Deconvolution of Clonal Heterogeneity in Acute Myeloid Leukemia using Single-Cell DNA Sequencing

2019 ◽  
Vol 19 ◽  
pp. S234
Author(s):  
Kiyomi Morita ◽  
Feng Wang ◽  
Katharina Jahn ◽  
Jack Kuipers ◽  
Yuanqing Yan ◽  
...  
Keyword(s):  
Acute Myeloid Leukemia ◽  
Dna Sequencing ◽  
Single Cell ◽  
Myeloid Leukemia ◽  
Clonal Heterogeneity ◽  
Acute Myeloid

scholarly journals Clonal Evolution of Acute Myeloid Leukemia Revealed by High-Throughput Single-Cell Genomics

2020 ◽  
Author(s):  
Kiyomi Morita ◽  
Feng Wang ◽  
Katharina Jahn ◽  
Jack Kuipers ◽  
Yuanqing Yan ◽  
...  
Keyword(s):  
Acute Myeloid Leukemia ◽  
Dna Sequencing ◽  
Single Cell ◽  
High Throughput ◽  
Myeloid Leukemia ◽  
Clonal Evolution ◽  
Treatment Resistance ◽  
Clonal Diversity ◽  
History Of ◽  
Acute Myeloid

SummaryOne of the pervasive features of cancer is the diversity of mutations found in malignant cells within the same tumor; a phenomenon called clonal diversity or intratumor heterogeneity. Clonal diversity allows tumors to adapt to the selective pressure of treatment and likely contributes to the development of treatment resistance and cancer recurrence. Thus, the ability to precisely delineate the clonal substructure of a tumor, including the evolutionary history of its development and the co-occurrence of its mutations, is necessary to understand and overcome treatment resistance. However, DNA sequencing of bulk tumor samples cannot accurately resolve complex clonal architectures. Here, we performed high-throughput single-cell DNA sequencing to quantitatively assess the clonal architecture of acute myeloid leukemia (AML). We sequenced a total of 556,951 cells from 77 patients with AML for 19 genes known to be recurrently mutated in AML. The data revealed clonal relationship among AML driver mutations and identified mutations that often co-occurred (e.g., NPM1/FLT3-ITD, DNMT3A/NPM1, SRSF2/IDH2, and WT1/FLT3-ITD) and those that were mutually exclusive (e.g., NRAS/KRAS, FLT3-D835/ITD, and IDH1/IDH2) at single-cell resolution. Reconstruction of the tumor phylogeny uncovered history of tumor development that is characterized by linear and branching clonal evolution patterns with latter involving functional convergence of separately evolved clones. Analysis of longitudinal samples revealed remodeling of clonal architecture in response to therapeutic pressure that is driven by clonal selection. Furthermore, in this AML cohort, higher clonal diversity (≥4 subclones) was associated with significantly worse overall survival. These data portray clonal relationship, architecture, and evolution of AML driver genes with unprecedented resolution, and illuminate the role of clonal diversity in therapeutic resistance, relapse and clinical outcome in AML.

scholarly journals High-throughput single-cell DNA sequencing of acute myeloid leukemia tumors with droplet microfluidics

2018 ◽  
Vol 28 (9) ◽  
pp. 1345-1352 ◽  
Author(s):  
Maurizio Pellegrino ◽  
Adam Sciambi ◽  
Sebastian Treusch ◽  
Robert Durruthy-Durruthy ◽  
Kaustubh Gokhale ◽  
...  
Keyword(s):  
Acute Myeloid Leukemia ◽  
Dna Sequencing ◽  
Single Cell ◽  
High Throughput ◽  
Myeloid Leukemia ◽  
Droplet Microfluidics ◽  
Acute Myeloid

scholarly journals Single-cell map of diverse immune phenotypes in the acute myeloid leukemia microenvironment

2021 ◽  
Vol 9 (1) ◽  
Author(s):  
Rongqun Guo ◽  
Mengdie Lü ◽  
Fujiao Cao ◽  
Guanghua Wu ◽  
Fengcai Gao ◽  
...  
Keyword(s):  
Acute Myeloid Leukemia ◽  
Single Cell ◽  
Myeloid Leukemia ◽  
Immune Cell ◽  
Immune Surveillance ◽  
Cell Types ◽  
Developmental Trajectory ◽  
Significant Difference ◽  
Cell Phenotypes ◽  
Acute Myeloid

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.

Clonal heterogeneity in patients with cytogenetically normal acute myeloid leukemia withNPM1mutations

2012 ◽  
Vol 54 (5) ◽  
pp. 1056-1060 ◽  
Author(s):  
Dana Dvorakova ◽  
Zdenek Racil ◽  
Marek Borsky ◽  
Blanka Robesova ◽  
Ivana Jeziskova ◽  
...  
Keyword(s):  
Acute Myeloid Leukemia ◽  
Myeloid Leukemia ◽  
Clonal Heterogeneity ◽  
Acute Myeloid

scholarly journals Massively parallel quantification of phenotypic heterogeneity in single cell drug responses

2020 ◽  
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.

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

2019 ◽  
Author(s):  
Kiyomi Morita ◽  
Feng Wang ◽  
Katharina Jahn ◽  
Yuanqing Yan ◽  
Robert Durruthy-Durruthy ◽  
...  
Keyword(s):  
Acute Myeloid Leukemia ◽  
Single Cell ◽  
Myeloid Leukemia ◽  
Driver Mutations ◽  
Acute Myeloid

scholarly journals Clonal heterogeneity of acute myeloid leukemia treated with the IDH2 inhibitor enasidenib

2018 ◽  
Vol 24 (8) ◽  
pp. 1167-1177 ◽  
Author(s):  
Lynn Quek ◽  
Muriel D. David ◽  
Alison Kennedy ◽  
Marlen Metzner ◽  
Michael Amatangelo ◽  
...  
Keyword(s):  
Acute Myeloid Leukemia ◽  
Myeloid Leukemia ◽  
Clonal Heterogeneity ◽  
Acute Myeloid
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