PD-1 Blockade in Solid Tumors with Defects in Polymerase Epsilon
Bibliographic record
Abstract
Missense mutations in the polymerase epsilon (POLE) gene have been reported to generate proofreading defects resulting in an ultramutated genome and to sensitize tumors to checkpoint blockade immunotherapy. However, many POLE-mutated tumors do not respond to such treatment. To better understand the link between POLE mutation variants and response to immunotherapy, we prospectively assessed the efficacy of nivolumab in a multicenter clinical trial in patients bearing advanced mismatch repair-proficient POLE-mutated solid tumors. We found that only tumors harboring selective POLE pathogenic mutations in the DNA binding or catalytic site of the exonuclease domain presented high mutational burden with a specific single-base substitution signature, high T-cell infiltrates, and a high response rate to anti-PD-1 monotherapy. This study illustrates how specific DNA repair defects sensitize to immunotherapy. POLE proofreading deficiency represents a novel agnostic biomarker for response to PD-1 checkpoint blockade therapy. SIGNIFICANCE: POLE proofreading deficiency leads to high tumor mutational burden with high tumor-infiltrating lymphocytes and predicts anti-PD-1 efficacy in mismatch repair-proficient tumors. Conversely, tumors harboring POLE mutations not affecting proofreading derived no benefit from PD-1 blockade. POLE proofreading deficiency is a new tissue-agnostic biomarker for cancer immunotherapy. This article is highlighted in the In This Issue feature, p. 1397.
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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 | 0.000 | 0.000 |
| 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".