Precision treatment with artificial intelligence assisted subtyping enhances therapeutic efficacy in HR+/HER2- breast cancer: The LINUXtrial

Abstract

We report the results of LINUX (NCT05594095), a multicenter, randomized, controlled phase II platform trial aiming to identify effective precision treatments for hormone receptor-positive/human epidermal growth factor receptor 2-negative metastatic breast cancer after resistance to cyclin-dependent kinase 4/6 inhibitor. A total of 105 patients were categorized into four similarity network fusion (SNF) subtypes by artificial intelligence-assisted classification and randomly assigned to receive subtyping-based precision therapy (N = 70) or treatment of physician's choice (N = 35). Results demonstrate superior primary endpoint of objective response rates in the subtyping-based groups compared to controls: 10% versus 0% for SNF1, 65% versus 30% for SNF2, 40% versus 30% for SNF3, and 70% versus 20% for SNF4. Grade 3-4 treatment-related adverse events occurred in 37% of both groups. These findings highlight the clinical benefits of subtyping-based precision therapies, particularly for SNF2 and SNF4 subtypes, warranting further validation in phase III trials.

Keywords: AI-assisted; HR+/HER2−; breast cancer; precision treatment; subtyping-based therapy.