Targeting Oncogenic Polyamines in Refractory Pediatric Leukemias Using the Anti-Protozoan Drug Α-Difluoromethylornithine (DFMO) in Combination with GC7 and Aurothioglucose
Bibliographic record
Abstract
Abstract Introduction: Polyamines (PAs) are cationic metabolites that enhance pro-tumorigenic cellular processes including the stimulation of cell division and proliferation, pro-survival gene expression, DNA and protein synthesis, regulation of apoptosis, oxidative stress and angiogenesis. Spermidine is a particularly important polyamine because it acts as an essential substrate of hypusine biosynthesis. Hypusination is required for the post-transcriptional activation of eukaryotic initiation factor 5A (eIF5A) which is critical to cell growth and protein synthesis. Spermidine production is highly regulated through the rate-limiting enzyme ornithine decarboxylase (ODC). Polyamines and eIF5A have been shown to be critical growth promoters in various pediatric cancers. Thus, targeted inhibition of ODC has therapeutic potential for this unique group of currently incurable pediatric malignancies. The anti-protozoan drug α-difluoromethylornithine (DFMO) is a suicide inhibitor of ODC and its effect may be potentiated by GC7 and Aurothioglucose. Previous mechanism based studies indicate that DFMO effectively reduces polyamine levels in neuroblastoma (NB) and provides rationale for ongoing Phase I and II clinical trials. To our knowledge, the effects of DFMO, GC7 and aurothioglucose have not yet been tested in pediatric leukemias. In this report, we evaluate the effectiveness of these therapeutic agents as a novel treatment of refractory hematological malignancies in the pediatric population. Methods: The recently published TARGET 2018 dataset was analyzed to determine the relationship between ODC expression and patient survival. Demographic data from this study was used to identify patients populations who may benefit from an ODC targeted therapy. Western blot analyses were then used to screen a comprehensive panel of pediatric cancer cell lines for ODC expression. Infant and pediatric leukemia cell lines were selected to represent the various subtypes including AML (TIB202, KASUMI) as well as B ALL (SUP-B15, MV411, RS4-11, CCRF-SB ) and T ALL (CEM/C1, MOLT-3). These cells were derived from children with relapsed disease and have varying degrees of resistance to conventional chemotherapy. Drug combination studies were used to define drug candidates having potential synergistic activity when combined with DFMO. Results: A Kaplan-Meier evaluation determined that high expression of ODC was associated with significantly lower patient survival and this finding was confirmed via cox regression analysis. The demographic analysis determined that females were significantly overrepresented in the high expression group whereas no variation was seen between race and ethnicity groups. Furthermore, a correlation was observed between high expression of eIF5A and younger diagnosis, indicating that ODC targeted therapy may be more effective in infant populations. DFMO-induced cytotoxicity was detected in many of the tumor cells including infant AML (TIB202) at an approximate physiologically achievable IC50 value of 200 uM. This was representative of values previously published for neuroblastoma. In infant AML, a western blot analysis determined that DFMO increased the abundance of cleaved PARP, cleaved caspase 3 and cleaved caspase 7 which serve as mechanistic markers of apoptosis. Probing for caspase 8 and caspase 9 showed no difference between treated cells and control. A combination screen of over 80 biologically active compounds was conducted to identify potential synergistic treatment partners of DFMO where GC7 and aurothioglucose (AuTG) ranked among the most promising. Further experimentation determined that doses of GC7 and AuTG, which were not effective as a singular treatment, were able to enhance the ability of DFMO to kill tumor cells. Discussion: The preclinical studies discussed here offer the first proof-of-concept data on a novel treatment approach for refractory leukemia in children. We provide mechanistic evidence to show the ability of DFMO to effectively kill polyamine-dependent tumor cells at physiologically attainable concentrations. In addition, effective drug combinations have been identified to further enhance their clinical utility. Our findings provide key preclinical information on the utility of these drugs as treatment for refractory hematological malignancies in the pediatric population. Disclosures No relevant conflicts of interest to declare.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".