Mammary tumors that become independent of the type I insulin-like growth factor receptor express elevated levels of platelet-derived growth factor receptors
Bibliographic record
Abstract
BACKGROUND: Targeted therapies are becoming an essential part of breast cancer treatment and agents targeting the type I insulin-like growth factor receptor (IGF-IR) are currently being investigated in clinical trials. One of the limitations of targeted therapies is the development of resistant variants and these variants typically present with unique gene expression patterns and characteristics compared to the original tumor. RESULTS: MTB-IGFIR transgenic mice, with inducible overexpression of the IGF-IR were used to model mammary tumors that develop resistance to IGF-IR targeting agents. IGF-IR independent mammary tumors, previously shown to possess characteristics associated with EMT, were found to express elevated levels of PDGFRα and PDGFRβ. Furthermore, these receptors were shown to be inversely expressed with the IGF-IR in this model. Using cell lines derived from IGF-IR-independent mammary tumors (from MTB-IGFIR mice), it was demonstrated that PDGFRα and to a lesser extent PDGFRβ was important for cell migration and invasion as RNAi knockdown of PDGFRα alone or PDGFRα and PDGFRβ in combination, significantly decreased tumor cell migration in Boyden chamber assays and suppressed cell migration in scratch wound assays. Somewhat surprisingly, concomitant knockdown of PDGFRα and PDGFRβ resulted in a modest increase in cell proliferation and a decrease in apoptosis. CONCLUSION: During IGF-IR independence, PDGFRs are upregulated and function to enhance tumor cell motility. These results demonstrate a novel interaction between the IGF-IR and PDGFRs and highlight an important, therapeutically relevant pathway, for tumor cell migration and invasion.
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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 | 0.001 |
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".