Cancer-induced nerve injury promotes resistance to anti-PD-1 therapy
Bibliographic record
Abstract
Perineural invasion (PNI) is a well-established factor of poor prognosis in multiple cancer types1, yet its mechanism remains unclear. Here we provide clinical and mechanistic insights into the role of PNI and cancer-induced nerve injury (CINI) in resistance to anti-PD-1 therapy. Our study demonstrates that PNI and CINI of tumour-associated nerves are associated with poor response to anti-PD-1 therapy among patients with cutaneous squamous cell carcinoma, melanoma and gastric cancer. Electron microscopy and electrical conduction analyses reveal that cancer cells degrade the nerve fibre myelin sheets. The injured neurons respond by autonomously initiating IL-6- and type I interferon-mediated inflammation to promote nerve healing and regeneration. As the tumour grows, the CINI burden increases, and its associated inflammation becomes chronic and skews the general immune tone within the tumour microenvironment into a suppressive and exhaustive state. The CINI-driven anti-PD-1 resistance can be reversed by targeting multiple steps in the CINI signalling process: denervating the tumour, conditional knockout of the transcription factor mediating the injury signal within neurons (Atf3), knockout of interferon-α receptor signalling (Ifnar1−/−) or by combining anti-PD-1 and anti-IL-6-receptor blockade. Our findings demonstrate the direct immunoregulatory roles of CINI and its therapeutic potential. Perineural invasion and cancer-induced nerve injury of tumour-associated nerves are associated with poor response to anti-PD-1 therapy, which can be reversed by combining anti-PD-1 therapy with anti-inflammatory interventions.
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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 | 0.001 |
| 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".