Immunophenotypic characterization of acute leukemias in Bahia, Brazil

Santos, Mariane Melo dos; Santos, Allan de souza; Santos, Herbert Henrique de Melo; Santos, Lorene da Silva; Meyer, Roberto; Torres, Alex José Leite

doi:10.31744/einstein_journal/2023ao0117

Cited by 3 publications

(1 citation statement)

References 52 publications

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“…Overall, we observed the expression of CD13 in 21.8% of the B‐ALL patients, CD33 in 19.7% of the B‐ALL patients, and CD117 in 4.7% of the B‐ALL patients. The authors from high‐income and low‐income countries reported the aberrant expression of myeloid marker expression including 10.5%–54.5% CD13, 2.6%–89% CD33, and 0%–26.2% CD117 38,39,60,62–75 . These differences in frequencies of expression of various myeloid markers might be attributed to the inherent genetic differences among ethnic subpopulations, technical factors such as lack of uniformity in using monoclonal antibody clone type, and differences in flow cytometry methodology, processing, and data analysis 74,76 .…”

Section: Discussionmentioning

confidence: 99%

Hematological, clinical, immunophenotypic characterization, and treatment outcomes of prognostically significant genetic subtypes of B‐lineage acute lymphoblastic leukemia: A report of 1021 patients from India

Gupta

Varma

Sharma

et al. 2023

Cancer

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BackgroundThe published literature on hematological, clinical, flowcytometric‐immunophenotyping, and minimal residual disease outcomes of the prognostically important genetic subtypes of acute lymphoblastic leukemia (ALL) is scarce from low‐income countries. For newer classifications such as BCR::ABL1‐like ALLs, the scarcity of patient‐level data is even more pronounced.MethodsThe authors performed comprehensive detection of recurrent gene fusions and BCR::ABL1‐like ALL cases followed by immunophenotypic profiling and obtained clinical outcome parameters for a large cohort (n = 1021) of patients from India. This cohort included a significant number of patients with BCR::ABL1‐like ALL subtype and other genetic subtypes of ALL.ResultsPatients with BCR::ABL1‐positive and BCR::ABL1‐like ALL were significantly older, had male preponderance, and expressed a higher white blood cell count than BCR::ABL1‐negative cases (p < .05). Logistic regression modeling of B‐lineage‐ALL (B‐ALL) subtypes revealed that cluster of differentiation (CD)36 is a strong statistically significant predictive marker of BCR::ABL1‐like ALL (p < .05). Furthermore, patients with BCR::ABL1‐like ALLs show a significantly higher frequency of CD36 expression compared to BCR::ABL1‐negative ALLs (p < .05). In terms of clinical symptoms, lymphadenopathy is a strong statistically significant predictive marker in BCR::ABL1‐like ALLs compared to BCR::ABL1‐negative ALL cases (p < .05). In terms of treatment outcomes, minimal residual disease (MRD) positivity in BCR::ABL1‐positive ALL cases were statistically significant (p < .05), and BCR::ABL1‐like ALL cases had high MRD‐positivity as compared to BCR::ABL1‐negative ALL cases but did not show statistical significance.ConclusionsThe findings evince the use of novel therapies and personalized treatment regimens to improve the overall survival of the newer incorporated entities in B‐ALLs. This is the first report characterizing the hematological, clinical, flowcytometric‐immunophenotyping, and minimal residual disease outcomes of the prognostically significant subtypes of ALLs in patients from India.Plain Language Summary Characterizing the hematological, clinical, flowcytometric‐immunophenotyping, and minimal residual disease outcomes of the prognostically significant subtypes (n = 1021) of acute lymphoblastic leukemia (ALLs) in patients from India. We have made two independent logistic regression models of cluster of differentiation (CD) markers and clinical symptoms to differentiate prognostically significant subtypes of ALLs. Logistic regression analysis of CD markers revealed CD36 as a strong predictor in BCR::ABL1‐like ALL cases compared to BCR::ABL1‐negative ALL cases. Logistic regression analysis of clinical symptoms revealed lymphadenopathy significantly predicts BCR::ABL1‐like ALLs (p < .05). In terms of treatment outcomes, BCR::ABL1‐positive ALL had statistically significant minimal residual disease (MRD) (p < .05), and BCR::ABL1‐like ALL cases had high MRD‐positivity but did not show statistical significance as compared to BCR::ABL1‐negative ALLs.

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Section: Discussionmentioning

confidence: 99%

Hematological, clinical, immunophenotypic characterization, and treatment outcomes of prognostically significant genetic subtypes of B‐lineage acute lymphoblastic leukemia: A report of 1021 patients from India

Gupta

Varma

Sharma

et al. 2023

Cancer

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Flow cytometric detection of leukemic blasts in Libyan pediatric patients with acute lymphoblastic leukemia

Elbnnani,

Elbasir,

Altabal

et al. 2024

Libyan Journal of Medicine

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Pattern Recognition of Acute Lymphoblastic Leukemia (ALL) Using Computational Deep Learning

et al. 2023

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Leukemia is a cancer of blood-producing cells, including the bone marrow. Abnormal white blood cells travel through blood vessels and multiply rapidly. Healthy cells in the body become a minority, and the imbalance increases the chances of infection in the body. Leukemia or blood cancer is the most common cancer in children ages 2 -14. Most leukemia in children is treated. Acute lymphocytic leukemia (ALL) is a type of cancer in the blood and bone marrow. It progresses rapidly when immature white blood cells are formed instead of mature ones. Treatments for acute lymphocytic leukemia include drugs and blood transfusions directly into veins, chemotherapy, and all transplantation, which involve transferring organs or tissues within the body or from one person to another. In this paper, Pattern Recognition of Acute Lymphoblastic Leukemia has been proposed using Computational Deep Learning. Pattern recognition technology uses mathematical algorithms to identify patterns in large datasets of data. Analyzing the data, the algorithms can identify patterns indicative of certain states or conditions. In the case of ALL, the algorithm would look for patterns in white blood cell count data that indicate the presence of ALL. These patterns may include changes in the number of white blood cells over time, changes in the composition of the white blood cells, or changes in the levels of certain proteins or gene expressions associated with ALL. The proposed ALLDM model achieved 81.53% (DDS) and 87.92% (SDS) of chemotherapy management, 79.16% (DDS) and 94.31% (SDS) of Stem Cell Transplantation Management, 63.77% (DDS) and 87.37% (SDS) of Radiation therapy Management and 88.92% (DDS) and 85.86% (SDS) of Targeted therapy drugs management.

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Immunophenotypic characterization of acute leukemias in Bahia, Brazil

Cited by 3 publications

References 52 publications

Hematological, clinical, immunophenotypic characterization, and treatment outcomes of prognostically significant genetic subtypes of B‐lineage acute lymphoblastic leukemia: A report of 1021 patients from India

Hematological, clinical, immunophenotypic characterization, and treatment outcomes of prognostically significant genetic subtypes of B‐lineage acute lymphoblastic leukemia: A report of 1021 patients from India

Flow cytometric detection of leukemic blasts in Libyan pediatric patients with acute lymphoblastic leukemia

Pattern Recognition of Acute Lymphoblastic Leukemia (ALL) Using Computational Deep Learning

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