FlowCT for the analysis of large immunophenotypic data sets and biomarker discovery in cancer immunology Article Swipe

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Cirino Botta , Catarina Maia , Juan‐José Garcés , Rosalinda Termini , Cristina Pérez , Irene Manrique , Leire Burgos , Aintzane Zabaleta , Diego Alignani , Sarai Sarvide , Juana Merino , Noemí Puig , María‐Teresa Cedena , Marco Rossi , Pierfrancesco Tassone , Massimo Gentile , Pierpaolo Correale , Ivan Borrello , Evangelos Terpos , Tomáš Jelı́nek , Artur Paiva , Aldo Roccaro , Hartmut Goldschmidt , Hervé Avet‐Loiseau , Laura Rosiñol , María‐Victoria Mateos , Joaquin Martínez‐López , Juan José Lahuerta , Joan Bladé , Jesús F. San Miguel , Bruno Paiva ·

YOU? · · 2021 · Open Access · · DOI: https://doi.org/10.1182/bloodadvances.2021005198

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.

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Computational Biology

Immunology

Internal Medicine

Biology

Concepts

Multiple myeloma Immune system Oncology Hazard ratio Bone marrow Immunotherapy Concordance Medicine Computational biology Immunology Internal medicine Biology Confidence interval

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Type: article
Language: en
Landing Page: https://doi.org/10.1182/bloodadvances.2021005198
PDF: https://ashpublications.org/bloodadvances/article-pdf/6/2/690/1862863/advancesadv2021005198.pdf
OA Status: gold
Cited By: 41
References: 58
Related Works: 10
OpenAlex ID: https://openalex.org/W3202791180

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DOI: https://doi.org/10.1182/bloodadvances.2021005198

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Title: FlowCT for the analysis of large immunophenotypic data sets and biomarker discovery in cancer immunology

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Type: article

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Language: en

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Publication year: 2021

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Publication date: 2021-09-29

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Authors: Cirino Botta, Catarina Maia, Juan‐José Garcés, Rosalinda Termini, Cristina Pérez, Irene Manrique, Leire Burgos, Aintzane Zabaleta, Diego Alignani, Sarai Sarvide, Juana Merino, Noemí Puig, María‐Teresa Cedena, Marco Rossi, Pierfrancesco Tassone, Massimo Gentile, Pierpaolo Correale, Ivan Borrello, Evangelos Terpos, Tomáš Jelı́nek, Artur Paiva, Aldo Roccaro, Hartmut Goldschmidt, Hervé Avet‐Loiseau, Laura Rosiñol, María‐Victoria Mateos, Joaquin Martínez‐López, Juan José Lahuerta, Joan Bladé, Jesús F. San Miguel, Bruno Paiva

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Landing page: https://doi.org/10.1182/bloodadvances.2021005198

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PDF URL: https://ashpublications.org/bloodadvances/article-pdf/6/2/690/1862863/advancesadv2021005198.pdf

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OA URL: https://ashpublications.org/bloodadvances/article-pdf/6/2/690/1862863/advancesadv2021005198.pdf

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Concepts: Multiple myeloma, Immune system, Oncology, Hazard ratio, Bone marrow, Immunotherapy, Concordance, Medicine, Computational biology, Immunology, Internal medicine, Biology, Confidence interval

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Cited by: 41

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Citations by year (recent): 2025: 8, 2024: 11, 2023: 11, 2022: 10, 2021: 1

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