Phase I Dose-Escalation Trial Combining Olaparib and Thoracic Radiotherapy in Extensive-Stage Small Cell Lung Cancer
Bibliographic record
Abstract
PURPOSE: Patients with extensive-stage small cell lung cancer are commonly treated with induction systemic therapy and consolidative thoracic radiotherapy (TRT). PARP inhibitors have demonstrated radiosensitization in preclinical lung cancer models. We performed an investigator-initiated, multi-institutional, single-arm, open-label phase I study of concurrent olaparib with TRT. PATIENTS AND METHODS: Patients without progression after induction platinum/etoposide ± atezolizumab were treated with oral olaparib for 3 weeks and concurrent low-dose TRT (30 Gy/10 fractions) in weeks 2 and 3. Olaparib dose started at 50 mg twice daily and escalated in 50 mg/dose increments in cohorts of three patients each. Primary objectives were the safety and maximum tolerated dose (MTD) of olaparib + TRT. Secondary objectives included in-field local recurrence rate, progression-free survival, and overall survival. RESULTS: Between October 2018 and March 2022, 24 patients with a median age of 68 years were treated (median follow-up, 11.4 months) with platinum/etoposide and 30 Gy/10 fractions TRT; 10 patients also received atezolizumab. The MTD of olaparib with TRT was 200 mg twice daily. There were three grade 3 (G3) dose-limiting adverse events (AE), including pneumonitis/pneumonia, esophagitis, and abdominal pain. The most common G2 to G3 treatment-related AE were esophagitis (n = 12) and pneumonitis/pneumonia (n = 2). There were no G4 or G5 AE. The 12-month cumulative incidence of local recurrence was 27%, and the median progression-free survival and overall survival were 3.6 and 17.7 months, respectively. CONCLUSIONS: This study established the MTD and recommended a phase II dose of olaparib at 200 mg twice daily with concurrent low-dose TRT, and the combination seemed safe without unexpected toxicities.
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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) | 0.001 | 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 | 0.004 |
| 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".