In Vitro Investigation Demonstrates IGFR/VEGFR Receptor Cross Talk and Potential of Combined Inhibition in Pediatric Central Nervous System Atypical Teratoid Rhabdoid Tumors
Bibliographic record
Abstract
BACKGROUND: Atypical teratoid rhabdoid tumor of the central nervous system (CNS ATRT) is a malignancy that commonly affects young children. The biological mechanisms contributing to tumor aggressiveness and resistance to conventional therapies in ATRT are unknown. Previous studies have shown the activity of insulin like growth factor-I receptor (IGF-1R) in ATRT tumor specimens and cell lines. IGF-1R has been shown to cross-talk with other receptor tyrosine kinases (RTKs) in a number of cancer types, leading to enhanced cell proliferation. OBJECTIVE: This study aims to evaluate the role of IGF-1 receptor cross-talk in ATRT biology and the potential for therapeutic targeting. METHODS: Cell lines derived from CNS ATRT specimens were analyzed for IGF-1 mediated cell proliferation. A comprehensive receptor tyrosine kinase (RTK) screen was conducted following IGF-1 stimulation. Bioinformatic analysis of publicly available cancer growth inhibition data to identify correlation between IC50 of a VEGFR inhibitor and IGF-1R expression. RESULTS: Comprehensive RTK screen identified VEGFR-2 cross-activation following IGF-1 stimulation. Bioinformatics analysis demonstrated a positive correlation between IC50 values of VEGFR inhibitor Axitinib and IGF-1R expression, supporting the critical influence of IGF-1R in modulating response to anti-angiogenic therapies. CONCLUSION: Overall, our data present a novel experimental framework to evaluate and utilize receptor cross-talk mechanisms to select effective drugs and combinations for future therapeutic trials in ATRT.
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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) | 0.002 | 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".