Strategies for the prevention or reversal of PARP inhibitor resistance
Bibliographic record
Abstract
INTRODUCTION: Advances in our understanding of tumor biology shed light on hallmarks of cancer development and progression that include dysregulated DNA damage repair (DDR) machinery. Leveraging the underlying tumor genomic instability and tumor-specific defects in DDR, Poly (ADP-ribose) polymerase (PARP) inhibitors (PARPi) induced DNA damage emerges as a novel non-chemotherapy therapeutic opportunity. PARPis are currently approved in multiple tumor types, with the largest benefit seen in tumors with homologous recombination repair (HRR) deficiency, including germline and somatic mutations in BRCA1/2 genes (BRCA) and other pathway members such as PALB2 and Rad51c. AREAS COVERED: This review article summarizes the current approval landscape and known and proposed mechanisms of resistance to PARPi. Further, therapeutic strategies to overcome PARPi resistance are discussed, including ongoing clinical trials. EXPERT OPINION: PARPi have proven to be a safe and effective therapy and represents a cornerstone treatment across multiple solid tumor types. Elucidating innate and acquired mechanisms of resistance, coupled with the emergence of novel therapeutic options to capitalize on the activity of PARPi and prevent or reverse the acquisition of resistance, provides an opportunity to further expand the role of PARPi in cancer therapy.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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) | 0.010 | 0.004 |
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".