Transcriptional Subtypes Resolve Tumor Heterogeneity and Identify Vulnerabilities to MEK Inhibition in Lung Adenocarcinoma
Bibliographic record
Abstract
Abstract Purpose: Lung adenocarcinomas comprise the largest fraction of non–small cell lung cancer, which is the leading cause of cancer-related deaths. Seventy-five percent of adenocarcinomas lack targeted therapies because of scarcity of druggable drivers. Here, we classified tumors on the basis of signaling similarities and discovered subgroups within this unmet patient population. Experimental Design: We leveraged transcriptional data from >800 early- and advanced-stage patients. Results: We identified three robust subtypes dubbed mucinous, proliferative, and mesenchymal with respective pathway phenotypes. These transcriptional states lack discrete and causative mutational etiology as evidenced by similarly distributed oncogenic drivers, including KRAS and EGFR. The subtypes capture heterogeneity even among tumors lacking known oncogenic drivers. Paired multi-regional intratumoral biopsies demonstrated unified subtypes despite divergently evolved prooncogenic mutations, indicating subtype stability during selective pressure. Heterogeneity among in vitro and in vivo preclinical models is expounded by the human lung adenocarcinoma subtypes and can be leveraged to discover subtype-specific vulnerabilities. As proof of concept, we identified differential subtype response to MEK pathway inhibition in a chemical library screen of 89 lung cancer cell lines, which reproduces across model systems and a clinical trial. Conclusions: Our findings support forward translational relevance of transcriptional subtypes, where further exploration therein may improve lung adenocarcinoma treatment. See related commentary by Skoulidis, p. 913
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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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".