Oncogenic driver mutations underlie the spatial tumour immune landscape of non-small cell lung cancer
Bibliographic record
Abstract
Lung adenocarcinoma (LUAD) is a molecularly diverse form of lung cancer characterized by distinct oncogenic driver mutations that influence both tumour biology and clinical outcomes. Understanding the interplay between these oncogenic drivers and the tumour microenvironment (TME) is crucial for improving therapeutic strategies and patient management. Here, we investigate the impact of driver mutations on the composition and spatial architecture of the TME in LUAD. Using imaging mass cytometry (IMC), we analyse tumour samples from 157 LUAD patients, integrating genomic and clinical data to link specific mutations with tumour characteristics. Unique patterns are associated with mutated KRAS and EGFR tumours with TP53 co-mutations, suggesting these co-mutations reshape the TME and promote resistance to tyrosine kinase inhibitors (TKIs). Overall, our findings highlight the complex interplay between oncogenic driver mutations and the TME in LUAD, underscoring the importance of integrating genomic and cellular data to understand the underlying tumour behaviour and prognosis. Oncogenic driver mutations can influence the tumour biology of lung adenocarcinomas. Here, authors analyse 157 LUAD samples using imaging mass cytometry to reveal how these mutations impact the tumour microenvironment and clinical outcomes.
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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 | 0.001 | 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".