Immune induction strategies to enhance responses to PD-1 blockade: lessons from the TONIC trial
Bibliographic record
Abstract
Programmed cell death protein 1 (PD-1) blockade is only effective in a minority of patients, prompting the search for combinatorial therapies that increase responses. Identifying effective combinations requires lengthy testing and so far has shown few successes. To accelerate progress Voorwerk and colleagues (Nat Med. 25(6):920-8, 2019) used an adaptive trial design to compare 4 short-course therapies (radiotherapy, cyclophosphamide, cisplatin and doxorubicin) for their ability to improve the tumor immune microenvironment and enhance responses to subsequent PD-1 blockade in women with metastatic triple negative breast cancer, a disease with low response rate to PD-1 blockade. They reported the first phase of the trial that enrolled 12 to 17 patients per arm to "pick the winner" induction treatment. Higher objective response rates (ORR) compared to no induction were observed only in the arm containing doxorubicin, which proceeded to phase II. These results raise a number of questions about testing local versus systemic induction treatments and whether sequencing with PD-1 blockade is appropriate in light of evidence supporting concomitant treatment, at least for radiotherapy. Small imbalances in baseline characteristics can also influence results obtained with limited numbers of patients per arm. We hope that these considerations will help future adaptive, signal-finding combination immunotherapy studies.
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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 | 0.007 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.006 | 0.010 |
| 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".