The association of complex genetic background with the prognosis of acute leukemia with ambiguous lineage.

Acute leukemia with ambiguous lineage (ALAL) is a rare and highly aggressive malignancy with limited molecular characterization and therapeutic recommendations. In this study, we retrospectively analyzed 1635 acute leukemia cases in our center from January 2012 to June 2018. The diagnose of ALAL was based on either EGIL or 2016 WHO criteria, a total of 39 patients were included. Four patients diagnosed as acute undifferentiated leukemia (AUL) by both classification systems. Among the patients underwent high-throughput sequencing, 89.5% were detected at least one mutation and the median number of gene mutation was 3 (0-8) per sample. The most frequently mutated genes were NRAS (4, 21%), CEBPA (4, 21%), JAK3 (3, 16%), RUNX1 (3, 16%). The mutations detected in mixed-phenotype acute leukemia (MPAL) enriched in genes related to genomic stability and transcriptional regulation; while AUL cases frequently mutated in genes involved in signaling pathway. The survival analysis strongly suggested that mutation burden may play important roles to predict the clinical outcomes of ALAL. In addition, the patients excluded by WHO criteria had even worse clinical outcome than those included. The association of the genetic complexity of blast cells with the clinical outcomes and rationality of the diagnostic criteria of WHO system need to be evaluated by more large-scale prospective clinical studies.

Accurate Machine-Learning-Based classification of Leukemia from Blood Smear Images.

BACKGROUND: Conventional identification of blood disorders based on visual inspection of blood smears through microscope is time consuming, error-prone and is limited by hematologist's physical acuity. Therefore, an automated optical image processing system is required to support the clinical decision-making. MATERIALS AND METHODS: Blood smear slides (n = 250) were prepared from clinical samples, imaged and analyzed in Jimma Medical Center, Hematology department. Samples were collected, analyzed and preserved from out and in-patients. The system was able to categorize four common types of leukemia's such as acute and chronic myeloid leukemia; and acute and chronic lymphoblastic leukemia, through a robust image segmentation protocol, followed by classification using the support vector machine. RESULTS: The system was able to classify leukemia types with an accuracy, sensitivity, specificity of 97.69%, 97.86% and 100%, respectively for the test datasets, and 97.5%, 98.55% and 100%, respectively, for the validation datasets. In addition, the system also showed an accuracy of 94.75% for the WBC counts that include both lymphocytes and monocytes. The computer-assisted diagnosis system took less than one minute for processing and assigning the leukemia types, compared to an average period of 30 minutes by unassisted manual approaches. Moreover, the automated system complements the healthcare workers' in their efforts, by improving the accuracy rates in diagnosis from ∼70% to over 97%. CONCLUSION: Importantly, our module is designed to assist the healthcare facilities in the rural areas of sub-Saharan Africa, equipped with fewer experienced medical experts, especially in screening patients for blood associated diseases including leukemia.

The Care of the Leukemic Patients in Times of SARS-CoV-2.

PURPOSE OF REVIEW: The spread of the novel coronavirus SARS-CoV-2 and its associated disease, coronavirus disease of 2019 (COVID-19), has significantly derailed cancer care. Patients with leukemia are more likely to have severe infection and increased rates of mortality. There is paucity of information on how to modify care of leukemia patients in view of the COVID-19 risks and imposed restrictions. We review the available literature on the impact of COVID-19 on different types of leukemia patients and suggest general as well as disease-specific recommendations on care based on available evidence. RECENT FINDINGS: The COVID-19 infection impacts leukemia subtypes in variable ways and the standard treatments for leukemia have similarly, varying effects on the course of COVID-19 infection. Useful treatment strategies include deferring treatment when possible, use of less intensive regimens, outpatient targeted oral agents requiring minimal monitoring, and prioritization of curative or life-prolonging strategies. Reducing health care encounters, rational transfusion standards, just resource allocation, and pre-emptive advance care planning will serve the interests of leukemia patients. Ad hoc modifications based on expert opinions and extrapolations of previous well-designed studies are the way forward to navigate the crisis. This should be supplanted with more rigorous prospective evidence.

RAMRSGL: A Robust Adaptive Multinomial Regression Model for Multicancer Classification.

In view of the challenges of the group Lasso penalty methods for multicancer microarray data analysis, e.g., dividing genes into groups in advance and biological interpretability, we propose a robust adaptive multinomial regression with sparse group Lasso penalty (RAMRSGL) model. By adopting the overlapping clustering strategy, affinity propagation clustering is employed to obtain each cancer gene subtype, which explores the group structure of each cancer subtype and merges the groups of all subtypes. In addition, the data-driven weights based on noise are added to the sparse group Lasso penalty, combining with the multinomial log-likelihood function to perform multiclassification and adaptive group gene selection simultaneously. The experimental results on acute leukemia data verify the effectiveness of the proposed method.

Assessment of ST2 and Reg3a levels in patients with acute graft-versus-host disease after allogeneic hematopoietic stem cell transplantation

Background/aim: Graft-versus-host disease (GVHD) is a crucial complication leading to significant morbidity and mortality allogeneic hematopoietic stem cell transplantation which occurs in approximately half of the transplant recipients. Suppression of tumorigenicity 2 (ST2) and regenerating islet-derived 3-alpha(Reg3a) might be important biomarkers to predict acute GVHD. Materials and methods: In the present study, blood samples were collected from 17 patients with acute GVHD and 12 control patients after allogeneic stem cell transplantation. ST2 and Reg3a were measured in plasma samples compared in patients with acute GVHD and the controls. Results: Median age of the study population was 42 years (range 19­49). When compared to controls, the mean ST2 levels was significant higher in acute GVHD (9794 ng/dL vs. 2646 ng/dL, P = 0.008). Mean Reg3a level did not show significant difference between control and acute GVHD group (8848 ng/dL vs. 5632 ng/dL, respectively, P = 0.190). Conclusion: The ST2 level might be used as a significant biomarker for predicting acute GVHD.

Introduction and Classification of Leukemias.

Classifying the hematological malignancies by assigning cells to their normal counterpart and describing the nature of disease progression are entirely reliant on an accurate picture for the development of the multifarious types of blood and immune cells. In recent years, our understanding of the complex relationships between the various hematopoietic stem cell-derived cell lineages has undergone substantial revision. There has been similar progress in how we describe the nature of the "target" cells that genetic insults transform to give rise to the hematological malignancies. Here I describe how both longstanding and new information has influenced classifying, for diagnosis, the hematological malignancies.

IoMT-Based Automated Detection and Classification of Leukemia Using Deep Learning.

For the last few years, computer-aided diagnosis (CAD) has been increasing rapidly. Numerous machine learning algorithms have been developed to identify different diseases, e.g., leukemia. Leukemia is a white blood cells- (WBC-) related illness affecting the bone marrow and/or blood. A quick, safe, and accurate early-stage diagnosis of leukemia plays a key role in curing and saving patients' lives. Based on developments, leukemia consists of two primary forms, i.e., acute and chronic leukemia. Each form can be subcategorized as myeloid and lymphoid. There are, therefore, four leukemia subtypes. Various approaches have been developed to identify leukemia with respect to its subtypes. However, in terms of effectiveness, learning process, and performance, these methods require improvements. This study provides an Internet of Medical Things- (IoMT-) based framework to enhance and provide a quick and safe identification of leukemia. In the proposed IoMT system, with the help of cloud computing, clinical gadgets are linked to network resources. The system allows real-time coordination for testing, diagnosis, and treatment of leukemia among patients and healthcare professionals, which may save both time and efforts of patients and clinicians. Moreover, the presented framework is also helpful for resolving the problems of patients with critical condition in pandemics such as COVID-19. The methods used for the identification of leukemia subtypes in the suggested framework are Dense Convolutional Neural Network (DenseNet-121) and Residual Convolutional Neural Network (ResNet-34). Two publicly available datasets for leukemia, i.e., ALL-IDB and ASH image bank, are used in this study. The results demonstrated that the suggested models supersede the other well-known machine learning algorithms used for healthy-versus-leukemia-subtypes identification.

Myeloid/lymphoid neoplasms with eosinophilia and FLT3 rearrangement.

Myeloid/lymphoid neoplasms with eosinophilia and gene rearrangement are a unique category in the WHO classification, and include cases with rearrangement of PDGFRA, PDGFRB, FGFR1, and PCM1-JAK2. We report three patients presented with eosinophilia and FLT3 rearrangement: the first case with chronic eosinophilic leukemia, not otherwise specified and T-lymphoblastic leukemia/lymphoma; the second case with myeloid sarcoma; and the last case with high-grade myelodysplastic syndrome. The first case showed t(13;14)(q12;q32), which encoded FLT3-TRIP11. The patient was treated with intense chemotherapy and subsequently sorafenib with clinical improvement. Unfortunately, the patient showed persistent residual disease and passed away 9 months after the diagnosis from pneumonia. The other two cases both showed ETV6-FLT3. The second patient was treated with local radiation and systemic chemotherapy including sorafenib and was alive. The third patient was treated with chemotherapy but showed transformation to acute myeloid leukemia and died 15 months after diagnosis. These cases are among a growing number of cases with FLT3 rearrangement that all showed similar clinicopathologic features characterized by myeloproliferative neoplasm with eosinophilia and frequent T lymphoblastic leukemia/lymphoma. Therefore, we propose that the myeloid/lymphoid neoplasms with eosinophilia and FLT3 rearrangement be included in the WHO category of myeloid/lymphoid neoplasms with eosinophilia and gene rearrangement.

Ranking Series of Cancer-Related Gene Expression Data by Means of the Superposing Significant Interaction Rules Method.

The Superposing Significant Interaction Rules (SSIR) method is a combinatorial procedure that deals with symbolic descriptors of samples. It is able to rank the series of samples when those items are classified into two classes. The method selects preferential descriptors and, with them, generates rules that make up the rank by means of a simple voting procedure. Here, two application examples are provided. In both cases, binary or multilevel strings encoding gene expressions are considered as descriptors. It is shown how the SSIR procedure is useful for ranking the series of patient transcription data to diagnose two types of cancer (leukemia and prostate cancer) obtaining Area Under Receiver Operating Characteristic (AU-ROC) values of 0.95 (leukemia prediction) and 0.80-0.90 (prostate). The preferential selected descriptors here are specific gene expressions, and this is potentially useful to point to possible key genes.

DNA methylation markers in the diagnosis and prognosis of common leukemias.

The ability to identify a specific type of leukemia using minimally invasive biopsies holds great promise to improve the diagnosis, treatment selection, and prognosis prediction of patients. Using genome-wide methylation profiling and machine learning methods, we investigated the utility of CpG methylation status to differentiate blood from patients with acute lymphocytic leukemia (ALL) or acute myelogenous leukemia (AML) from normal blood. We established a CpG methylation panel that can distinguish ALL and AML blood from normal blood as well as ALL blood from AML blood with high sensitivity and specificity. We then developed a methylation-based survival classifier with 23 CpGs for ALL and 20 CpGs for AML that could successfully divide patients into high-risk and low-risk groups, with significant differences in clinical outcome in each leukemia type. Together, these findings demonstrate that methylation profiles can be highly sensitive and specific in the accurate diagnosis of ALL and AML, with implications for the prediction of prognosis and treatment selection.

Significance of minimal residual disease in pediatric mixed phenotype acute leukemia: a multicenter cohort study.

The rarity of mixed phenotype acute leukemia (MPAL) has precluded adequate data to incorporate minimal residual disease (MRD) monitoring into therapy. Fluidity in MPAL classification systems further complicates understanding its biology and outcomes; this includes uncertainty surrounding the impact of shifting diagnostic requirements even between iterations of the World Health Organization (WHO) classification. Our primary objective was to address these knowledge gaps. To do so, we analyzed clinicopathologic features, therapy, MRD, and survival in a centrally-reviewed, multicenter cohort of MPAL uniformly diagnosed by the WHO classification and treated with acute lymphoblastic leukemia (ALL) regimens. ALL induction therapy achieved an EOI MRD negative (<0.01%) remission in most patients (70%). EOI MRD positivity was predictive of 5-year EFS (HR = 6.00, p < 0.001) and OS (HR = 9.57, p = 0.003). Patients who cleared MRD by EOC had worse survival compared with those EOI MRD negative. In contrast to adults with MPAL, ALL therapy without transplantation was adequate to treat most pediatric patients. Earlier MRD clearance was associated with better treatment success and survival. Prospective trials are now necessary to validate and refine MRD thresholds within the pediatric MPAL population and to identify salvage strategies for those with poor predicted survival.

The clinico-pathological spectrum of primary cutaneous lymphoma other than mycosis fungoides/Sezary syndrome.

The major aim of Session 1 of the 2018 European Association of Hematopathology/Society for Hematopathology Workshop was to collect examples of cutaneous lymphomas, excluding mycosis fungoides/Sezary syndrome, as defined in the current WHO classification of tumours of the haemetopoietic and lymphoid tissues. Overall 42 cases were submitted. These were considered in four main categories: primary cutaneous B cell lymphomas (12 cases), primary cutaneous T cell lymphomas/lymphoproliferations with CD8+/cytotoxic phenotype (12 cases), primary cutaneous CD30-positive lymphoproliferative disorders (15 cases) and primary cutaneous T cell lymphomas/leukaemias with CD4+ phenotype (4 cases). Using these cases as examples, we were able to present the full spectrum of cutaneous lymphoproliferations (excluding mycosis fungoides/Sezary syndrome), including examples of rare, provisional and new entities as listed in the 2017 update of the WHO classification. The findings are summarized in this report with emphasis on differential diagnostic considerations and the importance of clinico-pathological correlation for final subtyping. In presenting these findings we hope to raise awareness of this enigmatic group of neoplasms and to further our understanding of these rare disease entities.

Recognition and management of leukaemia in children.

Leukaemia is the most common cancer in children. The presenting manifestations can be wide-ranging, from a relatively well child to life-threatening complications. Symptoms can be manifested in any of the bodily systems. Undertaking a thorough clinical assessment of the child, in addition to recognising and addressing parental concerns, is vital. Furthermore, recognising that children can commonly present with musculoskeletal or abdominal symptoms increases the diagnostic yield, thereby preventing missed or late diagnoses. Childhood cancer has a huge impact on the child and their family, both at diagnosis and in the long term; providing advice and signposting families to appropriate support groups is an important aspect of their management. Nurses play a vital role in managing children with cancers, starting from raising suspicion and identifying the child with leukaemia, ensuring that high-quality care is delivered throughout their treatment, managing complications, and providing support and information to children and their families. An illustrative case study is included to highlight some of the challenges that health professionals may encounter in their clinical practice.

Automatic recognition of different types of acute leukaemia in peripheral blood by image analysis.

AIMS: Morphological differentiation among different blast cell lineages is a difficult task and there is a lack of automated analysers able to recognise these abnormal cells. This study aims to develop a machine learning approach to predict the diagnosis of acute leukaemia using peripheral blood (PB) images. METHODS: A set of 442 smears was analysed from 206 patients. It was split into a training set with 75% of these smears and a testing set with the remaining 25%. Colour clustering and mathematical morphology were used to segment cell images, which allowed the extraction of 2,867 geometric, colour and texture features. Several classification techniques were studied to obtain the most accurate classification method. Afterwards, the classifier was assessed with the images of the testing set. The final strategy was to predict the patient's diagnosis using the PB smear, and the final assessment was done with the cell images of the smears of the testing set. RESULTS: The highest classification accuracy was achieved with the selection of 700 features with linear discriminant analysis. The overall classification accuracy for the six groups of cell types was 85.8%, while the overall classification accuracy for individual smears was 94% as compared with the true confirmed diagnosis. CONCLUSIONS: The proposed method achieves a high diagnostic precision in the recognition of different types of blast cells among other mononuclear cells circulating in blood. It is the first encouraging step towards the idea of being a diagnostic support tool in the future.

Clinical, immunophenotypic, and genomic findings of acute undifferentiated leukemia and comparison to acute myeloid leukemia with minimal differentiation: a study from the bone marrow pathology group.

Acute undifferentiated leukemia is a rare type of acute leukemia that shows no evidence of differentiation along any lineage. Clinical, immunophenotypic and genetic data is limited and it is uncertain if acute undifferentiated leukemia is biologically distinct from acute myeloid leukemia with minimal differentiation, which also shows limited myeloid marker expression and has been reported to have a poor prognosis. We identified 92 cases initially diagnosed as acute undifferentiated leukemia or acute myeloid leukemia with minimal differentiation from pathology databases of nine academic institutions with available diagnostic flow cytometric data, cytogenetic findings, mutational and clinical data. Outcome analysis was performed using Kaplan Meier test for the 53 patients who received induction chemotherapy. Based on cytogenetic abnormalities (N = 30) or history of myelodysplastic syndrome (N = 2), 32 cases were re-classified as acute myeloid leukemia with myelodysplasia related changes. The remaining 24 acute undifferentiated leukemia patients presented with similar age, blood counts, bone marrow cellularity, and blast percentage as the remaining 30 acute myeloid leukemia with minimal differentiation patients. Compared to acute myeloid leukemia with minimal differentiation, acute undifferentiated leukemia cases were characterized by more frequent mutations in PHF6 (5/15 vs 0/19, p = 0.016) and more frequent expression of TdT on blasts (p = 0.003) while acute myeloid leukemia with minimal differentiation cases had more frequent CD123 expression (p = 0.042). Outcome data showed no difference in overall survival, relapse free survival, or rates of complete remission between acute undifferentiated leukemia and acute myeloid leukemia with minimal differentiation groups (p > 0.05). Acute myeloid leukemia with myelodysplasia-related changes patients showed shorter survival when censoring for bone marrow transplant as compared to acute undifferentiated leukemia (p = 0.03) and acute myeloid leukemia with minimal differentiation (p = 0.002). In this largest series to date, the acute undifferentiated leukemia group shows distinct characteristics from acute myeloid leukemia with minimal differentiation, including more frequent PHF6 mutations and expression of TdT.

The Diagnostic Work-Up of Hypereosinophilia.

Hypereosinophilia (HE) is defined as a persistent elevated eosinophil count of ≥1.5 × 109/L. HE can be one of the dominant manifestations of a hematopoietic myeloid neoplasm or secondary/reactive to an underlying medical condition. If a cause of HE and its associated tissue/organ damage is not determined, the condition is considered to be idiopathic hypereosinophilic syndrome (HES). The work-up of HE can be challenging due to a broad range of causes of HE that can be either reactive or neoplastic. In recent years, with the advent of molecular genetic testing and the introduction of targeted therapy in the management of these patients, there is a growing interest in better characterization of these diseases. Using a multimodality approach and following a proper -algorithm, a diagnosis can be made in a large proportion of patients. In idiopathic HES, myeloid neoplasm associated -somatic mutations as evidence of clonality are reported in -20-25% patients; however, the mutation data should be -interpreted cautiously considering the prevalence of clonal hematopoiesis of indeterminate potential (CHIP). Bone marrow morphology has been shown to have important value in the identification of a true myeloid neoplasm in these disorders. A genome-wide study may be needed to understand the "idiopathic" cases that would ultimately lead to better patient care.

Adult leukemia survival trends in the United States by subtype: A population-based registry study of 370,994 patients diagnosed during 1995-2009.

BACKGROUND: The lifetime risk of developing leukemia in the United States is 1.5%. There are challenges in the estimation of population-based survival using registry data because treatments and prognosis vary greatly by subtype. The objective of the current study was to determine leukemia survival estimates in the United States from 1995 to 2009 according to subtype, sex, geographical area, and race. METHODS: Five-year net survival was estimated using data for 370,994 patients from 43 registries in 37 states and in 6 metropolitan areas, covering approximately 81% of the adult (15-99 years) US population. Leukemia was categorized according to principal subtype (chronic lymphocytic leukemia, acute myeloid leukemia, and acute lymphocytic leukemia), and subcategorized in accordance with the HAEMACARE protocol. We analyzed age-standardized 5-year net survival by calendar period (1995-1999, 2000-2004, and 2005-2009), leukemia subtype, sex, race, and US state. RESULTS: The age-standardized 5-year net survival estimates increased from 45.0% for patients diagnosed during 1995-1999 to 49.0% for those diagnosed during 2000-2004 and 52.0% for those diagnosed during 2005-2009. For patients diagnosed during 2005-2009, 5-year survival was 18.2% (95% confidence interval [95% CI], 17.8%-18.6%) for acute myeloid leukemia, 44.0% (95% CI, 43.2%-44.8%) for acute lymphocytic leukemia, and 77.3% (95% CI, 76.9%-77.7%) for chronic lymphocytic leukemia. For nearly all leukemia subtypes, survival declined in successive age groups above 45 to 54 years. Men were found to have slightly lower survival than women; however, this discrepancy was noted to have fallen in successive calendar periods. Net survival was substantially higher in white than black patients in all calendar periods. There were large differences in survival noted between states and metropolitan areas. CONCLUSIONS: Survival from leukemia in US adults improved during 1995-2009. Some geographical differences in survival may be related to access to care. We found disparities in survival by sex and between black and white patients.

Toward the potential cure of leukemias in the next decade.

Historically, progress in leukemia research has been slow, but it has accelerated recently as a result of understanding the pathophysiology of leukemias and implementing more effective and targeted therapies. This review summarizes the progress across leukemia subsets and projects the potential cure of most leukemias in the next decade.