Detection of CD34, TdT, CD56, CD2, CD4, and CD14 by Flow Cytometry Is Associated With NPM1 and FLT3 Mutation Status in Cytogenetically Normal Acute Myeloid Leukemia

Automated Deep Learning-Based Diagnosis and Molecular Characterization of Acute Myeloid Leukemia Using Flow Cytometry

The current flow cytometric analysis of blood and bone marrow samples for diagnosis of acute myeloid leukemia (AML) relies heavily on manual intervention in the processing and analysis steps, introducing significant subjectivity into resulting diagnoses and necessitating highly trained personnel. Furthermore, concurrent molecular characterization via cytogenetics and targeted sequencing can take multiple days, delaying patient diagnosis and treatment. Attention-based multi-instance learning models (ABMILMs) are deep learning models that make accurate predictions and generate interpretable insights regarding the classification of a sample from individual events/cells; nonetheless, these models have yet to be applied to flow cytometry data. In this study, we developed a computational pipeline using ABMILMs for the automated diagnosis of AML cases based exclusively on flow cytometric data. Analysis of 1820 flow cytometry samples shows that this pipeline provides accurate diagnoses of acute leukemia (area under the receiver operating characteristic curve [AUROC] 0.961) and accurately differentiates AML vs B- and T-lymphoblastic leukemia (AUROC 0.965). Models for prediction of 9 cytogenetic aberrancies and 32 pathogenic variants in AML provide accurate predictions, particularly for t(15;17)(PML::RARA) [AUROC 0.929], t(8;21)(RUNX1::RUNX1T1) (AUROC 0.814), and NPM1 variants (AUROC 0.807). Finally, we demonstrate how these models generate interpretable insights into which individual flow cytometric events and markers deliver optimal diagnostic utility, providing hematopathologists with a data visualization tool for improved data interpretation, as well as novel biological associations between flow cytometric marker expression and cytogenetic/molecular variants in AML. Our study is the first to illustrate the feasibility of using deep learning-based analysis of flow cytometric data for automated AML diagnosis and molecular characterization.

Automated Deep Learning-Based Diagnosis and Molecular Characterization of Acute Myeloid Leukemia using Flow Cytometry

Current flow cytometric analysis of blood and bone marrow samples for diagnosis of acute myeloid leukemia (AML) relies heavily on manual intervention in both the processing and analysis steps, introducing significant subjectivity into resulting diagnoses and necessitating highly trained personnel. Furthermore, concurrent molecular characterization via cytogenetics and targeted sequencing can take multiple days, delaying patient diagnosis and treatment. Attention-based multi-instance learning models (ABMILMs) are deep learning models which make accurate predictions and generate interpretable insights regarding the classification of a sample from individual events/cells; nonetheless, these models have yet to be applied to flow cytometry data. In this study, we developed a computational pipeline using ABMILMs for the automated diagnosis of AML cases based exclusively on flow cytometric data. Analysis of 1,820 flow cytometry samples shows that this pipeline provides accurate diagnoses of acute leukemia [AUROC 0.961] and accurately differentiates AML versus B- and T-lymphoblastic leukemia [AUROC 0.965]. Models for prediction of 9 cytogenetic aberrancies and 32 pathogenic variants in AML provide accurate predictions, particularly for t(15;17)(PML::RARA) [AUROC 0.929], t(8;21)(RUNX1::RUNX1T1) [AUROC 0.814], and NPM1 variants [AUROC 0.807]. Finally, we demonstrate how these models generate interpretable insights into which individual flow cytometric events and markers deliver optimal diagnostic utility, providing hematopathologists with a data visualization tool for improved data interpretation, as well as novel biological associations between flow cytometric marker expression and cytogenetic/molecular variants in AML. Our study is the first to illustrate the feasibility of using deep learning-based analysis of flow cytometric data for automated AML diagnosis and molecular characterization.

Flow Cytometric Identification of Hematopoietic and Leukemic Blast Cells for Tailored Clinical Follow-Up of Acute Myeloid Leukemia

Acute myeloid leukemia (AML) is a myeloid malignancy that is characterized by the accumulation of leukemic blast cells, which originate from hematopoietic stem cells that have undergone leukemic transformation and/or are more mature progenitors that have gained stemness features. Currently, no consensus exists for the flow cytometric identification of normal blast cells and their leukemic counterparts by their antigenic expression profile. Differentiating between the benign cells and the malignant cells is crucial for the further deployment of immunophenotype panels for the clinical follow-up of AML patients. This review provides an overview of immunophenotypic markers that allow the identification of leukemic blast cells in the bone marrow with multiparameter flow cytometry. This technique allows the identification of hematopoietic blast cells at the level of maturing cells by their antigen expression profile. While aberrant antigen expression of a single immunophenotypic marker cell cannot be utilized in order to differentiate leukemic blast cells from normal blast cells, combinations of multiple immunophenotypic markers can enable the distinction of normal and leukemic blast cells. The identification of these markers has provided new perspectives for tailored clinical follow-up, including therapy management, diagnostics, and prognostic purposes. The immunophenotypic marker panels, however, should be developed by carefully considering the variable antigen marker expression profile of individual patients.

Quantitative analyses of CD7, CD33, CD34, CD56, and CD123 within the FLT3-ITD/NPM1-MUT myeloblastic/monocytic bulk AML blastic populations

The most frequent mutations in acute myeloid leukemia (AML) - FLT3-ITD and NPM1 - are associated with a specific immunophenotype. We evaluated the levels of surface antigens in an uninvestigated AML patient population according to the combination of FLT3-ITD/NPM1 mutations. Antigen levels were calculated as the geometric mean fluorescence index (MFI) ratio between myeloblasts or monoblasts/monocytes and a negative population for the specific antigen. In myeloblastic populations, FLT3-ITD cases presented CD7^high MFI values (p < .001), while NPM1-MUT cases presented CD33^high (p < .001), and CD34^low (p < .001) MFI values. Within the monoblastic/monocytic populations, CD56^high expression was observed only in the FLT3-WT/NPM1-MUT population (p=.003). The single common antigen expression between myeloblasts and monoblasts/monocytes was CD123^high expression only within the FLT3-ITD/NPM1-MUT subgroup. Our results present a subtle influence of FLT3-ITD/NPM1 mutations upon antigen expression profiles in myeloblasts vs monoblasts/monocytes, and we described a novel correlation between the presence of NPM1 and CD56^high values within bulk leukemic monoblasts/monocytes.

Next-generation sequencing reveals gene mutations landscape and clonal evolution in patients with acute myeloid leukemia

OBJECTIVES

The study aims to understand geneome diversification and complexity that developed in Acute myeloid leukemia (AML).

METHODS

Next-generation sequencing (NGS) was used to identify the genetic profiles of 22 genes relevant to hematological malignancy in 204 patients with de novo non-M3 AML.

RESULTS

At time of initial diagnosis, at least one mutation was identified in 80.9% of patients (165/204). The most commonly mutated gene was NPM1 (22.1%), followed by ASXL1 (18.1%), TET2 (18.1%), IDH2 (15.7%), CEBPA (14.7%), FLT3-ITD (13.2%) and DNMT3A (11.8%). Mutations landscape analysis indicated several patterns of co-occurring and mutual exclusive gene mutations. Some correlation was observed between gene mutations and clinicohematological features. Multivariate analysis showed that age >60 years, karyotypes, IDH2 and KIT mutations were the independent unfavorable prognostic factors for OS; NPM1-mut/ FLT3-ITD-wt was independently correlated with prolonged OS; whereas the independent poor risk factors for RFS were karyotypes, high WBC and RUNX1 mutation. According to different genotype demonstrated by multivariate analysis, 163 patients with intermediate-risk cytogenetics were classified into three subgroups: patients with NPM1-mut/ FLT3-ITD-wt or biallelic CEBPA mutation as favorable risk, patients with KIT, IDH2, TP53 or NRAS mutations as unfavorable risk, and the remaining was the intermediate risk. We also obtain information of clonal evolution during leukemia progression by observing five patients who underwent repeat NGS at relapse in our cohort.

CONCLUSION

NGS techniques is a useful tool for discovering related gene mutations and clonal evolution in AML genomes, leading to novel targeted therapeutic approaches that could improve patients outcomes.

Mixed Phenotype Acute Leukemia: Current Approaches to Diagnosis and Treatment

PURPOSE OF REVIEW

Mixed phenotype acute leukemia (MPAL) is a rare subtype of acute leukemia with features of both acute lymphoblastic leukemia (ALL) and acute myeloid leukemia (AML). The review examines current definitions and controversies in classification of MPAL, new insights into genomic drivers and pathogenesis, recent evidence to support treatment recommendations, and opportunities for future research.

RECENT FINDINGS

Recent collaborative efforts have made progress in understanding the genomic landscape and optimal therapy for MPAL. The preponderance of retrospective data supports beginning therapy with ALL directed regimens. Differences in prognosis for adult and children with MPAL have led to divergent approaches for therapy intensity, including use of stem cell transplantation consolidation. MPAL remains a challenging leukemia to understand, research, and treat due to low incidence, shifting and subjective approaches to classification, and innate biological heterogeneity. Ongoing research hopes to surmount these obstacles through prospective studies within large cooperative groups to provide new insight into targetable biology and further refine optimal therapy.

Acute myeloid leukemia with mutated NPM1 mimics acute promyelocytic leukemia presentation

OBJECTIVES

To investigate clinicopathological and molecular features of NPM1-mutated acute myeloid leukemia that presented with infrequent acute promyelocytic leukemia (APL)-like phenotype and clinical presentation.

METHODS

Cases with both de novo or secondary Acute Myeloid Leukemia (AML) were retrieved. Data from flow cytometry immunophenotyping, cytogenetics, molecular studies, and clinical presentation were analyzed.

RESULTS

Cases presented with abnormal coagulation parameters and low platelets count; four of them showed a DIC index compatible with overt DIC. Two cases showed Auer rods. In all cases, immunophenotypes mimicked APL: blasts expressed CD33, CD13, and cytoplasmic MPO but did not express CD34, HLA-DR, or CD11b. Notably, CD4 expression was observed in all cases. Neither t(15;17) nor PML/RARα gene rearrangement was detected. NPM1 gene mutation was identified in all cases. In four cases, TET2 or IDH2 co-mutations were identified.

CONCLUSIONS

Our findings provide additional evidence of association between NPM1-mutated AML with TET2 or IDH2 co-mutations and the APL-like immunophenotype. This AML subset was found to exist in both de novo and secondary AML. High WBC count and blasts with low to moderate side scatter and significant expression of CD4 are observed features that could assist in the differential diagnosis with APL. The occurrence of significant elevated D-dimer levels, or even overt DIC observed at diagnosis in these cases could be relevant for this AML subtype.

Morphological and Immunophenotypic Clues to the WHO Categories of Acute Myeloid Leukaemia

Diagnosis and classification of acute myeloid leukaemia (AML) require cytogenetic and molecular genetic investigation. However, while these evaluations are pending, morphology supplemented by immunophenotyping can provide clues to the diagnosis of specific cytogenetic/genetic categories of AML. Most importantly, acute promyelocytic leukaemia can be diagnosed with a high degree of certainty. However, provisional identification of cases associated with t(8; 21), inv(16), t(1; 22), and NPM1 mutation may also be possible. In addition, transient abnormal myelopoiesis of Down's syndrome can generally be diagnosed morphologically.

Mutational spectrum and risk stratification of intermediate-risk acute myeloid leukemia patients based on next-generation sequencing

Intermediate-risk acute myeloid leukemia (IR-AML), which accounts for a substantial number of AML cases, is highly heterogeneous. Although several mutations have been identified, the heterogeneity of AML is uncertain because novel mutations have yet to be discovered. Here we applied next generation sequencing (NGS) platform to screen mutational hotspots in 410 genes relevant to hematological malignancy. IR-AML samples (N=95) were sequenced by Illumina Hiseq and mutations in 101 genes were identified. Only seven genes (CEBPA, NPM1, DNMT3A, FLT3-ITD, NRAS, IDH2 and WT1) were mutated in more than 10% of patients. Genetic interaction analysis identified several cooperative and exclusive patterns of overlapping mutations. Mutational analysis indicated some correlation between genotype and phenotype. FLT3-ITD mutations were identified as independent factors of poor prognosis, while CEBPA mutations were independent favorable factors. Co-occurrence of FLT3-ITD, NPM1 and DNMT3A mutations was identified with associated with specific clinical AML features and poor outcomes. Furthermore, by integrating multiple mutations in the survival analysis, 95 IR-AML patients could be stratified into three distinct risk groups allowing reductions in IR-AML by one-third. Our study offers deep insights into the molecular pathogenesis and biology of AML and indicated that the prognosis of IR-AML could be further stratified by different mutation combinations which may direct future treatment intervention.

Feature genes predicting the FLT3/ITD mutation in acute myeloid leukemia

In the present study, gene expression profiles of acute myeloid leukemia (AML) samples were analyzed to identify feature genes with the capacity to predict the mutation status of FLT3/ITD. Two machine learning models, namely the support vector machine (SVM) and random forest (RF) methods, were used for classification. Four datasets were downloaded from the European Bioinformatics Institute, two of which (containing 371 samples, including 281 FLT3/ITD mutation-negative and 90 mutation-positive samples) were randomly defined as the training group, while the other two datasets (containing 488 samples, including 350 FLT3/ITD mutation-negative and 138 mutation-positive samples) were defined as the test group. Differentially expressed genes (DEGs) were identified by significance analysis of the micro-array data by using the training samples. The classification efficiency of the SCM and RF methods was evaluated using the following parameters: Sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV) and the area under the receiver operating characteristic curve. Functional enrichment analysis was performed for the feature genes with DAVID. A total of 585 DEGs were identified in the training group, of which 580 were upregulated and five were downregulated. The classification accuracy rates of the two methods for the training group, the test group and the combined group using the 585 feature genes were >90%. For the SVM and RF methods, the rates of correct determination, specificity and PPV were >90%, while the sensitivity and NPV were >80%. The SVM method produced a slightly better classification effect than the RF method. A total of 13 biological pathways were overrepresented by the feature genes, mainly involving energy metabolism, chromatin organization and translation. The feature genes identified in the present study may be used to predict the mutation status of FLT3/ITD in patients with AML.

Deep profiling of multitube flow cytometry data

MOTIVATION

Deep profiling the phenotypic landscape of tissues using high-throughput flow cytometry (FCM) can provide important new insights into the interplay of cells in both healthy and diseased tissue. But often, especially in clinical settings, the cytometer cannot measure all the desired markers in a single aliquot. In these cases, tissue is separated into independently analysed samples, leaving a need to electronically recombine these to increase dimensionality. Nearest-neighbour (NN) based imputation fulfils this need but can produce artificial subpopulations. Clustering-based NNs can reduce these, but requires prior domain knowledge to be able to parameterize the clustering, so is unsuited to discovery settings.

RESULTS

We present flowBin, a parameterization-free method for combining multitube FCM data into a higher-dimensional form suitable for deep profiling and discovery. FlowBin allocates cells to bins defined by the common markers across tubes in a multitube experiment, then computes aggregate expression for each bin within each tube, to create a matrix of expression of all markers assayed in each tube. We show, using simulated multitube data, that flowType analysis of flowBin output reproduces the results of that same analysis on the original data for cell types of >10% abundance. We used flowBin in conjunction with classifiers to distinguish normal from cancerous cells. We used flowBin together with flowType and RchyOptimyx to profile the immunophenotypic landscape of NPM1-mutated acute myeloid leukemia, and present a series of novel cell types associated with that mutation.

Expression of costimulatory molecule B7-H3 and its prognostic implications in human acute leukemia

OBJECTIVES

This study focused on the expression pattern and clinical significance of B7-H3 expression in human acute leukemia.

METHODS

We systematically analyzed 134 patients with acute myeloid leukemia (101 cases) and acute lymphocytic leukemia (33 cases) by flow cytometry.

RESULTS

The frequency of B7-H3(+) cases was 44.8% in total. The B7-H3 expression rate differed from 0% to 74.8% in individual cases. The correlation between B7-H3 expression and traditional prognostic factors, such as age and gender, the white blood cell count was not confirmed. However, B7-H3 had a significant higher expression in CD34(+) cases and high risk karyotypes.

CONCLUSIONS

Owing to the expression of B7-H3 being statistically relevant in predicting disease progression and a shorter life survival, our results demonstrated that B7-H3 expression in acute leukemia predicts an unfavorable outcome.

Aberrant expression of CD13 identifies a subgroup of standard-risk adult acute lymphoblastic leukemia with inferior survival

BACKGROUND

The standard-risk (SR) subgroup of acute lymphoblastic leukemia in adults (aALL) is a heterogeneous category, with a 20% to 40% relapse rate and a wide range of relapse-free survival (RFS) and overall survival (OS). There is a need to identify at the outset those patients with SR-aALL who are likely to have shorter RFS and OS, so they can be treated more aggressively.

PATIENTS AND METHODS

Flow cytometric data of 81 patients with SR-aALL treated with a standardized protocol were retrospectively analyzed. Thirty-two patients (40%) relapsed; the median RFS and OS were 12.5 months (range, 1-136 months) and 30 months (range, 3-235 months), respectively. Twenty-six patients survived ≥ 48 months.

RESULTS

Expression of myeloid antigen CD13, using the conventional ≥ 20% threshold and a lower ≥ 5% threshold, was seen in 17 (29%) of 59 and 29 (49%) of 59 patients, respectively, whereas dual expression of CD13 and CD33 was seen in 8 patients. CD13 positivity at ≥ 20% and ≥ 5% threshold was associated with a shorter RFS (P = .0158 and P < .0001, respectively) and OS (P = .0072 and P < .0001, respectively). Dual expression of CD13 (at ≥ 5% or ≥ 20% threshold) and CD33 was associated with inferior OS (P = .0038 and P = .0032, respectively) and RFS (P = .0705 and P = .2516, respectively). For ≥ 20% and ≥ 5% threshold of positivity, 16 of 42 and 28 of 42 who survived < 48 months were positive, compared with 1 of 17 and 1 of 17 who survived ≥ 48 months (P = .0133 and P < .0001, respectively).

CONCLUSION

Aberrant expression of CD13 in ≥ 5% of blasts of patients with SR-aALL is an adverse prognostic factor, delineating a subgroup of patients with SR-aALL that should be considered for more aggressive treatment.