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Multi-Factor Clustering Incorporating Cell Motility Predicts T Cell Expansion Potential.

ABSTRACT: Expansion of an initial population of T cells is essential for cellular immunotherapy. In Chronic Lymphocytic Leukemia (CLL), expansion is often complicated by lack of T cell proliferation, as these cells frequently show signs of exhaustion. This report seeks to identify specific biomarkers or measures of cell function that capture the proliferative potential of a starting population of cells. Mixed CD4+/CD8+ T cells from healthy donors and individuals previously treated for CLL were characterized on the basis of proliferative potential and in vitro cellular functions. Single-factor analysis found little correlation between the number of populations doublings reached during expansion and either Rai stage (a clinical measure of CLL spread) or PD-1 expression. However, inclusion of in vitro IL-2 secretion and the propensity of cells to align onto micropatterned features of activating proteins as factors identified three distinct groups of donors. Notably, these group assignments provided an elegant separation of donors with regards to proliferative potential. Furthermore, these groups exhibited different motility characteristics, suggesting a mechanism that underlies changes in proliferative potential. This study describes a new set of functional readouts that augment surface marker panels to better predict expansion outcomes and clinical prognosis.

SUBMITTER: Lee JH

PROVIDER: S-EPMC8063612 | biostudies-literature | 2021

REPOSITORIES: biostudies-literature

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Multi-Factor Clustering Incorporating Cell Motility Predicts T Cell Expansion Potential.

Lee Joanne H JH Shao Shuai S Kim Michelle M Fernandes Stacey M SM Brown Jennifer R JR Kam Lance C LC

Frontiers in cell and developmental biology 20210409

Expansion of an initial population of T cells is essential for cellular immunotherapy. In Chronic Lymphocytic Leukemia (CLL), expansion is often complicated by lack of T cell proliferation, as these cells frequently show signs of exhaustion. This report seeks to identify specific biomarkers or measures of cell function that capture the proliferative potential of a starting population of cells. Mixed CD4+/CD8+ T cells from healthy donors and individuals previously treated for CLL were characteriz ...[more]

PMID: 33898440

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Project description:Computational models of cell signaling networks typically are aimed at capturing dynamics of molecular components to derive quantitative insights from prior experimental data, and to make predictions concerning altered dynamics under different conditions. However, signaling network models have rarely been used to predict how cell phenotypic behaviors result from the integrated operation of these networks. We recently developed a decision tree model for how EGF-induced fibroblast cell motility across two-dimensional fibronectin-coated surfaces depends on the integrated activation status of five key signaling nodes, including a proximal regulator of transcellular contractile force generation, MLC (myosin light chain) [Hautaniemi et al, Bioinformatics 21: 2027 {2005}], but we have not previously attempted predictions of new experimental effects from this model.In this new work, we construct an improved decision tree model for the combined influence of EGF and fibronectin on fibroblast cell migration based on a wider spectrum of experimental protein signaling and cell motility measurements, and directly test a significant and non-intuitive a priori prediction for the outcome of a targeted molecular intervention into the signaling network: that partially reducing activation of MLC would increase cell motility on moderately adhesive surfaces. This prediction was indeed confirmed experimentally: partial inhibition of the activating MLC kinase (MLCK) upstream using the pharmacologic agent ML-7 resulted in increased motility of NR6 fibroblasts. We further extended this exciting finding by showing that partial reduction of MLC activation similarly enhanced the transmigration of the human breast carcinoma cell line MDA-213 through a Matrigel barrier.These findings specifically highlight a central regulatory role for transcellular contractility in governing cell motility, while at the same time demonstrating the value of a decision tree approach to a systems "signal-response" model in discerning non-intuitive behavior arising from integrated operation a cell signaling network.

Project description:Background: Several studies have shown that the hyaluronan-mediated motility receptor (HMMR) is overexpressed in various cancers and could be a potential prognostic factor. However, further research is still required to determine the prognostic value and potential function of HMMR in head and neck squamous cell carcinoma (HNSCC). Materials and Methods: Transcriptomic expression data were collected from the Cancer Genome Atlas database (TCGA) and Gene Expression Omnibus and the differences in HMMR expression between normal and tumor tissues were analyzed. The correlation between the methylation level of HMMR and its mRNA expression was analyzed via cBioPortal. Additionally, the data obtained from TCGA was analyzed with MethSurv to determine the prognostic value of the HMMR methylation levels in HNSCC. Gene set enrichment analysis (GSEA) and single sample GSEA (ssGSEA) were used to explore the potential biological functions of HMMR. Results: HMMR was highly expressed in HNSCC tumor tissue compared to normal tissue (p < 0.001). Multivariate analysis (MAV) showed that high HMMR mRNA expression was an independent prognostic factor of overall survival (OS) in TCGA (HR = 1.628, 95% CI: 1.169-2.266, p = 0.004) and GSE41613 data (HR = 2.238, p = 0.013). The methylation level of HMMR negatively correlated with the HMMR expression (R = -0.12, p < 0.001), and patients with low HMMR methylation had worse OS than patients with high methylation (p < 0.001). GSEA found that HMMR expression was associated with the KARS, EMT, and G2M checkpoint pathways, as well as the interferon-gamma and interferon-alpha responses, whereas ssGSEA showed that HMMR expression positively correlated with the infiltration level of Th2 cells. MAV confirmed that high HMMR protein expression was an inferior independent factor for OS (HR = 2.288, p = 0.045) and progression-free survival (HR = 2.247, p = 0.038) in 70 HNSCC. Conclusions: This study demonstrated that the upregulation of HMMR mRNA and protein in HNSCC is a biomarker for poor prognosis. The biological functions of HMMR are potentially related to the KARS, EMT, and G2M checkpoint pathways, as well as the interferon-gamma and interferon-alpha responses. These findings help to elucidate the role of HMMR in carcinogenesis and lay a foundation for further study.

Dataset Information

Multi-Factor Clustering Incorporating Cell Motility Predicts T Cell Expansion Potential.

Publications

Multi-Factor Clustering Incorporating Cell Motility Predicts T Cell Expansion Potential.

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