Gahee Park, Sangjoon Choi, Seokhwi Kim, Soo Ick Cho, Wonkyung Jung, Jeongun Ryu, Minuk Ma, Donggeun Yoo, Kyunghyun Paeng, Chan-Young Ock, Sanghoon Song, Heon Song, Sergio Pereira, Seonwook Park<\/p>\n
USCAP, 2022<\/strong><\/p>\n Background:<\/strong> The advanced genomic analysis capturing tumor-specific profiles in DNA or RNA allowed us to estimate tumor purities more precisely. However, the direct evaluation of tumor purity assessment in a whole slide image (WSI) was limited due to technical challenges. Here, we present an artificial intelligence (AI)-powered tumor purity (AI-P) method using Lunit SCOPE IO to capture and quantify its tumor heterogeneity by defining characteristics of tissue area and cell types from WSI. Furthermore, we implemented AI-P for H&E stained WSI in The Cancer Genome Atlas (TCGA) cohorts to validate previous methodologies of next- generation sequencing (NGS)-based tumor purity estimates.<\/p>\n Design:<\/strong> Lunit SCOPE IO embedded two computer vision models: a tissue segmentation model and a cell detection model to analyze the spatial analysis of heterogeneity on WSI from the pan-cancer analysis. The performance of Lunit SCOPE IO was trained and validated with 3,166 H&E WSI of multiple cancer types, annotated by 52 board-certified pathologists. First, we implemented Lunit SCOPE IO to extract the characteristics of tissue and cell images from H&E stained WSI in TCGA cohorts. The AI-powered tumor purity (AI-P) was calculated as the total number of tumor cell counts in the cancer area over the total cell counts. Next, we evaluated AI-P and differential expression of tumor purity estimates from Aran et al., including breast cancer, lung cancer, and colorectal cancer of TCGA cohorts in 16 cancer types (n= 6,573). Finally, to assess clinical relevance driven by purity estimates in treatment effectiveness and prognosis, we classified AI P into \u201chigh\u201d and \u201clow\u201d by a threshold of 50%.<\/p>\n Results:<\/strong> The overall average tissue size from the TCGA cohorts was 315.3 \u00b1 165.6 (Mean \u00b1 SD) mm2 with the average cancer region of 65.9 \u00b1 57.6 (Mean \u00b1 SD) mm2 which provided average AI-P estimates of 36.4% \u00b1 18.7% (Mean \u00b1 SD). Ovarian cancer had the highest AI-P estimate of 51.2% amongst the 16 cancers. The purity estimates from AI-P and multiple genomic profiling methods (ABSOLUTE, ESTIMATE, and LUMP) had high concordance between most cancer types (|R| > 0.40, p < 0.001), but there was a loose correlation between AI-P and pathologic examination (|R| = 0.18, p < 0.001, Figure 1). Overall survival of the \u201chigh\u201d AI- P group (n=1,663) prolonged significantly for those with above 50% of AI-P score compared to the \u201clow\u201d AI-P group (n=4,910, median OS: 85.1 versus 110.9 months, P=0.016). In a subgroup analysis, the \"\"high\"\" AI-P group had significantly poor prognosis in a total of 5 out of 16 cancers (p < 0.05) including kidney clear cell carcinoma (HR 2.26, p = 0.006), uterine corpus endometrial carcinoma (HR 1.71, p = 0.032), colorectal cancer (HR 1.67, p = 0.009), breast cancer (HR 1.47, p = 0.021), and hepatocellular carcinoma (HR 1.45, p = 0.037, Figure 2).<\/p>\n