The Role of CD4+ Tissue Resident Memory Cells in Oncogene Driven Lung Adenocarcinoma
Bibliographic record
Abstract
Abstract Lung cancer presents a global health risk that is responsible for over 2 million deaths worldwide every year. Tissue resident memory T cells (Trm) are critical for rapid protection to pathogens at entry points in the body, including the lung. Trm are identified as CD69+ CD103+ cells which remain in the tissue (i.e. lung), are longer-lived memory cells, and have antigen specificity. Many studies have shown clinical significance with tumour infiltrating lymphocytes (TIL) markers of CD103+ and CD69+ in solid tumour cancers. Few studies have addressed the direct or indirect mechanism of anti-tumour function of CD4+ Trm cells during tumour progression and how the tumour microenvironment impacts this. One primary role of CD4+ T cells in anti-tumour immunity is to provide aid to CD8+ effector cells. CD4+ Trm are known to have Th1 interferon responses to viral infection and Th2 type responses in allergic response leading to excess fibrosis. Little is known of the diversity of CD4+ Trm and their Type-1 or -2 phenotypes during cancer. In this study, we used a genetically engineered mouse model (GEMM) of lung adenocarcinoma (LAC), with doxycycline-inducible oncogene mutation KRASG12D. We observed a significant increase in PD-1+ CD4+ Trm with a Th2-like phenotype (CD44+ CD62L−CD103+ CD69+ PD-1+GATA3hi) in the KRASG12D tumour bearing lungs. When stimulated ex vivo with PMA/ionomycin, we found CD4+ CD69+ cells produced more IFNγ in the KRASG12DGEMM. To examine clinical relevance, we used the program KMplotter and found signatures of Trm and CD4+ Trm significantly correlated with increased survival in LAC patients. We intend to determine the functional relevance of these Th2-like CD4+ TRM cells in the tumour microenvironment.
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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 | 0.001 |
| 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".