Recent advancements in spatial transcriptomics technologies have significantly enhanced resolution and throughput, underscoring an urgent need for systematic benchmarking. Here, we generate serial tissue sections from colon adenocarcinoma, hepatocellular carcinoma, and ovarian cancer samples for systematic evaluation. Using these uniformly processed samples, we generate spatial transcriptomics data across four high-throughput platforms with subcellular resolution: Stereo-seq v1.3, Visium HD FFPE, CosMx 6K, and Xenium 5K. To establish ground truth datasets, we profile proteins on tissue sections adjacent to all platforms using CODEX and perform single-cell RNA sequencing on the same samples. Leveraging manual nuclear segmentation and detailed annotations, we systematically assess each platform’s performance across capture sensitivity, specificity, diffusion control, cell segmentation, cell annotation, spatial clustering, and concordance with adjacent CODEX. The uniformly generated and processed multi-omics dataset could advance computational method development and biological discoveries. The dataset is accessible via SPATCH, a user-friendly web server for visualization and download.

Recent studies have indicated the possible function of miR-217 in tumorigenesis. However, the roles of miR-217 in colorectal cancer (CRC) are still largely unknown. We examined the expression of miR-217 and AEG-1 in 50 CRC tissues and the corresponding noncancerous tissues by qRT-PCR. The clinical significance of miR-217 was analyzed. CRC cell lines with miR-217 upregulation and AEG-1 silencing were established and the effects on tumor growth in vitro and in vivo were assessed. Dual-luciferase reporter gene assays were also performed to investigate the interaction between miR-217 and AEG-1. Our data demonstrated that miR-217 was significantly downregulated in 50 pairs of colorectal cancer tissues. MiR-217 expression levels were closely correlated with tumor differentiation. Moreover, decreased miR-217 expression was also associated with shorter overall survival of CRC patients. MiR-217 overexpression significantly inhibited proliferation, colony formation and invasiveness of CRC cells by promoting apoptosis and G0/G1 phase arrest. Interestingly, ectopic miR-217 expression decreased AEG-1 expression and repressed luciferase reporter activity associated with the AEG-1 3′-untranslated region (UTR). AEG-1 silencing resulted in similar biological behavior changes to those associated with miR-217 overexpression. Finally, in a nude mouse xenografted tumor model, miR-217 overexpression significantly suppressed CRC cell growth. Our findings suggest that miR-217 has considerable value as a prognostic marker and potential therapeutic target in CRC.

Purpose: Traditional risk stratification schemes in gastrointestinal stromal tumors (GIST) were defined in the pre-imatinib era and rely solely on clinicopathologic metrics. We hypothesize that genomic-based risk stratification is prognostically relevant in the current era of tyrosine kinase inhibitor (TKI) therapeutics. Experimental Design: Comprehensive mutational and copy-number profiling using MSK-IMPACT was performed. We integrated clinicopathologic and genomic parameters and utilized an elastic-net penalized Cox proportional hazards machine learning model for outcome risk stratification. Results: A 3-tier genomic risk stratification model for recurrence-free survival (RFS) in 152 primary localized gastric and 80 small bowel GISTs was proposed. Gastric GISTs were classified as high risk if chr1p deletion or SDHB loss was present, and intermediate risk if chr14q deletion was present or KIT exon 11 mutation was absent. Small bowel GISTs were classified as high risk if MAX/MGA/MYC, CDKN2A, or RB1 alterations were present, and intermediate risk if chr1p deletion or chr5q amplification was present. Compared with conventional risk stratification, genomic risk stratification both upgrades and downgrades, suggesting that conventional risk stratification may underestimate or overtreat some high-risk and low-risk patients, respectively. Longitudinal sequencing detected most KIT-independent genomic alterations at baseline. Subanalysis in 26 SDH-deficient GISTs revealed that presence of TP53 mutations or chr1q amplifications portends worse RFS and disease-free survival. Conclusions: We developed a novel, next-generation genomic risk stratification model for primary gastric and small bowel GISTs, complementing traditional clinicopathologic models. Future independent validation of our model in external cohorts is essential.