Abstract 3422: Synthetic lethal siRNA screening reveals novel modifiers of insulin-like growth factor-1 receptor (IGF1R) inhibitor activity in childhood sarcomas
Bibliographic record
Abstract
Abstract The insulin-like growth factor-1 receptor (IGF1R) is emerging as a therapeutic target in many cancers including Ewing family tumors and other childhood sarcomas, where IGF1R blocking antibodies show promising anti-tumor activity in some but not all patients. A key question remains as to what underlies IGF1R inhibitor sensitivity versus resistance. IGF1R mutations have to date not been found in these tumors. Instead, we hypothesize that compensatory signaling circuits by-passing targeted IGF1R inhibition are involved. We performed synthetic lethal siRNA screens in Ewing family tumor cell lines to study the IGF1R signaling axis and related protein tyrosine kinase networks in the presence and absence of sub-lethal doses of an IGF1R kinase inhibitor, with the goal of identifying siRNAs that restored full inhibitor activity. This strategy revealed (1) that Ewing sarcoma and rhabdomyosarcoma cells are exquisitely sensitive to loss of distal IGF1R signaling components such as the ribosomal protein S6 (RPS6); (2) IGF1R inhibitors fail to block RPS6 activation in resistant cell lines; and (3) that siRNA knock-down of certain protein tyrosine kinases such as the MET family receptor MST1R restores inhibitor efficacy, even in highly drug resistant cell lines. Moreover, we confirmed MST1R expression in childhood sarcomas and found that loss of MST1R blocks downstream RPS6 activation when combined with IGF1R inhibition in vitro. Taken together, we present an approach in principle applicable to any promising drug of interest. We demonstrate that key signaling effectors of the IGF1R axis in childhood sarcomas represent distal pathway proteins such as RPS6, and we identify the MST1R receptor tyrosine kinase as a potential therapeutic target whose knock-down markedly enhances sensitivity to IGF1R inhibition in high-risk sarcoma cell types, including resistant lines. These data underscore the importance of fully understanding protein tyrosine kinase networks for successful implementation of kinase inhibitor strategies. Note: This abstract was not presented at the AACR 101st Annual Meeting 2010 because the presenter was unable to attend. Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 101st Annual Meeting of the American Association for Cancer Research; 2010 Apr 17-21; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2010;70(8 Suppl):Abstract nr 3422.
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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 | 0.001 |
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".