Immunological characterization of monoclonal gammopathies using multidimensional and computational flow cytometry

Resumen

In this PhD thesis, we hypothesized that the advent of multidimensional and computational flow cytometry immunophenotyping and cell sorting may help addressing unresolved clinical and biological questions in patients with monoclonal gammopathies, which could result in the identification of new biomarkers for their risk stratification. To answer our hypothesis, the aim of this PhD thesis was to leverage on the growing multidimensionality and computational power of flow cytometry immunophenotyping and cell sorting, to identify new applications of this single-cell technology in patients with monoclonal gammopathies. For that, we investigated the presence and clinical significance of dysplastic phenotypes in patients with newly diagnosed multiple myeloma and developed a semi-automated computational flow cytometry workspace, labelled FlowCT, to analyze large immunophenotypic datasets. FlowCT was used to identify immune signatures of disease progression in patients with smoldering and active multiple myeloma and to analyze a possible correlation between impaired immune response and severity of coronavirus disease 2019 (COVID-19) in patients with monoclonal gammopathies and other hematological malignancies. Regarding the first objective, we performed an immunological characterization of MM patients with MDS-like phenotypic abnormalities (MDS-PA) using multidimensional flow cytometry. Briefly,our results showed that MDS-PA were detected in 11.6% of newly-diagnosed patients with active MM. MDS associated mutations were found in CD34+ HPCs or in mature cell lineages from half of the patients with MDS-PA. MDS-PA and MDS-associated mutations infrequently emerged after HDT/ASCT. Presence of MDS-PA at diagnosis was associated with alterations in the cellular composition of the tumor microenvironment. Detection of monocytic MDS-PA at diagnosis was associated with more frequent anemia and neutropenia during treatment, and was an independent prognostic factor of PFS and OS. The negative impact of monocytic MDS-PA in survival was solely observed in patients receiving maintenance without IMID. It was abrogated whenever thalidomide or lenalidomide were used. Relatively to the second objective, we developed a computational flow cytometry tool for immunological characterization of smoldering and active multiple myeloma patients. The semi-automated all-in-one package developed, FlowCT, includes pre-processing, normalization, multiple dimensionality reduction, automated clustering and predictive modeling tools. An immune signature predictive of malignant transformation was identified in patients with SMM, using FlowCT. Stratification according to low and high-risk immune scores could be complementary to the IMWG risk model. An immune signature significantly associated with PFS and OS, was identified after treatment intensification in patients with active MM. This immune score was independent of MRD status and remained relatively stable throughout maintenance therapy. Lastly, we used multidimensional and computational flow cytometry (using FlowCT) for the immunological characterization of hematological patients in the context of COVID-19. Here, we observed that the frequency of hematological patients requiring intensive care and dying from COVID-19 was significantly higher than that observed in patients without blood cancer. Hematological patients showed an altered immune response to COVID-19. When compared to cases without blood cancer, the former displayed significantly decreased percentages of distinct innate and adaptive immune cell types. Furthermore, hematological patients dying from COVID-19 tended to have increased numbers of neutrophils counterbalanced by reduced percentages of other immune cell types versus those who survived.