Identification of Compounds That Target Acute Myeloid Leukemia Stem Cells Using a Scalable Next-Generation Screening Platform
Notice bibliographique
Résumé
Abstract The main barrier to curing acute myeloid leukemia (AML) is disease relapse, which occurs due to therapy resistance and persistence of leukemic stem cells (LSCs) after conventional induction chemotherapy. LSCs possess stem cell properties such as quiescence and self-renewal, as well as a transcriptional signature that resembles that of normal hematopoietic stem and progenitor cells. While patient-derived xenograft (PDX) models are the most stringent way to examine the properties of LSCs in human AML, this labor-intensive assay is not amenable to high throughput approaches to identify druggable vulnerabilities. Although current therapies effectively reduce tumor burden, achieving lasting event-free survival remains a challenge in the management of patients with AML. Current drug discovery efforts are focused on finding agents that eradicate bulk tumor, but not necessarily LSCs. Here, we report a next-generation screening strategy as a novel paradigm that allows the direct elucidation of molecules which antagonize LSC properties. This screening workflow is based on two scalable models of LSCs. The first is the gene expression profile of a core set of 104 genes (LSC104) that are differentially expressed between LSC+ and LSC‒ fractions obtained from primary AML samples and validated in xenotransplantation assays (Ng et al, Nature 2016). This LSC gene expression signature is strongly associated with overall survival and chemotherapy response in independent cohorts of AML patients comprising all subtypes. The second is a continuous AML culture system derived from a patient with relapsed AML that maintains, and allows the prospective enrichment of, a rare population of quiescent, self-renewing LSCs restricted to the CD34+CD38‒ fraction. These LSCs, but not the bulk tumor cells in this system, demonstrate leukemia-initiating capacity both in vitro and in PDX models. Similarly, the LSC fraction of this system, but not the bulk cells, has a LSC104 expression profile that correlates strongly with that of LSC-enriched fractions from patient samples. Together, these components form a clinically-relevant scalable model system to identify compounds with activity against LSC stemness properties, based on alterations in the proportion of LSCs in the continuous AML culture system as well as in the core LSC104 gene expression profile. To this end, we assembled a collection of 1200 curated bioactive small molecules, including 150 metabolic inhibitors and 35 targeted epigenetic probes, and performed a high-dimensional throughput screen using flow-cytometry to examine the effect of these compounds on the LSC-enriched CD34+CD38‒ fraction in our AML culture system. Candidate hits were then tested for their effects on the LSC104 profile. Rigorous analyses unveiled a number of candidate compounds with the potential to antagonize LSC properties, including several already in clinical use or in clinical trials against AML, such as cytarabine, and inhibitors of FLT3, CDK, PLK1, and aurora kinase, as well as several classes of compounds not previously described against AML, targeting NAMPT, BRPF1B, CHK1, and KSP, among others. In conclusion, we describe a next-generation scalable throughput approach that integrates stem cell biology to uncover modulators of stemness in AML. This multi-parametric screening strategy serves as a platform for the deeper understanding of druggable vulnerabilities of LSCs, and serves as a starting point to achieve lasting event-free survival in AML patients. Disclosures Chan: AbbVie: Research Funding; Celgene: Research Funding; Genentech: Research Funding.
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 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) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,001 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,000 |
| Science ouverte | 0,000 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 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 ».