Stepwise discriminant function analysis for rapid identification of acute promyelocytic leukemia from acute myeloid leukemia with multiparameter flow cytometry

A Bioinformatics View on Acute Myeloid Leukemia Surface Molecules by Combined Bayesian and ABC Analysis

“Big omics data” provoke the challenge of extracting meaningful information with clinical benefit. Here, we propose a two-step approach, an initial unsupervised inspection of the structure of the high dimensional data followed by supervised analysis of gene expression levels, to reconstruct the surface patterns on different subtypes of acute myeloid leukemia (AML). First, Bayesian methodology was used, focusing on surface molecules encoded by cluster of differentiation (CD) genes to assess whether AML is a homogeneous group or segregates into clusters. Gene expressions of 390 patient samples measured using microarray technology and 150 samples measured via RNA-Seq were compared. Beyond acute promyelocytic leukemia (APL), a well-known AML subentity, the remaining AML samples were separated into two distinct subgroups. Next, we investigated which CD molecules would best distinguish each AML subgroup against APL, and validated discriminative molecules of both datasets by searching the scientific literature. Surprisingly, a comparison of both omics analyses revealed that CD339 was the only overlapping gene differentially regulated in APL and other AML subtypes. In summary, our two-step approach for gene expression analysis revealed two previously unknown subgroup distinctions in AML based on surface molecule expression, which may guide the differentiation of subentities in a given clinical–diagnostic context.

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.

Contemporary Challenges in Clinical Flow Cytometry: Small Samples, Big Data, Little Time

Background

Immunophenotypic analysis of cell populations by flow cytometry has an established role in primary diagnosis and disease monitoring of many hematologic diseases. A persistent problem in evaluation of specimens is suboptimal cell counts and low cell viability, which results in an undesirable rate of analysis failure. In addition, the increased amount of data generated in flow cytometry challenges existing data analysis and reporting paradigms.

Content

We describe current and emerging technological improvements in cell analysis that allow the clinical laboratory to perform multiparameter analysis of specimens, including those with low cell counts and other quality issues. These technologies include conventional multicolor flow cytometry and new high-dimensional technologies, such as spectral flow cytometry and mass cytometry that enable detection of over 40 antigens simultaneously. The advantages and disadvantages of each approach are discussed. We also describe new innovations in flow cytometry data analysis, including artificial intelligence-aided techniques.

Summary

Improvements in analytical technology, in tandem with innovations in data analysis, data storage, and reporting mechanisms, help to optimize the quality of clinical flow cytometry. These improvements are essential because of the expanding role of flow cytometry in patient care.

Prognosis of sexual dimorphism with unfused hyoid bone: Artificial intelligence informed decision making with discriminant analysis

Depending on the metric and non-metric skeletal features of various bones, forensic experts proposed diverse sex identification methods. The main focus of the present study is to calculate sexual dimorphism in human unfused or disarticulated hyoid bone and compared it with studies conducted by different researchers. For this study, 293 unfused hyoid bones were accumulated and investigated from 173 male and 120 female cadavers of the northwest Indian population from the age of 15 to 80 years. Initially, discriminant analysis was performed on the dataset to predict sex and to get an idea for the crucial variables for sexual dimorphism. Later, significant variables predicted by the discriminant analysis were used for machine learning approaches to improve accuracy for sex determination. The standard scaler method is used for pre-processing of the data before machine learning analysis and to prevent overfitting and underfitting, 70 % of the whole dataset was utilized in the training of the model and the remaining data were used for testing the model. According to the discriminant analysis, body length (BL) and body height (BH) were found to be highly significant for the sex determination and predicted sex with 75.1 % accuracy. However, implementation of machine learning approaches such as the XG Boost classifier increased the accuracy to 83 % with sensitivity, and specificity scores of 0.81 and 0.84, respectively. Moreover, the ROC-AUC score achieved by the XG Boost classifier is 0.89; indicating machine learning investigation can improve the sex determination accuracy up to the appropriate standard.

miR-638 in circulating leukaemia cells as a non-invasive biomarker in diagnosis, treatment response and MRD surveillance of acute promyelocytic leukaemia

BACKGROUND

MicroRNA (miRNA) expression has been implicated in leukaemia. In recent years, miRNAs have been under investigation for their potential as non-invasive biomarkers in acute promyelocytic leukaemia (APL). We investigated whether miR-638 in circulating leukaemia cells is a non-invasive biomarker in diagnosis, assessment of the treatment response and minimal residual disease (MRD) surveillance of APL.

METHODS

Sixty cases of acute myeloid leukaemia (AML), including 30 cases of APL and 30 cases of non-APL AML, were selected. Thirty healthy controls were also selected. Bone marrow (BM) and peripheral blood (PB) samples were collected from APL patients at diagnosis and post-induction. Microarray analysis and quantitative real-time PCR (qRT-PCR) were performed for miRNA profiling and miR-638 expression analysis, respectively. For statistical analysis, Mann-Whitney U test, Wilcoxon Signed Rank test, receiver operating characteristic (ROC) curve analysis and Spearman's rho correlation test were used.

RESULTS

Both microarray and qRT-PCR data showed that miR-638 was significantly upregulated in BM after APL patients received induction therapy. Moreover, miR-638, which is specifically downregulated in APL cell lines, was upregulated after all-trans retinoic acid (ATRA)-induced myeloid differentiation. Receiver operating characteristic (ROC) curve analyses revealed that miR-638 could serve as a valuable biomarker for differentiating APL from controls or non-APL AML. Furthermore, miR-638 expression was sharply increased after induction therapy and complete remission (CR). An inverse correlation was observed between miR-638 and PML-RARα transcripts levels in BM samples, while a positive correlation was revealed between PB miR-638 and BM miR-638 levels in APL patients after induction therapy.

CONCLUSIONS

Our study suggested that miR-638 may serve as a potential APL biomarker for diagnosis and assessment of the response to targeted therapy, and PB miR-638 could be used for non-invasive MRD surveillance in APL.

The curious case of HLA-DR-positive APL

The triad of weak/absent CD34, negative HLA-DR expression, and positivity to CD117 is pathognomonic for the diagnosis of acute promyelocytic leukemia. However, in rare cases, strong positivity to HLD-DR and CD34 may be noted.

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.

Computational analysis of flow cytometry data in hematological malignancies: future clinical practice?

PURPOSE OF REVIEW

This review outlines the advancements that have been made in computational analysis for clinical flow cytometry data in hematological malignancies.

RECENT FINDINGS

In recent years, computational analysis methods have been applied to clinical flow cytometry data of hematological malignancies with promising results. Most studies combined dimension reduction (principle component analysis) or clustering methods (FlowSOM, generalized mixture models) with machine learning classifiers (support vector machines, random forest). For diagnosis and classification of hematological malignancies, many studies have reported results concordant with manual expert analysis, including B-cell chronic lymphoid leukemia detection and acute leukemia classification. Other studies, e.g. concerning diagnosis of myelodysplastic syndromes and classification of lymphoma, have shown to be able to increase diagnostic accuracy. With respect to treatment response monitoring, studies have focused on, for example, computational minimal residual disease detection in multiple myeloma and posttreatment classification of healthy or diseased in acute myeloid leukemia. The results of these studies are encouraging, although accurate relapse prediction remains challenging. To facilitate clinical implementation, collaboration and (prospective) validation in multicenter setting are necessary.

SUMMARY

Computational analysis methods for clinical flow cytometry data hold the potential to increase ease of use, objectivity and accuracy in the clinical work-up of hematological malignancies.

Early treatment of acute promyelocytic leukaemia is accurately guided by the PML protein localisation pattern: real-life experience from a tertiary New Zealand centre

Current guidelines recommend that a rapid test be used to assist diagnosis of acute promyelocytic leukaemia (APL), but the choice of an assay is discretionary. PML immunofluorescence (PML IF) identifies the microparticulate pattern of the PML protein localisation, highly specific for APL. The aim of this study was to evaluate clinical utility of PML IF in a real-life setting based on a retrospective records review for all patients who had PML IF performed in our centre between 2000 and 2017. Final analysis included 151 patients, 70 of whom had APL. PML IF was reported on average 3 days faster than cytogenetics. Compared with genetic results, PML IF showed sensitivity of 96% and specificity of 100%. PML IF accurately predicted APL in four APL cases with cryptic karyotype/FISH and excluded APL in 98% cases tested based on the suspicious immunophenotype alone, 21/28 of whom had mutated NPM1. Results of PML IF influenced decision to start ATRA in 25 (36%) APL patients and led to its termination in six non-APL patients. In conclusion, PML IF is a fast and reliable test that facilitates accurate treatment decisions when APL is suspected. This performance of PML IF remains hard to match in a real-life setting.

The CD9+ CD11b- HLA-DR- immunophenotype can be used to diagnose acute promyelocytic leukemia

OBJECTIVE

To investigate the immunophenotypic characteristics of acute promyelocytic leukemia (APL) and explore the sensitivity and specificity of various antibody combinations for the timely and accurate diagnosis APL.

METHODS

A retrospective analysis was performed using morphological, immunological, genetic, and molecular biological data from 92 patients diagnosed with APL and 190 controls diagnosed with non-APL acute myeloid leukemia.

RESULTS

For APL diagnosis, the CD9/CD11b/human leukocyte antigen (HLA)-DR antibody combination had 85% sensitivity and 95% specificity, AUC = 0.85. However, the sensitivity and specificity were 39% and 92%, AUC = 0.65, respectively, for the HLA-DR/CD34/CD117 combination, and 80% and 80%, AUC = 0.80, respectively for the CD11b/HLA-DR combination. Significant differences were observed between the different antibody combinations.

CONCLUSIONS

The CD9/CD11b/HLA-DR antibody combination displays high sensitivity and specificity and can be used to diagnose APL.

Basophil-lineage commitment in acute promyelocytic leukemia predicts for severe bleeding after starting therapy

Severe hemorrhagic events occur in a significant fraction of acute promyelocytic leukemia patients, either at presentation and/or early after starting therapy, leading to treatment failure and early deaths. However, identification of independent predictors for high-risk of severe bleeding at diagnosis, remains a challenge. Here, we investigated the immunophenotype of bone marrow leukemic cells from 109 newly diagnosed acute promyelocytic leukemia patients, particularly focusing on the identification of basophil-related features, and their potential association with severe bleeding episodes and patient overall survival.From all phenotypes investigated on leukemic cells, expression of the CD203c and/or CD22 basophil-associated markers showed the strongest association with the occurrence and severity of bleeding (p ≤ 0.007); moreover, aberrant expression of CD7, coexpression of CD34⁺/CD7⁺ and lack of CD71 was also more frequently found among patients with (mild and severe) bleeding at baseline and/or after starting treatment (p ≤ 0.009). Multivariate analysis showed that CD203c expression (hazard ratio: 26.4; p = 0.003) and older age (hazard ratio: 5.4; p = 0.03) were the best independent predictors for cumulative incidence of severe bleeding after starting therapy. In addition, CD203c expression on leukemic cells (hazard ratio: 4.4; p = 0.01), low fibrinogen levels (hazard ratio: 8.8; p = 0.001), older age (hazard ratio: 9.0; p = 0.002), and high leukocyte count (hazard ratio: 5.6; p = 0.02) were the most informative independent predictors for overall survival.In summary, our results show that the presence of basophil-associated phenotypic characteristics on leukemic cells from acute promyelocytic leukemia patients at diagnosis is a powerful independent predictor for severe bleeding and overall survival, which might contribute in the future to (early) risk-adapted therapy decisions.

HLA-DR antigen-positive acute promyelocytic leukemia

Acute promyelocytic leukemia (APL) with t(15;17)(q22;q21)/PML-RARα is a subtype of acute myeloid leukemia (AML) with distinct morphologic and immunophenotypic characteristics. It is a highly aggressive disease that requires rapid diagnosis and early intervention. In addition to morphologic evaluation, flow cytometry has been widely used to facilitate prompt diagnosis of this disease. Compared with other types of AML, APL typically displays a triad of absent or weak CD34, absent HLA-DR, and positive CD117. HLA-DR positive APL is extremely rare and its clinical and pathological features have not been reported. A total of 45 cases of APL with t(15,17)/PML-RARα were diagnosed at Harbor-UCLA Medical Center from year 2006 to 2015. Among them, only two cases were positive for HLA-DR by flow cytometry immunophenotyping. Here we describe the clinical, morphologic, immunophenotypic, and cytogenetic features of these two cases.