Subversion of NK-cell and TNFα Immune Surveillance Drives Tumor Recurrence
Bibliographic record
Abstract
Abstract Understanding how incompletely cleared primary tumors transition from minimal residual disease (MRD) into treatment-resistant, immune-invisible recurrences has major clinical significance. We show here that this transition is mediated through the subversion of two key elements of innate immunosurveillance. In the first, the role of TNFα changes from an antitumor effector against primary tumors into a growth promoter for MRD. Second, whereas primary tumors induced a natural killer (NK)–mediated cytokine response characterized by low IL6 and elevated IFNγ, PD-L1hi MRD cells promoted the secretion of IL6 but minimal IFNγ, inhibiting both NK-cell and T-cell surveillance. Tumor recurrence was promoted by trauma- or infection-like stimuli inducing VEGF and TNFα, which stimulated the growth of MRD tumors. Finally, therapies that blocked PD-1, TNFα, or NK cells delayed or prevented recurrence. These data show how innate immunosurveillance mechanisms, which control infection and growth of primary tumors, are exploited by recurrent, competent tumors and identify therapeutic targets in patients with MRD known to be at high risk of relapse. Cancer Immunol Res; 5(11); 1029–45. ©2017 AACR.
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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) | 0.002 | 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".