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Enregistrement W4389248130 · doi:10.1182/blood-2023-190180

Single Cell Transcriptomic Analysis of B-Cell Acute Lymphoblastic Leukemia (B-ALL) Reveals Correlation between Leukemia Cell State, Genetic Alterations and Clinical Outcome

2023· article· en· W4389248130 sur OpenAlexaff
Ilaria Iacobucci, Andy G.X. Zeng, Qingsong Gao, Laura Garcia Prat, Pradyumna Baviskar, Sayyam Shah, Alexander Murison, Véronique Voisin, Colin Bailey, Matthew Lear, Anjali S. Advani, Selina M. Luger, Elisabeth Paietta, Wendy Stock, John E. Dick, Charles G. Mullighan

Notice bibliographique

RevueBlood · 2023
Typearticle
Langueen
DomaineBiochemistry, Genetics and Molecular Biology
ThématiqueCancer Genomics and Diagnostics
Établissements canadiensUniversity of TorontoPrincess Margaret Cancer CentreUniversity Health Network
Organismes subventionnairesnon disponible
Mots-clésBiologyTranscriptomeGeneLeukemiaGeneticsGene expressionCell cycleB cellGene expression profilingMolecular biologyCancer research

Résumé

récupéré en direct d'OpenAlex

I.I., A.G.X.Z, Q.G., L.G.P.: co-first authors; J.E.D., C.G.M.: co-senior authors Introduction: Genomic analyses of bulk ALL samples have improved our understanding of the genetic basis and risk stratification of B-ALL, but do not directly examine intratumor heterogeneity or enable inference of leukemia developmental state and cell of origin. Methods: We profiled 89 B-ALL samples by single-cell RNA-seq (scRNA-seq) (10X Genomics 5'v2) and compared them to a scRNA-seq reference map of normal human B-cell development. Results: Analysis of heterogeneity of inferred DNA copy number alterations at single cell level showed that aneuploid ALL with near haploidy and hyperdiploid harbor chromosomal losses or gains in all blasts, consistent with early, synchronous origin rather than sequential accumulation. A subset of TCF3::PBX1 samples harbored clones with progressive overexpression of genes surrounding PBX1 which scWGS confirmed caused by accumulation of DNA gains. Extensive intra-sample heterogeneity was observed by non-negative matrix factorization and driven by five consensus gene expression signatures: cell cycle (S; G2/M), metabolism, differentiation and inflammation. Signature composition refined leukemic subtyping; for example, within DUX4-r B-ALL two groups were discerned: one with higher inflammation score, expression of stem cell genes and worse outcome and one with lower inflammation score and expression of Pro-B related genes. To understand variation in developmental states among B-ALL blasts, we first developed an atlas of human B cell development. Marker genes elucidated from bulk RNA-seq on purified human HSC, MPP, LMPP, MLP, CLP, Pre-Pro-B, Pro-B, Pre-B, and B populations guided the development of a new scRNA-seq atlas of human B cell development comprising 130,085 cells spanning 90 fetal, pediatric, and adult samples from 8 studies (Fig 1A). Unexpectedly, we found that human CLPs retain transcriptional programs governed by CEBPA; in vitro and in vivo functional assays validated the capacity of CLPs for extensive myeloid differentiation prior to lineage restriction at the Pro-B stage. We next mapped our 89 B-ALL sc samples to precise cellular states along B cell development and clustered samples by leukemia cell composition. As expected, most samples exhibited similarity of GEP to that of pro-B cells, and this was most pronounced among hyperdiploid samples. High pre-B abundance was observed in MEF2D-r and TCF3::PBX1, while an HSC/MPP/LMPP enriched cluster encompassed ZNF384-r and a subset of DUX4-r B-ALL. Notably, one ZNF384-r B-ALL patient with LMPP involvement at diagnosis retained these LMPP-like cells at relapse despite a lineage switch to AML. Finally, we identified a group of patients with high early lymphoid (MLP, CLP, Pre-Pro-B) abundance representing a subset of Ph+ and KMT2A-r samples (Fig. 1B). We next utilized marker genes from each B-ALL cell state to estimate their relative abundance within a bulk RNA-seq cohort of 2046 B-ALL patients. In addition to validating these associations between genomic class and leukemia cell state, we identified age-dependent patterns of B-ALL state involvement wherein early lymphoid involvement is highest in infancy and adulthood while Pro-B involvement is highest in childhood. In childhood, high-risk disease is associated with higher early lymphoid ( P=4.9e-12) and lower Pro-B ( P=0.0085) abundance. Further, patients with residual disease (RD) levels above 1% are enriched for higher upfront early lymphoid abundance ( P=1e-7) and lower Pro-B ( P=4.1e-5) and Pre-B ( P=2.2e-11) abundance compared to patients with RD < 0.01%. Finally, B-ALL cell state improved resolution of previously reported transcriptional heterogeneity within defined genomic classes. Early lymphoid abundance discerns between two subtypes within Ph+ (Kim Nat Gen 2023) with AUC=0.953 ( P=2.4e-9), as well as two subtypes within KMT2A-r (Brady Nat Gen 2022) with AUC=0.996 ( P=1.1e-6). Notably we find that KMT2A::AFF1 fusions produce early lymphoid-enriched disease compared to KMT2A::MLLT3 ( P=0.0024) and KMT2A::MLLT10 ( P=0.0037), wherein the latter two fusions exhibit Pre-B enrichment ( P=0.025 and P=0.0037, respectively). Conclusions: Understanding variation in transcriptional programs and developmental states of B-ALL blasts by sc transcriptome refines existing clinical and genomic classifications and provides novel prognostic markers.

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 Opus0,019
Tête enseignante GPT0,274
Écart entre enseignants0,255 · 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

Citations2
Publié2023
Routes d'admission1
Résumé présentoui

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