AKT/mTOR as a targetable hub to overcome multimodal resistance to EGFR inhibitors in oesophageal squamous cell carcinoma
Bibliographic record
Abstract
BACKGROUND: Oesophageal squamous cell carcinoma (ESCC) is associated with late-stage diagnosis, limited treatment options, the development of drug resistance and poor outcome. Epidermal growth factor receptor is frequently dysregulated in ESCC. EGFR copy number gain and/or protein overexpression are beneficial as predictive biomarkers for EGFR inhibitor therapy; however, inherent and acquired resistance limit response rates, and durable disease control is infrequent. METHODS: This study investigates the causes of resistance to the off-patent EGFR inhibitor gefitinib in three gefitinib-resistance model systems: intrinsic, acquired resistance and growth factor (TGFβ)-induced resistance. Findings from studies in 13 ESCC cell lines were validated in tumour specimens from the GO2 clinical trial (n = 32), publicly available ESCC datasets (n = 264), cell line-derived xenograft (CDX) and patient-derived organoid (PDO) model systems. RESULTS: Gefitinib resistance in ESCC was associated with diverse mechanisms, including RTK signalling via PDGFRβ and IGFBP3/IGF1/IGF1R, as well as EMT, but was consistently associated with the maintenance of signalling via AKT across multiple cell lines and model systems. AKT or mTOR inhibitors synergised with gefitinib in 2D and anchorage-independent 3D assays. Gefitinib plus the AKT inhibitor capivasertib (Truqap™) was efficacious in human CDX and PDO models. DISCUSSION: Combining AKT/mTOR inhibitors with EGFR inhibitors in EGFR-driven ESCC shows synergism but with elevated toxicity. Monotherapy AKT/mTOR inhibitors or combined therapy at reduced doses could offer improved, cost-effective therapy options for gefitinib-resistant 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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.002 | 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".