Carregant...
Tipus de document
ArticleVersió
Versió publicadaData de publicació
Llicència de publicació
Si us plau utilitzeu sempre aquest identificador per citar o enllaçar aquest document: https://hdl.handle.net/2445/202034
FlowCT for the analysis of large immunophenotypic datasets and biomarker discovery in cancer immunology
Títol de la revista
Director/Tutor
ISSN de la revista
Títol del volum
Recurs relacionat
Resum
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.© 2022 by The American Society of Hematology. Licensed under Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0), permitting only noncommercial, nonderivative use with attribution. All other rights reserved.
Matèries
Matèries (anglès)
Citació
Citació
BOTTA, Cirino, MAIA, Catarina, GARCÉS, Juan josé, TERMINI, Rosalinda, PÉREZ, Cristina, MANRIQUE, Irene, BURGOS, Leire, ZABALETA, Aintzane, ALIGNANI, Diego, SARVIDE, Sarai, MERINO, Juana, PUIG, Noemí, CEDENA, María teresa, ROSSI, Marco, TASSONE, Pierfrancesco, GENTILE, Massimo, CORREALE, Pierpaolo, BORRELLO, Ivan, TERPOS, Evangelos, JELINEK, Tomas, PAIVA, Artur, ROCCARO, Aldo, GOLDSCHMIDT, Hartmut, AVET-LOISEAU, Hervé, ROSIÑOL DACHS, Laura, MATEOS, María victoria, MARTÍNEZ LÓPEZ, Joaquín, LAHUERTA, Juan josé, BLADÉ, J. (joan), SAN MIGUEL, Jesús f., PAIVA, Bruno. FlowCT for the analysis of large immunophenotypic datasets and biomarker discovery in cancer immunology. _Blood Advances_. 2022. Vol. 6, núm. 2, pàgs. 690-703. [consulta: 24 de gener de 2026]. ISSN: 2473-9537. [Disponible a: https://hdl.handle.net/2445/202034]