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Background: The Crohn's-like lymphoid reaction (CLR) is manifested as peritumoral lymphocytes aggregation in colon cancer, which is a major component of the host immune response to cancer. However, the lack of a unified and objective CLR evaluation standard limits its clinical application. We, therefore, developed a deep learning model for the fully automated CLR density quantification on routine hematoxylin and eosin (HE)-stained whole-slide images (WSIs) and further investigated its prognostic validity for patient stratification. Methods: The CLR density was calculated by using a deep learning method on HE-stained WSIs. A training (N = 279) and a validation (N = 194) cohorts were used to evaluate the prognostic value of CLR density for overall survival (OS). Result: The fully automated quantified CLR density was an independent prognostic factor, with high CLR density associated with increased OS in the discovery (HR 0.58, 95% CI 0.38–0.89, P = 0.012) and validation cohort (0.45, 0.23–0.88, 0.020). Integrating CLR density into a Cox model with other risk factors showed improved prognostic capability. Conclusion: We developed a new immune indicator (CLR density) quantified by a deep learning method to evaluate the lymphocytes aggregation in colon cancer. The CLR density was demonstrated its predictive value for OS in two independent cohorts. This approach allows for the objective and standardized quantification while reducing pathologists' workload. Therefore, this fully automated standardized method of CLR evaluation had potential clinical value. [ABSTRACT FROM AUTHOR]

MHC class I-related chain A (MICA) is one of the major ligands for natural killer group 2 member D (NKG2D), which is an activating NK receptor. MICA is expressed on the surface of human epithelial tumor cells, and its shedding from tumor cells leads to immunosuppression. To activate immune response in the tumor microenvironment, we designed an anti-VEGFR2–MICA bispecific antibody (JZC01), consisting of MICA and an anti-VEGFR2 single chain antibody fragment (JZC00) and explored its potential anti-tumor activity. JZC01 targeted vascular endothelial growth factor receptor 2 (VEGFR2) and inhibited tumorigenesis by blocking the VEGFR2 signaling pathway. Additionally, JZC01 promoted NK and CD8+ T cells to release IFN-γ and engaged activated lymphocytes to lysis of VEGFR2-expressing tumor cells. The in vivo anti-tumor activity of JZC01 was investigated by establishing a Lewis lung cancer cell-transplanted mouse model. It effectively reduced the tumor vascular density and increased the infiltration and activation of NK and CD8+ T cells in the tumor microenvironment. Thus, JZC01 functions in anti-tumor angiogenesis and anti-tumor immune activation, and showed improved anti-tumor efficacy combined with docetaxel, which provides a new insight into anti-tumor therapy. [ABSTRACT FROM AUTHOR]