Abstract 5222: AI-driven, mIF-based cell-omics reveals spatially resolved cell signature for outcome prediction in NSCLC patients

Abstract Background: Despite the rapidly increasing number of new targeted and immunotherapeutic options over the past two decades, the prognosis of patients with NSCLC, even with early-stage tumors, is still poor and novel biomarkers are needed to better stratify patients in terms of survival and treatment response. A novel approach is to gain a holistic understanding of the cellular composition and formation of the tumor microenvironment (TME). Therefore, we developed a miF-based, AI-driven approach for spatially resolved TME characterization at the cellular level and used this to successfully predict clinical outcome. Methods: We assembled a large bicentric real-world sample group of 1168 patients with resected NSCLC from the Charite and the University Hospital Cologne. For tissue microarray construction, four 1. 5 mm tissue cores were punched from each formalin-fixed and paraffin-embedded tumor block. Sections were stained with a 12-plex IF panel followed by H Part 1 (Regular Abstracts) ; 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84 (6Suppl): Abstract nr 5222.