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Record 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 on 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

Bibliographic record

VenueBlood · 2020
Typearticle
Languageen
FieldMedicine
TopicAcute Myeloid Leukemia Research
Canadian institutionsMcGill Genome CentreMcGill University Health CentreMcGill UniversityJewish General Hospital
Fundersnot available
KeywordsBiologySingle-cell analysisTranscriptomeGene expression profilingMyeloid leukemiaCD34MyeloidLeukemiaStem cellCellCancer researchComputational biologyGeneMolecular biologyGene expressionGenetics

Abstract

fetched live from 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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.032
GPT teacher head0.289
Teacher spread0.257 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

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Citations0
Published2020
Admission routes1
Has abstractyes

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