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FlowCT for the analysis of large immunophenotypic data sets and biomarker discovery in cancer immunology
Cirino Botta1,2, Catarina Maia2, Juan-José Garcés2
1Department of Health Promotion, Mother and Child Care, Internal Medicine and Medical Specialties, University of Palermo, Palermo, Italy.
Insights
FlowCT is a new open-source tool for analyzing large immune monitoring datasets. It identifies immune biomarkers for multiple myeloma progression and survival, aiding clinical trial analysis.
Area of Science:
- Immunology
- Computational Biology
- Biostatistics
Background:
- Large-scale immune monitoring is crucial for identifying treatment response predictors in clinical trials, especially for immunotherapies.
- Flow cytometry is a high-throughput single-cell analysis method, but manual data interpretation is challenging for large datasets.
- Manual analysis of complex flow cytometry data limits the capture of cellular diversity and reproducibility.
Purpose of the Study:
- To introduce FlowCT, a semi-automated workspace for analyzing large-scale flow cytometry datasets.
- To demonstrate FlowCT's utility in multiple myeloma research, including biomarker discovery and survival prediction.
- To facilitate reproducible and objective analysis of high-dimensional immune monitoring data.
Main Methods:
- Development of FlowCT, incorporating pre-processing, normalization, dimensionality reduction, automated clustering, and predictive modeling.
- Application of FlowCT to analyze T-cell compartments in bone marrow (BM) and peripheral blood (PB) from smoldering multiple myeloma (SMM) and active multiple myeloma (MM) patients.
- Analysis of 354 samples to identify immune signatures, prognostic T-cell subsets, and longitudinal effects of maintenance therapy.
Main Results:
- Identification of immune signatures predictive of malignant transformation in SMM patients (HR, 1.7; P < .001).
- Determination of progression-free survival (HR, 4.09; P < .0001) and overall survival (HR, 3.12; P = .047) in active MM patients.
- New insights into stem cell memory T cells, BM-PB immune profile concordance, and maintenance therapy effects.
Conclusions:
- FlowCT is an open-source computational tool enabling quality control and analysis of high-dimensional immune monitoring data.
- FlowCT facilitates the objective identification of biomarkers and unveils cellular diversity in large studies.
- The tool supports reproducible research in immunology and clinical trials, particularly for multiple myeloma.
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 = .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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