Riu Yamashita

Yokosu K, et al. J Gynecol Oncol. 2027 ;38:e30. https://doi.org/10.3802/jgo.2027.38.e30

Tumors contain diverse non-cancer cells that constitute the tumor microenvironment; thus, identifying which cells are cancerous is a foundational and critical first step in cancer research. However, to our knowledge, no transcriptome-based classifier has been trained using pathologist-defined annotations from matched H&E images at single-cell resolution. Moreover, few tools can be shared across diverse platforms. Here, we present SPATNIC, a variational autoencoder that classifies individual cells as cancer or normal, trained on Xenium cells labeled by a pathologist review of matched H&E images. It matches or exceeds the performance of existing tools in primary colorectal tumors and liver and lung metastases. SPATNIC generalizes zero-shot across Visium, Visium HD, CosMx, Xenium, MERSCOPE, and single-cell RNA sequencing. In public single-cell atlases, it identified a rare POU2F3 ⁺ tuft-like cancer stem cell population missed by existing annotations and associated with worse survival. In rectal cancer profiled before and after chemoradiotherapy, it separated cancer from normal epithelium in tissue, thus enabling per-patient screening of antibody–drug conjugate (ADC) targets.

Supplementary Table 4. Univariable and multivariate analyses for PFS and OS

Supplementary Table 14. Comparison of expression levels of specific immune-related genes stratified by histopathology

Supplementary Table 16. Characteristics of patients whose tumor samples were used for flow cytometry stratified by histopathology

Supplementary Figure 5. Prognostic impact of TIME phenotype in ICI combination therapy

Supplementary Table 6. Patient characteristics of the IO-TKI treatment group

Supplementary Table 13. Sample list

Supplementary Table 8. Tumor responses in the IO-TKI treatment group