Purpose: Human epidermal growth factor receptor 2 (HER2)-targeted therapies have shown promise in treating -amplified metastatic colorectal cancer (mCRC). Identifying optimal biomarkers for treatment decisions remains challenging. This study explores the potential of artificial intelligence (AI) in predicting treatment responses to trastuzumab plus pertuzumab (TP) in patients with -amplified mCRC from the phase II TRIUMPH trial.
View Article and Find Full Text PDFPurpose: HER2-targeted therapies in ERBB2-amplified metastatic colorectal cancer (mCRC) are effective; however, a notable portion of patients do not respond to treatment, and secondary resistance occurs in most patients receiving these treatments. The purpose of this study was to investigate determinants of treatment efficacy and resistance in patients with ERBB2-amplified mCRC who received HER2-targeted therapy by analyzing multiomics data.
Experimental Design: We investigated genomic data from a nationwide large cancer genomic screening project, the SCRUM-Japan project.
Aim: Atezolizumab plus bevacizumab combination therapy (Atezo + Beva) is used as the first-line therapy for unresectable hepatocellular carcinoma (u-HCC). Serious adverse events (AEs), including rupture of esophagogastric varices, have been seen during treatment. Therefore, the relationships of efficacy, safety, and portal hypertension (PH) were analyzed.
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