Abstract B32: A specialized post-transcriptional program in chemoresistant, quiescent cancer cells
Notice bibliographique
Résumé
Abstract Quiescent (G0) cells are a clinically relevant fraction in cancers, which include dormant cancer stem cells, and resist clinical therapy. G0 cells reveal extensive changes in gene expression at the protein and translation levels. We previously identified that the translation mechanism is altered in G0 cancer cells. MicroRNAs, noncoding RNAs that target distinct mRNAs to alter gene expression, were found to associate with a key RNA-binding protein and enable specialized functions in G0, where they recruit noncanonical translation factors to regulate specific mRNA translation. We find that G0 leukemic cells show similar proteome and translatome to cells isolated post-chemotherapy. These data suggest that specialized post-transcriptional mechanisms in G0 leukemic cells regulate a distinct translatome to mediate chemoresistance. To understand the role of post-transcriptional regulation in chemoresistance, we compared global transcriptome, translatome and proteome profiling in chemoresistant G0 acute monocytic leukemic (AML) cells. We find that chemotherapy or G0 induction leads to DNA damage responsive ATM and stress signaling, which alter post-transcriptional and translational mechanisms. ATM and stress-activated p38 MAPK/MK2 increase AU-rich-element (ARE) bearing proinflammatory cytokine and immune gene mRNAs, by regulating a key ARE RNA binding protein and modifying canonical translation. AREs are present on 3'UTRs of tightly regulated oncogenes and cytokines, to post-transcriptionally control their expression. Both rate-limiting steps—mRNA cap recognition and tRNA recruitment—in canonical translation are altered. These signaling pathways lead to low mTOR activity in G0, which activates the cap complex inhibitor, eIF4EBP to impair canonical translation, leading to noncanonical translation of specific mRNAs with specialized cap binding and ribosome recruitment factors. In addition, stress and STAT1/interferon signaling are activated to reduce the canonical tRNA recruitment mechanism, enabling noncanonical translation of specific mRNAs. These changes permit translation of ARE bearing proinflammatory cytokine TNFa, and immune and cell-migration modulators that promote survival. Co-inhibiting p38 MAPK and TNFa that promote antiapoptosis—prior to or along with chemotherapy—decreases chemoresistance in AML cells, in vivo, and in patient samples without affecting normal cells. Our studies reveal a proinflammatory subpopulation in AML that mediates resistance, enabled by DNA damage- and stress-regulated post-transcriptional and translational mechanisms that are mediated by AU-rich-elements and a critical ARE RNA binding protein. Disrupting ARE regulation reduces TNFα and chemoresistance, revealing AREs, an important ARE RNA binding protein and noncanonical translation as regulators of chemoresistance. These studies reveal the significance of post-transcriptional regulation of proinflammatory and immune gene-mediated chemoresistance. Citation Format: Sooncheol Lee, Syed I.A. Bukhari, Samuel S. Truesdell, Swapna Kollu, Richard D. Mortensen, Myriam Boukhali, Esha Jain, Dongjun Lee, Maria Mazzola, Radhika Raheja, Adam Langenbucher, Nicholas Haradhwala, Akiko Yanagiya, Michael Lawrence, Roopali Gandhi, Ruslan Sadreyev, David Sweetser, Wilhelm Haas, Shobha Vasudevan. A specialized post-transcriptional program in chemoresistant, quiescent cancer cells [abstract]. In: Proceedings of the AACR Special Conference on Targeting PI3K/mTOR Signaling; 2018 Nov 30-Dec 8; Boston, MA. Philadelphia (PA): AACR; Mol Cancer Res 2020;18(10_Suppl):Abstract nr B32.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,001 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 0,000 |
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 tête enseignante, 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 ».