Grady, William M

Abstract Colorectal cancer develops through a normal-adenoma-carcinoma sequence, yet only 5-10% of adenomas progress to malignancy, and the cellular programs governing that sequence remain poorly defined. Here we generate a spatial multi-omics atlas of human colon adenomas, combining Visium CytAssist and protein co-detection across 24 nonadvanced and advanced tubular adenomas with single-cell resolution Xenium Prime 5K profiling of 101 patient-matched normal, adenoma, and carcinoma cores from 16 patients. Integrating whole-transcriptome and 31-plex protein data identifies nine spatial clusters and two dysplastic epithelial populations that co-express stemness, proliferation, and senescence programs. These programs occupy a shared, spatially confined epithelial niche that expands from adenoma to carcinoma. Spatial analysis revealed GDF15, a senescence-associated secretory factor, mediated the coupling between senescence and stemness in advanced adenomas, and that GDF15-high epithelium locally excludes CD8+ T cells in adenoma and, more broadly, in carcinoma. These findings position senescence as a spatially instructive rather than merely tumor-suppressive program during colorectal carcinogenesis and suggest GDF15 might be a potential candidate target for cancer prevention and interception in the colon.

Abstract AI-powered pathology foundation models provide general-purpose representations of histopathological images by encoding image tiles into numerical embeddings. However, these embeddings are not directly interpretable in biological or clinical terms and must be translated into biologically meaningful features, such as cell-type composition or gene expression, to enable downstream clinical applications. To bridge this gap, we developed STpath, a framework that integrates histopathology image embeddings derived from existing pathology foundation models with matched, spatially resolved transcriptomics data. STpath consists of cancer-specific XGBoost models trained to infer cell-type compositions and gene expression from histopathology image tiles. We evaluated STpath in colorectal and breast cancer datasets and showed that it provides accurate estimates of the composition of major cell types and the expression of a subset of genes, with further performance gains achieved by combining embeddings from multiple foundation models. Finally, we demonstrated that STpath inferred features that can be used in downstream studies to evaluate their associations with clinical outcomes.

INTRODUCTION: A substantial proportion of patients with esophageal adenocarcinoma (EAC) do not report gastroesophageal reflux disease (GERD) symptoms. This study aimed to compare the risk factor profiles and cancer stage at presentation of patients with EAC with and without prior GERD. METHODS: In this retrospective cross-sectional study, patients with EAC were divided into 2 cohorts: (i) EAC with prior GERD: patients who reported typical GERD symptoms (heartburn or regurgitation) ≥1 year before cancer diagnosis and (ii) EAC without prior GERD: patients who did not report prior GERD symptoms or reported symptoms within 1 year of their cancer diagnosis. Baseline demographics, risk factors, and cancer stage at presentation were compared between the 2 cohorts. In addition, the distribution of patients based on numbers of BE/EAC–associated risk factors (1, 2, 3, 4, and 5 or more) was examined in the symptomatic and asymptomatic cohorts. RESULTS: Over 13 years, 388 patients with EAC with prior GERD and 245 patients with EAC without prior GERD were recruited. Both groups had similar baseline demographics and risk factors, but patients with EAC with prior GERD were more likely to have a history of BE. Asymptomatic patients had more advanced disease. Patients with 3 or more BE/EAC–related risk factors formed the largest proportion of patients in both the symptomatic and asymptomatic cohorts. DISCUSSION: Patients with EAC with and without prior GERD symptoms are phenotypically similar, suggesting that BE screening efforts to prevent or detect early EAC should not be restricted to just those with GERD.

Abstract Purpose: Current endoscopy-based screening and surveillance programs have not been proven effective at decreasing esophageal adenocarcinoma (EAC) mortality, creating an unmet need for effective molecular tests for early detection of this highly lethal cancer. We conducted a genome-wide methylation screen to identify novel methylation markers that distinguish EAC and high-grade dysplasia (HGD) from normal squamous epithelium (SQ) or nondysplastic Barrett's esophagus (NDBE). Experimental Design: DNA methylation profiling of samples from SQ, NDBE, HGD, and EAC was performed using HM450 methylation arrays (Illumina) and reduced-representation bisulfate sequencing. Ultrasensitive methylation-specific droplet digital PCR and next-generation sequencing (NGS)-based bisulfite-sequencing assays were developed to detect the methylation level of candidate CpGs in independent esophageal biopsy and endoscopic brushing samples. Results: Five candidate methylation markers were significantly hypermethylated in HGD/EAC samples compared with SQ or NDBE (P < 0.01) in both esophageal biopsy and endoscopic brushing samples. In an independent set of brushing samples used to construct biomarker panels, a four-marker panel (model 1) demonstrated sensitivity of 85.0% and 90.8% for HGD and EACs respectively, with 84.2% and 97.9% specificity for NDBE and SQ respectively. In a validation set of brushing samples, the panel achieved sensitivity of 80% and 82.5% for HGD and EAC respectively, at specificity of 67.6% and 96.3% for NDBE and SQ samples. Conclusions: A novel DNA methylation marker panel differentiates HGD/EAC from SQ/NDBE. DNA-methylation–based molecular assays hold promise for the detection of HGD/EAC using esophageal brushing samples.