B16 melanoma inhibits cellular response to Interleukin-12 via multiple mechanisms including paracrine action of Wnt-inducible Signaling Protein-1 (46.10)
Bibliographic record
Abstract
Abstract Interleukin-12 (IL12) enhances anti-tumor immunity when delivered locally within the tumor microenvironment. However, immunoregulatory elements within the tumor microenvironment are thought to dampen the effect of IL12 as an adjuvant. The objective of this study was to identify biochemical mechanisms of tumor-immune cell cross-talk that locally suppress the action of IL12. A high content in vitro assay was developed to quantify tumor-mediated suppression of the action of IL12 using a T helper type 1 cell line. Following an induction period, the melanoma cell model, B16F0, inhibited the cellular response to IL12 in vitro. This paracrine effect was not explained by induction of apoptosis or creation of a cytokine sink, despite both mechanisms at work within the co-culture assay. Secreted protein, acidic and rich in cysteine (SPARC); exosomes; and Wnt-inducible signaling protein-1 (WISP-1) were identified using a proteomics workflow and confirmed to be enriched in B16F0-conditioned media. Neutralization of WISP-1 recovered and recombinant WISP-1 dose-dependently inhibited the activity of Signal Transducer of Activated Tyrosine 4 (STAT4), a key transcription factor within the IL12 pathway, within the in vitro co-culture assay. Moreover, WISP-1 was expressed in vivo following intradermal challenge with B16F10 cells and exhibited an inverse dependence on tumor size.
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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 | 0.000 |
| 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".