Post-Transcriptional Deregulation of MYC Via PTEN Constitutes a Major Alternative Pathway of MYC Activation In T-Cell Acute Lymphoblastic Leukemia
Notice bibliographique
Résumé
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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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 0,001 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».