Integrative Profiling of T790M-Negative EGFR-Mutated NSCLC Reveals Pervasive Lineage Transition and Therapeutic Opportunities
Bibliographic record
Abstract
Abstract Purpose: Despite the established role of EGFR tyrosine kinase inhibitors (TKIs) in EGFR-mutated NSCLC, drug resistance inevitably ensues, with a paucity of treatment options especially in EGFRT790M-negative resistance. Experimental Design: We performed whole-exome and transcriptome analysis of 59 patients with first- and second-generation EGFR TKI-resistant metastatic EGFR-mutated NSCLC to characterize and compare molecular alterations mediating resistance in T790M-positive (T790M+) and -negative (T790M−) disease. Results: Transcriptomic analysis revealed ubiquitous loss of adenocarcinoma lineage gene expression in T790M− tumors, orthogonally validated using multiplex IHC. There was enrichment of genomic features such as TP53 alterations, 3q chromosomal amplifications, whole-genome doubling and nonaging mutational signatures in T790M− tumors. Almost half of resistant tumors were further classified as immunehot, with clinical outcomes conditional on immune cell-infiltration state and T790M status. Finally, using a Bayesian statistical approach, we explored how T790M− and T790M+ disease might be predicted using comprehensive genomic and transcriptomic profiles of treatment-naïve patients. Conclusions: Our results illustrate the interplay between genetic alterations, cell lineage plasticity, and immune microenvironment in shaping divergent TKI resistance and outcome trajectories in EGFR-mutated NSCLC. Genomic and transcriptomic profiling may facilitate the design of bespoke therapeutic approaches tailored to a tumor's adaptive potential.
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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 | 0.000 |
| 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".