Characterizing the non-genetic mechanisms of EGFR-TKI tolerance and resistance in lung adenocarcinoma
Bibliographic record
Abstract
Lung cancer is the most commonly diagnosed cancer and the leading cause of cancer-related deaths in the world. While effective targeted therapies are available for the treatment of epidermal growth factor receptor (EGFR) driven lung adenocarcinoma (LUAD), resistance to these therapies remains a major hurdle to effective patient care. In particular, non-genetic mechanisms of EGFR inhibitor resistance are poorly understood. The progression towards non-genetic mechanisms of EGFR inhibitor resistance are known to involve an intermediate, transitory drug-tolerant persister (DTP) state. In this thesis, I studied two non-genetic mechanisms of therapy resistance to EGFR inhibition: 1) epithelial-mesenchymal transition (EMT) and 2) small cell transformation (SCT). In the first part, I used an integrative approach to characterize an ILK-SFK-YAP molecular mechanism that underlies DTP survival in the progression towards EMT-mediated EGFR inhibitor resistance. In the second part, I described our progressive steps in creating an in vitro model of SCT. At the end of this part, we developed an in vitro LUAD DTP-like model that was induced to express multiple small cell lung cancer markers, clarifying the molecular pathways involved with neuroendocrine transdifferentiation. Overall, this thesis characterizes the molecular alterations that drive two non-genetic mechanisms of EGFR inhibitor resistance, furthering our understanding of how treatment-sensitive LUAD cells can transform into a drug-resistant state.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".