Lecture
Recent Advances in Flow Cytometry for Hematologic Malignancies
- at -
- ICM Saal 4a
- Type: Lecture
Lecture description
S. Genduso, Hamburg/DE, H. Hauspurg, Hamburg/DE
Since its invention in the 1960s by Wolfgang Göhde and Leonard Herzenberg, flow cytometry has continuously evolved as a high-throughput single-cell analysis technology for the diagnosis and monitoring of hematological malignancies. In recent years,
technological and analytical advances have markedly influenced its application in both clinical practice and research. Multiparametric and full-spectrum flow cytometry have significantly improved the resolution and sensitivity of malignant cell immunophenotyping, enabling detailed high-dimensional analyses of rare and complex hematopoietic cell populations that cannot be fully resolved by conventional approaches.
In parallel, progress in the development of standardized antibody panels, protocols, and data analysis workflows has enhanced inter-laboratory reproducibility and diagnostic consistency, which is critical for the harmonized detection of rare malignant cell populations. Indeed, flow cytometry-based assessment of minimal residual disease (MRD) is now widely implemented in acute leukemia, multiple myeloma, and lymphoproliferative disorders, supporting risk stratification and guiding therapeutic decisions.
Furthermore, flow cytometry has improved the assessment of T-cell clonality, which was previously limited by the lack of specific antibodies, in contrast to established methods for B-cell clonality. Antibodies targeting the T-cell receptor beta constant regions (TRBC1
and TRBC2) now allow precise identification of clonal T-cell populations. These innovations, however, pose clinical interpretative challenges, including distinguishing clonal from reactive T-cell expansions and therapy-induced immunophenotypic changes.
The increasing complexity of flow cytometry-generated data necessitates the use of machine learning and artificial intelligence (AI), which have the potential to accelerate workflows and provide reliable, observer-independent results. Nevertheless, widespread implementation of AI in routine diagnostics remains limited.
In summary, flow cytometry remains a cornerstone of leukemia and lymphoma diagnostics, enabling rapid, accurate, and reproducible analyses. Recent advances have further enhanced its resolution, broadened its diagnostic scope (including the precise assessment of T-cell clonality), and reinforced its role as a standard tool for MRD detection. Looking forward, AI-driven data analysis promises to further enhance the analytical power and efficiency of flow cytometry.
Since its invention in the 1960s by Wolfgang Göhde and Leonard Herzenberg, flow cytometry has continuously evolved as a high-throughput single-cell analysis technology for the diagnosis and monitoring of hematological malignancies. In recent years,
technological and analytical advances have markedly influenced its application in both clinical practice and research. Multiparametric and full-spectrum flow cytometry have significantly improved the resolution and sensitivity of malignant cell immunophenotyping, enabling detailed high-dimensional analyses of rare and complex hematopoietic cell populations that cannot be fully resolved by conventional approaches.
In parallel, progress in the development of standardized antibody panels, protocols, and data analysis workflows has enhanced inter-laboratory reproducibility and diagnostic consistency, which is critical for the harmonized detection of rare malignant cell populations. Indeed, flow cytometry-based assessment of minimal residual disease (MRD) is now widely implemented in acute leukemia, multiple myeloma, and lymphoproliferative disorders, supporting risk stratification and guiding therapeutic decisions.
Furthermore, flow cytometry has improved the assessment of T-cell clonality, which was previously limited by the lack of specific antibodies, in contrast to established methods for B-cell clonality. Antibodies targeting the T-cell receptor beta constant regions (TRBC1
and TRBC2) now allow precise identification of clonal T-cell populations. These innovations, however, pose clinical interpretative challenges, including distinguishing clonal from reactive T-cell expansions and therapy-induced immunophenotypic changes.
The increasing complexity of flow cytometry-generated data necessitates the use of machine learning and artificial intelligence (AI), which have the potential to accelerate workflows and provide reliable, observer-independent results. Nevertheless, widespread implementation of AI in routine diagnostics remains limited.
In summary, flow cytometry remains a cornerstone of leukemia and lymphoma diagnostics, enabling rapid, accurate, and reproducible analyses. Recent advances have further enhanced its resolution, broadened its diagnostic scope (including the precise assessment of T-cell clonality), and reinforced its role as a standard tool for MRD detection. Looking forward, AI-driven data analysis promises to further enhance the analytical power and efficiency of flow cytometry.
