Targeting DNA repair in lung cancer with PARP inhibitors
Bibliographic record
Abstract
Lung cancer is a global burden affecting millions of individuals worldwide. Projections indicate a substantial rise in incidence by 2045, with an estimated increase of 131% in males and 105% in females, respectively. Although many therapies are approved for treating lung cancers, the development of acquired resistance to current anti-cancer therapies is a current clinical challenge. This highlights the urgent need for the identification of new biomarkers and the development of novel therapies to further improve treatment outcomes in patients with lung cancer. The emergence of Poly (ADP-ribose) polymerase PARP inhibitors (PARPis) as novel agents in cancer treatment has become a recent focus of research. Although these drugs are approved for other cancer types, such as prostate, ovarian, pancreatic, and breast cancer, they have yet to be approved for use in lung cancer patients. Studies using in vitro, in vivo, and 3D models have shown that PARPis hold promise for targeting lung cancer cells by blocking PARP function in DNA repair pathways. Key challenges associated with the clinical use of PARPis include toxicity and the development of resistance mechanisms. This review aims to critically examine the clinical relevance of PARPis in the context of lung cancer, with a focus on their mechanisms of action, drug interactions, dosing considerations, preclinical and clinical research findings, and the principal limitations hindering their therapeutic efficacy, particularly resistance mechanisms.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 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) | 0.002 | 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".