DOI: 10.5061/dryad.2ngf1vhxd
title: Exploratory mass cytometry analysis reveals immunophenotypes of cancer treatment-related pneumonitis

description: Anti-cancer treatments can result in various adverse effects, including
 infections due to immune suppression/dysregulation and drug-induced
 toxicity in the lung. One of the major opportunistic infections is
 Pneumocystis jirovecii pneumonia (PCP), which can cause severe respiratory
 complications and high mortality rates. Cytotoxic drugs and
 immune-checkpoint inhibitors (ICIs) can induce interstitial lung diseases
 (ILDs). Nonetheless, the differentiation of these diseases can be
 difficult, and the pathogenic mechanisms of such diseases are not yet
 fully understood. To better comprehend the immunophenotypes, we conducted
 an exploratory mass cytometry analysis of immune cell subsets in
 bronchoalveolar lavage fluid from patients with PCP, cytotoxic
 drug-induced ILD (DI-ILD), and ICI-associated ILD (ICI-ILD) using two
 panels containing 64 markers. In PCP, we observed an expansion of the
 CD16+ T cell population, with the highest CD16+ T proportion in a fatal
 case. In ICI-ILD, we found an increase in CD57+ CD8+ T cells expressing
 immune checkpoints (TIGIT+ LAG3+ TIM-3+ PD-1+), FCRL5+ B cells, and CCR2+
 CCR5+ CD14+ monocytes. These findings uncover the diverse immunophenotypes
 and possible pathomechanisms of cancer treatment-related pneumonitis.
description: Cisplatin-positive cells and doublets were excluded to select
 live cells, and CD45<sup>+</sup> cells were further analyzed.
 For T cells, CD2<sup>+</sup> CD3<sup>+</sup> cells
 were gated and subjected to UMAP and Citrus algorithms. The UMAP analysis
 included clustering channels for CD4, CD8a, CD27, CD28, CD45RA, CD45RO,
 Fas, and used the parameters: numbers of neighbors = 15, minimum distance
 = 0.01. The Citrus algorithm for T cells included clustering channels for
 CD4, CD5, CD7, CD8a, CD11a, CD16, CD27, CD28, CD44, CD45RA, CD45RO, CD49d,
 CD57, CD69, CD226, Fas, IL-2R, PD-L1, PD-L2, PD-1, OX40, TIGIT, TIM3,
 CTLA-4, CD223 (LAG-3), BTLA, ICOS, ST2, CCR7, CXCR3, HLA-DR, and used the
 parameters: association models = nearest shrunken centroid (PAMR), cluster
 characterization = abundance, minimum cluster size = 5%, cross validation
 folds = 5, false discovery rate = 1%. The citrus analysis excluded
 nivolumab-induced ILD cases due to the apparent loss of PD-1 detection
 caused by competitive inhibition by nivolumab (Supplementary Figure 1B)
 (Yanagihara et al., 2020). The viSNE analysis for T cells included
 clustering channels for CD4, CD5, CD7, CD8a, CD11a, CD16, CD27, CD28,
 CD44, CD45RA, CD45RO, CD49d, CD57, CD69, CD226, Fas, IL-2R, PD-L1, PD-L2,
 PD-1, OX40, TIGIT, TIM3, CTLA-4, CD223 (LAG-3), BTLA, ICOS, ST2, CCR7,
 CXCR3, and HLA-DR, and used the parameters: iterations = 1000, perplexity
 = 30, theta = 0.5. For myeloid cells,
 CD3<sup>–</sup> CD11b<sup>+</sup>
 CD11c<sup>+</sup> cells were gated, and UMAP and Citrus
 algorithms were used. The UMAP analysis included clustering channels for
 CD11b, CD11c, CD64, CD14, CD16, CD206, HLA-DR, and CCR2, and used the
 parameters: numbers of neighbors = 10, minimum distance = 0.01. The Citrus
 algorithm included clustering channels for CD11b, CD11c, CD64, CD14, CD16,
 CD32, CD36, CD38, CD84, CD86, CD163, CD206, CD209, CD223, HLA-DR, CCR2,
 CCR5, and ST2, and used the parameters: association models = nearest
 shrunken centroid (PAMR), cluster characterization = abundance, minimum
 cluster size = 5%, cross validation folds = 5, false discovery rate = 1%.
 One case of PCP was excluded from the Citrus analysis due to low cell
 numbers. B cells and Plasma cells were identified using
 gating of CD3<sup>–</sup>CD64<sup>–</sup> and
 CD19<sup>+</sup> or CD138<sup>+</sup> cells. viSNE
 analysis was performed to cluster B cells using the following markers:
 CD19, CD38, CD11c, IgA, IgG, CD138, CD21, ST2, CXCR5, CD24, CD27, TIM-1,
 IgM, HLA-DR, IgD, and FCRL5. The analysis was performed on both individual
 and concatenated files using the parameters of 1000 iterations, perplexity
 of 30, and theta of 0.5. The selection of the
 dimensionality reduction technique, UMAP or viSNE, was made based on their
 ability to retain the relationships between global structures and the
 distances between cell clusters (where UMAP outperformed viSNE) and their
 ability to present a distinct and non-overlapping portrayal of cell
 subpopulations, facilitating the identification of inter-group variations
 (where viSNE performed better than UMAP).
description: # Exploratory mass cytometry analysis reveals immunophenotypes of cancer
 treatment-related pneumonitis [https://doi.org/10.5061/dryad.2ngf1vhxd
 ](https://doi.org/10.5061/dryad.2ngf1vhxd) ## Description of the data and
 file structure The dataset comprises mass cytometry analyses, with each
 subject represented by two distinct panels: a T-cell panel and a B-cell
 panel. The data are stored in Flow Cytometry Standard (FCS) format.
 Summaries of each subject's data are compiled in an Excel file
 "Cases1.xlsx". Additionally, an R Markdown script has been
 developed for the analysis of the dataset GSE145926.