Comprehensive Single-Cell RNA-Sequencing Mapping of Primary Acute Myeloid Leukemias and Profiling of NPM1-Mutated Cells
Notice bibliographique
Résumé
Abstract Introduction: Acute myeloid leukemia (AML) evolution is a multistep process in which cells evolve from hematopoietic stem and progenitor cells (HSPCs) that acquire genetic anomalies, such as chromosomal rearrangements and mutations, which define distinct subgroups. Mutations in Nucleophosmin 1 (NPM1), which occur in ~30% patients, are the most frequent subgroup-defining mutations in AML and appear to be a late driver event in this disease. Bulk RNA-sequencing studies have identified differentially expressed genes between AML subgroups, but they are uninformative of the composition of cell types populating each sample. Large scale Single-cell RNA sequencing (scRNA-seq) technologies now enable a detailed characterization of intra tumoral heterogeneity, and could help to better understand the stepwise evolution from normal to malignant cells. Methods: Twelve primary human AML specimens from MSKCC and Quebec Leukemia Cell Bank, including 8 with NPM1 mutations, were included in this cohort. Cells were subjected to scRNA-seq using 10X Genomics Chromium Single Cell 3' protocols and libraries were sequenced on Illumina HiSeq or NovaSeq platforms. FASTQ files were processed using SEQC pipeline (Azizi E et al, Cell 2018), resulting in a carefully filtered count matrix of > 100,000 single cells (4877 to 11532 cells per sample). Results: Using euclidean distance metrics and t-Distributed Stochastic Neighbor Embedding (t-SNE) visualization, we explored the phenotypic overlap between samples and showed that leukemia cells from different patients were mostly dissimilar, suggesting inter-sample heterogeneity. However, samples with similar morphology and similar NPM1 mutational status were phenotypically closer (Fig A), as anticipated from bulk RNA-sequencing data (TCGA, NEJM 2013). We partitioned cells into distinct clusters using Phenograph (Levine J et al, Cell 2015) (Fig B) and measured the diversity of samples per cluster using Shannon's entropy metric, revealing that mature cell types (B/plasma cells, T/NK and erythroid cells, Fig C), presumably excluded from the tumor bulk, are transcriptionally similar across samples. Most notably, the next most diverse cluster (C36), comprising 438 cells from 11/12 samples, contains cells with a HSPC-like phenotype, as suggested by i) highest correlation of the centroid of this cluster with HSC1 (lin-/CD133+/CD34dim) population from sorted bulk RNA-sequencing data (Novershtern N et al, Cell 2011), and ii) marked GSEA enrichment for stem cell signatures (top enrichment: Jaatinen_hematopoeitic_stem_cell_up, NES = 9.04, FDR q-val = 0). To study the extent to which NPM1 or other mutations drive heterogeneity in leukemia populations, we interrogated 3'-derived single-cell sequences for all recurrent mutations in AML and found that NPM1 gene has unique features (e.g. relatively high single-cell expression and 3' localization) that allow specific identification of mutations in 5 to 34% of cells per mutated sample. To control for the high frequency of false negatives caused by dropouts in scRNA-seq data, we normalized the abundance of mutated vs wild-type cells to provide an estimation of mutation frequency in different cell types (Fig D). As expected, NPM1 mutations were rare in B and T/NK lymphoid cells (also observed using RT-qPCR in sorted populations by Dvorakova D et al, Leuk Lymphoma 2013) and were found in the majority of leukemia and myeloid cells. Interestingly, these mutations were detected at various frequencies in erythroid cells, suggesting that NPM1 mutations are acquired in cells with different lineage commitment in different patients. Most notably, the HSPC-like cluster C36 also contained a subpopulation of cells that have acquired NPM1 mutations and are transcriptionally different from wild-type cells. Conclusion: This study presents a first comprehensive single-cell map of primary AML, and the first 3'-based interrogation of mutations in single cells. It led to the identification phenotypically distinct cells presenting a HSPC-like expression profile which were sub-clonally harboring NPM1 mutations, providing the means to identify deregulated genes in these important leukemia subpopulations. Figure Figure. Disclosures Levine: Epizyme: Patents & Royalties; Celgene: Consultancy, Research Funding; Janssen: Consultancy, Honoraria; Isoplexis: Equity Ownership; C4 Therapeutics: Equity Ownership; Prelude: Research Funding; Gilead: Honoraria; Imago: Equity Ownership; Novartis: Consultancy; Roche: Consultancy, Research Funding; Loxo: Consultancy, Equity Ownership; Qiagen: Equity Ownership, Membership on an entity's Board of Directors or advisory committees.
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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 | 0,001 | 0,001 |
| 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) | 0,002 | 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 ».