Abstract 2725: The single-cell atlas of driver mutations in acute myeloid leukemia

Abstract Background Knowledge of immune cell phenotypes, function, and developmental trajectory in acute myeloid leukemia (AML) microenvironment is essential for understanding mechanisms of evading immune surveillance and immunotherapy response of targeting special microenvironment components. Methods Using a single-cell RNA sequencing (scRNA-seq) dataset, we analyzed the immune cell phenotypes, function, and developmental trajectory of bone marrow (BM) samples from 16 AML patients and 4 healthy donors, but not AML blasts. Results We observed a significant difference between normal and AML BM immune cells. Here, we defined the diversity of dendritic cells (DC) and macrophages in different AML patients. We also identified several unique immune cell types including T helper cell 17 (TH17)-like intermediate population, cytotoxic CD4+ T subset, T cell: erythrocyte complexes, activated regulatory T cells (Treg), and CD8+ memory-like subset. Emerging AML cells remodels the BM immune microenvironment powerfully, leads to immunosuppression by accumulating exhausted/dysfunctional immune effectors, expending immune-activated types, and promoting the formation of suppressive subsets. Conclusion Our results provide a comprehensive AML BM immune cell census, which can help to select pinpoint targeted drug and predict efficacy of immunotherapy.

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Mutational profiling of acute myeloid leukemia with normal cytogenetics in Brazilian patients: the value of next-generation sequencing for genomic classification

Journal of Investigative Medicine ◽

10.1136/jim-2017-000566 ◽

2017 ◽

Vol 65 (8) ◽

pp. 1155-1158 ◽

Cited By ~ 6

Author(s):

Thiago Rodrigo de Noronha ◽

Miguel Mitne-Neto ◽

Maria de Lourdes Chauffaille

Keyword(s):

Acute Myeloid Leukemia ◽

Next Generation Sequencing ◽

Myeloid Leukemia ◽

Regulation Of Gene Expression ◽

Uniparental Disomy ◽

Driver Mutations ◽

Next Generation ◽

Normal Cytogenetics ◽

Generation Sequencing ◽

Acute Myeloid

Karyotype (KT) aberrations are important prognostic factors for acute myeloid leukemia (AML); however, around 50% of cases present normal results. Single nucleotide polymorphism array can detect chromosomal gains, losses or uniparental disomy that are invisible to KT, thus improving patients’ risk assessment. However, when both tests are normal, important driver mutations can be detected by the use of next-generation sequencing (NGS). Fourteen adult patients with AML with normal cytogenetics were investigated by NGS for 19 AML-related genes. Every patient presented at least one mutation:DNMT3Ain nine patients;IDH2in six;IDH1in three;NRASandNPM1in two; andTET2,ASXL1,PTPN11, andRUNX1in one patient. No mutations were found inFLT3,KIT,JAK2,CEBPA,GATA2,TP53,BRAF,CBL,KRAS,andWT1genes. Twelve patients (86%) had at least one mutation in genes related with DNA methylation (DNMT3A,IDH1,IDH2,andTET2), which is involved in regulation of gene expression and genomic stability. All patients could be reclassified based on genomic status and nine had their prognosis modified. In summary, NGS offers insights into the molecular pathogenesis and biology of cytogenetically normal AML in Brazilian patients, indicating that the prognosis could be further stratified by different mutation combinations. This study shows a different frequency of mutations in Brazilian population that should be confirmed.

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Massively parallel quantification of phenotypic heterogeneity in single cell drug responses

10.1101/2020.12.18.423559 ◽

2020 ◽

Cited By ~ 1

Author(s):

Benjamin B. Yellen ◽

Jon S. Zawistowski ◽

Eric A. Czech ◽

Caleb I. Sanford ◽

Elliott D. SoRelle ◽

...

Keyword(s):

Machine Learning ◽

Acute Myeloid Leukemia ◽

Single Cell ◽

Myeloid Leukemia ◽

Single Cell Analysis ◽

Phenotypic Heterogeneity ◽

Massively Parallel ◽

Cell Analysis ◽

On Chip ◽

Acute Myeloid

AbstractSingle cell analysis tools have made significant advances in characterizing genomic heterogeneity, however tools for measuring phenotypic heterogeneity have lagged due to the increased difficulty of handling live biology. Here, we report a single cell phenotyping tool capable of measuring image-based clonal properties at scales approaching 100,000 clones per experiment. These advances are achieved by exploiting a novel flow regime in ladder microfluidic networks that, under appropriate conditions, yield a mathematically perfect cell trap. Machine learning and computer vision tools are used to control the imaging hardware and analyze the cellular phenotypic parameters within these images. Using this platform, we quantified the responses of tens of thousands of single cell-derived acute myeloid leukemia (AML) clones to targeted therapy, identifying rare resistance and morphological phenotypes at frequencies down to 0.05%. This approach can be extended to higher-level cellular architectures such as cell pairs and organoids and on-chip live-cell fluorescence assays.

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