Dataset Information

B7-H3/CD276: An Emerging Cancer Immunotherapy.

ABSTRACT: Immunotherapy aiming at suppressing tumor development by relying on modifying or strengthening the immune system prevails among cancer treatments and points out a new direction for cancer therapy. B7 homolog 3 protein (B7-H3, also known as CD276), a newly identified immunoregulatory protein member of the B7 family, is an attractive and promising target for cancer immunotherapy because it is overexpressed in tumor tissues while showing limited expression in normal tissues and participating in tumor microenvironment (TME) shaping and development. Thus far, numerous B7-H3-based immunotherapy strategies have demonstrated potent antitumor activity and acceptable safety profiles in preclinical models. Herein, we present the expression and biological function of B7-H3 in distinct cancer and normal cells, as well as B7-H3-mediated signal pathways in cancer cells and B7-H3-based tumor immunotherapy strategies. This review provides a comprehensive overview that encompasses B7-H3's role in TME to its potential as a target in cancer immunotherapy.

SUBMITTER: Zhou WT

PROVIDER: S-EPMC8326801 | biostudies-literature | 2021

REPOSITORIES: biostudies-literature

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Publications

B7-H3/CD276: An Emerging Cancer Immunotherapy.

Zhou Wu-Tong WT Jin Wei-Lin WL

Frontiers in immunology 20210719

Immunotherapy aiming at suppressing tumor development by relying on modifying or strengthening the immune system prevails among cancer treatments and points out a new direction for cancer therapy. B7 homolog 3 protein (B7-H3, also known as CD276), a newly identified immunoregulatory protein member of the B7 family, is an attractive and promising target for cancer immunotherapy because it is overexpressed in tumor tissues while showing limited expression in normal tissues and participating in tum ...[more]

PMID: 34349762

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Project description:CD276 is a promising prognostic indicator and an attractive therapeutic target in various malignancies. However, current methods for CD276 detection are time-consuming and expensive, limiting extensive studies and applications of CD276. We aimed to develop a pathomic model for CD276 prediction from H&E-stained pathological images, and explore the underlying mechanism of the pathomic features by associating the pathomic model with transcription profiles. A dataset of colon adenocarcinoma (COAD) patients was retrieved from the Cancer Genome Atlas (TCGA) database. The dataset was divided into the training and validation sets according to the ratio of 8:2 by a stratified sampling method. Using the gradient boosting machine (GBM) algorithm, we established a pathomic model to predict CD276 expression in COAD. Univariate and multivariate Cox regression analyses were conducted to assess the predictive performance of the pathomic model for overall survival in COAD. Gene Set Enrichment Analysis (GESA) was performed to explore the underlying biological mechanisms of the pathomic model. The pathomic model formed by three pathomic features for CD276 prediction showed an area under the curve (AUC) of 0.833 (95%CI: 0.784-0.882) in the training set and 0.758 (95%CI: 0.637-0.878) in the validation set, respectively. The calibration curves and Hosmer-Lemeshow goodness of fit test showed that the prediction probability of high/low expression of CD276 was in favorable agreement with the real situation in both the training and validation sets (P=0.176 and 0.255, respectively). The DCA curves suggested that the pathomic model acquired high clinical benefit. All the subjects were categorized into high pathomic score (PS) (PS-H) and low PS (PS-L) groups according to the cutoff value of PS. Univariate and multivariate Cox regression analysis indicated that PS was a risk factor for overall survival in COAD. Furthermore, through GESA analysis, we found several immune and inflammatory-related pathways and genes were associated with the pathomic model. We constructed a pathomics-based machine learning model for CD276 prediction directly from H&E-stained images in COAD. Through integrated analysis of the pathomic model and transcriptomics, the interpretability of the pathomic model provide a theoretical basis for further hypothesis and experimental research.

Dataset Information

B7-H3/CD276: An Emerging Cancer Immunotherapy.

Publications

B7-H3/CD276: An Emerging Cancer Immunotherapy.

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