TY - JOUR
T1 - FlowCT for the analysis of large immunophenotypic data sets and biomarker discovery in cancer immunology
AU - Botta, Cirino
AU - Maia, Catarina
AU - Garcés, Juan-José
AU - Termini, Rosalinda
AU - Perez, Cristina
AU - Manrique, Irene
AU - Burgos, Leire
AU - Zabaleta, Aintzane
AU - Alignani, Diego
AU - Sarvide, Sarai
AU - Merino, Juana
AU - Puig, Noemi
AU - Cedena, María-Teresa
AU - Rossi, Marco
AU - Tassone, Pierfrancesco
AU - Gentile, Massimo
AU - Correale, Pierpaolo
AU - Borrello, Ivan
AU - Terpos, Evangelos
AU - Jelinek, Tomas
AU - Paiva, Artur
AU - Roccaro, Aldo
AU - Goldschmidt, Hartmut
AU - Avet-Loiseau, Hervé
AU - Rosinol, Laura
AU - Mateos, Maria-Victoria
AU - Martinez-Lopez, Joaquin
AU - Lahuerta, Juan-José
AU - Bladé, Joan
AU - San-Miguel, Jesús F
AU - Paiva, Bruno
PY - 2022
Y1 - 2022
N2 - 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 singlecell 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.
AB - 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 singlecell 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.
KW - N/A
KW - N/A
UR - http://hdl.handle.net/10807/305438
U2 - 10.1182/bloodadvances.2021005198
DO - 10.1182/bloodadvances.2021005198
M3 - Article
SN - 2473-9537
VL - 6
SP - 690
EP - 703
JO - Blood advances
JF - Blood advances
ER -