Hepatology (Baltimore, Md.) · 2026
TESERA: morphologic subtyping and prognostic stratification of hepatocellular carcinoma from H&E whole-slide images.
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Yasaka Tyler M, Afrin Shaila, Chen Po-Yuan, Molina Laura M, Kim Chang Kyung, Chakkingal Prerna, Jain Anisha, Lehrich Brandon M, Singh Sucha, Poddar Minakshi, Dadey Rebekah E, Singhi Aatur D, Bao Riyue, Oshima Kiyoko, Jain Dhanpat, Monga Satdarshan P, Chiu Yu-Chiao
Аннотация
BACKGROUND AIMS: Hepatocellular carcinoma (HCC) remains among the most lethal cancers worldwide, yet few tools exist to characterize HCC biology and stratify patient risk at the point of care. We developed TESERA (Tissue Encoded Subtype Elucidation and Risk Assessment), a computational pathology framework that derives both unsupervised subtypes and a prognostic index directly from hematoxylin and eosin (H&E) whole-slide images (WSIs).
APPROACH RESULTS: Applied to The Cancer Genome Atlas Liver Hepatocellular Carcinoma (TCGA-LIHC) cohort, unsupervised clustering of TESERA embeddings grouped tumors into Tissue Encoded Subtypes (TES1 and TES2) associated with different overall survival (OS; TES2 HR=4.20). TES1, the favorable-prognosis subtype, was enriched for metabolic pathways and CTNNB1 mutations, while TES2 was enriched for proliferative pathways and TP53 mutations. To predict patient-level survival outcomes, we trained a supervised model, the TESERA morphologic index (TESERA-MI), on TESERA embeddings, which predicted both OS (HR=3.17) and disease-free survival (HR=2.19) in TCGA-LIHC. TESERA-MI achieved the highest discrimination in a head-to-head comparison with clinical variables and molecular markers (C-index, 0.73; 1-, 3-, and 5-year AUROCs, 0.82, 0.80, and 0.79), remained independently prognostic after multivariable adjustment (HR=2.97), and stratified OS in an independent in-house cohort (HR=2.16).
CONCLUSIONS: Released as a publicly accessible pipeline applicable to H&E WSIs, TESERA unifies morphologically derived metabolism-versus-proliferation subtypes with an externally validated, prognostic morphologic index competitive with existing clinical and molecular markers. These findings position routine histology as a low-cost, widely available substrate for biology-aware risk stratification in HCC.
APPROACH RESULTS: Applied to The Cancer Genome Atlas Liver Hepatocellular Carcinoma (TCGA-LIHC) cohort, unsupervised clustering of TESERA embeddings grouped tumors into Tissue Encoded Subtypes (TES1 and TES2) associated with different overall survival (OS; TES2 HR=4.20). TES1, the favorable-prognosis subtype, was enriched for metabolic pathways and CTNNB1 mutations, while TES2 was enriched for proliferative pathways and TP53 mutations. To predict patient-level survival outcomes, we trained a supervised model, the TESERA morphologic index (TESERA-MI), on TESERA embeddings, which predicted both OS (HR=3.17) and disease-free survival (HR=2.19) in TCGA-LIHC. TESERA-MI achieved the highest discrimination in a head-to-head comparison with clinical variables and molecular markers (C-index, 0.73; 1-, 3-, and 5-year AUROCs, 0.82, 0.80, and 0.79), remained independently prognostic after multivariable adjustment (HR=2.97), and stratified OS in an independent in-house cohort (HR=2.16).
CONCLUSIONS: Released as a publicly accessible pipeline applicable to H&E WSIs, TESERA unifies morphologically derived metabolism-versus-proliferation subtypes with an externally validated, prognostic morphologic index competitive with existing clinical and molecular markers. These findings position routine histology as a low-cost, widely available substrate for biology-aware risk stratification in HCC.