Molecular genetic and clinical characteristics of MEF2D-BCL9 fusion gene-positive acute B-cell lymphoblastic leukemia / 白血病·淋巴瘤

Objective:To investigate the molecular genetic and clinical characteristics of MEF2D-BCL9 fusion gene-positive acute B-cell lymphoblastic leukemia (B-ALL), and to provide the reference for the diagnosis and treatment of the disease.Methods:The medical record and experimental examination data of a 18-year-old female MEF2D-BCL9 fusion gene-positive B-ALL patient were retrospectively analyzed. The clinical manifestations and biological characteristics of MEF2D-BCL9 fusion gene-positive B-ALL were summarized.Results:This 18-year-old female patient was treated in a local hospital in December 2018 and was diagnosed as B-ALL. She achieved complete remission after chemotherapy and recurred at 6 months after the initial onset, and then she was admitted to Hebei Yanda Ludaopei Hospital in the 9 months after the initial onset.MEF2D-BCL9 fusion gene was detected through RNA-sequencing (RNA-seq) and verified by using polymerase chain reaction and Sanger sequencing. Bone marrow cell morphology was similar to mature B cells with vacuoles but without characteristic chromosome karyotype abnormalities. The patient achieved remission after VLD regimen chemotherapy, chimeric antigen receptor T-cell (CAR-T) therapy and bridged to allogeneic hematopoietic stem cell transplantation (allo-HSCT). She has maintained complete remission for 2 years at the last follow-up in February 2022.Conclusions:MEF2D-BCL9 fusion gene-positive B-ALL is characterized with high risk, early relapse and poor prognosis. These patients may benefit from CAR-T and allo-HSCT. It further emphasizes the importance of taking MEF2D-BCL9 fusion gene into the detection or identification by using RNA-seq, particularly for those newly diagnosed B-ALL patients in children and adolescents with specific bone marrow morphology.

Identification of TCF3-ZNF384 fusion by transcriptome sequencing in B cell acute lymphoblastic leukemia and its laboratory and clinical characteristics / 中华医学遗传学杂志

OBJECTIVE@#To detect fusion gene with pathological significance in a patient with refractory and relapsed acute B cell lymphoblastic leukemia (B-ALL) and to explore its laboratory and clinical characteristics.@*METHODS@#Transcriptome sequencing was used to detect potential fusion transcripts. Other laboratory results and clinical data of the patient were also analyzed.@*RESULTS@#The patient was found to harbor TCF3 exon 17-ZNF384 exon 7 in-frame fusion transcript. The minimal residual disease (MRD) has remained positive after multiple chemotherapy protocols including CD19-, CD22- targeted chimeric antigen receptor T cells immunotherapy. The patient eventually achieved complete remission and sustained MRD negativity after allogeneic hemopoietic stem cell transplantation (allo-HSCT).@*CONCLUSION@#Transcriptome sequencing can effectively detect potential fusion genes with clinical significance in leukemia. TCF3-ZNF384 positive B-ALL has unique laboratory and clinical characteristics, may not well respond to chemotherapy and immunotherapy, and is more likely to relapse. Timely allo-HSCT treatment may help such patients to achieve long-term disease-free survival. TCF3-ZNF384 positive B-ALL is not uncommon in pediatric patients but has not been effectively identified.

Progress of big data analysis and artificial intelligence technology for hematologic neoplasms / 白血病·淋巴瘤

The new wave of artificial intelligence pushed by deep learning algorithms has dramatically promoted the development of big data analysis technology. On the other hand, advances in life sciences represented by high-throughput genome sequencing have provided massive medical data. Artificial intelligence technology has also provided a powerful tool for hematological malignancy research. This article introduces related research progress in the 61st American Society of Hematology Annual Meeting.

Progress of big data analysis and artificial intelligence technology for hematologic neoplasms / 白血病·淋巴瘤

The new wave of artificial intelligence pushed by deep learning algorithms has dramatically promoted the development of big data analysis technology. On the other hand, advances in life sciences represented by high-throughput genome sequencing have provided massive medical data. Artificial intelligence technology has also provided a powerful tool for hematological malignancy research. This article introduces related research progress in the 61st American Society of Hematology Annual Meeting.

Mutation profiling of 16 candidate genes in de novo acute myeloid leukemia patients / 医学前沿

This retrospective analysis aimed to investigate the mutation profile of 16 common mutated genes in de novo acute myeloid leukemia (AML) patients. A total of 259 patients who were diagnosed of de novo AML were enrolled in this study. Mutation profiling of 16 candidate genes were performed in bone marrow samples by using Sanger sequencing.We identified at least 1 mutation in 199 of the 259 samples (76.8%), and 2 or more mutations in 31.7% of samples. FLT3-ITD was the most common mutated gene (16.2%, 42/259), followed by CEBPA (15.1%, 39/259), NRAS (14.7%, 38/259), and NPM1 (13.5%, 35/259). Concurrence was observed in 97.1% of the NPM1 mutated cases and in 29.6% of the double mutated CEBPA cases. Distinct patterns of co-occurrence were observed for different hotspot mutations within the IDH2 gene: R140 mutations were associated with NPM1 and/or FLT3-ITD mutations, whereas R172 mutations co-occurred with DNMT3A mutations only. Concurrence was also observed in 86.6% of epigenetic regulation genes, most of which co-occurred with NPM1 mutations. The results showed certain rules in the mutation profiling and concurrence of AML patients, which was related to the function classification of genes. Defining the mutation spectrum and mutation pattern of AML will contribute to the comprehensive assessment of patients and identification of new therapeutic targets.