Abstract 827: Identification of the insulin-like growth factor 2 mRNA binding protein (IGF2BP1) as an important regulator of cIAP1 translation and apoptosis in rhabdomyosarcomas.
Bibliographic record
Abstract
Abstract Rhabdomyosarcoma, a neoplasm characterized by undifferentiated myoblats-like cells, is the most common soft tissue sarcoma of childhood and represents 3-4% of all childhood cancers. IGF2BP1 is an oncofetal protein that was first identified in the rhabdomyosarcoma RD cell line and was shown to be overexpressed in a variety of cancers. Our group has previously identified IGF2BP1 as a potential translation modulator of the cellular inhibitor of apoptosis protein 1 (cIAP1), a key regulator of the NFκB signaling pathway and of caspase-8 mediated cell death in mammalian cells. In this study, we report that IGF2BP1 and cIAP1 expression is upregulated in a panel of rhabdomyosarcoma cell lines. We also show that IGF2BP1 is a positive regulator of cIAP1 translation, specifically through an internal ribosome entry site (IRES) mechanism. Finally, we report that altering the levels of cIAP1 in two rhabdomyosarcoma cell lines, RH36 and RH41, either by IGF2BP1 knock-down or by a Smac mimetic coumpound, sensitizes these cells to TNFα-mediated cell death. Our results identify IGF2BP1 and cIAP1 as important regulators of apoptosis in rhabdomyosarcomas. Citation Format: Mame Daro Faye, Tyson Graber, Stephanie Langlois, Kyle Cowan, Martin Holcik. Identification of the insulin-like growth factor 2 mRNA binding protein (IGF2BP1) as an important regulator of cIAP1 translation and apoptosis in rhabdomyosarcomas. [abstract]. In: Proceedings of the 104th Annual Meeting of the American Association for Cancer Research; 2013 Apr 6-10; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2013;73(8 Suppl):Abstract nr 827. doi:10.1158/1538-7445.AM2013-827
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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 | 0.001 | 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) | 0.008 | 0.002 |
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".