Potentiality of multiple modalities for single-cell analyses to evaluate the tumor microenvironment in clinical specimens

Kashima, Yukie; Togashi, Yosuke; Fukuoka, Shota; Kamada, Tetsuo; Irie, Takuma; Suzuki, Ayako; Nakamura, Yoshiaki; Shitara, Kohei; Minamide, Tatsunori; Yoshida, Taku; Taoka, Naofumi; Kawase, Tatsuya; Wada, Teiji; Inaki, Koichiro; Chihara, Masataka; Ebisuno, Yukihiko; Tsukamoto, Sakiyo; Fujii, Ryo; Ohashi, Akihiro; Suzuki, Yutaka; Tsuchihara, Katsuya; Nishikawa, Hiroyoshi; Doi, Toshihiko

doi:10.1038/s41598-020-79385-w

Cited by 19 publications

(18 citation statements)

References 49 publications

(58 reference statements)

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“…Compared to scRNA‐seq, CYTOF offers the advantage of a higher throughput for the evaluation of the TME in clinical samples, as it allows for a more accurate targeting of immune cell subsets through the use of >30 selected antigen markers. Moreover, CyTOF “narrow and distinct” 78 , 79 data are generated from the analysis of over 250 000 cells, whereas transcriptome‐based platforms detect wider unbiased populations from several thousands of cells. 80 , 81…”

Section: Discussionmentioning

confidence: 99%

Assessment of the immune landscapes of advanced ovarian cancer in an optimized in vivo model

Pisano

Lenna

Healey

et al. 2021

Clinical & Translational Med

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Background Ovarian cancer (OC) is typically diagnosed late, associated with high rates of metastasis and the onset of ascites during late stage disease. Understanding the tumor microenvironment and how it impacts the efficacy of current treatments, including immunotherapies, needs effective in vivo models that are fully characterized. In particular, understanding the role of immune cells within the tumor and ascitic fluid could provide important insights into why OC fails to respond to immunotherapies. In this work, we comprehensively described the immune cell infiltrates in tumor nodules and the ascitic fluid within an optimized preclinical model of advanced ovarian cancer. Methods Green Fluorescent Protein (GFP)‐ID8 OC cells were injected intraperitoneally into C57BL/6 mice and the development of advanced stage OC monitored. Nine weeks after tumor injection, mice were sacrificed and tumor nodules analyzed to identify specific immune infiltrates by immunohistochemistry. Ascites, developed in tumor bearing mice over a 10‐week period, was characterized by mass cytometry (CyTOF) to qualitatively and quantitatively assess the distribution of the immune cell subsets, and their relationship to ascites from ovarian cancer patients. Results Tumor nodules in the peritoneal cavity proved to be enriched in T cells, antigen presenting cells and macrophages, demonstrating an active immune environment and cell‐mediated immunity. Assessment of the immune landscape in the ascites showed the predominance of CD8 + , CD4 + , B – , and memory T cells, among others, and the coexistance of different immune cell types within the same tumor microenvironment. Conclusions We performed, for the first time, a multiparametric analysis of the ascitic fluid and specifically identify immune cell populations in the peritoneal cavity of mice with advanced OC. Data obtained highlights the impact of CytOF as a diagnostic tool for this malignancy, with the opportunity to concomitantly identify novel targets, and define personalized therapeutic options.

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Section: Discussionmentioning

confidence: 99%

Assessment of the immune landscapes of advanced ovarian cancer in an optimized in vivo model

Pisano

Lenna

Healey

et al. 2021

Clinical & Translational Med

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“…CyToF is a mass cytometry method that analyzes multiple proteins tagged with labels. It has previously been alternatively used or combined with scRNA-seq in cancer immunity studies [3,20]. For example, single-cell mass cytometry (CyToF) is commonly used to classify cell surface proteins of immune cells into specific cell classifications.…”

Section: Single-cell Sequencing Technologies and Integrated Databasesmentioning

confidence: 99%

Single-Cell Sequencing: Current Applications in Precision Onco-Genomics and Cancer Therapeutics

Mustachio

Roszik

2022

Cancers

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Single-cell sequencing encompasses a variety of technologies that evaluate cells at the genomic, transcriptomic, epigenomic, and proteomic levels. Each of these levels can be split into additional techniques that enable specific and optimized sequencing for a specialized purpose. At the transcriptomic level, single-cell sequencing has been used to understand immune-malignant cell networks, as well as differences between primary versus metastatic tumors. At the genomic and epigenomic levels, single-cell sequencing technology has been used to study genetic mutations involved in tumor evolution or the reprogramming of regulatory elements present in metastasized disease, respectively. Lastly, at the proteomic level, single-cell sequencing has been used to identify biomarkers important for predicting patient prognosis, as well as biomarkers essential for evaluating optimal treatment strategies. Integrated databases and atlases, as a result of large sequencing experiments, provide a vast array of information that can be applied to various studies and accessed by researchers to further answer scientific questions. This review summarizes recent, high-impact literature covering these aspects, as well as single-cell sequencing in the translational setting. Specifically, we review the potential that single-cell sequencing has in the clinic and its implementation in current clinical studies.

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“…They found that pre-existing TILs were neither activated nor expanded after PD-1 blockade; instead, novel T cell clones appeared to undergo sequential reactions. GAPF REE2 consortium, including the authors of this manuscript, also conducted scRNA-seq using clinical samples from gastrointestinal cancer patients to characterize the transcriptome features of a series of cells constructing cancers, primarily focusing on immune cells [15] (Fig. 1a).…”

Section: Single-cell Analysis For Characterizing Microenvironmental Non-tumor Cellsmentioning

confidence: 99%

Single-cell and spatial analyses of cancer cells: toward elucidating the molecular mechanisms of clonal evolution and drug resistance acquisition

et al. 2021

Self Cite

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Even within a single type of cancer, cells of various types exist and play interrelated roles. Each of the individual cells resides in a distinct microenvironment and behaves differently. Such heterogeneity is the most cumbersome nature of cancers, which is occasionally uncountable when effective prevention or total elimination of cancers is attempted. To understand the heterogeneous nature of each cell, the use of conventional methods for the analysis of “bulk” cells is insufficient. Although some methods are high-throughput and compressive regarding the genes being detected, the obtained data would be from the cell mass, and the average of a large number of the component cells would no longer be measured. Single-cell analysis, which has developed rapidly in recent years, is causing a drastic change. Genome, transcriptome, and epigenome analyses at single-cell resolution currently target cancer cells, cancer-associated fibroblasts, endothelial cells of vessels, and circulating and infiltrating immune cells. In fact, surprisingly diverse features of clonal evolution of cancer cells, during the development of cancer or acquisition of drug resistance, accompanied by corresponding gene expression changes in the circumstantial stromal cells, appeared in recent single-cell analyses. Based on the obtained novel insights, better optimal drug selection and new drug administration sequences were started. Even a remaining concern of the single cell analyses is being addressed. Until very recently, it was impossible to obtain positional information of cells in cancer via single-cell analysis because such information is lost during preparation of single-cell suspensions. A new method, collectively called spatial transcriptome (ST) analysis, has been developed and rapidly applied to various clinical specimens. In this review, we first outline the recent achievements of single-cell cancer analysis in analyzing the molecular basis underlying the acquisition of drug resistance, particularly focusing on the latest anti-epidermal growth factor receptor tyrosine kinase inhibitor, osimertinib. Further, we review the currently available ST analysis methods and introduce our recent attempts regarding the respective topics.

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Potentiality of multiple modalities for single-cell analyses to evaluate the tumor microenvironment in clinical specimens

Cited by 19 publications

References 49 publications

Assessment of the immune landscapes of advanced ovarian cancer in an optimized in vivo model

Assessment of the immune landscapes of advanced ovarian cancer in an optimized in vivo model

Single-Cell Sequencing: Current Applications in Precision Onco-Genomics and Cancer Therapeutics

Single-cell and spatial analyses of cancer cells: toward elucidating the molecular mechanisms of clonal evolution and drug resistance acquisition

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