Detalle Publicación

FlowCT for the analysis of large immunophenotypic data sets and biomarker discovery in cancer immunology
Autores: Botta, C. (Autor de correspondencia); Da Silva Maia, Catarina Alexandra; Garcés Latre, Juan José; Termini, R.; Pérez Ruiz, Cristina; Manrique Sáenz de Tejada, Irene; Burgos Rodríguez, Leire; Zabaleta Azpiroz, Aintzane; Alignani, Diego Oscar; Sarvide Plano, Sarai; Merino Roncal, Juana María; Puig, N.; Cedena, M. T.; Rossi, M.; Tassone, P.; Gentile, M.; Correale, P.; Borrello, I.; Terpos, E.; Jelinek, T.; Paiva, A.; Roccaro, A.; Goldschmidt, H.; Avet-Loiseau, H.; Rosinol, L.; Mateos, M. V.; Martínez-López, J.; Lahuerta, J. J.; Blade, J.; San Miguel Izquierdo, Jesús; Paiva, Bruno (Autor de correspondencia); Programa Estudio Terapeutica Hemop; iMMunocell Study Grp
Título de la revista: BLOOD ADVANCES
ISSN: 2473-9529
Volumen: 6
Número: 2
Páginas: 690 - 703
Fecha de publicación: 2022
Lugar: WOS
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 5 .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. as #NCT01916252 and #NCT02406144.