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Enregistrement W3096141131 · doi:10.1182/blood-2020-143221

Single-Cell Transcriptomic Profiling of De Novo and Relapsed Acute Myeloid Leukemia Identifies a Leukemic Stemness Program Shared across Diverse Phenotypes

2020· article· en· W3096141131 sur OpenAlexaff
Samantha Worme, Selin Jessa, William Poon, Maja Jankovic, Gabriela Galicia-Vázquez, Alexandre Bazinet, Katharine Fooks, Isabella Iasenza, Patricia Arreba-Tutusaus, Kolja Eppert, Ioannis Ragoussis, Yu Chang Wang, Nathalie A. Johnson, Sarit Assouline, Claudia L. Kleinman, François Mercier

Notice bibliographique

RevueBlood · 2020
Typearticle
Langueen
DomaineMedicine
ThématiqueAcute Myeloid Leukemia Research
Établissements canadiensMcGill Genome CentreMcGill University Health CentreMcGill UniversityJewish General Hospital
Organismes subventionnairesnon disponible
Mots-clésBiologySingle-cell analysisTranscriptomeGene expression profilingMyeloid leukemiaCD34MyeloidLeukemiaStem cellCellCancer researchComputational biologyGeneMolecular biologyGene expressionGenetics

Résumé

récupéré en direct d'OpenAlex

Introduction: Relapse remains the major cause of mortality in acute myeloid leukemia (AML). Prior work indicates that a rare subset of leukemic stem cells (LSCs) self-renew and propagate AML. However, characterizing LSCs is complicated by their scarcity, the lack of universal markers and the heterogeneity across patients. Here, we aim to define a transcriptional program associated with LSCs in patient samples. Methods: We performed single-cell RNA sequencing (scRNA-seq) scRNA-seq of bone marrow from 19 AML samples (14 patients) using the 10X Chromium 3' v2 platform. These samples span multiple morphologies, genetic alterations, and disease stages. Leukemic and normal cells were distinguished based on agreement of three methods: (i) canonical marker expression, (ii) clustering analysis in a multi-sample dataset, and (iii) inferred chromosomal alterations. Leukemic cells were mapped to a panel of signatures from the Human Cell Atlas to infer the most similar normal cell-type, using single-cell gene-set enrichment analysis. Transcription factor activity was inferred at the single-cell level using the SCENIC workflow. Cell state trajectories were constructed using Monocle v2. Common driver mutations were detected at the bulk level using targeted gDNA sequencing and in single cells with targeted amplification of cDNA libraries. A validation cohort of samples was processed with the CITE-seq protocol to capture single-cell gene expression and surface protein levels for CD34, CD38, CD123, CLL1, and TIM3. Results: We captured a total of 55,355 cells meeting quality thresholds, with a median of ~2,800 cells/sample. We observed a large inter-patient heterogeneity with cells segregating largely by sample (Fig. 1A), which was not explained by morphological subtype, treatment received, or driver mutations. As previously described, similarity in gene expression of longitudinal samples did not depend on time before relapse. However, we found transcriptional similarity in a group of samples with relatively silent CNV profiles, suggesting that large chromosomal alterations are a main driver of inter-patient variability. We also observed variation in terms of nearest normal cell assignment: while some samples contained cells resembling diverse mature cell types, others had an abundance of stem-like cells, confirmed by high activity of transcription factors involved in self-renewal (e.g. HOXA9, GATA2). To analyze intrasample variation, we performed Principal Component Analysis and found that, in over half of the samples, LSC and maturation genes were the main source of transcriptional variation. A gradient of activation of known LSC signatures was detected in these samples (Fig 1B). Cell state trajectory reconstruction indicated a continuum of LSC gene expression in leukemic cells. Interestingly, expression of known LSC genes was mostly diffuse is a small subset of samples, a finding that suggests that LSC activity may be widespread in these cases but remains to be validated functionally. Finally, we derived a stemness signature correlated with LSC in our cohort, by extracting concordant genes in a ranked correlation analysis and reconstruction of gene regulatory networks. This yielded a recurrent stemness signature that included previously described LSC-associated genes that were not part of our input, as well as novel factors with expression highly specific to the most LSC-like cells (Fig 1C). To validate this novel stemness signature, we experimentally determined LSC frequencies in a separate cohort (N=5) by xenotransplantation according to expression of CD34 and CD38, and confirmed higher expression of our signature in the LSC fraction. Conclusions: Within a genetically and phenotypically diverse cohort of patients, we could identify, at single-cell resolution, recurrent programs of stemness and myeloid maturation. Altogether, we provide novel candidates for a transcriptional program of putative LSC drivers with therapeutic relevance in AML. Figure Disclosures Johnson: AbbVie: Research Funding; Roche/Genentech, Merck, Bristol-Myers Squibb, AbbVie: Consultancy; Roche/Genentech, Merck: Honoraria. Assouline:Takeda: Research Funding; AbbVie: Consultancy, Honoraria, Speakers Bureau; AstraZeneca: Consultancy, Honoraria, Speakers Bureau; Pfizer: Consultancy, Honoraria; BeiGene: Consultancy, Honoraria, Research Funding; Janssen: Consultancy, Honoraria, Speakers Bureau; F. Hoffmann-La Roche Ltd: Consultancy, Honoraria, Research Funding. Mercier:Sanofi-Genzyme: Consultancy.

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 enseignants

Ni 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.

score de la tête « metaresearch » (Codex)0,000
score de la tête « metaresearch » (Gemma)0,000
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: aucune
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,001
Score d'incertitude au seuil0,003

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0000,000
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0000,001
Études des sciences et des technologies0,0000,000
Communication savante0,0000,000
Science ouverte0,0000,000
Intégrité de la recherche0,0000,000
Charge utile insuffisante (le modèle a refusé de juger)0,0010,000

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.

Tête enseignante Opus0,032
Tête enseignante GPT0,289
Écart entre enseignants0,257 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_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 découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeObservationnel
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations0
Publié2020
Routes d'admission1
Résumé présentoui

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