AimsCytogenetic abnormalities involving the IGH gene are seen in up to 55% of patients with multiple myeloma. Current testing is performed manually by fluorescence in situ hybridisation (FISH) on purified plasma cells. We aimed to assess whether an automated imaging flow cytometric method that uses immunophenotypic cell identification, and does not require cell isolation, can identify IGH abnormalities.MethodsAspirated bone marrow from 10 patients with multiple myeloma were studied. Plasma cells were identified by CD38 and CD138 coexpression and assessed with FISH probes for numerical or structural abnormalities of IGH. Thousands of cells were acquired on an imaging flow cytometer and numerical data and digital images were analysed.ResultsUp to 30 000 cells were acquired and IGH chromosomal abnormalities were detected in 5 of the 10 marrow samples. FISH signal patterns seen included fused IGH signals for IGH/FGFR3 and IGH/MYEOV, indicating t(4;14) and t(11;14), respectively. In addition, three IGH signals were identified, indicating trisomy 14 or translocation with an alternate chromosome. The lowest limit of detection of an IGH abnormality was in 0.05% of all cells.ConclusionsThis automated high-throughput immuno-flowFISH method was able to identify translocations and trisomy involving the IGH gene in plasma cells in multiple myeloma. Thousands of cells were analysed and without prior cell isolation. The inclusion of positive plasma cell identification based on immunophenotype led to a lowest detection level of 0.05% marrow cells. This imaging flow cytometric FISH method offers the prospect of increased precision of detection of critical genetic lesions involving IGH and other chromosomal defects in multiple myeloma.

Three types of chromosomal translocations, t(4;14)(p16;q32), t(14;16)(q32;q23), and t(11;14)(q13;q32), are associated with prognosis and the decision making of therapeutic strategy for multiple myeloma (MM). In this study, we developed a new diagnostic modality of the multiplex FISH in immunophenotyped cells in suspension (Immunophenotyped-Suspension-Multiplex (ISM)-FISH). For the ISM-FISH, we first subject cells in suspension to the immunostaining by anti-CD138 antibody and, then, to the hybridization with four different FISH probes for genes of IGH, FGFR3, MAF, and CCND1 tagged by different fluorescence in suspension. Then, cells are analyzed by the imaging flow cytometry MI-1000 combined with the FISH spot counting tool. By this system of the ISM-FISH, we can simultaneously examine the three chromosomal translocations, i.e, t(4;14), t(14;16), and t(11;14), in CD138-positive tumor cells in more than 2.5 × 104 nucleated cells with the sensitivity at least up to 1%, possibly up to 0.1%. The experiments on bone marrow nucleated cells (BMNCs) from 70 patients with MM or monoclonal gammopathy of undetermined significance demonstrated the promising qualitative diagnostic ability in detecting t(11;14), t(4;14), and t(14;16) of our ISM-FISH, which was more sensitive compared with standard double-color (DC) FISH examining 200 interphase cells with its best sensitivity up to 1.0%. Moreover, the ISM-FISH showed a positive concordance of 96.6% and negative concordance of 98.8% with standard DC-FISH examining 1000 interphase cells. In conclusion, the ISM-FISH is a rapid and reliable diagnostic tool for the simultaneous examination of three critically important IGH translocations, which may promote risk-adapted individualized therapy in MM.

Long-term tyrosine kinase inhibitor (TKI) treatment for patients with chronic myeloid leukemia (CML) causes various adverse events. Achieving a deep molecular response (DMR) is necessary for discontinuing TKIs and attaining treatment-free remission. Thus, early diagnosis is crucial as a lower DMR achievement rate has been reported in high-risk patients. Therefore, we attempted to identify CML cells using a novel technology that combines artificial intelligence (AI) with flow cytometry and investigated the basis for AI-mediated identification. Our findings indicate thatBCR-ABL1-transduced cells and leukocytes from patients with CML showed significantly fragmented mitochondria and decreased mitochondrial membrane potential. Additionally,BCR-ABL1enhanced the phosphorylation of Drp1 via the mitogen-activated protein kinase pathway, inducing mitochondrial fragmentation. Finally, the AI identified cell line models and patient leukocytes that showed mitochondrial morphological changes. Our study suggested that this AI-based technology enables the highly sensitive detection ofBCR-ABL1-positive cells and early diagnosis of CML.

No abstract