<scp>B7‐H3</scp> drives immunosuppression and Co‐targeting with <scp>CD47</scp> is a new therapeutic strategy in β‐catenin activated melanomas
Bibliographic record
Abstract
In melanoma, immune cell infiltration into the tumor is associated with better patient outcomes and response to immunotherapy. T-cell non-inflamed tumors (cold tumors) are associated with tumor cell-intrinsic Wnt/β-catenin activation, and are typically resistant to anti-PD-1 alone or in combination with anti-CTLA-4 therapy. Reversal of the 'cold tumor' phenotype and identifying new effective immunotherapies are challenges. We sought to investigate the role of a newer immunotherapy agent, B7-H3, in this setting. RNA sequencing was used to identify co-targeting strategies upon B7-H3 inhibition in a well-defined preclinical melanoma model driven by β-catenin. We found that immune checkpoint molecule B7-H3 confers a suppressive tumor microenvironment by modulating antiviral signals and innate immunity. B7-H3 inhibition led to an inflamed microenvironment, up-regulation of CD47/SIRPa signaling, and together with blockade of the macrophage checkpoint CD47 resulted in additive antitumor responses. We found that the antitumor effects of the B7-H3/CD47 antibody combination were dependent on cytokine signaling pathways (CCR5/CCL5 and IL4).
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".