Systematic Identification of Oncogenic EGFR Interaction Partners
Bibliographic record
Abstract
The epidermal growth factor receptor (EGFR) is a receptor tyrosine kinase (TK) that-once activated upon ligand binding-leads to receptor dimerization, recruitment of protein complexes, and activation of multiple signaling cascades. The EGFR is frequently overexpressed or mutated in various cancers leading to aberrant signaling and tumor growth. Hence, identification of interaction partners that bind to mutated EGFR can help identify novel targets for drug discovery. Here, we used a systematic approach to identify novel proteins that are involved in cancerous EGFR signaling. Using a combination of high-content imaging and a mammalian membrane two-hybrid protein-protein interaction method, we identified eight novel interaction partners of EGFR, of which half strongly interacted with oncogenic, hyperactive EGFR variants. One of these, transforming acidic coiled-coil proteins (TACC) 3, stabilizes EGFR on the cell surface, which results in an increase in downstream signaling via the mitogen-activated protein kinase and AKT pathway. Depletion of TACC3 from cells using small hairpin RNA (shRNA) knockdown or small-molecule targeting reduced mitogenic signaling in non-small cell lung cancer cell lines, suggesting that targeting TACC3 has potential as a new therapeutic approach for non-small cell 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 | 0.001 | 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".