis. Finally, the therapeutic potential of RTK inhibitors is discussed in the context of combinatorial targeting via co-inhibiting proliferative and anti-apoptotic pathways, epigenomic/transcriptional programs, and harnessing the efficacy of PARP inhibitors and programmed cell death 1/ligand 1 immune checkpoint therapies.
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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 | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".