Prognostic Impact of Unsupervised Early Assessment of Bulk and Leukemic Stem Cell Measurable Residual Disease in Acute Myeloid Leukemia

Acute myeloid leukemias (AML) are clonal diseases that develop from self-renewing leukemic stem cells (LSC). LSCs, enriched within the CD34+CD38− subpopulation (1), represent a tiny minority of all leukemic cells at diagnosis (2). LSCs are more resistant than the overall leukemic population to intensive chemotherapy (i.e., anthracyclines and cytarabine; refs. 3–5). Furthermore, LSCs levels have been shown to carry an independent and strong prognostic impact on both the initial response to induction chemotherapy and overall survival (OS), demonstrating the clinical relevance of stem cell abundance in AML (6, 7). This has been reinforced by studies showing that relapsing patients have an enriched population of LSCs (8) with an increase in LSC-related gene signatures (9).

The detection of measurable residual disease (MRD) is becoming increasingly important in therapeutic adaptation. Several methods have been developed for its evaluation, including multiparametric flow cytometry (MFC). It has been widely demonstrated that MFC is a mandatory diagnostic tool and is very efficient for the follow-up of any type of AML. The "different from normal (DfN)" and "leukemia-associated immunophenotype (LAIP)" analysis strategies are complementary (10). Unfortunately, exhaustive identification of LAIPs may require a large number of marker combinations, which could significantly increase the cost of the method and complicate both analysis and comparison with reference bone marrow (RBM) counterparts. In 2018, the European LeukemiaNet (ELN) has published recommendations, including a combined approach incorporating "DfN" and "LAIP" (10). Simultaneously, the ELN expert panel mentioned that the use of new software, based on unsupervised analysis, should be considered to reduce the subjectivity of interpretation although only few studies addressed this issue (11). Among these methods, the unsupervised FlowSOM methodology seems to be the most suitable for routine work and especially for the analysis of MRD in AML. It combines short analysis time, high sensitivity (12) and complies with ELN recommendations by combining "DfN" and "LAIP" approaches.

In its latest paper (13), ELN recommends an initial MFC MRD after two induction cycles. Among the many articles on which these recommendations are based, few have investigated the early response after a single cycle and even fewer have done so with an LSC analysis. We therefore decided to conduct this study in such a way as to provide a new perspective.

In this work, we evaluated the performance of unsupervised clustering for the post-induction assessment of bulk and LSC MRD in 155 patients with AML who received intensive conventional chemotherapy treatment.

Between January 1, 2017, and December 31, 2020, 1,044 patients with AML (>18 years, excluding promyelocytic AML) were included in the DATAML registry (authorization #915285). Immunophenotyping at diagnosis was available for 701 patients from Toulouse University Hospital (median age, 62 years) of whom 336 received a standard intensive chemotherapy regimen combining 3 days of daunorubicin or 5 days of idarubicin and 7 days of cytarabine. Treatments have been described in detail elsewhere (14) and in some situations, a third drug may be added such as midostaurin, lomustine, etc. Finally, 155 patients underwent bone marrow (BM) MRD assessment by MFC after hematologic recovery from induction chemotherapy, between day 35 and day 42 (Supplementary Table S1 and Supplementary Fig. S1). AML patient samples were obtained after written informed consent in accordance with the Declaration of Helsinki. BM samples were stored in the HIMIP collection (BB-0033–00060). In accordance with French law, the HIMIP collection was declared to the Ministry of Higher Education and Research (DC 2008–307 collection 1) and obtained a transfer agreement (AC 2008–129) after approval by the Comité de Protection des Personnes Sud-Ouest et Outremer II (institutional review board).

MFC analysis of leukemic cells at diagnosis and post-induction was performed on fresh BM samples collected on EDTA-K. Two antibody combinations were used. One combination, for leukemic bulk analysis, associated CD34, CD13, CD38, CD7, CD33, CD56, CD117, HLA-DR, and CD45 as recommended by the ELN (13) and experimented by Tettero and colleagues (15) The second combination comprised CD34, CD38, CD133, CD135, CD45, CD45RA for CD34+ subpopulation analysis (Supplementary Table S2). Samples were processed on Navios instruments (Beckman-Coulter, Miami, FL). In parallel, 10 RBM samples were obtained and analyzed by MFC on the same instrument with the same antibody combinations to construct unsupervised immunophenotypic model patterns as reported (16).

For each antibody combination, compensations were checked and fluorescence intensities of each marker were normalized for each file. All CD45+ BM cells of RBM were used to construct the model pattern used for bulk MRD and only CD34+ BM cells were used to construct the model pattern for LSC MRD. The normalized RBM files were then merged and processed to obtain two 64-nodes FlowSOM minimal spanning tree (MST; ref. 16): a first one containing all CD45+ BM cells and a second one focused on CD34+ progenitors cells. For each patient, the merged RBM file was then processed together with the normalized diagnosis and post-induction follow-up samples in the FlowSOM module (Bioconductor version 3.3.2 with flowsom and flocore packages) integrated with the Kaluza analysis software (Beckman Coulter) as reported (16). The MST (nodes) of the 3 files (RBM, diagnosis and follow-up) were redistributed in an unsupervised manner, according to the set of immunophenotypic characteristics of the merged samples.

Specific linked gates were defined to track each unique node concomitantly in the RBM, diagnosis and follow-up files. When this tracking gate was moved from one node to another, the respective statistics of the mean fluorescence intensity for each marker and the number of cells in the considered node were displayed for the RBM, diagnosis and follow-up BM. This allowed the detection of MRD nodes with the "DfN" and "LAIP" approach in an unsupervised manner. Specifically, for the search for LSCs, nodes of interest were selected from CD34+CD38− cells included in the CD34+ population after excluding CD34+CD38+ cells. MRD was considered positive if the percentage of cells in a given node of the follow-up sample was at least twice that of the same node in RBM (11) and if the sum of those nodes was greater than 0.1% of the analyzed cells, as recommended by the ELN (10). For the tube based on the LSC search, because no recommendation exists, we chose a patient-dependent threshold representing twice the value found in the RBM files.

The presence of FLT3-ITD was tested as described (17). Electrophoregram peaks were quantified using GeneMarker 2.2 (SoftGenetics, State College, PA). Extended DNA resequencing was performed using Illumina NextSeq500 and Haloplex HS (Agilent, Santa Clara, CA) targeted on the complete coding regions of 52 genes: ASXL1, ASXL2, ATM, BCOR, BCORL1, CBL, CCND2, CEBPA, CSF3R, CUX1, DDX41, DHX15, DNMT3A, EP300, ETV6, EZH2, FLT3, GATA1, GATA2, IDH1, IDH2, JAK2, KDM5A, KDM6A, KIT, KMT2D, KRAS, MGA, MPL, MYC, NF1, NPM1, NRAS, PHF6, PIGA, PPM1D, PRPF8, PTPN11, RAD21, RUNX1, SETBP1, SF3B1, SMC1A, SMC3, SRSF2, STAG2, TET2, TP53, U2AF1, WT1, ZBTB7A, and ZRSR2. Data were processed through two algorithms from GATK (https://software.broadinstitute.org/gatk), HaplotypeCaller (scaling accurate genetic variant discovery) to tens of thousands of samples (18, 19). The mean depth was 2,190 reads. Identified variants were curated manually and named according to the rules of the Human Genome Variation Society (hgvs.org). Molecular data are stored in the European Nucleotide Archive (https://www.ebi.ac.uk/ena/).

Complete remission, relapse, leukemia-free survival (LFS) and OS were defined according to standard ELN 2017 criteria (20).

The follow-up of patients in complete remission were censored at the time of last contact. Risk groups for prognosis were evaluated for OS and LFS by univariate analysis (log-rank test) and by a multivariate model of Cox regression. All calculations were performed using STATA version 13 software (STATA Corp., College Station, TX), all graphs were drawn using Graph Pad Prism software (San Diego, CA).

Comparisons were performed using a Mann–Whitney test for continuous variables and Fisher exact test for categorical variables with GraphPad Prism. Statistical test results are graphically expressed: *, P < 0.05; **, P < 0.01; ***, P < 0.001; ****, P < 0.0001.

The datasets generated and/or analyzed during the current study are available from the corresponding author on reasonable request.

In order to evaluate post-induction MRD levels in the 155 patients with AML treated with intensive chemotherapy, we applied an unsupervised clustering method (FlowSOM, Supplementary Fig. S2A) to RBM samples of healthy donors and cells from diagnosis and follow-up. Using this method, we highlighted a small number of nodes, specific to leukemic bulk, at diagnosis with the myeloid panel (CD34, CD13, CD38, CD7, CD33, CD56, CD117, HLADR, and CD45) and specific to CD34+CD38− LSCs with the LSC panel (CD34, CD38, CD133, CD135, CD45, CD45RA). After applying the node tree to the RBM and to the follow-up point, we could measure the node size and define the unique sensitivity threshold of the technique for each patient (on the RBM) and the MRD value (on the follow-up point).

The median size of bulk AML nodes was 20.6% of white blood cells at diagnosis [interquartile range (IQR), 11.9%–45.7%; Supplementary Fig. S2B]. The sensitivity threshold calculated in RBM was 0.08% (IQR, 0.05%–0.19%; Supplementary Fig. S2B). The levels of bulk AML MRD were very heterogenous with a median of 0.10% (IQR, 0.02%–0.48%; Supplementary Fig. S2B). Post-induction fold reduction of bulk leukemic cells was 206 (IQR, 43–1,197; Supplementary Fig. S2C), i.e., 2.31 log reduction, with 58 patients (37%) having a positive MRD above the ELN-defined threshold of 0.1% (10).

Stem cell nodes were specifically defined on the CD34+CD38− subpopulation using the LSC panel. Accordingly, LSC nodes comprised 69.4% of total CD34+CD38− leukemic cells at diagnosis (IQR, 48.3%–85.3%; Supplementary Fig. S2D) versus 1.3% in RBM samples (IQR, 0.5%–2.7%), validating the LSC clustering strategy. To note, we were able to identify CD34+CD38− LSC populations DfN in all AML even those with low CD34 expression (CD34 < 20%; Supplementary Fig. S3). The rate of post-induction residual LSCs was highly heterogeneous with a median of 2.42% of CD34+CD38− cells (IQR, 0.43%–9.91%) and a fold reduction of 27 (IQR, 6–157; Supplementary Fig. S2C), i.e., 1.43 log reduction. After induction, 85 patients (55%) still had detectable LSC above the technical threshold.

The median follow-up of patients was 26.2 months. The median OS for patients with a positive bulk MRD was 15.2 months and was not reached for negative patients (HR, 3.61; P < 0.0001; Fig. 1A). One-year (y) and 3-y OS were respectively 57% and 38% in bulk MRD-positive patients and 92% and 74% in negative patients. The median OS of patients with a positive LSC MRD was 25.0 months and not reached for negative patients (HR, 3.05; P = 0.0001; Fig. 1B). One-y and 3-y OS were respectively 73% and 46% in LSC MRD-positive patients and 85% and 77% in negative patients. In multivariate analysis, a positive bulk MRD was significantly associated with a worse OS [HR, 2.64; 95% confidence interval (CI), 1.44–4.84; P = 0.002; Supplementary Table S3]. AML de novo status retained a significant impact on OS (HR, 0.44; P = 0.011).

The median LFS for bulk MRD-positive patients was 7.2 months and was not reached for bulk MRD-negative patients (HR, 2.80; P < 0.0001; Fig. 1C). One-y and 3-y LFS were respectively 42% and 16% in bulk MRD-positive patients and 71% and 50% in negative patients. Median LFS was respectively 12 months and not reached for LSC MRD-positive and negative patients (HR, 2.49; P = 0.0002; Fig. 1D). One-y and 3-y LFS were respectively 51% and 24% in LSC MRD-positive patients and 74% and 55% in negative patients. In multivariate analysis, positive bulk MRD (HR, 2.91; 95% CI, 1.66–5.10; P < 0.001) and positive LSC MRD (HR, 1.9; 95% CI, 1.09–3.30; P = 0.023) were both independently associated with a shorter LFS (Supplementary Table S4).

To better describe the prognostic impact of AML MRD, bulk and LSC MRD data were associated to define 3 groups of patients based to their early response to chemotherapy: (i) chemosensitive patients with both negative Bulk and LSC MRD (Bulk-LSC−; n = 57, 37%), (ii) patients with AML with negative bulk MRD but still positive LSC MRD (Bulk-LSC+; n = 40, 26%), and (iii) chemoresistant patients with positive bulk MRD (Bulk+; n = 58, 37%). LSC phenotypic profile of those 3 groups were highly similar (Supplementary Fig. S4).

Median OS were 15.2 months in Bulk+, 38.4 months in Bulk-LSC+ and not reached in Bulk-LSC− (Fig. 2A). One-y and 3-y OS were 57% and 38% in Bulk+, 90% and 57% in Bulk-LSC+ and 95% and 86% in Bulk-LSC−. In multivariate analysis, Bulk+ (HR, 6.09; 95% CI, 2.45–15.1; P < 0.001) was associated with a worse OS (Supplementary Table S5). Median LFS were 7.1 months in Bulk+, 13.2 months in Bulk-LSC+ and not reached in Bulk-LSC− (Fig. 2B). In multivariate analysis, Bulk+ (HR, 5.73; 95% CI, 2.84–11.5; P < 0.001) and Bulk-LSC+ (HR, 2.78; 95% CI, 1.37–5.65; P = 0.005) were associated with a shorter LFS (Supplementary Table S6).

Of the 155 patients, 67 (43.2%) were transplanted, including 24, 19 and 24 with Bulk-LSC−, Bulk-LSC+ and Bulk+ MRD, corresponding to 42.1%, 47.5% and 41.4% of each group, respectively (Supplementary Table S1). In a landmark analysis at 6 months from diagnosis, when 142 (91.6%) patients were still alive, the OS of transplant recipients were similar in Bulk-LSC− and Bulk-LSC+ MRD patients (Supplementary Fig. S5A and S5B); in contrast, Bulk+ MRD transplant recipients had significantly longer OS (1y OS 79.2%) than that of non-transplanted Bulk+ MRD patients (57.2%, P = 0.011; Supplementary Fig. S5C).

As there was no significant interaction in our multivariate analysis, between Bulk and LSC MRD and age, cytogenetics, NPM1 mutation or FLT3-ITD for each end point, we concluded that the prognostic significance of MRD was independent of these pretherapy prognostic parameters.

Although the overall immunophenotypic profiles of AML blasts, depending on MRD status, were similar (Supplementary Fig. S6A), some specific markers at diagnostic showed significant, even if moderate, differences (Supplementary Fig. S6B). In our cohort, markers such as CD36 and CD11b (which are part of the markers we study at diagnosis but which we do not use in our follow-up method) have a significantly higher expression in Bulk+ MRD patients, as well as CD16 and CD56. Conversely, CD13 expression was significantly decreased in this group of patients.

To explore cytogenetic and molecular differences between chemoresistant (Bulk+) and chemosensitive (Bulk-LSC−) patients, we focused on 72 patients of the cohort (47%) for whom genetic and next-generation sequencing mutational data on 46 genes were available. The distribution of abnormalities in these patients was representative of what is known in AML (Fig. 3A). Abnormalities were grouped into 7 functional classes defined by The Cancer Genome Atlas (TCGA; ref. 21). As expected, NPM1 mutations were positively correlated to the chemosensitive Bulk-LSC− group (OR, 3.3; 95% CI, 1.2–9.5; Fig. 3B).

Although our cohort size was relatively small, we wanted to study the impact of Bulk and LSC MRD in specific subgroups of AML. Indeed, the recent increase in therapeutic choices offers new options to clinicians and early MRD could be an aid to therapeutic decisions.

FLT3-ITD, IDH1/2 mutations, NPM1 mutation associated with wild-type FLT3 and adverse cytogenetics were detected in 38, 40, 28, and 36 patients, respectively. In these groups, 3-y OS and 3-y LFS ranged between 21% to 57% and 0% to 11% in Bulk+ and between 75% to 91% and 56% to 75% in Bulk-LSC−, respectively (Fig. 4; Table 1). The number of patients did not allow us to interpret the prognostic impact of Bulk-LSC+ MRD. Finally, to explore those clinically relevant AML groups, we compared the phenotypic characteristics of LSC. It appeared that LSC from NPM1 m AMLs were low expressers of CD45RA, CD135, and CD133 compared with LSC from AML with adverse cytogenetics (Supplementary Fig. S7).

In this study, we confirm the prognostic impact of post-induction MFC Bulk MRD in patients with AML treated with intensive chemotherapy, complementing previous studies (22–27). We wanted to evaluate MRD in this early therapeutic point, on one hand to homogenize our results and on the other hand because the different publications on the subject were performed after two cycles of chemotherapy or without a specific follow-up point (24, 28). Of note, analysis of MRD panels with the unsupervised multivariate clustering FlowSOM method identified LAIP/DfN clusters in 100% of patients with AML in this cohort. This artificial intelligence-based strategy greatly facilitated the homogenization of the results (data normalization phase, follow-up over time with the reference diagnosis, computerized assistance for the choice of LAIP/DfN). The speed of the analysis is also a determining factor for its applicability in an MFC platform. The power of clustering relies largely on the choice of markers. For example, we showed that CD13 and CD56 were differentially expressed between AML with negative and positive post-induction MRD. Although these markers are part of the classical LAIP-DfN profiles and thus influenced our ability to detect residual cells, they seemed to be related to the intrinsic chemoresistance of leukemic cells. Indeed, CD56 expression was associated with shorter survival of patients with AML and a low probability of achieving complete remission (29). CD56 plays a critical role in the regulation of cell survival, drug resistance, and self-renewal of AML blasts in connection with activation of the MAPK or glycolysis signaling pathway (30).

Nevertheless, a sizable group of Bulk- MRD patients still relapsed with only 69% LFS after 1 year. One reason for this disappointing result could be that the threshold for MRD positivity was set too high (0.1% as recommended by ELN; ref. 10). Unfortunately, this threshold is mainly dictated by the choice of flow cytometry markers and normal hematopoietic subpopulations present in the BM. To circumvent this problem, we focused on LSCs, a well-known chemoresistant subpopulation (31, 32). Using this strategy, we isolated a group of patients with negative Bulk MRD but positive LSC MRD (25.8% of this cohort). As expected, these patients had intermediate survival between Bulk+ MRD and Bulk-LSC− MRD patients. Similarly, our study of immunophenotypic markers in AML highlighted that CD36 expression correlated with a poorer response to chemotherapy. This membrane fatty acid receptor is of great interest in AML. CD36 is indeed involved in lipid metabolism and chemoresistance of LSCs. Indeed, according to Ye and colleagues (33), CD36+ LSCs secrete inflammatory cytokines that enable lipolysis of adipocytes. The release of fatty acids and their recovery by CD36 allows their use by LSCs as an additional energy source that is normally scarce in the marrow.

This first study combining Bulk and LSC MFC MRD and unsupervised analysis strategy has some weaknesses. Indeed, it is a monocentric study with a relatively small cohort size and a short follow-up time. But despite this, we observe survival results comparable with the study of Zeijlemaker and colleagues (28) who carried out their MRD analyses after two cycles of chemotherapy. In addition, our study allows interesting observations on groups of patients classified by genetic or molecular abnormalities and suggests an additional prognostic value of post-induction MRD. Indeed, we can observe that in patients with adverse cytogenetics, OS and LFS were significantly different between Bulk-LSC− and Bulk-LSC+ MRD patients. Astonishingly, in the Bulk-LSC− MRD patients relatively small population, no deaths or relapses were reported, whereas Bulk-LSC+ and Bulk+ patients had a poor prognosis. Similarly, in the group of good prognosis NPM1 mutated and FLT3wt, a Bulk+ MRD was predictive of a relapse within 14 months. Of course, all these results will have to be confirmed in a larger cohort of patients and could be extended to more genetics subgroups. Indeed, it would be interesting to study whether LSCs show differences in phenotypic expression according to genetic subgroups.

The design of our LSC panel can certainly be improved in the future. First, we chose to focus on markers such as CD45RA and CD133 that were already familiar in our laboratory. Some markers like CD96 (34), CLL1 (35), TIM3 (36), CD36 (33, 37), CD39 (38), or CALCRL (39) could improve the sensitivity and specificity of this follow-up method, but we could not add them because the calculation of our MRD depends on the markers used for diagnosis. Then the threshold can also be questioned. In the absence of a specific study on LSC MRD by unsupervised multivariate method, we chose to keep the validated threshold in Bulk MRD, i.e., a threshold representing twice the value found in the same RBM node (11).

One of the major advantages of MFC remains the speed of the technique. FlowSOM's unsupervised clustering analysis significantly improves the speed of analysis and the rendering of results. In a period where OS and relapse-free survival is increasing thanks to therapeutic improvements, it becomes essential to have a quick and reliable answer on the effectiveness of a treatment. We believe that early assessment MRD in MFC may have a role to play in future clinical trials, as it does in other hematologic diseases, and even more with unsupervised analysis.

In the future era of personalized medicine, we can envision an important place for MRD by MFC combined with unsupervised clustering software and a chemoresistant cell monitoring strategy as done here with the LSC. The use of new markers for cohorts of patients treated with new therapies will certainly require the adaptation of CMF MRD analysis tools and will need many additional studies.

S. Bertoli reports personal fees from AbbVie, Astellas, BMS-Celgene, Novartis, Pfizer, and Jazz Pharmaceuticals outside the submitted work. S. Tavitian reports other support from Novartis, Servier, and Pfizer outside the submitted work. F. Huguet reports personal fees from Novartis, Incyte, Pfizer, and Servier outside the submitted work. P. Bories reports honoraria from Kite-Gilead and AbbVie on topics unrelated to the present manuscript. C. Récher reports grants, personal fees, and nonfinancial support from AbbVie, Astellas, BMS, and Jazz Pharmaceuticals; grants from Amgen, Iqvia, and MaatPharma; personal fees and nonfinancial support from Novartis and Servier; and personal fees from Takeda outside the submitted work. F. Vergez reports grants from Pierre Fabre and nonfinancial support from Sysmex outside the submitted work. No disclosures were reported by the other authors.

A. Canali: Conceptualization, data curation, software, formal analysis, validation, investigation, writing–original draft, writing–review and editing. I. Vergnolle: Formal analysis, validation, investigation. S. Bertoli: Investigation. L. Largeaud: Investigation. M.-L. Nicolau: Investigation. J.-B. Rieu: Investigation. S. Tavitian: Investigation. F. Huguet: Investigation. M. Picard: Investigation. P. Bories: Investigation. J.P. Vial: Software, investigation. N. Lechevalier: Software. M.C. Béné: Software, writing–review and editing. I. Luquet: Investigation. V. Mansat-De Mas: Investigation. E. Delabesse: Investigation. C. Récher: Conceptualization, investigation, writing–review and editing. F. Vergez: Conceptualization, data curation, software, formal analysis, validation, investigation, writing–original draft, writing–review and editing.

We would like to thank the technicians of the hematology flow cytometry laboratory at the Toulouse Oncopole University Cancer Institute for processing all the samples used for this work.

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