Safety and Antitumor Activity of Pembrolizumab in Patients with Estrogen Receptor–Positive/Human Epidermal Growth Factor Receptor 2–Negative Advanced Breast Cancer
Bibliographic record
Abstract
Abstract Purpose: We investigated the safety and antitumor activity of the anti–programmed death 1 monoclonal antibody pembrolizumab in patients with estrogen receptor–positive (ER+)/human epidermal growth factor receptor 2–negative (HER2−) advanced breast cancer with programmed death ligand 1–positive (PD-L1–positive) tumors in the phase Ib open-label, multicohort KEYNOTE-028 (NCT02054806) study. Patients and Methods: Patients with ER+/HER2− advanced breast cancer with PD-L1–positive tumors (combined positive score ≥1) received pembrolizumab (10 mg/kg every 2 weeks) up to 2 years or until confirmed progression/intolerable toxicity. Primary endpoints were safety and overall response rate (ORR), based on Response Evaluation Criteria in Solid Tumors, version 1 (RECIST v1.1) as assessed by investigator review. Results: Between April 2014 and January 2015, 25 patients were enrolled. Median number of prior therapies for breast cancer, including endocrine agents, was 9 (range, 3–15). Median follow-up was 9.7 months (range, 0.7–31.8 months). Three patients experienced partial response (PR) and none experienced complete response (CR), resulting in an ORR of 12.0% (95% CI, 2.5%–31.2%); 16% of patients had stable disease (SD) and clinical benefit rate (CR + PR + [SD for ≥24 weeks]) was 20% (95% CI, 7–41). Median duration of response was 12.0 months (range, 7.4–15.9 months). The incidence of treatment-related adverse events was 64%; nausea (20%) and fatigue (12%) were most common and were predominantly grade 1/2. No treatment-related discontinuations or deaths occurred. Conclusions: Pembrolizumab was well tolerated with modest but durable overall response in certain patients with previously treated, advanced, PD-L1–positive, ER+/HER2− breast cancer. Clin Cancer Res; 24(12); 2804–11. ©2018 AACR.
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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) | 0.000 | 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 | 0.001 |
| 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".