VISTA expression and patient selection for immune-based anticancer therapy
Bibliographic record
Abstract
V-domain Ig suppressor of T-cell activation (VISTA) is a B7 family member that plays key roles in maintaining T cell quiescence and regulation of myeloid cell populations, which together establish it as a novel immunotherapy target for solid tumors. Here we review the growing literature on VISTA expression in relation to various malignancies to better understand the role of VISTA and its interactions with both tumor cells and immune cells expressing other checkpoint molecules within the tumor microenvironment (TME). The biology of VISTA creates several mechanisms to maintain the TME, including supporting the function of myeloid-derived suppressor cells, regulating natural killer cell activation, supporting the survival of regulatory T cells, limiting antigen presentation on antigen-presenting cells and maintaining T cells in a quiescent state. Understanding these mechanisms is an important foundation of rational patient selection for anti-VISTA therapy. We provide a general framework to describe distinct patterns of VISTA expression in correlation with other known predictive immunotherapy biomarkers (programmed cell death ligand 1 and tumor-infiltrating lymphocytes) across solid tumors to facilitate investigation of the most efficacious TMEs for VISTA-targeted treatment as a single agent and/or in combination with anti-programmed death 1/anti-cytotoxic T lymphocyte antigen-4 therapies.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".