Targeting FEN1 to enhance efficacy of PARP inhibition in triple-negative breast cancer
Bibliographic record
Abstract
Abstract Patients with triple-negative breast cancer (TNBC) have limited targeted therapeutic options. PARP inhibitors (PARPi) have demonstrated an important role for BRCA -mutant patients with early TNBC. Combination approaches with PARPi can broaden the use of PARPi to a larger cohort of TNBC patients. We selected six genes from our previously identified 63-gene signature that was associated with PARPi response. siFEN1 increased cells in G2/M arrest, DNA damage and particularly apoptosis. Targeting FEN1 with a chemical inhibitor enhanced the efficacy of PARPi in 7/10 cell lines, and synergy was demonstrated mainly in PARPi-resistant TNBC cell lines. A BRCA2 -mutant cell line with acquired resistance to olaparib (HCC1395-OlaR) was strongly synergistic, with a combination index value of 0.20. The combination of PARPi and FEN1 inhibition also showed synergy in a PARPi-resistant xenograft-derived organoid model. Two mechanisms which explain the underlying efficacy are rapid progression in DNA replication fork speed and enhancement of DNA damage. The combination induced the highest fork speed (47% difference in comparison to control, P<0.0001) when FEN1 inhibition and PARPi equally increased fork speed individually in a cell line with a pre-existing increase in replication stress. The combination also increased DNA damage at lower drug concentrations, driving response in most of the synergistic cell lines. Gene expression analysis suggested that the sensitizing role of FEN1 inhibition in PARPi-resistant cell lines may be due to downregulation of pathways including mismatch repair. Therefore, targeting FEN1 shows great therapeutic potential as a targeted combination approach, particularly in the context of PARPi-resistant TNBC.
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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) | 0.001 | 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".