Data from Preoperative Chemoradiotherapy plus Nivolumab before Surgery in Patients with Microsatellite Stable and Microsatellite Instability–High Locally Advanced Rectal Cancer
Bibliographic record
Abstract
AbstractPurpose: Preoperative chemoradiotherapy (CRT) and surgical resection are the standard treatment for locally advanced rectal cancer (LARC). Combining immune checkpoint inhibitors with radiation suggests a promising approach for enhancing efficacy. We investigated the efficacy of CRT followed by nivolumab and surgery in patients with LARC. Patients and Methods: In phase I, we investigated the feasibility of sequentially combined CRT, 5 cycles of nivolumab, and radical surgery. In phase II, patients with microsatellite stable (MSS) and microsatellite instability-high (MSI-H) LARC were evaluated. Results: Three patients in phase I received full courses of CRT and nivolumab without dose modification; the schedule was recommended for phase II. A pathologic complete response (pCR) was centrally confirmed in 30% [11/37; 90% confidence interval (CI), 18%–44%] and 60% (3/5) of the MSS and exploratory MSI-H cohorts, respectively. While immune-related severe adverse events were observed in 3 patients, no treatment-related deaths were observed. In 38 patients with MSS who underwent surgery, pCR rates of 75% (6/8) and 17% (5/30; P = 0.004, Fisher exact test) were observed in those with programmed cell death ligand 1 (PD-L1) tumor proportion score ≥1% and <1%, respectively; IHC staining was performed using pre-CRT samples. In 24 patients with MSS, pre-CRT samples were analyzed by flow cytometry; pCR rates of 78% (7/9) and 13% (2/15; P = 0.003, Fisher exact test) were observed for CD8+ T cell/effector regulatory T cell (CD8/eTreg) ratios of ≥2.5 and <2.5, respectively, in tumor-infiltrating lymphocytes. Conclusions: CRT followed by consolidation nivolumab could increase pCR. PD-L1 expression and an elevated CD8/eTreg ratio were positive predictors in patients with MSS LARC.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".