Abstract Background: Copy-number variation (CNV) is a hallmark of prostate cancer, including PTEN loss and TP53 alterations that shape tumor progression. Whole-genome spatial DNA sequencing technologies are not yet mature or widely accessible. Spatial transcriptomics-based CNV inference (SpatialCNV) produces genome-wide CNV maps and preserves histologic and spatial context. Unlike bulk DNA assays, it enables tracing of tumor subclones across tissue architecture. SpatialCNV can also identify tumor-containing spots when histology annotation is not provided, which is an essential feature for a lot of spatial cancer studies. Methods: We performed a systematic benchmark across simulated and real prostate spatial transcriptomics datasets, including sections with matched whole-exome truth and standard pathology. We evaluated twelve CNV tools, spanning single-cell-derived and spatial transcriptomics-specific methods. Performance was assessed in two scenarios: without histology (spot-level tumor detection) and with histology (CNV calling). We compared different strategies (stromal/immune, pure-benign epithelium, reference-free, etc) using correlation, sensitivity/specificity, and cross-patient robustness. Results: For detecting tumor-containing spots without histology, CopyKAT achieved the most reliable automatic identification when run in a reference-free mode after deconvolution-based selection of epithelial enriched spots. For CNV calling, inferCNV achieved the best performance when using a ‘pure-benign’ reference—histologically benign epithelial spots without CNV changes. We developed an automated pipeline to define pure-benign references and applied it to multi-patient cohorts, revealing spatially coherent clones shared between primary and metastatic tumors. Conclusions: SpatialCNV enables genome-wide characterization of prostate cancer clonality and metastatic relevant CNV events. Our benchmark provides pratical guidance and workflows, and supports understanding of prostate cancer clonality and spatial distribution. Citation Format: Jintong Shi. Benchmarking SpatialCNV in Prostate Cancer: tools, reference strategies, and workflows across simulated and real spatial transcriptomics abstract. In: Proceedings of the AACR Special Conference in Cancer Research: Innovations in Prostate Cancer Research and Treatment; 2026 Jan 20-22; Philadelphia PA. Philadelphia (PA): AACR; Cancer Res 2026;86 (2Suppl): Abstract nr A059.
Jintong Shi (Tue,) studied this question.
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