ETV6-NTRK3–Mediated Breast Epithelial Cell Transformation Is Blocked by Targeting the IGF1R Signaling Pathway
Bibliographic record
Abstract
The insulin-like growth factor (IGF) 1 receptor (IGF1R) is an important therapeutic target under study in many cancers. Here, we describe a breast cancer model based on expression of the ETV6-NTRK3 (EN) chimeric tyrosine kinase that suggests novel therapeutic applications of IGF1R inhibitors in secretory breast cancers. Originally discovered in congenital fibrosarcomas with t(12;15) translocations, EN was identified subsequently in secretory breast carcinoma (SBC) which represent a variant of invasive ductal carcinoma. Because fibroblast transformation by EN requires the IGF1R axis, we hypothesized a similar dependency may exist in mammary cells and, if so, that IGF1R inhibitors might be useful to block EN-driven breast oncogenesis. In this study, we analyzed EN expressing murine and human mammary epithelial cell lines for transformation properties. Various IGF1R signaling inhibitors, including the dual specificity IGF1R/insulin receptor (INSR) inhibitor BMS-536924, were then tested for effects on three-dimensional Matrigel cell growth, migration, and tumor formation. We found that EN expression increased acinar size and luminal filling in Matrigel cultures and promoted orthotopic tumor growth in mice. Tumors were well differentiated and nonmetastatic, similar to human SBC. The known EN effector pathway, PI3K-Akt, was activated in an IGF1- or insulin-dependent manner. BMS-536924 blocked EN transformation in vitro, whereas BMS-754807, another IGIFR/INSR kinase inhibitor currently in clinical trials, significantly reduced tumor growth in vivo. Importantly, EN model systems mimic the clinical phenotype observed in human SBC. Moreover, EN has a strict requirement for IGF1R or INSR in breast cell transformation. Thus, our findings strongly encourage the evaluation of IGF1R/INSR inhibitors to treat EN-driven breast cancers.
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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) | 0.001 | 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".