Data from Molecular Target Characterization and Antimyeloma Activity of the Novel, Insulin-like Growth Factor 1 Receptor Inhibitor, GTx-134
Bibliographic record
Abstract
Abstract Purpose: Therapeutic strategies that target insulin-like growth factor 1 receptor (IGF-1R) hold promise in a wide variety of cancers including multiple myeloma (MM). In this study, we describe GTx-134, a novel small-molecule inhibitor of IGF-1R and insulin receptor (IR) and characterized its antitumor activity in preclinical models of MM. Experimental Design: The activity of GTx-134 as a single agent and in combination was tested in MM cell lines and primary patient samples. Downstream effector proteins and correlation with apoptosis was evaluated. Cytotoxcity in bone marrow stroma coculture experiments was assessed. Finally, the in vivo efficacy was evaluated in a human myeloma xenograft model. Results: GTx-134 inhibited the growth of 10 of 14 myeloma cell lines (<5 μmol/L) and induced apoptosis. Sensitivity to GTx-134 correlated with IGF-1R signal inhibition. Expression of MDR-1 and CD45 were associated with resistance to GTx-134. Coculture with insulin-growth factor-1 (IGF-1) or adherence to bone marrow stroma conferred modest resistance, but did not overcome GTx-134–induced cytotoxicity. GTx-134 showed in vitro synergies when combined with dexamethasone or lenalidomide. Further, GTx-134 enhanced the activity of PD173074, a fibroblast growth factor receptor 3 (FGFR3) inhibitor, against t(4;14) myeloma cells. Therapeutic efficacy of GTx-134 was shown against primary cells and xenograft tumors. Although dysregulation of glucose homeostasis was observed in GTx-134–treated mice, impairment of glucose tolerance was modest. Conclusions: These studies support the potential therapeutic efficacy of GTx-134 in MM. Further, they provide a rationale for clinical application in combination with established antimyeloma treatments and novel targeted therapies. Clin Cancer Res; 17(14); 4693–704. ©2011 AACR.
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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) | 0.005 | 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".