Tumour-associated macrophages are associated with poor prognosis and programmed death ligand 1 expression in oesophageal cancer

Yagi, Taisuke; Baba, Yoshifumi; Okadome, Kazuo; Kiyozumi, Yuki; Hiyoshi, Yukiharu; Ishimoto, Takatsugu; Iwatsuki, Masaaki; Miyamoto, Yuji; Yoshida, Naoya; Watanabe, Masayuki; Komohara, Yoshihiro; Baba, Hideo

doi:10.1016/j.ejca.2019.01.018

Cited by 100 publications

(76 citation statements)

References 45 publications

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“…In addition, our experimental studies showed that cell invasion and migration ability were significantly more upregulated in esophageal cancer cell lines co‐cultured with activated macrophages than that in control cell lines. Furthermore, co‐culture with activated macrophages elevated the PD‐L1 expression in cancer cells 52 . Given the significant interest in cancer immunotherapies targeting TAMs and PD‐L1, our findings might have considerable clinical implications.…”

Section: Tamsmentioning

confidence: 63%

Tumor immune microenvironment and immune checkpoint inhibitors in esophageal squamous cell carcinoma

et al. 2020

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Esophageal squamous cell carcinoma (ESCC) is the main prevalent histological type of esophageal cancer, predominantly constituting 90% of cases worldwide. Despite the development of multidisciplinary therapeutic approaches, its prognosis remains unfavorable. Recently, the development of monoclonal antibodies inhibiting programmed death 1 (PD‐1) or programmed death‐ligand 1 (PD‐L1) has led to marked therapeutic responses among multiple malignancies including ESCC. However, only a few patients achieved clinical benefits due to resistance. Therefore, precise and accurate predictive biomarkers should be identified for personalized immunotherapy in clinical settings. Because the tumor immune microenvironment can potentially influence the patient's response to immune checkpoint inhibitors, tumor immunity, such as PD‐L1 expression on tumors, tumor‐infiltrating lymphocytes, tumor‐associated macrophages, and myeloid‐derived suppressor cells, in ESCC should be further investigated. In this review, accumulated evidence regarding the tumor immune microenvironment and immune checkpoint inhibitors in ESCC are summarized.

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Section: Tamsmentioning

confidence: 63%

Tumor immune microenvironment and immune checkpoint inhibitors in esophageal squamous cell carcinoma

et al. 2020

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“…TAMs play a controversial role in tumor progression depending on different tumor types. TAMs infiltration has been shown to correlate with worse outcome in cancers, including breast cancer [36], oesophageal cancer [37], gastric cancer [38], and pancreatic cancer [39]. However, some studies indicated that TAMs were correlated with better prognosis in non-small cell lung cancer [40] and colorectal carcinoma [41].…”

Section: Discussionmentioning

confidence: 99%

Profiles of immune cell infiltration in head and neck squamous carcinoma

Liang

Wang

2020

Bioscience Reports

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Tumor immune infiltration cells (TIICs) are highly heterogeneous, not only in different cancer subtypes but also within different cancer regions. We conducted the Cell-type Identification using Estimating Relative Subsets Of RNA Transcripts (CIBERSORT) method. We assessed the relative proportions of 22 TIICs in HNSC using publicly available TCGA transcriptional datasets, analyzed the proportions of TIICs between HNSC tissues and normal tissues, along with accompanying clinicopathological data, and the impact of TIICs on clinical outcome. After the filter criteria, a total of 395 patients were included in the analysis. We found significant differences in naïve B cells, monocytes, resting mast cells, activated mast cells, CD8+ T cells, and M0 macrophages between HNSC tissues and adjacent non-cancer tissues. We also found that some TIIC subgroups were significantly associated with clinical parameters. Moreover, the patients with low Tregs fraction had worse OS and DFS than those with high Tregs fraction. However, low M0 macrophages fraction was associated with better OS and DFS in HNSC patients. Moreover, Tregs and M0 macrophages are likely to be important determinants of prognosis, which may serve as a potential immunotherapy target for HNSC. Then, we screened the immune-related differentially expressed genes (DEGs), performed the GO and KEGG enrichment analysis, constructed the protein–protein interaction network, and screened the prognosis-related hub genes in HNSC. However, further clinical investigation and basic experiments are needed to validate our results, and uncover the molecular mechanisms interlinking TIICs in HNSC and their roles in prognosis and therapy.

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“…Accurate patient prognosis is crucial for improving the survival rates of cancer patients since it is a prerequisite to delivering the most effective treatment for each patient. In fact, multiple papers have shown that the quantitative characterization of the tumour-immune microenvironment components, including TILs, TAMs, and immune checkpoints, can yield information of prognostic relevance [51,52,53]. Particularly, tumour cells surrounded by a large number of prominent intra-tumoural and peri-tumoural TILs and M1 macrophages have been related to better prognosis in several types of cancer [54,55], whereas a high content of M2 macrophages and TBs has been associated with poorer outcome [52,56].…”

Section: Discussionmentioning

confidence: 99%

Identification of Immunological Features Enables Survival Prediction of Muscle-Invasive Bladder Cancer Patients Using Machine Learning

Gavriel

Dimitriou

Brieu³

et al. 2020

Preprint

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Clinical staging and prognosis of muscle-invasive bladder cancer (MIBC) routinely includes assessment of patient tissue samples by a pathologist. Recent studies corroborate the importance of image analysis in identifying and quantifying immunological markers from tissue samples that can provide further insights into patient prognosis. In this paper, we apply multiplex immunofluorescence on MIBC tissue sections to capture whole slide images and quantify potential prognostic markers related to lymphocytes, macrophages, tumour buds, and PD-L1. We propose a machine learning based approach for the prediction of 5 year prognosis with different combinations of image, clinical, and spatial features. An ensemble model comprising several functionally different models successfully stratifies MIBC patients into two risk groups with high statistical significance (p value < 1e − 05). Critical to improving MIBC survival rates, our method classifies correctly 71.4% of the patients who succumb to MIBC within 5 years, significantly higher than the 28.6% of the current clinical gold standard, the TNM staging system.

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Tumour-associated macrophages are associated with poor prognosis and programmed death ligand 1 expression in oesophageal cancer

Cited by 100 publications

References 45 publications

Tumor immune microenvironment and immune checkpoint inhibitors in esophageal squamous cell carcinoma

Tumor immune microenvironment and immune checkpoint inhibitors in esophageal squamous cell carcinoma

Profiles of immune cell infiltration in head and neck squamous carcinoma

Identification of Immunological Features Enables Survival Prediction of Muscle-Invasive Bladder Cancer Patients Using Machine Learning

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