@article{3034202,
    title = "FlowCT for the analysis of large immunophenotypic data sets and
biomarker discovery in cancer immunology",
    author = "Botta, Cirino and Maia, Catarina and Garces, Juan-Jose and Termini, and Rosalinda and Perez, Cristina and Manrique, Irene and Burgos, Leire and and Zabaleta, Aintzane and Alignani, Diego and Sarvide, Sarai and Merino, and Juana and Puig, Noemi and Cedena, Maria-Teresa and Rossi, Marco and and Tassone, Pierfrancesco and Gentile, Massimo and Correale, Pierpaolo and and Borrello, Ivan and Terpos, Evangelos and Jelinek, Tomas and Paiva, Artur and and Roccaro, Aldo and Goldschmidt, Hartmut and Avet-Loiseau, Herve and and Rosinol, Laura and Mateos, Maria-Victoria and Martinez-Lopez, Joaquin and and Lahuerta, Juan-Jose and Blade, Joan and San-Miguel, Jesus F. and and Paiva, Bruno and Programa Estudio Terapeutica Hemop and iMMunocell Study and Grp",
    journal = "Blood advances",
    year = "2022",
    volume = "6",
    number = "2",
    pages = "690-703",
    publisher = "Elsevier",
    doi = "10.1182/bloodadvances.2021005198",
    abstract = "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. clinicaltrials.gov as #NCT01916252 and
#NCT02406144."
}