OncoSpot enables benchmarking of cancer spatial transcriptomics methods across tumor evolutionary modes

Abstract

Spatial transcriptomics (ST) is increasingly used to map clonal architecture within tumors, yet methods for analyzing cancer ST data are difficult to evaluate because real tumors lack ground truth for copy-number alterations (CNAs), subclonal identity, and malignant cell fraction. Existing ST simulators model normal tissue and do not represent tumor evolution, CNA-driven expression, or the spatial organization of malignant subclones. We present OncoSpot, a cancer-aware simulator of Visium-like spot-based ST data. OncoSpot combines a spatial evolutionary scaffold from DEMON, CNAs generated along the resulting clonal phylogeny with CNAsim, cancer-type-specific malignant transcriptional programs, and spatially clustered nonmalignant cell types, and aggregates cells into spots with known malignant fraction, subclone, and copy-number profiles. We demonstrate OncoSpot on high-grade serous ovarian carcinoma, colorectal cancer, and hepatocellular carcinoma, which represent three distinct modes of tumor evolution. Simulated subclones were spatially clustered and showed stronger phylogeny-space concordance than those generated by scCube under matched subclone populations. Benchmarking CopyKAT, CancerFinder, Clonalscope, and RCTD revealed cancer-dependent performance: Clonalscope recovered copy-number signals more accurately in ovarian and colorectal cancer, whereas CopyKAT performed better in hepatocellular carcinoma, reflecting both the assumptions of each method and the copy-number landscapes of the three cancers. OncoSpot's ground truth also exposed a failure mode in Clonalscope's malignant spot identification, in which tumor and normal labels were inverted, and allowed us to trace its cause. These method-dependent behaviors would be difficult to detect in real data. OncoSpot provides a framework for benchmarking CNA inference, subclone detection, malignant spot identification, and normal spot deconvolution, and its modular design and guidelines allow extension to additional cancer types. OncoSpot is freely available at https://github.com/compbio-mallory/OncoSpot.