1706P Artificial intelligence-powered tumor purity assessment from H&E whole slide images associates with variant allele frequency of somatic mutations across 23 cancer types in TCGA cohorts

Abstract

Precise tumor purity assessment is a crucial preparation step to determine target sequencing depth to detect oncogenic variants from tissue samples. Moreover, knowing tumor purity improves somatic variant calling during post-sequencing analysis. We developed an artificial intelligence (AI)-powered model to quantify tumor purity from whole slide images (AI-P), named Lunit SCOPE TP, to address these challenges. In this study, we analyzed the correlation between AI-P, variant allele frequency (VAF), and average depth of sequencing across 23 cancer types.