Cancer-induced nerve injury promotes resistance to anti-PD-1 therapy
Bibliographic record
Abstract
While the nervous system has reciprocal interactions with both cancer and the immune system, little is known about the potential role of tumor associated nerves (TANs) in modulating anti-tumoral immunity. Moreover, while peri-neural invasion is a well establish poor prognostic factor across cancer types, the mechanisms driving this clinical effect remain unknown. Here, we provide clinical and mechniastic association between TANs damage and resistance to anti-PD-1 therapy. Using electron microscopy, electrical conduction studies, and tumor samples of cutaneous squamous cell carcinoma (cSCC) patients, we showed that cancer cells can destroy myelin sheath and induce TANs degeneration. Multi-omics and spatial analyses of tumor samples from cSCC patients who underwent neoadjuvant anti-PD-1 therapy demonstrated that anti-PD-1 non-responders had higher rates of peri-neural invasion, TANs damage and degeneration compared to responders, both at baseline and following neoadjuvant treatment. Tumors from non-responders were also characterized by a sustained signaling of interferon type I (IFN-I) - known to both propagate nerve degeneration and to dampen anti-tumoral immunity. Peri-neural niches of non-responders were characterized by higher immune activity compared to responders, including immune-suppressive activity of M2 macrophages, and T regulatory cells. This tumor promoting inflammation expanded to the rest of the tumor microenvironment in non-responders. Anti-PD-1 efficacy was dampened by inducing nerve damage prior to treatment administration in a murine model. In contrast, anti-PD-1 efficacy was enhanced by denervation and by interleukin-6 blockade. These findings suggested a potential novel anti-PD-1 resistance drived by TANs damage and inflammation. This resistance mechanism is targetable and may have therapeutic implications in other neurotropic cancers with poor response to anti-PD-1 therapy such as pancreatic, prostate, and breast cancers.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".