Platelet–Acute Leukemia Interactions

Zhang, Li; Liu, Jing; Qin, Xiang; Liu, Wenjun

doi:10.1016/j.cca.2022.09.015

Cited by 7 publications

(2 citation statements)

References 95 publications

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“…Studies showed that cytogenetic or molecular abnormalities had an influence on proliferation and differentiation of MKs as well as platelets production in AML patients [ 32 , 50 ]. The thrombopoietin (TPO)/myeloproliferative leukemia virus oncogene (MPL) pathway plays a critical role in both normal and malignant hematopoiesis and megakaryopoiesis [ 51 , 52 ].…”

Section: Discussionmentioning

confidence: 99%

Influence on therapeutic outcome of platelet count at diagnosis in patients with de novo non-APL acute myeloid leukemia

Zhang,

Wu,

Yuan

et al. 2023

BMC Cancer

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Background Platelet (PLT) count at diagnosis plays an important role in cancer development and progression in solid tumors. However, it remains controversial whether PLT count at diagnosis influences therapeutic outcome in patients with non-acute promyelocytic leukemia (APL) acute myeloid leukemia (AML). Methods This study analyzed the relationship between PLT count at diagnosis and genetic mutations in a cohort of 330 newly diagnosed non-APL AML patients. The impact of PLT count on complete remission, minimal residual disease status and relapse-free survival (RFS) were evaluated after chemotherapy or allogeneic hematopoietic stem cell transplantation (allo-HSCT). Results Our studies showed that patients with DNMT3A mutations have a higher PLT count at diagnosis, while patients with CEBPA biallelic mutations or t(8;21)(q22; q22) translocation had lower PLT count at diagnosis. Furthermore, non-APL AML patients with high platelet count (> 65 × 109/L) at diagnosis had worse response to induction chemotherapy and RFS than those with low PLT count. In addition, allo-HSCT could not absolutely attenuated the negative impact of high PLT count on the survival of non-APL AML patients. Conclusion PLT count at diagnosis has a predictive value for therapeutic outcome for non-APL AML patients.

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Section: Discussionmentioning

confidence: 99%

Influence on therapeutic outcome of platelet count at diagnosis in patients with de novo non-APL acute myeloid leukemia

Zhang,

Wu,

Yuan

et al. 2023

BMC Cancer

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show abstract

“…Similarly, eosinophils and basophils also infiltrate multiple tumors and are activated to regulate tumor progression, either by directly interacting with cancer cells or indirectly by modulating the TME ( 185 – 188 ). Platelets, small anucleate structures derived from BM megakaryocytes, have also been shown to play a broad role in tumor progression, and favor proliferation from the release of growth factors and drug resistance ( 189 – 191 ). In addition, they act in the formation of secondary niches through a mechanism called “cloaking”, in which platelets produce physical protection in cancer cells, thus assisting in vascular migration and extravasation to the tissues to form metastases ( 192 – 194 ).…”

Section: Concluding Remarks and Future Perspectivesmentioning

confidence: 99%

The Yin-Yang of myeloid cells in the leukemic microenvironment: Immunological role and clinical implications

et al. 2022

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The leukemic microenvironment has a high diversity of immune cells that are phenotypically and functionally distinct. However, our understanding of the biology, immunology, and clinical implications underlying these cells remains poorly investigated. Among the resident immune cells that can infiltrate the leukemic microenvironment are myeloid cells, which correspond to a heterogeneous cell group of the innate immune system. They encompass populations of neutrophils, macrophages, and myeloid-derived suppressor cells (MDSCs). These cells can be abundant in different tissues and, in the leukemic microenvironment, are associated with the clinical outcome of the patient, acting dichotomously to contribute to leukemic progression or stimulate antitumor immune responses. In this review, we detail the current evidence and the many mechanisms that indicate that the activation of different myeloid cell populations may contribute to immunosuppression, survival, or metastatic dissemination, as well as in immunosurveillance and stimulation of specific cytotoxic responses. Furthermore, we broadly discuss the interactions of tumor-associated neutrophils and macrophages (TANs and TAMs, respectively) and MDSCs in the leukemic microenvironment. Finally, we provide new perspectives on the potential of myeloid cell subpopulations as predictive biomarkers of therapeutical response, as well as potential targets in the chemoimmunotherapy of leukemias due to their dual Yin-Yang roles in leukemia.

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Evaluation of artificial intelligence‐assisted morphological analysis for platelet count estimation

Guo,

Zhang,

Liu

et al. 2024

Int J Lab Hematology

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IntroductionThis study aims to assess the performance of the platelet count estimation using artificial intelligence technology on the MC‐80 digital morphology analyzer.MethodsDigital morphology analyzer uses two different computational principles for platelet count estimation: based on PLT/RBC ratio (PLT‐M1) and estimate factor (PLT‐M2). 977 samples with various platelet counts (low, median, and high) were collected. Out of these, 271 samples were immunoassayed using CD61 and CD41 antibodies. The platelet counts obtained from the hematology analyzer (PLT‐I and PLT‐O), digital morphology analyzer (PLT‐M1 and PLT‐M2), and flow cytometry (PLT‐IRM) were compared.ResultsThere was no significant deviation observed before and after verification for both PLT‐M1 and PLT‐M2 across the analysis range (average bias: −0.845/−0.682, 95% limit of agreement (LOA): −28.675–26.985/−29.420–28.056). When platelet alarms appeared, PLT‐M1/PLT‐M2 showed the strongest correlation with PLT‐IRM than PLT‐I with PLT‐IRM (r: 0.9814/0.9796 > 0.9601). The correlation between PLT‐M1/PLT‐M2 and PLT‐IRM was strong for samples with interference, such as large platelets or RBC fragments, but relatively weak in small RBCs. The deviation between PLT‐M1 and PLT‐M2 is related to the number of RBCs. Compared with PLT‐I, PLT‐M1/PLT‐M2 showed higher accuracy for platelet transfusion decisions, especially for samples with low‐value PLT.ConclusionThe novel platelet count estimation on the MC‐80 digital morphology analyzer provides high accuracy, especially the reviewed result, which can effectively confirm suspicious platelet count.

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Platelet–Acute Leukemia Interactions

Cited by 7 publications

References 95 publications

Influence on therapeutic outcome of platelet count at diagnosis in patients with de novo non-APL acute myeloid leukemia

Influence on therapeutic outcome of platelet count at diagnosis in patients with de novo non-APL acute myeloid leukemia

The Yin-Yang of myeloid cells in the leukemic microenvironment: Immunological role and clinical implications

Evaluation of artificial intelligence‐assisted morphological analysis for platelet count estimation

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