Drug tolerance and persistence to EGFR inhibitor treatment are mediated by an ILK-SFK-YAP signaling axis in lung adenocarcinoma
Bibliographic record
Abstract
Combating resistance to targeted therapy remains a major challenge to improving lung cancer care. Epithelial-mesenchymal transition (EMT) in tumour cells is an established non-genetic resistance mechanism to EGFR tyrosine kinase inhibitors (TKI) that is also associated with worse outcome in patients. Here we demonstrate that integrin-linked kinase (ILK) is an important driver of EMT-mediated TKI resistance in lung adenocarcinoma (LUAD) by promoting a drug-tolerant persister (DTP) cell phenotype. Our results indicate that high ILK expression is associated with EMT in LUAD patients and that genetic suppression of ILK can limit EMT progression and reduce the viability of DTP cells by impairing YAP activation, ultimately improving osimertinib (Osi) sensitivity in LUAD cells. Importantly, LUAD cells with high ILK expression are able to persist during EGFR-TKI treatment, acquiring additional genetic and phenotypic alterations to develop EGFR-TKI resistance. To improve clinical translatability of our findings, we showed that pharmacological inhibition of ILK can suppress EMT and improve Osi response in LUAD cells. Lastly, we found that strong immunohistochemistry staining of ILK in patient biopsies was significantly associated with and may be used to predict receptor tyrosine kinase-independent mechanisms of EGFR-TKI resistance. Overall, our results suggest that ILK is an important regulator of EGFR-TKI response and may be exploited as a predictor for acquired resistance, providing evidence for co-targeting ILK with EGFR to better control minimal residual disease and EGFR-TKI resistance in lung cancer.
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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 | 0.001 |
| 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".