Toro, Paula

3118 Background: Improved risk stratification is critically needed across multiple cancers to guide treatment decisions. This includes reducing overtreatment in Ductal Carcinoma in Situ (DCIS), addressing late recurrence risk in estrogen receptor-positive/lymph node-negative (ER+/LN-) breast cancer (BC), and refining prognosis in head & neck squamous cell carcinomas (HNSCC), and high-grade serous ovarian carcinoma (HGSOC). Tumor-infiltrating lymphocytes (TILs) reflect anti-tumor immunity, whereas tumor-associated macrophages (TAMs) polarize toward anti-tumor M1-like (CD68+) or immunosuppressive M2-like (CD163+) phenotypes. Integrating TILs with TAM polarization markers may better capture inflammatory balance and improve outcome prediction. We developed a hematoxylin and eosin (H&E)-only AI method (VAIP) to quantify TILs and virtual CD68/CD163 TAMs to develop multi-cancer based potential prognostic models. Methods: TILs were detected on H&E with HoVer-UNet, and virtual CD68 and CD163 macrophages were inferred with VISTA. In cancer regions, we computed cell densities and immune-balance ratios and trained cancer-specific Cox models with compact feature sets (DCIS 6; HGSOC 7; ER+ 4; HNSCC 4). Prognostic stratification used disease-free survival (DFS) for DCIS (N=273), HGSOC (training N=165, independent test N=220), and ER+ BC (training N=144, test N=622), and overall survival (OS) for HNSCC (N=77), reflecting clinical endpoints. Evaluation used cross-validation for cohorts without an external test set (DCIS, HNSCC) and independent training and testing when independent cohort was available (HGSOC, ER+). Results: Risk stratification performance across cancer types is in Table 1. The DCIS model used 6 features, largely TIL-macrophage contrast ratios (TIL/M2, TIL/M1). HGSOC used 7 features with a similar ratio-heavy immune-balance signature, plus macrophage-normalized density and polarization metrics (e.g., M1/N, M/N). In contrast, ER+ BC and HNSCC achieved best performance with 4 features dominated by macrophage measures, particularly M2 density and the M1/M2 ratio. Across sites, CD163-related macrophage metrics and immune-balance ratios were consistently informative. Conclusions: Integrating H&E-visible TILs with virtual CD68/CD163 TAMs enables fully automated, H&E-only prognostic biomarkers that stratify OS and DFS across multiple cancers, demonstrating consistent prognostic value in DCIS, ER+ BC, HGSOC, and HNSCC. These findings support further study of this histomorphometric risk classifier through broader external and prospective validation. Performance across cancer types. Cancer Type Cohort C-index HR [95% CI] p-value DCIS All 0.616 1.76 [1.20-2.59] <0.001 DCIS RT/No-treatment 0.624 1.94 [1.15-3.26] 0.003 HGSOC Test 0.553 1.38 [1.02-1.87] 0.030 ER+ BC Test 0.577 1.51 [1.00-2.28] 0.004 HNSCC All 0.603 1.92 [1.03-3.36] 0.012

Abstract Objective β-Catenin–mutated hepatocellular adenomas (HCAs) carry an increased malignant transformation risk and are screened by interpreting glutamine synthetase (GS) and β-catenin by immunohistochemistry (IHC). Our study aims to assess GS and β-catenin interpretation guidelines for applicability and reproducibility in predicting high-risk HCA and other relevant molecular alterations. Methods Hematoxylin and eosin (H&E), β-catenin, GS, and CD34 stains from 75 HCAs were interpreted by three pathologists using Method A (GS interpretation: negative, perivenular patchy, map-like, diffuse, and indeterminate) and Method B criteria (similar GS interpretation scheme based on a recent publication, with and without CD34 expression patterns). Ease of application and interpretation confidence level were assessed. High-risk IHC was defined as nuclear β-catenin and/or diffuse homogeneous GS. Molecular testing was performed on a subset of HCAs and controls. Results There were 57 resections and 18 biopsy specimens examined. Methods A and B (GS only) were rated as easy to apply, with high interpretation confidence (≥90% using both methods). Consensus rate was comparable in biopsy specimens (100% for both methods) and resections (88% for Method A, 93% for Method B). While the same cases were stratified into high-risk GS categories using both systems, clinically significant genetic alterations (TERT promoter, EGFR, MTOR, and TP53) were identified in 25% of cases stratified as not high risk by IHC. Conclusions Both methods have a similar ease of application and level of interpretation confidence, and they also detected β-catenin mutations as expected. Other relevant molecular alterations associated with risk of neoplastic progression and/or bleeding were detected in 25% of HCAs with the non-high-risk IHC phenotype, suggesting the value of molecular testing in this subset.