Identification of a Resistance Mechanism to IGF-IR Targeting in Human Triple Negative MDA-MB-231 Breast Cancer Cells
Bibliographic record
Abstract
Triple negative breast cancer (TNBC) is associated with unfavorable prognosis and high relapse rates following chemotherapy. There is an urgent need to develop effective targeted therapy for this BC subtype. The type I insulin-like growth factor receptor (IGF-IR) was identified as a potential target for BC management. We previously reported on the production of the IGF-Trap, a soluble IGF-1R fusion protein that reduces the bioavailability of circulating IGF-1 and IGF-2 to the cognate receptor, impeding signaling. In nude mice xenotransplanted with the human TNBC MDA-MB-231 cells, we found variable responses to this inhibitor. We used this model to investigate potential resistance mechanisms to IGF-targeted therapy. We show here that prolonged exposure of MDA-MB-231 cells to the IGF-Trap in vitro selected a resistant subpopulation that proliferated unhindered in the presence of the IGF-Trap. We identified in these cells increased fibroblast growth factor receptor 1 (FGFR1) activation levels that sensitized them to the FGFR1-specific tyrosine kinase inhibitor PD166866. Treatment with this inhibitor caused cell cycle arrest in both the parental and resistant cells, markedly increasing cell death in the latter. When combined with the IGF-Trap, an increase in cell cycle arrest was observed in the resistant cells. Moreover, FGFR1 silencing increased the sensitivity of these cells to IGF-Trap treatment in vivo. Our data identify increased FGFR1 signaling as a resistance mechanism to targeted inhibition of the IGF-IR and suggest that dual IGF-1R/FGFR1 blockade may be required to overcome TNBC cell resistance to IGF-axis inhibitors.
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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 | 0.000 |
| 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".