Synergistic activity of PARP inhibitors (PARPi) in combination with standard chemotherapy (CTx) in leiomyosarcoma.
Bibliographic record
Abstract
11560 Background: Leiomyosarcomas (LMS) are genetically heterogeneous tumors that arise from smooth muscle. Currently, the mainstay of systemic treatment for patients with advanced/metastatic disease is doxorubicin (Dox) based CTx. Several genomic analyses of LMS reveal defects in homologous recombination (HR) DNA repair pathway in about half of patients, consistent with a druggable “BRCAness” phenotype. Thus, we sought to determine which combinations of standard CTx and PARPi might be synergistic promising therapeutic strategies for LMS. Methods: Dox, Docetaxel (Doc), Temozolomide (Tmz) were evaluated in combination with PARPi (Olaparib [Ola], Niraparib [Nira] and Talazoparib [Tala]) at 12 different drug concentrations. Four LMS cell lines of different origins (gynecological - GY, abdominal - A, extremity - E) were tested in a high throughput manner. All drug concentrations were chosen according to EC50. Cells were incubated with each combination for 7 days. Viability was assessed by ATPlite Luminescence Assay System.Evaluation of drug combination effect was performed using a Bliss synergy score. This system quantifies the degree of synergy as multiplicative effect of single drugs as if they acted independently. With a synergy score of -5 to 5, the interaction between two drugs is considered as additive; <-5 antagonistic and > 5 synergistic, and therefore a promising combination. Results: Anticancer activity, ranging from additive to synergistic was seen with all combinations. Results were consistent among all cell lines, independent of site of cell line origin (Table) Most synergistic combination in the majority of LMS cell lines were Dox or Tmz when combined with Tala, reaching up to 15 % and 27% above Bliss respectively. In contrast, Doc showed only additive effect with all analyzed PARPi. Conclusions: The data suggest that the combination of Dox or Tmz with PARPi may represent promising treatment options for LMS patients. Recent clinical studies support this notion in uterine LMS. Importantly these results suggest that such approach may be extended to all sites of LMS. Pre-clinical studies are underway to identify the most promising combinations for future clinical trial design. [Table: see text]
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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 | 0.001 |
| Bibliometrics | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".