MET Exon 14 Skipping Mutations in Lung Cancer: Clinical–Pathological Characteristics and Immune Microenvironment
Bibliographic record
Abstract
MET exon 14 skipping mutations have emerged as significant driver alterations in non-small-cell lung cancer (NSCLC), contributing to tumor progression. This study examines the immune microenvironment in NSCLC patients with these mutations and its prognostic implications. We performed multiplex immunofluorescence (mIF) staining on formalin-fixed paraffin-embedded (FFPE) tissue samples from nine NSCLC patients, including four recurrent/metastatic and five non-recurrent/non-metastatic patients. Two panels assessed immune cell markers (CD8, CD4, CD20, CD68, and FoxP3) and immune checkpoints (PD-L1, LAG3, and TIM3). Immune cell infiltration and checkpoint expression were analyzed using HALOTM software (version 3.6.4134.464). Nearest neighbor analysis was conducted to assess the proximity of immune cells to tumor cells. Univariate Cox regression analysis assessed factors associated with disease-free survival (DFS). CD8+TIM3+ and CD8+LAG3+ cells were predominantly located in the tumor parenchyma of recurrent/metastatic patients but localized to the stroma in non-recurrent/non-metastatic patients. Non-recurrent/non-metastatic patients exhibited a higher density of tertiary lymphoid structures and closer proximity of CD20+ B cells, CD8+TIM3+, and CD8+LAG3+ cells to tumor cells compared to recurrent/metastatic patients, though the differences were not statistically significant. Cox regression analysis suggested a potential association between higher densities of CD8+TIM3+ cells and improved DFS (HR = 0.89), though these findings did not reach statistical significance. Our findings suggest that differences in immune microenvironmental factors, particularly those related to immune checkpoint expression (TIM3 and LAG3), may influence clinical outcomes in NSCLC patients with MET exon 14 skipping mutations. Further studies are needed to validate these observations and explore potential therapeutic implications.
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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 | 0.001 |
| 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".