Unsupervised Flow Cytometry Analysis Allows for an Accurate Identification of Minimal Residual Disease Assessment in Acute Myeloid Leukemia.

Jean Philippe Vial,François Vergez,Pierre-Yves Dumas,Audrey Bidet,Thibaut Leguay,Francis Lacombe,Marie C Béné,Arnaud Pigneux,Nicolas Lechevalier

doi:10.3390/cancers13040629

Jean Philippe Vial, François Vergez + Show 7 more

Open Access

https://doi.org/10.3390/cancers13040629

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Abstract

Simple SummaryIn acute myeloid leukemia (AML), minimal/measurable residual disease (MRD) can be assessed based on molecular markers or immunophenotypic features evaluated at diagnosis, through multiparameter flow cytometry (MFC) for the latter. New artificial intelligence tools allow to perform unsupervised analysis of MFC data. The Flow-Self-Organizing-Maps (FlowSOM) tool was used here to concomitantly compare MFC features of normal bone marrow together with diagnosis and follow-up bone marrow samples from 40 AML patients for the evaluation of MRD. MFC results were compared to molecular MRD, showing high concordance. This opens the road for a new easy and objective way of assessing MRD even in AML patients without molecular markers.The assessment of minimal residual disease (MRD) is increasingly considered to monitor response to therapy in hematological malignancies. In acute myeloblastic leukemia (AML), molecular MRD (mMRD) is possible for about half the patients while multiparameter flow cytometry (MFC) is more broadly available. However, MFC analysis strategies are highly operator-dependent. Recently, new tools have been designed for unsupervised MFC analysis, segregating cell-clusters with the same immunophenotypic characteristics. Here, the Flow-Self-Organizing-Maps (FlowSOM) tool was applied to assess MFC-MRD in 96 bone marrow (BM) follow-up (FU) time-points from 40 AML patients with available mMRD. A reference FlowSOM display was built from 19 healthy/normal BM samples (NBM), then simultaneously compared to the patient’s diagnosis and FU samples at each time-point. MRD clusters were characterized individually in terms of cell numbers and immunophenotype. This strategy disclosed subclones with varying immunophenotype within single diagnosis and FU samples including populations absent from NBM. Detectable MRD was as low as 0.09% in MFC and 0.051% for mMRD. The concordance between mMRD and MFC-MRD was 80.2%. MFC yielded 85% specificity and 69% sensitivity compared to mMRD. Unsupervised MFC is shown here to allow for an easy and robust assessment of MRD, applicable also to AML patients without molecular markers.

Highlights

The prognosis of acute myeloblastic leukemia (AML) has considerably improved in the past few years through progress in therapeutic schedules, emergence of new therapies and a better definition of risk-groups
From the laboratory database of Bordeaux University Hospital (Bordeaux, France), diagnosis data were selected from untreated AML patients who had been tested between April 2015 and March 2020 and carried a molecular marker allowing for molecular FU
Data were available for all samples after multiparameter flow cytometry (MFC) analysis with both Tube 1 and Tube 2 panels

Summary

Introduction

The prognosis of acute myeloblastic leukemia (AML) has considerably improved in the past few years through progress in therapeutic schedules, emergence of new therapies and a better definition of risk-groups. AMLs are very heterogeneous hematological malignancies, but three prognostic risk groups have been defined by the European. It is largely admitted that this threshold and this technique do not allow to detect small numbers of persisting leukemic cells, called ”Measurable Residual Disease”. MRD detection has become one of the major challenges to adapt therapy and prognosis. Several methods have been developed, allowing to reach very low detection thresholds: reverse transcriptase-quantitative PCR (RT-qPCR), multiparameter flow cytometry (MFC), next-generation sequencing (NGS) and, more recently, digital droplet PCR (ddPCR) [5]

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