PURPOSE: The existence of patients with multiple myeloma (MM) and light-chain (AL) amyloidosis who present with a monoclonal gammopathy of undetermined significance (MGUS)-like phenotype has been hypothesized, but methods to identify this subgroup are not standardized and its clinical significance is not properly validated. PATIENTS AND METHODS: An algorithm to identify patients having MGUS-like phenotype was developed on the basis of the percentages of total bone marrow (BM) plasma cells (PC) and of clonal PC within the BM PC compartment, determined at diagnosis using flow cytometry in 548 patients with MGUS and 2,011 patients with active MM. The clinical significance of the algorithm was tested and validated in 488 patients with smoldering MM, 3,870 patients with active MM and 211 patients with AL amyloidosis. RESULTS: Patients with smoldering MM with MGUS-like phenotype showed significantly lower rates of disease progression (4.5% and 0% at 2 years in two independent series). There were no statistically significant differences in time to progression between treatment versus observation in these patients. In active newly diagnosed MM, MGUS-like phenotype retained independent prognostic value in multivariate analyses of progression-free survival (PFS; hazard ratio [HR], 0.49; P = .001) and overall survival (OS; HR, 0.56; P = .039), together with International Staging System, lactate dehydrogenase, cytogenetic risk, transplant eligibility, and complete remission status. Transplant-eligible patients with active MM with MGUS-like phenotype showed PFS and OS rates at 5 years of 79% and 96%, respectively. In this subgroup, there were no differences in PFS and OS according to complete remission and measurable residual disease status. Application of the algorithm in two independent series of patients with AL predicted for different survival. CONCLUSION: We developed an open-access algorithm for the identification of MGUS-like patients with distinct clinical outcomes. This phenotypic classification could become part of the diagnostic workup of MM and AL amyloidosis.
- MeSH
- fenotyp MeSH
- klinická relevance MeSH
- lidé MeSH
- mnohočetný myelom * diagnóza MeSH
- monoklonální gamapatie nejasného významu * diagnóza terapie MeSH
- paraproteinemie * diagnóza terapie MeSH
- primární amyloidóza * MeSH
- progrese nemoci MeSH
- Check Tag
- lidé MeSH
- Publikační typ
- časopisecké články MeSH
- práce podpořená grantem MeSH
Large-scale immune monitoring is becoming routinely used in clinical trials to identify determinants of treatment responsiveness, particularly to immunotherapies. Flow cytometry remains one of the most versatile and high throughput approaches for single-cell analysis; however, manual interpretation of multidimensional data poses a challenge when attempting to capture full cellular diversity and provide reproducible results. We present FlowCT, a semi-automated workspace empowered to analyze large data sets. It includes pre-processing, normalization, multiple dimensionality reduction techniques, automated clustering, and predictive modeling tools. As a proof of concept, we used FlowCT to compare the T-cell compartment in bone marrow (BM) with peripheral blood (PB) from patients with smoldering multiple myeloma (SMM), identify minimally invasive immune biomarkers of progression from smoldering to active MM, define prognostic T-cell subsets in the BM of patients with active MM after treatment intensification, and assess the longitudinal effect of maintenance therapy in BM T cells. A total of 354 samples were analyzed and immune signatures predictive of malignant transformation were identified in 150 patients with SMM (hazard ratio [HR], 1.7; P < .001). We also determined progression-free survival (HR, 4.09; P < .0001) and overall survival (HR, 3.12; P = .047) in 100 patients with active MM. New data also emerged about stem cell memory T cells, the concordance between immune profiles in BM and PB, and the immunomodulatory effect of maintenance therapy. FlowCT is a new open-source computational approach that can be readily implemented by research laboratories to perform quality control, analyze high-dimensional data, unveil cellular diversity, and objectively identify biomarkers in large immune monitoring studies. These trials were registered at www.clinicaltrials.gov as #NCT01916252 and #NCT02406144.
- MeSH
- biologické markery MeSH
- doutnající mnohočetný myelom * MeSH
- imunofenotypizace MeSH
- kostní dřeň MeSH
- lidé MeSH
- průtoková cytometrie metody MeSH
- Check Tag
- lidé MeSH
- Publikační typ
- časopisecké články MeSH
- práce podpořená grantem MeSH
