How can artificial intelligence models assist PD-L1 expression scoring in breast cancer: results of multi-institutional ring studies.

Programmed death ligand-1 (PD-L1) expression is a key biomarker to screen patients for PD-1/PD-L1-targeted immunotherapy. However, a subjective assessment guide on PD-L1 expression of tumor-infiltrating immune cells (IC) scoring is currently adopted in clinical practice with low concordance. Therefore, a repeatable and quantifiable PD-L1 IC scoring method of breast cancer is desirable. In this study, we propose a deep learning-based artificial intelligence-assisted (AI-assisted) model for PD-L1 IC scoring. Three rounds of ring studies (RSs) involving 31 pathologists from 10 hospitals were carried out, using the current guideline in the first two rounds (RS1, RS2) and our AI scoring model in the last round (RS3). A total of 109 PD-L1 (Ventana SP142) immunohistochemistry (IHC) stained images were assessed and the role of the AI-assisted model was evaluated. With the assistance of AI, the scoring concordance across pathologists was boosted to excellent in RS3 (0.950, 95% confidence interval (CI): 0.936-0.962) from moderate in RS1 (0.674, 95% CI: 0.614-0.735) and RS2 (0.736, 95% CI: 0.683-0.789). The 2- and 4-category scoring accuracy were improved by 4.2% (0.959, 95% CI: 0.953-0.964) and 13% (0.815, 95% CI: 0.803-0.827) (p < 0.001). The AI results were generally accepted by pathologists with 61% "fully accepted" and 91% "almost accepted". The proposed AI-assisted method can help pathologists at all levels to improve the PD-L1 assay (SP-142) IC assessment in breast cancer in terms of both accuracy and concordance. The AI tool provides a scheme to standardize the PD-L1 IC scoring in clinical practice.

Nuclear factor-κB p65 regulates glutaminase 1 expression in human hepatocellular carcinoma.

BACKGROUND: Glutaminase (GLS), the key enzyme that catalyzes glutamine catabolism, facilitates the production of energy, building blocks, and factors resisting stresses. Two isoforms of GLS have been identified: GLS1 and GLS2. Elevated GLS1 contributes to tumorigenesis and tumor progression. This study investigates the molecular mechanism by which GLS1 is regulated in human hepatocellular carcinoma (HCC). METHODS: Online databases were investigated to search for factors that co-overexpress with GLS1. siRNA knockdown or chemical compounds were utilized to manipulate the activation or inactivation of nuclear factor-κB (NF-κB) p65 signaling. Both the mRNA and protein levels of GLS1 were detected. The biological and clinical importance of p65-GLS1 in HCC was also demonstrated. RESULTS: NF-κB p65 regulates GLS1 expression in HCC cells. Knockdown or suppression of GLS1 compromises HCC cell proliferation. Elevated GLS1 expression correlates with neoplasm histological grade, and the dysregulation of p65-GLS1 is associated with poor prognosis in human HCC patients. CONCLUSION: GLS1 can be developed as a diagnostic and therapeutic target for human HCC.