Abstract 4251: Evaluating novel targeted therapeutic agents in T-cell acute lymphoblastic leukemia using a zebrafish xenotransplantation model
Bibliographic record
Abstract
Abstract T-cell acute lymphoblastic leukemia (T-ALL) is an aggressive hematologic malignancy accounting for 25% of adult and 10-15% of pediatric ALL cases. The disease is characterized by infiltration of the bone marrow and peripheral blood with immature lymphoblasts bearing T-lineage markers. Although current multi-agent chemotherapy regimens have dramatically improved cancer outcomes, a significant proportion of T-ALL do not respond to therapy or relapse post therapy. Novel molecular-based treatment strategies are desperately needed to improve outcome and limit toxicity. Mutations that activate the NOTCH and PTEN/PI3K pathways are common in human T-ALL and may drive T-ALL cell proliferation. Gamma secretase inhibiting compounds that block NOTCH signaling have been effective against subtypes of T-ALL with NOTCH mutations but have had limited success in clinical trials due to severe gastrointestinal toxicity, and problems with drug resistance. Recently, inhibition of the PI3K pathway (including the downstream kinase AKT) have shown efficacy in vitro and in pre-clinical animal models. We have recently developed a novel human cancer xenotransplantation (XT) animal model using the zebrafish, which can be used for high throughput pre-clinical testing of human tumour-drug interactions for both leukemia and solid tumours (Corkery et al., 2011 Brit. J. Haemat.). Using the zebrafish XT model we are studying a number of compounds that specifically target the PI3K/AKT kinase pathway for ability to inhibit T-ALL cell proliferation in vivo. Preliminary proof-of-principal studies have shown that we can successfully engraft the PTEN-deficient T-ALL leukemia Jurkat cell line in zebrafish embryos. In agreement with published data, we have shown that treating Jurkat transplanted embryos with the mTOR inhibitor rapamycin inhibits in vivo proliferation of the human leukemia cells line by 60%, while, by contrast, the AKT inhibitor triciribine does not significantly inhibit proliferation. We have also successfully transplanted primary bone marrow from pediatric T-ALL patients which go on to engraft in the zebrafish embryos. The ability to screen novel compounds against a panel of primary patient samples could provide a more accurate means of predicting the efficacy of said compounds across heterogeneous patient populations. We are now employing the zebrafish XT model to study the effects of novel PI3K/AKT kinase inhibition on the proliferation of a panel of human T-ALL cell lines and primary patient samples as a means of rapidly selecting new drug candidates to move forward into the rodent model and Phase I clinical trials. Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 103rd Annual Meeting of the American Association for Cancer Research; 2012 Mar 31-Apr 4; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2012;72(8 Suppl):Abstract nr 4251. doi:1538-7445.AM2012-4251
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".