Single cell guided deconvolution of bulk transcriptomics recapitulates differentiation stages of acute myeloid leukemia and predicts drug response
bioRxiv (Cold Spring Harbor Laboratory)
Abstract
Abstract The diagnostic spectrum for AML patients is increasingly based on genetic abnormalities due to their prognostic and predictive value. However, information on the AML blast phenotype regarding their maturational arrest has started to regain importance due to its predictive power on drug responses. Here, we deconvolute 1350 bulk RNA-seq samples from five independent AML cohorts on a single-cell healthy BM reference and demonstrate that the morphological differentiation stage (FAB classification) could be faithfully reconstituted using estimated cell compositions (ECCs). Moreover, we show that the ECCs reliably predict ex-vivo drug resistances as demonstrated for Venetoclax, a BCL-2 inhibitor, resistance specifically in AML with CD14+ monocyte phenotype. We further validate these predictions using in-house proteomics data by showing that BCL-2 protein abundance is split into two distinct clusters for NPM1-mutated AML at the extremes of CD14+ monocyte percentages, which could be crucial for the Venetoclax dosing for these patients. Our results suggest that Venetoclax resistance predictions can also be extended to AML without recurrent genetic abnormalities (NOS), and possibly to MDS-related AML and secondary AML. Collectively, we propose a framework for allowing a joint mutation and maturation stage modeling that could be used as a blueprint for testing sensitivity for new agents across the various subtypes of AML.
Authors 13
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Emin Onur Karakaslar Aachen Department of Biomedical Data Sciences Leiden Center for Computational Oncology
Leiden University Medical Center · Delft University of Technology
Affiliation as printed
Department of Biomedical Data Sciences, Leiden University Medical Center, Leiden, The Netherlands
Leiden Center for Computational Oncology, Leiden University Medical Center, Leiden, The Netherlands
Pattern Recognition & Bioinformatics, Delft University of Technology, Delft, The Netherlands
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Jeppe F. Severens Aachen Department of Biomedical Data Sciences Leiden Center for Computational Oncology
Leiden University Medical Center · Delft University of Technology
Affiliation as printed
Department of Biomedical Data Sciences, Leiden University Medical Center, Leiden, The Netherlands
Leiden Center for Computational Oncology, Leiden University Medical Center, Leiden, The Netherlands
Pattern Recognition & Bioinformatics, Delft University of Technology, Delft, The Netherlands
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Elena Sánchez‐López Aachen Center for Proteomics and Metabolomics Leiden Center for Computational Oncology
Leiden University Medical Center
Affiliation as printed
Center for Proteomics and Metabolomics, Leiden University Medical Center, Leiden, The Netherlands
Leiden Center for Computational Oncology, Leiden University Medical Center, Leiden, The Netherlands
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Leiden University Medical Center
Affiliation as printed
Center for Proteomics and Metabolomics, Leiden University Medical Center, Leiden, The Netherlands
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Leiden University Medical Center · Institutul Regional de Oncologie
Affiliation as printed
Department of Flow Cytometry, Medical Laboratory, Regional Institute of Oncology, Iasi, Romania
Department of Immunology, Leiden University Medical Center, Leiden, The Netherlands
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Leiden University Medical Center
Affiliation as printed
Department of Immunology, Leiden University Medical Center, Leiden, The Netherlands
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Leiden University Medical Center
Affiliation as printed
Center for Proteomics and Metabolomics, Leiden University Medical Center, Leiden, The Netherlands
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Leiden University Medical Center
Affiliation as printed
Department of Hematology, Leiden University Medical Center, Leiden, The Netherlands
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Leiden University Medical Center
Affiliation as printed
Department of Hematology, Leiden University Medical Center, Leiden, The Netherlands
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Hendrik Veelken Aachen Department of Hematology, Leiden University Medical Center Leiden Center for Computational Oncology
Leiden University Medical Center
Affiliation as printed
Department of Hematology, Leiden University Medical Center, Leiden, The Netherlands
Leiden Center for Computational Oncology, Leiden University Medical Center, Leiden, The Netherlands
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Marcel J. T. Reinders Aachen Department of Biomedical Data Sciences Leiden Center for Computational Oncology
Leiden University Medical Center · Delft University of Technology
Affiliation as printed
Department of Biomedical Data Sciences, Leiden University Medical Center, Leiden, The Netherlands
Leiden Center for Computational Oncology, Leiden University Medical Center, Leiden, The Netherlands
Pattern Recognition & Bioinformatics, Delft University of Technology, Delft, The Netherlands
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Leiden University Medical Center
Affiliation as printed
Department of Hematology, Leiden University Medical Center, Leiden, The Netherlands
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Erik B. van den Akker corresponding Aachen Department of Biomedical Data Sciences Leiden Center for Computational Oncology
Leiden University Medical Center · Delft University of Technology
Affiliation as printed
Department of Biomedical Data Sciences, Leiden University Medical Center, Leiden, The Netherlands
Leiden Center for Computational Oncology, Leiden University Medical Center, Leiden, The Netherlands
Pattern Recognition & Bioinformatics, Delft University of Technology, Delft, The Netherlands
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