Common inherited loss-of-function mutations in the innate sensor NOD2 contribute to exceptional immune response to cancer immunotherapy
Bibliographic record
Abstract
Lung cancers and melanomas have many somatically mutated self-proteins that would be expected to trigger an immune rejection response, yet therapeutic responses can only be induced in a subset of patients. Here, we investigated the possibility that inherited differences in immune tolerance checkpoints contribute to variability in outcomes. Whole genome sequencing revealed biallelic germline loss-of-function (LOF) mutations in the immune tolerance checkpoint gene, NOD2 , in an exceptional immune responder to targeted radiotherapy for metastatic melanoma. In 40 exceptional immune responders to anti-PD1 monotherapy for non–small cell lung cancer (NSCLC), genome sequencing showed 30% had inherited a NOD2 LOF variant, more than twice the population frequency ( P = 0.0021). Conversely, a gain-of-function RIPK2 allele known to increase NOD2 signaling was inherited by 61% of nonresponders from the same cohort, compared to 10% of exceptional responders and much higher than the population frequency ( P < 0.0001). Within the overall recruited cohort of 144 NSCLC anti-PD1 patients, individuals with immune-related adverse events (irAE) had better overall survival, further improved in those with NOD2 LOF. In independent anti-PD1 monotherapy cohorts with a range of cancers, inherited NOD2 LOF was associated with complete or partial response ( P = 0.0107). Experimental validation in mice showed germline Nod2 LOF enhanced therapeutic immune responses elicited by anti-PD1 monotherapy against a high mutation burden colorectal cancer, increasing tumor infiltration by effector memory CD8 T cells. Collectively these results reveal common inherited human variation in an immune tolerance checkpoint is a determinant of cancer immune responses elicited by pharmacological inhibition of another checkpoint.
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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 | 0.001 |
| 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".