Tumor-Associated Regulatory T Cell Expression of LAIR2 Is Prognostic in Lung Adenocarcinoma
Bibliographic record
Abstract
Cancer development requires a permissive microenvironment that is shaped by interactions between tumor cells, stroma, and the surrounding matrix. As collagen receptors, the leukocyte-associated immunoglobulin-like receptor (LAIR) family allows the immune system to interact with the extracellular matrix. However, little is known about their role in regulating tumor immunity and cancer progression. Methods: Genetic analysis of resected human lung adenocarcinoma was correlated to clinical-pathological characteristics, gene ontologies, and single cell RNA sequencing (scRNASeq). LAIR2 production was determined in subsets of immune cells isolated from blood leukocytes and lung adenocarcinoma tumor. Functional assays were used to determine the role of LAIR2 in tumorigenesis. Results: LAIR2 expression was adversely prognostic in lung adenocarcinoma. LAIR2 was preferentially produced by activated CD4+ T cells and enhanced in vitro tumor invasion into collagen. scRNASeq analysis of tumor infiltrating T cells revealed that LAIR2 expression co-localized with FOXP3 expressing cells and shared a transcriptional signature with tumor-associated regulatory T (Treg) cells. A CD4+ LAIR2+ Treg gene signature was prognostically significant in the TCGA dataset (n = 439; hazard ratio (HR) = 1.37; 95% confidence interval (CI), 1.05–1.77, p = 0.018) and validated in NCI Director’s Challenge lung adenocarcinoma dataset (n = 488; HR = 1.54; 95% CI, 1.14–2.09, p = 0.0045). Conclusions: Our data support a role for LAIR2 in lung adenocarcinoma tumorigenesis and identify a CD4+ LAIR2+ Treg gene signature in lung adenocarcinoma prognosis. LAIR2 provides a novel target for development of immunotherapies.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".