Data from Resuscitating Cancer Immunosurveillance: Selective Stimulation of DLL1-Notch Signaling in T cells Rescues T-cell Function and Inhibits Tumor Growth
Bibliographic record
Abstract
Abstract Deficiencies in immune function that accumulate during cancer immunoediting lead to a progressive escape from host immunosurveillance. Therapies that correct or overcome these defects could have a powerful impact on cancer management, but current knowledge of the types and mechanisms of immune escape is still incomplete. Here, we report a novel mechanism of escape from T-cell immunity that is caused by reduction in levels of the Delta family Notch ligands DLL1 and DLL4 in hematopoietic microenvironments. An important mediator of this effect was an elevation in the levels of circulating VEGF. Selective activation of the DLL1-Notch signaling pathway in bone marrow precursors enhanced T-cell activation and inhibited tumor growth. Conversely, tumor growth led to inhibition of Delta family ligand signaling through Notch in the hematopoietic environment, resulting in suppressed T-cell function. Overall, our findings uncover a novel mechanism of tumoral immune escape and suggest that a soluble multivalent form of DLL1 may offer a generalized therapeutic intervention to stimulate T-cell immunity and suppress tumor growth. Cancer Res; 71(19); 6122–31. ©2011 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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 0.004 |
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".