Sequential targeting of PARP with carboplatin inhibits primary tumour growth and distant metastasis in triple-negative breast cancer
Bibliographic record
Abstract
Abstract Background Patients with triple-negative breast cancer (TNBC) develop early recurrence. While PARP inhibitors (PARPi) have demonstrated potential in BRCA1/2 -mutant (BRCA MUT ) TNBC, durable responses will likely be achieved if PARPi are used in combination. It is plausible that sequential administration of a potent PARPi like talazoparib in combination with carboplatin can enhance primary tumour and metastasis inhibition in BRCA MUT and BRCA1/2 wild-type (BRCA WT ) TNBCs, and decrease toxicity. Methods We evaluated the impact of the concurrent combination of talazoparib and carboplatin on cell survival in 13 TNBC cell lines. We compared the concurrent and sequential combination upon fork replication, migration and invasion. We also used three orthotopic xenograft models to evaluate primary tumour growth, distant metastasis, and toxicity. Results Concurrent talazoparib and carboplatin was synergistic in 92.3% of TNBC cell lines, independent of BRCA1/2 -mutation status. The sequential combination decreased fork speed in normal cells, but not in TNBC cells. The talazoparib-first sequential combination resulted in a strong reduction in migration (70.4%, P < 0.0001), invasion (56.9%, P < 0.0001), lung micrometastasis (56.4%, P < 0.0001), and less toxicity in a BRCA WT model. Conclusion The sequential combination of talazoparib and carboplatin is an effective approach to inhibit micrometastatic disease, providing rationale for the use of this combination in early TNBC patients.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".