Hyperion Image Analysis Depicts a Preliminary Landscape of Tumor Immune Microenvironment in OSCC with Lymph Node Metastasis

Abstract

Background. Oral squamous cell carcinoma (OSCC) constitutes the most common types of oral cancer. Because its prognosis varies significantly, identification of a tumor immune microenvironment could be a critical tool for treatment planning and predicting a more accurate prognosis. This study is aimed at utilizing the Hyperion imaging system to depict a preliminary landscape of the tumor immune microenvironment in OSCC with lymph node metastasis. Methods. We collected neoplasm samples from OSCC patients. Their formalin-fixed, paraffin-embedded (FFPE) tissue sections were obtained and stained utilizing a panel of 26 clinically relevant metal-conjugated antibodies. Detection and analysis were performed for these stained cells with the Hyperion imaging system. Results. Four patients met our inclusion criteria. We depicted a preliminary landscape of their tumor immune microenvironment and identified 25 distinct immune cell subsets from these OSCC patients based on phenotypic similarity. All these patients had decreased expression of CD8+ T cells in tumor specimens. Variety in cell subsets was seen, and more immune activated cells were found in patient A and patient B than those in patient C and patient D. Such differences in tumor immune microenvironments can contribute to forecasting of individual prognoses. Conclusion. The Hyperion imaging system helped to delineate a preliminary and multidimensional landscape of the tumor immune microenvironment in OSCC with lymph node metastasis and provided insights into the influence of the immune microenvironment in determination of prognoses. These results reveal possible contributory factors behind different prognoses of OSCC patients with lymph node metastasis and provide reference for individual treatment planning.

Adaptation of Imaging Mass Cytometry to Explore the Single Cell Alloimmune Landscape of Liver Transplant Rejection

“…Among these, only one was unique to CR, Macrophage-1. Also, not all subpopulations expressed CD45, which is consistent with prior work in clinical LT biopsies using multiplexed immunohistochemistry and IMC ( 15 , 19 – 21 , 34 ). While neutrophils and Other Leukocytes (CD45+ cells) were rare overall at <10% of the immune cell microenvironment, NR had a higher proportion of these populations when compared to CR ( Figure 4 ).…”

“…no CD3 expression on CD68+ macrophages). Interestingly, not all T- or B-cell populations were strongly CD45+, which has been observed by other groups using the IMC technique ( 15 , 19 – 21 ). Immune meta-clusters based on solitary markers included B-cells (CD20+), HLADR+ cells, Naïve T-cells (CD45RA+), Other Leukocytes (CD45+), and Neutrophils (CD66a+).…”

Identification and validation of transcription factor-driven enhancers of genes related to lipid metabolism in metastatic oral squamous cell carcinomas

“…Prognosis of OSCC is poor due to the occurrence of metastasis [ 1 , 19 , 20 ]. Development and metastasis of OSCC are influenced by a variety of factors, among which metabolic factors have attracted increasing attention [ 21 – 24 ]. In this study, we identified the MAE-regulated lipid metabolism-related genes and the key transcription factor in OSCC.…”

Application of High-Throughput Imaging Mass Cytometry Hyperion in Cancer Research

“…Among cancer-dedicated studies using IMC, several have focused mainly on reporting the feasibility of IMC analyses in various cancer subtypes and describing the composition of different cancer tissues as a technical preliminary step prior to future additional works investigating biological and/or clinical questions. These technical descriptive studies are particularly interesting as they contribute with other studies using IMC on reporting panels of Abs usable for IMC analyses ( 2 , 20 , 23 – 29 ). Beside quantifying cell-subpopulations, these studies have also pointed the feasibility of quantifying finely markers using IMC as PD-L1 expression in lung cancer and p53 in colorectal cancer samples ( 26 , 28 ).…”

“…Cell analysis itself consists in cell phenotyping and quantifications through cytometry-inspired approaches based on the phenotypic characteristics of the cells (using non-supervised or supervised classification strategies) but also neighborhood analysis to point the spatial interactions between the cells within the tissue. Several free or commercial softwares and pipelines are available and keep on being developed to achieve the analysis of IMC data as proposed in the different studies summarized hereafter ( 14 , 15 , 20 – 22 ). Both the morphological approach and the bioinformatics analyses provide complementary data in the interpretation of IMC data.…”