Oncogenic EGFR rewires STING-TBK1 immune machinery by tyrosine phosphorylation to license DNA damage tolerance
Bibliographic record
Abstract
Abstract EGFR hotspot mutations (mEGFR), including primary L858R, exon 19 deletion, and secondary T790M, are pivotal oncogenic drivers in human non-small cell lung cancer (NSCLC). Meanwhile, NSCLC resistance to third-generation tyrosine kinase inhibitors (TKIs) is a major clinical challenge and remains mechanistically unresolved. Here, we uncover a previously unrecognized immunological mechanism whereby mEGFR exploits cGAS-STING innate immune signaling, conventionally regarded as tumor-suppressive, to sustain oncogenic signaling and therapeutic resistance. Mechanistically, mutant EGFR kinase aberrantly incorporates into STING signalosomes, directly phosphorylating STING (Y245/Y314) and TBK1 (Y577/Y677), stabilizing and hyperactivating TBK1 proteins, and establishing an unexpected and kinase loop critical for DNA damage repair. Disruption of this mEGFR-STING-TBK1 axis, genetically or pharmacologically, profoundly sensitized resistant patient-derived NSCLC organoids to chemotherapy. Combining TBK1 inhibition with cisplatin notably eradicated mEGFR-driven tumors in spontaneous and immunocompetent NSCLC murine models and patient-derived organoids. Our findings suggest a new function of cGAS-STING in the DNA damage repair program, its paradoxical exploitation by oncogenic driver mutations, and an innate immune therapeutic vulnerability in NSCLC.
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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) | 0.002 | 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".