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A quantitative analysis of heterogeneities and hallmarks in acute myelogenous leukaemia

Nature Biomedical Engineering volume 3, pages 889–901 (2019)Cite this article

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Abstract

Acute myelogenous leukaemia (AML) is associated with risk factors that are largely unknown and with a heterogeneous response to treatment. Here, we provide a comprehensive quantitative understanding of AML proteomic heterogeneities and hallmarks by using the AML Proteome Atlas, a proteomics database that we have newly derived from MetaGalaxy analyses, for the proteomic profiling of 205 patients with AML and 111 leukaemia cell lines. The analysis of the dataset revealed 154 functional patterns based on common molecular pathways, 11 constellations of correlated functional patterns and 13 signatures that stratify the outcomes of patients. We find limited overlap between proteomics data and both cytogenetics and genetic mutations. Moreover, leukaemia cell lines show limited proteomic similarities with cells from patients with AML, suggesting that a deeper focus on patient-derived samples is needed to gain disease-relevant insights. The AML Proteome Atlas provides a knowledge base for proteomic patterns in AML, a guide to leukaemia cell line selection, and a broadly applicable computational approach for quantifying the heterogeneities of protein expression and proteomic hallmarks in AML.

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Fig. 1: MetaGalaxy analysis workflow.
Fig. 2: Functional patterns indicate varied functional states and alternative mechanisms.
Fig. 3: Example prognostic functional patterns.
Fig. 4: The co-clustering of functional patterns generates biologically insightful constellations and prognostic signatures.
Fig. 5: A tree of proteomic hallmarks in AML and its clinical relevance.
Fig. 6: Proteomic comparison between clinical AML samples and leukaemia cell lines.

Data availability

The datasets generated and analysed in the study are available on the AML Proteome Atlas: LeukemiaAtlas.org (direct dataset-download at https://www.leukemiaatlas.org/adultaml). Source data for the figures in this study are also provided in .xlsx and .csv formats in figshare at https://figshare.com/s/5ec75fb971747383c0be53 and are accessible directly from the AML Proteome Atlas (https://www.leukemiaatlas.org/adultaml).

Code availability

Access to the entire code used in the MetaGalaxy analysis is listed in Supplementary Table 7. In particular, the Progeny Clustering code is available on the R repository at https://cran.r-project.org/web/packages/progenyClust/index.html, and the MetaGalaxy pipeline is available in a R Shiny portal at https://meta-galaxy-analysis.shinyapps.io/mpa_r/, with a corresponding tutorial and demo files (main.csv and pathway.csv) available in the supplement and online at the AML Proteome Atlas (https://www.leukemiaatlas.org/code).

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Acknowledgements

This research was funded in part by a translational research grant from the Leukemia and Lymphoma Society to S.M.K., NSF CAREER 1150645, NSF NCS 1533708 and NIH R01 GM106027 grants to A.A.Q., and a HHMI Med-into-Grad fellowship to C.W.H.

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Author notes

  1. These authors contributed equally: A. A. Qutub, S. M. Kornblau.

Authors and Affiliations

  1. Department of Bioengineering, Rice University, Houston, TX, USA

    C. W. Hu & A. A. Qutub

  2. Department of Leukemia, The University of Texas MD Anderson Cancer Center, Houston, TX, USA

    Y. Qiu & S. M. Kornblau

  3. Biophysics Graduate Program, University of California, Berkeley, CA, USA

    A. Ligeralde

  4. Department of Biomedical Engineering, The University of Texas at San Antonio, San Antonio, TX, USA

    A. Y. Raybon & A. A. Qutub

  5. Department of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA

    S. Y. Yoo

  6. Department of Biomedical Informatics, The Ohio State University, Columbus, OH, USA

    K. R. Coombes

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Contributions

A.A.Q., S.M.K. and C.W.H. conceived and designed the study. Y.Q. performed the experiments. C.W.H., S.Y.Y., A.L., A.Y.R. and K.R.C. performed the computational and statistical analyses. C.W.H., S.M.K. and A.A.Q wrote and revised the manuscript.

Corresponding authors

Correspondence to A. A. Qutub or S. M. Kornblau.

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The authors declare no competing interests.

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Supplementary information

Supplementary Information

Supplementary figures, tables and software tutorial.

Reporting Summary

Supplementary Dataset 1

Antibody nomenclature table.

Supplementary Dataset 2

Correlation between post-translational modifications and total protein expression levels.

Supplementary Dataset 3

Functional group memberships.

Supplementary Dataset 4

Constellation memberships for cell lines.

Supplementary Dataset 5

Functional pattern memberships for cell lines.

Supplementary Dataset 6

Demographic information of patients overall and in each signature (S1–S13).

Supplementary Dataset 7

Method implementation and parameter specification.

Supplementary Dataset 8

Stability scores from Progeny Clustering for co-clustering.

Supplementary Dataset 9

Demo. 1

Supplementary Dataset 10

Demo. 2

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Hu, C.W., Qiu, Y., Ligeralde, A. et al. A quantitative analysis of heterogeneities and hallmarks in acute myelogenous leukaemia. Nat Biomed Eng 3, 889–901 (2019). https://doi.org/10.1038/s41551-019-0387-2

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