Data-driven phenotypic dissection of AML reveals progenitor-like cells that correlate with prognosis

Levine, Jacob H.; Simonds, Erin F.; Bendall, Sean C.; Davis, Kara L.; Amir, El-ad D.; Tadmor, Michelle; Litvin, Oren; Fienberg, Harris; Jager, Astraea; Zunder, Eli; Finck, Rachel; Gedman, Amanda L.; Radtke, Ina; Downing, James R.; Pe’er, Dana; Nolan, Garry P.

doi:10.1016/j.cell.2015.05.047

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Data-driven phenotypic dissection of AML reveals progenitor-like cells that correlate with prognosis

Published Date:

Jun 18 2015

Publisher's site:

http://dx.doi.org/10.1016/j.cell.2015.05.047 New Site Icon

Source:

Cell. 2015; 162(1):184-197.

[PDF-4.17 MB]

Details:

Personal Authors:

Levine, Jacob H. ; Simonds, Erin F. ; Bendall, Sean C. ; Davis, Kara L. ; Amir, El-ad D. ; ... More ▼

Keywords:

[+]

Pubmed ID:

26095251

Pubmed Central ID:

PMC4508757

Description:

Acute myeloid leukemia (AML) manifests as phenotypically and functionally diverse cells, often within the same patient. Intratumor phenotypic and functional heterogeneity have been linked primarily by physical sorting experiments, which assume that functionally distinct subpopulations can be prospectively isolated by surface phenotypes. This assumption has proven problematic, and we therefore developed a data-driven approach. Using mass cytometry, we profiled surface and intracellular signaling proteins simultaneously in millions of healthy and leukemic cells. We developed PhenoGraph, which algorithmically defines phenotypes in high-dimensional single-cell data. PhenoGraph revealed that the surface phenotypes of leukemic blasts do not necessarily reflect their intracellular state. Using hematopoietic progenitors, we defined a signaling-based measure of cellular phenotype, which led to isolation of a gene expression signature that was predictive of survival in independent cohorts. This study presents new methods for large-scale analysis of single-cell heterogeneity and demonstrates their utility, yielding insights into AML pathophysiology.

Document Type:

Journal Article

Collection(s):

CDC Public Access

Funding:

Main Document Checksum:

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Supporting Files:

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