Resistance to dual blockade of the kinases PI3K and mTOR in <i>KRAS</i> -mutant colorectal cancer models results in combined sensitivity to inhibition of the receptor tyrosine kinase EGFR
Bibliographic record
Abstract
Targeted blockade of aberrantly activated signaling pathways is an attractive therapeutic strategy for solid tumors, but drug resistance is common. KRAS is a frequently mutated gene in human cancer but remains a challenging clinical target. Inhibitors against KRAS signaling mediators, namely, PI3K (phosphatidylinositol 3-kinase) and mTOR (mechanistic target of rapamycin), have limited clinical efficacy as single agents in KRAS-mutant colorectal cancer (CRC). We investigated potential bypass mechanisms to PI3K/mTOR inhibition in KRAS-mutant CRC. Using genetically engineered mouse model cells that had acquired resistance to the dual PI3K/mTOR small-molecule inhibitor PF-04691502, we determined with chemical library screens that inhibitors of the ERBB [epidermal growth factor receptor (EGFR)] family restored the sensitivity to PF-04691502. Although EGFR inhibitors alone have limited efficacy in reducing KRAS-mutant tumors, we found that PF-04691502 induced the abundance, phosphorylation, and activity of EGFR, ERBB2, and ERBB3 through activation of FOXO3a (forkhead box O 3a), a transcription factor inhibited by the PI3K to AKT pathway. PF-04691502 also induced a stem cell-like gene expression signature. KRAS-mutant patient-derived xenografts from mice treated with PF-04691502 had a similar gene expression signature and exhibited increased EGFR activation, suggesting that this drug-induced resistance mechanism may occur in patients. Combination therapy with dacomitinib (a pan-ERBB inhibitor) restored sensitivity to PF-04691502 in drug-resistant cells in culture and induced tumor regression in drug-resistant allografts in mice. Our findings suggest that combining PI3K/mTOR and EGFR inhibitors may improve therapeutic outcome in patients with KRAS-mutant CRC.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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".