Post-Transcriptional Deregulation of MYC Via PTEN Constitutes a Major Alternative Pathway of MYC Activation In T-Cell Acute Lymphoblastic Leukemia
Bibliographic record
Abstract
Abstract Abstract 4188 Cumulative evidence indicates that MYC, one of the major downstream effectors of NOTCH1, is a critical component of T-cell acute lymphoblastic leukemia (T-ALL) oncogenesis and a potential candidate for targeted therapy. However, MYC is a complex oncogene, involving both fine protein dosage and cell-context dependency, and detailed understanding of MYC-mediated oncogenesis in T-ALL is still lacking. To better understand how MYC is interspersed in the complex T-ALL oncogenic networks, we performed the first thorough molecular and biochemical analysis of MYC activation in a comprehensive collection of primary adult and pediatric patient samples (n=163). RQ-PCR analysis first revealed that levels of MYC transcript are highly variable, and that high MYC expression can be generated in a large number of cases in absence of NOTCH1/FBXW7 mutations, suggesting the occurrence of multiple activation pathways in addition to NOTCH1. MYC protein levels were next assessed by western blot in a subpanel of 29 primary T-ALL samples. Although a large spectrum of protein expression was also observed, no direct correlation was apparent between transcript level and protein abundance. This discrepancy is in line with the frequent deregulation of factors controlling MYC protein turn-over in cancer cells. In Burkitt Lymphoma (BL) for example, mutations in the MYC-box (in and around the critical T58 phosphorylation site) impede efficient addressing to the proteasome, and results in oncogenic MYC protein stabilization. We sequenced the MYC-box region of 116 T-ALL samples and found no such mutations, suggesting that BL and T-ALL use different routes to achieve oncogenic MYC levels. An alternative candidate is the E3 ubiquitin ligase FBXW7 which has been shown to target both NOTCH1 and MYC for proteosomal degradation, and the loss-of-function mutations of which were previously shown to impair MYC protein stability. Although T-ALL harboring inactivating FBXW7 mutations were associated with high MYC protein level as expected, such mutants represented only 10% of the cases, and could thus not account for the numerous cases showing MYC protein accumulation in our series. This implied the frequent occurrence of other, alternative mechanisms of MYC stabilization in T-ALL. To identify these processes, we performed a detailed pan-genomic analysis of a T-ALL case showing high MYC protein levels at relapse but low MYC at diagnosis, despite identical transcript levels. Among a total of ten genomic aberrations found in this patient, only one difference was found between diagnosis and relapse, namely the progression of mono-allelic to bi-allelic deletion of a 10q23 region including the PTEN tumor suppressor gene. PTEN is considered the main negative regulator of the PI3K-AKT signaling. As AKT inactivates GSK3β, the serine-threonine kinase involved in MYC phosphorylation at T58, PTEN inactivation constitutes a likely candidate of MYC protein accumulation. To further test this hypothesis, we probed by western blot PTEN and MYC proteins in the previous subpanel of 29 T-ALL, and observed an inverse inter-relationship between PTEN and MYC protein levels. This anti-correlation was particularly clear in cases with abundant PTEN protein levels, where only faint MYC could be found despite relatively high transcription levels. Conversely, high MYC protein levels were in most cases associated with rather low PTEN protein levels. Finally, we functionally demonstrate that in T-ALL cell lines, the PI3K chemical inhibitor LY294002 which antagonizes AKT signaling and mimics PTEN function, is able to downmodulate MYC levels and activity. Altogether, our data reveals that post-transcriptional deregulation of MYC constitutes a major alternative pathway of MYC activation in T-ALL, operating partly via the PI3K/AKT axis through down-regulation of PTEN, and that NOTCH1 mutations might play a dual transcriptional and post-transcriptional role in this process. Our data lend further support to the significance of therapeutic targeting of MYC and/or the PTEN/AKT pathways, both in γ-secretase inhibitor-resistant and identified NOTCH1-independent/MYC-mediated T-ALL patients. Disclosures: No relevant conflicts of interest to declare.
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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) | 0.001 | 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".