Leukemia diagnosis: today and tomorrow

Digital PCR as a New Method for Minimal Residual Disease Monitoring and Treatment Free Remission Management in Chronic Myeloid Leukemia Patients: Is It Reliable?

The effective and sensitive monitoring of Minimal Residual Disease or Measurable Residual Disease (MRD) is a very important aspect in the management of patients affected by hematologic malignancies. The recent availability of new technologies has opened to the improvement of MRD monitoring. It is particularly relevant in patients affected by Chronic Myeloid Leukemia (CML). MRD monitoring is key in the management of CML patients thanks to the efficacy of TKIs therapy. Moreover, the policies of TKIs discontinuation aimed at treatment free remission are strongly based on the good selection of patients eligible for stopping TKIs therapy. The recently described application of digital PCR in CML patients monitoring seems to improve the accuracy and precision in the identification of optimal responders. The present review reports an overview on the application of digital PCR in the monitoring of MRD in CML and its impact on TKIs discontinuation trials and, consequently, on TFR success.

Diagnosis of Acute Leukemia by Multiparameter Flow Cytometry with the Assistance of Artificial Intelligence

We developed an artificial intelligence (AI) model that evaluates the feasibility of AI-assisted multiparameter flow cytometry (MFC) diagnosis of acute leukemia. Two hundred acute leukemia patients and 94 patients with cytopenia(s) or hematocytosis were selected to study the AI application in MFC diagnosis of acute leukemia. The kappa test analyzed the consistency of the diagnostic results and the immunophenotype of acute leukemia. Bland–Altman and Pearson analyses evaluated the consistency and correlation of the abnormal cell proportion between the AI and manual methods. The AI analysis time for each case (83.72 ± 23.90 s, mean ± SD) was significantly shorter than the average time for manual analysis (15.64 ± 7.16 min, mean ± SD). The total consistency of diagnostic results was 0.976 (kappa (κ) = 0.963). The Bland–Altman evaluation of the abnormal cell proportion between the AI analysis and manual analysis showed that the bias ± SD was 0.752 ± 6.646, and the 95% limit of agreement was from −12.775 to 13.779 (p = 0.1225). The total consistency of the AI immunophenotypic diagnosis and the manual results was 0.889 (kappa, 0.775). The consistency and speedup of the AI-assisted workflow indicate its promising clinical application.

Development of an algorithm for the identification of leukemic hematolymphoid neoplasms in Primary Care patients

BACKGROUND

Diagnosis of hematolymphoid neoplasm (HLN) requires different technologies which are performed on a patient basis instead of per protocol. We hypothesize that integration of hematimetric and cytological analysis along with multiparametric flow cytometry (MFC) provides a framework to evaluate peripheral blood (PB) samples from Primary Care.

METHODS

Samples from patients with persistent (>3 months) lymphocytosis (>5 × 109/L) and/or monocytosis (>109/L) or the presence of atypical and/or blast cells upon the smear review were analyzed by MFC concurrent to cytological analysis. MFC studies were carried out following standardized procedures.

RESULTS

In a 3-year period, smear review and MFC were performed simultaneously in 350 samples, demonstrating HLN in 194 cases (55.4%). In 156 cases, reactive cell populations were found. The combination of age, absolute lymphocyte count (ALC), hemoglobin and platelets provided the best correlation with MFC for the presence of a chronic lymphoproliferative disorder (CLPD) in lymphocytosis [area under the curve (AUC) 0.891, p < 0.05]. A model evaluating the probability of CLPD has been proposed and validated in an independent cohort.

CONCLUSIONS

A strategy to perform MFC studies following standardized procedures has proven to be useful to evaluate samples from patients in Primary Care centers for HLN diagnosis or reactive conditions, providing a sensitive and rapid clinical orientation and avoiding unnecessary consultations in routine clinical practice. The probability for the presence of CLPD in PB can be calculated and help guide decision-making regarding further testing.

Modeling leukemia with pediatric acute leukemia patient-derived iPSCs

OBJECTIVE

ediatric acute leukemia (AL) is the most common hematological malignancy in childhood. However, the limitation of clinical specimens hindered the progress of research. Therefore, new research platforms are urgently needed to establish and clarify the pathogenesis of pediatric AL, and it is necessary to try to find novel targeted therapies for the clinical use. Here, the induced pluripotent stem cells (iPSCs) derived from AL provide a reliable model for basic research.

METHODS

eukemia cells were sorted by flow cytometry and then reprogrammed into iPSCs by Sendai virus. Cell cycle assay was used to analyze cell proliferation.

RESULTS

iPS cell lines from T cell acute lymphoblastic leukemia (T-ALL) and acute myeloid leukemia (AML) cells were successfully established. The reprogramming efficiency of AML cells was much higher than that of ALL cells. Disease iPS cells switched off the expression of the disease marker genes at iPS and HPC stage. When different subtypes of AML-iPSCs were differentiated into hematopoietic progenitor cells, iPS derived from acute megakaryocytic leukemia was more readily differentiated into megakaryocyte-erythroid progenitors. Whereas, the differentiation of multipotent lymphoid progenitor (MLP) and granulocyte macrophage progenitor (GMP) were blocked. The iPS derived from acute monocyte leukemia (AMCL) also showed the differentiation of common myeloid progenitors (CMP), GMP and monocytes significantly increased but MLP differentiation was inhibited. The AML-iPSC could form teratomas and we could obverse three germ layers in vivo, indicating that the AML-iPSCs have full pluripotency. However, there were not enough blood cells in teratoma to identify the leukemia.

CONCLUSIONS

Our results provide a novel platform for AL research and critical insight into the difference of hematopoietic differentiation between ALL and AML.

Development and Evaluation of a Leukemia Diagnosis System Using Deep Learning in Real Clinical Scenarios

Leukemia is the most common malignancy affecting children. The morphologic analysis of bone marrow smears is an important initial step for diagnosis. Recent publications demonstrated that artificial intelligence is able to classify blood cells but a long way from clinical use. A total of 1,732 bone marrow images were used for the training of a convolutional neural network (CNN). New techniques of deep learning were integrated and an end-to-end leukemia diagnosis system was developed by using raw images without pre-processing. The system creatively imitated the workflow of a hematologist by detecting and excluding uncountable and crushed cells, then classifying and counting the remain cells to make a diagnosis. The performance of the CNN in classifying WBCs achieved an accuracy of 82.93%, precision of 86.07% and F1 score of 82.02%. And the performance in diagnosing acute lymphoid leukemia achieved an accuracy of 89%, sensitivity of 86% and specificity of 95%. The system also performs well at detecting the bone marrow metastasis of lymphoma and neuroblastoma, achieving an average accuracy of 82.93%. This is the first study which included a wider variety of cell types in leukemia diagnosis, and achieved a relatively high performance in real clinical scenarios.

How I investigate difficult cells at the optical microscope

Blood cell morphological identification on the peripheral blood and bone marrow films remains a cornerstone for the diagnosis of hematological neoplasms to be integrated with immunophenotyping, molecular genetics, and histopathology. Although standardization is still far from being achieved, with high interobserver variability, in recent years, several classification approaches, from the 1976 FAB to the 2016 WHO classification, have provided hematologists with detailed morphological descriptions for a large number of diseases. Counting blasts and detecting dysplastic specimens are two cornerstones of morphological diagnosis. This review deals with identifying difficult cells, with particular reference of those with relevant diagnostic implications.

Knockout Of BIRC5 Gene By CRISPR/Cas9 Induces Apoptosis And Inhibits Cell Proliferation In Leukemic Cell Lines, HL60 And KG1

INTRODUCTION

Human Baculoviral inhibitor of apoptosis repeat-containing 5 (BIRC5) which encodes survivin exhibits multiple biological activities, such as cell proliferation and apoptosis. Survivin is overexpressed in numerous malignant diseases including acute myeloid leukemia (AML). Recent studies have shown that the CRISPR/Cas9 nuclease-mediated gene-editing systems are suitable approach's for editing or knocking out various genes including oncogenes.

METHODS AND MATERIALS

We used CRISPR-Cas9 to knockout the BIRC5 in the human leukemic cell line, HL60, and KG1, and these cell lines were transfected with either the Cas9- and three sgRNAs expressing plasmids or negative control (scramble) using Lipofectamine 3000. The efficacy of the transfection was determined by quantitative reverse transcription-polymerase chain (RT-qPCR) and surveyor mutation assays. Cell proliferation and apoptosis were measured by MTT assay and flow cytometry, respectively.

RESULTS

We have successfully knocked out the BIRC5 gene in these leukemic cells and observed that the BIRC5-knocked out cells by CRISPR/Cas9 showed a significant decrease (30 folds) of survivin at mRNA levels. Moreover, cell death and apoptosis were significantly induced in BIRC5-CRISPR/Cas9-transfected cells compared to the scramble vector.

CONCLUSION

We demonstrated for the first time that targeting BIRC5 by CRISPR/Cas9 technology is a suitable approach for the induction of apoptosis in leukemic cells. However, further studies targeting this gene in primary leukemic cells are required.

The predictive value of morphological findings in early diagnosis of acute myeloid leukemia with recurrent cytogenetic abnormalities

This study explores cytomorphologic features and their predictive role for early identification of acute myeloid leukemia (AML) with morphological distinctive recurrent cytogenetic abnormalities (RCA): t(15;17), t(8;21) and inv(16)/t(16;16). We retrospectively evaluated 396 de novo AML cases, diagnosed and treated at single institution, between 2013-2017. Specific cytomorphologic features suggesting distinctive AML-RCA were revealed at diagnosis in 62 (15.65%) patients, including AML with t(15;17) in 41 (66.13%), t(8;21) in 13 (20.97%) and inv(16)/t(16;16) in 8 (12.90%). Final diagnoses of AML-RCA according to WHO integrated diagnostic criteria were established in 66 (16.66%) cases, including AML with t(15;17) 40 (60.60%), t(8;21) 17 (25.76%), and inv(16)/t(16;16) 9 (13.64%). Discordance between cytomorphological and other integrated criteria was detected as missed/wrong-call in 0/1 for t(15;17), 6/2 for t(8;21) and 2/1 for inv(16)/t(16;16). The cytomorphological accuracy was 97.56% (40/41) for t(15;17), 57.89% (11/19) for t(8;21) and 70% (7/10) for inv (16)/t(16;16). Positive/negative predictive values of cytomorphological evaluation were: 97.56%/100% for t(15;17); 84.62%/88.68% for t(8;21); 87.50%/96.65% for inv(16)/t(16;16). Sensitivity/specificity were: 100%/96.15% for t(15;17); 64.10%/95.92% for t(8;21); 77.78%/98.25% for inv(16)/t(16;16). We confirmed that morphology is still a highly relevant evaluation method in diagnosing several common AML-RCAs before completing cytogenetic and molecular studies, enabling early detection, particularly of AML with t(15;17).

Trisomy 21 with t(5; 11) chromosomal translocation as new unfavorable cytogenetic abnormalities in pediatric acute myeloid leukemia type M2: One case report of nine-year follow-up and literature review

We report one case of pediatric acute myeloid leukemia type 2 (AML-M2) who presented with karyotypic aberration of trisomy 21 with the t(5;11) chromosomal translocation. The patient achieved complete remission after two cycles of chemotherapy of daunorubicin, cytarabine and etoposide. Then, follow-up cytogenetic analysis from bone marrow cell cultures demonstrated a normal karyotype of 46, XY. After 9 years, the patient relapsed and the karyotypic abnormalities of trisomy 21 with t(5;11) reappeared. It was concluded that trisomy 21 with t(5; 11) is a new unfavorable cytogenetic aberration in AML-M2.

Flow cytometric analysis of CD64 expression pattern and density in the diagnosis of acute promyelocytic leukemia: a multi-center study in Shanghai, China

No unified immunophenotypic profiles and corresponding analytic strategies have been established for the rapid diagnosis of acute promyelocytic leukemia (APL) using flow cytometry (FCM). Here we describe a characteristic immunophenotypic panel that can rapidly and accurately distinguish APL from other types of adult acute myeloid leukemia (AML) using only FCM. By comparing APL cells and non-APL AML cells that share APL common immunophenotypes (CD34⁻CD117⁺HLA⁻DR⁻) we found that CD64 was a significant factor that differentiated APL from other AMLs. Further retrospective analyses of 205 APL and 629 non-APL AML patients from different hematology centers showed that either the CD64^{dim and homo}CD13^+homo CD33^+homoMPO⁺ (myeloperoxidase) CD11c⁻ panel or the CD64^{dim and homo}CD13^+homo CD33^+homoMPO⁺ CD11c⁺CD10⁻CD117⁺ SSC^high (high side scatter signal) panel could distinguish APL from non-APL AML patients with nearly 100% sensitivity, specificity and accuracy. Moreover, relative quantification of CD64 expression enhanced the applicability of our APL diagnostic immunophenotypic panels (ADI-panels) in different hematology centers. Application of the ADI-panels will decrease diagnosis time and improve personalized treatment for APL, a life-threatening disease with very rapid progression.

Detailed Characterization of Mesenchymal Stem/Stromal Cells from a Large Cohort of AML Patients Demonstrates a Definitive Link to Treatment Outcomes

Bone marrow mesenchymal stem/stromal cells (BM-MSCs) are key components of the hematopoietic niche thought to have a direct role in leukemia pathogenesis. BM-MSCs from patients with acute myeloid leukemia (AML) have been poorly characterized due to disease heterogeneity. We report a functional, genetic, and immunological characterization of BM-MSC cultures from 46 AML patients, stratified by molecular/cytogenetics into low-risk (LR), intermediate-risk (IR), and high-risk (HR) subgroups. Stable MSC cultures were successfully established and characterized from 40 of 46 AML patients irrespective of the risk subgroup. AML-derived BM-MSCs never harbored tumor-specific cytogenetic/molecular alterations present in blasts, but displayed higher clonogenic potential than healthy donor (HD)-derived BM-MSCs. Although HD- and AML-derived BM-MSCs equally provided chemoprotection to AML cells in vitro, AML-derived BM-MSCs were more immunosuppressive/anti-inflammatory, enhanced suppression of lymphocyte proliferation, and diminished secretion of pro-inflammatory cytokines. Multivariate analysis revealed that the level of interleukin-10 produced by AML-derived BM-MSCs as an independent prognostic factor negatively affected overall survival. Collectively our data show that AML-derived BM-MSCs are not tumor related, but display functional differences contributing to therapy resistance and disease evolution.

Network Biomarkers Constructed from Gene Expression and Protein-Protein Interaction Data for Accurate Prediction of Leukemia

Leukemia is a leading cause of cancer deaths in the developed countries. Great efforts have been undertaken in search of diagnostic biomarkers of leukemia. However, leukemia is highly complex and heterogeneous, involving interaction among multiple molecular components. Individual molecules are not necessarily sensitive diagnostic indicators. Network biomarkers are considered to outperform individual molecules in disease characterization. We applied an integrative approach that identifies active network modules as putative biomarkers for leukemia diagnosis. We first reconstructed the leukemia-specific PPI network using protein-protein interactions from the Protein Interaction Network Analysis (PINA) and protein annotations from GeneGo. The network was further integrated with gene expression profiles to identify active modules with leukemia relevance. Finally, the candidate network-based biomarker was evaluated for the diagnosing performance. A network of 97 genes and 400 interactions was identified for accurate diagnosis of leukemia. Functional enrichment analysis revealed that the network biomarkers were enriched in pathways in cancer. The network biomarkers could discriminate leukemia samples from the normal controls more effectively than the known biomarkers. The network biomarkers provide a useful tool to diagnose leukemia and also aids in further understanding the molecular basis of leukemia.

Differentiation of normal and leukemic cells by 2D light scattering label-free static cytometry

Two-dimensional (2D) light scattering patterns of single microspheres, normal granulocytes and leukemic cells are obtained by label-free static cytometry. Statistical results of experimental 2D light scattering patterns obtained from standard microspheres with a mean diameter of 4.19 μm agree well with theoretical simulations. High accuracy rates (greater than 92%) for label-free differentiation of normal granulocytes and leukemic cells, both the acute and chronic leukemic cells, are achieved by analyzing the 2D light scattering patterns. Our label-free static cytometry is promising for leukemia screening in clinics.

Clinical evaluation of panel testing by next-generation sequencing (NGS) for gene mutations in myeloid neoplasms

BACKGROUND

Genomic techniques in recent years have allowed the identification of many mutated genes important in the pathogenesis of acute myeloid leukemia (AML). Together with cytogenetic aberrations, these gene mutations are powerful prognostic markers in AML and can be used to guide patient management, for example selection of optimal post-remission therapy. The mutated genes also hold promise as therapeutic targets themselves. We evaluated the applicability of a gene panel for the detection of AML mutations in a diagnostic molecular pathology laboratory.

METHODS

Fifty patient samples comprising 46 AML and 4 other myeloid neoplasms were accrued for the study. They consisted of 19 males and 31 females at a median age of 60 years (range: 18-88 years). A total of 54 genes (full coding exons of 15 genes and exonic hotspots of 39 genes) were targeted by 568 amplicons that ranged from 225 to 275 bp. The combined coverage was 141 kb in sequence length. Amplicon libraries were prepared by TruSight myeloid sequencing panel (Illumina, CA) and paired-end sequencing runs were performed on a MiSeq (Illumina) genome sequencer. Sequences obtained were analyzed by in-house bioinformatics pipeline, namely BWA-MEM, Samtools, GATK, Pindel, Ensembl Variant Effect Predictor and a novel algorithm ITDseek.

RESULTS

The mean count of sequencing reads obtained per sample was 3.81 million and the mean sequencing depth was over 3000X. Seventy-seven mutations in 24 genes were detected in 37 of 50 samples (74 %). On average, 2 mutations (range 1-5) were detected per positive sample. TP53 gene mutations were found in 3 out of 4 patients with complex and unfavorable cytogenetics. Comparing NGS results with that of conventional molecular testing showed a concordance rate of 95.5 %. After further resolution and application of a novel bioinformatics algorithm ITDseek to aid the detection of FLT3 internal tandem duplication (ITD), the concordance rate was revised to 98.2 %.

CONCLUSIONS

Gene panel testing by NGS approach was applicable for sensitive and accurate detection of actionable AML gene mutations in the clinical laboratory to individualize patient management. A novel algorithm ITDseek was presented that improved the detection of FLT3-ITD of varying length, position and at low allelic burden.