Checkpoint blockade treatment sensitises relapsed/refractory non‐Hodgkin lymphoma to subsequent therapy
Bibliographic record
Abstract
Patients with relapsed/refractory (R/R) non-Hodgkin lymphoma (NHL) have limited options for salvage, and checkpoint blockade therapy (CBT) has little efficacy. Usage in solid malignancies suggests that CBT sensitises tumours to subsequent chemotherapy. We performed the first analysis of CBT on subsequent NHL treatment. Seventeen North American centres retrospectively queried records. The primary aim was to evaluate the overall response rate (ORR) to post-CBT treatment. Secondary aims included progression-free survival (PFS), duration of response (DOR) and overall survival (OS). Fifty-nine patients (68% aggressive NHL, 69% advanced disease) were included. Patients received a median of three therapies before CBT. Fifty-three (90%) discontinued CBT due to progression. Post-CBT regimens included chemotherapy (49%), targeted therapy (30%), clinical trial (17%), transplant conditioning (2%) and chimeric antigen receptor T cell (CAR-T) therapy (2%). The ORR to post-CBT treatment was 51%, with median PFS of 6·1 months. In patients with at least stable disease (SD) to post-CBT, the median DOR was significantly longer than to pre-CBT (310 vs. 79 days, P = 0·005) suggesting sensitisation. Nineteen patients were transplanted after post-CBT therapy. Median overall survival was not reached, nor affected by regimen. Prospective trials are warranted, as this may offer R/R NHL patients a novel therapeutic approach.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.000 |
| 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".