Characterization of innate lymphoid cell subsets infiltrating melanoma and epithelial ovarian tumors
Bibliographic record
Abstract
The innate lymphoid cell (ILC) family is composed of heterogeneous innate effector and helper immune cells that preferentially reside in tissues where they promote tissue homeostasis. In cancer, they have been implicated in driving both pro- and anti-tumor responses. This apparent dichotomy highlights the need to better understand differences in the ILC composition and phenotype within different tumor types that could drive seemingly opposite anti-tumor responses. Here, we characterized the frequency and phenotype of various ILC subsets in melanoma metastases and primary epithelial ovarian tumors. We observed high PD-1 expression on ILC subsets isolated from epithelial ovarian tumor samples, while ILC populations in melanoma samples express higher levels of LAG-3. In addition, we found that the frequency of cytotoxic ILCs and NKp46+ILC3 in tumors positively correlates with monocytic cells and conventional type 2 dendritic cells, revealing potentially new interconnected immune cell subsets in the tumor microenvironment. Consequently, these observations may have direct relevance to tumor microenvironment composition and how ILC subset may influence anti-tumor immunity.
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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 | 0.001 | 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".