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

被引：29

作者：

Botta, Cirino ^{[1
,2
]}

Maia, Catarina ^{[2
]}

Garces, Juan-Jose ^{[2
]}

Termini, Rosalinda ^{[2
]}

Perez, Cristina ^{[2
]}

Manrique, Irene ^{[2
]}

Burgos, Leire ^{[2
]}

Zabaleta, Aintzane ^{[2
]}

Alignani, Diego ^{[2
]}

Sarvide, Sarai ^{[2
]}

Merino, Juana ^{[2
]}

Puig, Noemi ^{[3
]}

Cedena, Maria-Teresa ^{[4
]}

Rossi, Marco ^{[5
]}

Tassone, Pierfrancesco ^{[5
]}

Gentile, Massimo ^{[6
]}

Correale, Pierpaolo ^{[7
]}

Borrello, Ivan ^{[8
]}

Terpos, Evangelos ^{[9
]}

Jelinek, Tomas ^{[10
]}

Paiva, Artur ^{[11
,12
,13
]}

Roccaro, Aldo ^{[14
]}

Goldschmidt, Hartmut ^{[15
,16
]}

Avet-Loiseau, Herve ^{[17
]}

Rosinol, Laura ^{[18
]}

Mateos, Maria-Victoria ^{[3
]}

Martinez-Lopez, Joaquin ^{[4
]}

Lahuerta, Juan-Jose ^{[4
]}

Blade, Joan ^{[18
]}

San-Miguel, Jesus F. ^{[2
]}

Paiva, Bruno ^{[2
]}

机构：

[1] Univ Palermo, Dept Hlth Promot, Internal Med & Med Specialties, Mother & Child Care, Via Vespro, I-90127 Palermo, Italy

[2] Clin Univ Navarra, Ctr Invest Med Aplicada, Inst Invest Sanitaria Navarra Hematol Unit, Ctr Invest Biomed Red,Canc Hematol Unit, Pamplona, Spain

[3] Inst Invest Biomed Salamanca, Hosp Univ Salamanca Hematol, Salamanca, Spain

[4] Hosp Univ 12 Octubre, Madrid, Spain

[5] Magna Graecia Univ Catanzaro, Clin & Expt Med Dept, Catanzaro, Italy

[6] Annunziata Hosp Cosenza, Dept Oncol, Hematol Unit, Cosenza, Italy

[7] Great Metropolitan Hosp Riuniti Reggio Calabria, Med Oncol Unit, Reggio Di Calabria, Italy

[8] Johns Hopkins Univ, Sidney Kimmel Comprehens Canc Ctr, Baltimore, MD USA

[9] Natl & Kapodistrian Univ Athens, Alexandra Gen Hosp, Dept Clin Therapeut, Sch Med, Athens, Greece

[10] Univ Hosp Ostrava, Dept Haemato Oncol, Ostrava, Czech Republic

[11] Ctr Hosp & Univ Coimbra, Unidade Gestao Operac Citometria, Coimbra, Portugal

[12] Univ Coimbra, Fac Med, Coimbra Inst Clin & Biomed Res, Coimbra, Portugal

[13] Inst Politecn Coimbra, Ciencias Biomed Lab, Escola Super Tecnol Saude Coimbra, Coimbra, Portugal

[14] Azienda Socio Sanitaria Territoriale Spedali Civi, Clin Res Dev & Phase 1 Unit, Brescia, Italy

[15] Univ Hosp Heidelberg, Internal Med 5, Heidelberg, Germany

[16] Natl Ctr Tumor Dis, Heidelberg, Germany

[17] Ctr Rech Cancerol Toulouse, Unite 1037, INSERM, Toulouse, France

[18] Inst Invest Biomed August Pi & Sunyer, Hosp Clin, Barcelona, Spain

来源：

BLOOD ADVANCES | 2022年 / 6卷 / 02期

基金：

欧洲研究理事会;

关键词：

MULTIPLE-MYELOMA PATIENTS; CYTOMETRY; CLASSIFICATION; PREDICTORS; ULTRAFAST;

D O I：

10.1182/bloodadvances.2021005198

中图分类号：

R5 [内科学];

学科分类号：

1002 ; 100201 ;

摘要：

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.

引用

页码：690 / 703

页数：14

共 54 条

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