Tumor heterogeneity of acute myeloid leukemia: insights from single-cell sequencing

AbstractAcute myeloid leukemia is an aggressive cancer of the blood forming system. The malignant cell population is composed of multiple clones that evolve over time. Clonal data reflect the mechanisms governing treatment response and relapse. Single cell sequencing provides most direct insights into the clonal composition of the leukemic cells, however it is still not routinely available in clinical practice. In this work we develop a computational algorithm that allows identifying all clonal hierarchies that are compatible with bulk variant allele frequencies measured in a patient sample. The clonal hierarchies represent descendance relations between the different clones and reveal the order in which mutations have been acquired. The proposed computational approach is tested using single cell sequencing data that allow comparing the outcome of the algorithm with the true structure of the clonal hierarchy. We investigate which problems occur during reconstruction of clonal hierarchies from bulk sequencing data. Our results suggest that in many cases only a small number of possible hierarchies fits the bulk data. This implies that bulk sequencing data can be used to obtain insights in clonal evolution.

Download Full-text

Computational Reconstruction of Clonal Hierarchies From Bulk Sequencing Data of Acute Myeloid Leukemia Samples

Frontiers in Physiology ◽

10.3389/fphys.2021.596194 ◽

2021 ◽

Vol 12 ◽

Author(s):

Thomas Stiehl ◽

Anna Marciniak-Czochra

Keyword(s):

Acute Myeloid Leukemia ◽

Single Cell ◽

Myeloid Leukemia ◽

Clonal Evolution ◽

Malignant Cell ◽

Leukemic Cells ◽

Sequencing Data ◽

Single Cell Sequencing ◽

Computational Reconstruction ◽

Acute Myeloid

Acute myeloid leukemia is an aggressive cancer of the blood forming system. The malignant cell population is composed of multiple clones that evolve over time. Clonal data reflect the mechanisms governing treatment response and relapse. Single cell sequencing provides most direct insights into the clonal composition of the leukemic cells, however it is still not routinely available in clinical practice. In this work we develop a computational algorithm that allows identifying all clonal hierarchies that are compatible with bulk variant allele frequencies measured in a patient sample. The clonal hierarchies represent descendance relations between the different clones and reveal the order in which mutations have been acquired. The proposed computational approach is tested using single cell sequencing data that allow comparing the outcome of the algorithm with the true structure of the clonal hierarchy. We investigate which problems occur during reconstruction of clonal hierarchies from bulk sequencing data. Our results suggest that in many cases only a small number of possible hierarchies fits the bulk data. This implies that bulk sequencing data can be used to obtain insights in clonal evolution.

Download Full-text

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

Biomarker Research ◽

10.1186/s40364-021-00265-0 ◽

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.

Download Full-text

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.

Download Full-text