Phase 2 Trial (POLA Study) of Lurbinectedin plus Olaparib in Patients with Advanced Solid Tumors: Results of Efficacy, Tolerability, and the Translational Study
Bibliographic record
Abstract
We hypothesized that the combination of olaparib and lurbinectedin maximizes DNA damage, thus increasing its efficacy. The POLA phase 1 trial established the recommended phase 2 dose of lurbinectedin as being 1.5 mg (day 1) and that of olaparib as being 250 mg/12 h (days 1–5) for a 21-day cycle. In phase 2, we explore the efficacy of the combination in terms of clinical response and its correlation with mutations in the HRR genes and the genomic instability (GI) parameters. Results: A total of 73 patients with high-grade ovarian (n = 46), endometrial (n = 26), and triple-negative breast cancer (n = 1) were treated with lurbinectedin and olaparib. Most patients (62%) received ≥3 lines of prior therapy. The overall response rate (ORR) and disease control rate (DCR) were 9.6% and 72.6%, respectively. The median progression-free survival (PFS) was 4.54 months (95% CI 3.0–5.2). Twelve (16.4%) patients were considered long-term responders (LTR), with a median PFS of 13.3 months. No clinical benefit was observed for cases with HRR gene mutation. In ovarian LTRs, although a direct association with GI and a total loss of heterozygosity (LOH) events was observed, the association did not reach statistical significance (p = 0.055). Globally, the total number of LOHs might be associated with the ORR (p =0.074). The most common grade 3–4 toxicities were anemia and thrombocytopenia, in 6 (8.2%) and 3 (4.1%) patients, respectively. Conclusion: The POLA study provides evidence that the administration of lurbinectedin and olaparib is feasible and tolerable, with a DCR of 72.6%. Different GI parameters showed associations with better responses.
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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".