Immunohistochemical analysis of B7-H3 expression in patients with lung cancer following various anti-cancer treatments

Establishment of a pathomic-based machine learning model to predict CD276 (B7-H3) expression in colon cancer

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.

New Emerging Targets in Cancer Immunotherapy: The Role of B7-H3

Immune checkpoints (ICs) are molecules implicated in the fine-tuning of immune response via co-inhibitory or co-stimulatory signals, and serve to secure minimized host damage. Targeting ICs with various therapeutic modalities, including checkpoint inhibitors/monoclonal antibodies (mAbs), antibody-drug conjugates (ADCs), and CAR-T cells has produced remarkable results, especially in immunogenic tumors, setting a paradigm shift in cancer therapeutics through the incorporation of these IC-targeted treatments. However, the large proportion of subjects who experience primary or secondary resistance to available IC-targeted options necessitates further advancements that render immunotherapy beneficial for a larger patient pool with longer duration of response. B7-H3 (B7 Homolog 3 Protein, CD276) is a member of the B7 family of IC proteins that exerts pleiotropic immunomodulatory effects both in physiologic and pathologic contexts. Mounting evidence has demonstrated an aberrant expression of B7-H3 in various solid malignancies, including tumors less sensitive to current immunotherapeutic options, and has associated its expression with advanced disease, worse patient survival and impaired response to IC-based regimens. Anti-B7-H3 agents, including novel mAbs, bispecific antibodies, ADCs, CAR-T cells, and radioimmunotherapy agents, have exhibited encouraging antitumor activity in preclinical models and have recently entered clinical testing for several cancer types. In the present review, we concisely present the functional implications of B7-H3 and discuss the latest evidence regarding its prognostic significance and therapeutic potential in solid malignancies, with emphasis on anti-B7-H3 modalities that are currently evaluated in clinical trial settings. Better understanding of B7-H3 intricate interactions in the tumor microenvironment will expand the oncological utility of anti-B7-H3 agents and further shape their role in cancer therapeutics.