Abstract A067 Unlocking asparaginase resistance: MondoA's role in pediatric B-ALL's adaptation to nutrient scarcity
Bibliographic record
Abstract
Abstract Background: MLXIP (Myc-associated factor X-like protein X-interacting protein), also known as MondoA, is a crucial metabolic sensor that we found to promote malignancy in pediatric acute lymphoblastic B-cell leukemia (B-ALL). In our group’s previous work, a link between MondoA overexpression and resistance to glutamine deprivation (GD) was observed, though the mechanisms are not yet completely understood. Aim: To further understand the behavior of leukemia cells during amino acid shortage, we conducted experiments with L-asparaginase (ASNase), an enzyme used in leukemia treatment, to deprive cells of asparagine (ASN) and, to a lesser extent, of glutamine (GLN). Methods and Results: RNA expression and proteomic analysis of MondoA knockout (MKO) revealed a downregulation of asparagine synthetase (ASNS), an enzyme crucial for ASNase sensitivity. This trend was reproducible in publicly available gene expression data of leukemic patients (UCSC Xena). Downregulated proteins involved in pyruvate metabolism as well as the Tricarboxylic acid (TCA) cycle (e.g. citrate synthase and mitochondrial aconitase) indicated an insufficient isocitrate production in MKO. Conversely, isocitrate dehydrogenases (IDH2 and IDH3) were upregulated. There was no alteration in the enzymes further along the cycle that utilize α-KG as a substrate. We measured relative cell viability of Nalm6 B-ALL cells under different metabolic and treatment conditions, as well as with the rescue substrate α-KG. MKO showed a faster loss of viability after ASNase treatment compared to MondoA-expressing cells. A similar trend was observed under glutamine deprivation (GD). As ASNS converts GLN to ASN, we assessed the effects of combined ASN and GLN starvation. Coherent with our hypothesis, MondoA sufficient cells had a strongly diminished advantage under these conditions. Notably, the addition of α-ketoglutarate (αKG) to MKO cells mitigated the effects of ASNase and GD as well as it improved cell viability in normal media. Of interest, α-KG can also be derived from glutamine through glutaminolysis. This appears to be sufficient to sustain MKO cell growth even under conditions of GD, albeit at a reduced rate. Thus, a compromised TCA cycle in MKO cells can be overcome by αKG-substitution, compensating reduced pyruvate metabolism. Conclusion: Our results demonstrate the importance of MondoA in the metabolic adaptation of leukemia cells under nutrient deprivation. The genetic inactivation of MondoA expression leads to decreased viability of the cells under metabolic stress conditions likely due to impaired ASNS expression and a disrupted TCA cycle. In perspective, these data suggest that MondoA expression could be a critical factor in determining the efficacy of L-asparaginase therapy and predicting outcomes for pediatric B-ALL patients. Citation Format: Alissia Fernandes Madeira, Constantin Segner, Christian Brückner, Alisa Kolesnikova, Mansour Poorebrahim, Busheng Xue, Alexandra Sipol, Poul Sørensen, Julia Hauer, Stefan Burdach. Unlocking asparaginase resistance: MondoA's role in pediatric B-ALL's adaptation to nutrient scarcity [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Advances in Pediatric Cancer Research; 2024 Sep 5-8; Toronto, Ontario, Canada. Philadelphia (PA): AACR; Cancer Res 2024;84(17 Suppl):Abstract nr A067.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".