Jiazhen (JoJo) Rong

Somatic mutations such as copy number alterations accumulate during cancer progression, driving intratumor heterogeneity that impacts therapy effectiveness. Understanding the characteristics and spatial distribution of genetically distinct subclones is essential for unraveling tumor evolution and improving cancer treatment. Here we present Clonalscope, a subclone detection method using copy number profiles, applicable to spatial transcriptomics and single-cell sequencing data. Clonalscope implements a nested Chinese Restaurant Process to identify de novo tumor subclones, which can incorporate prior information from matched bulk DNA sequencing data for improved subclone detection and malignant cell labeling. On single-cell RNA sequencing and single-cell assay for transposase-accessible chromatin using sequencing data from gastrointestinal tumors, Clonalscope successfully labeled malignant cells and identified genetically different subclones with thorough validations. On spatial transcriptomics data from various primary and metastasized tumors, Clonalscope labeled malignant spots, traced subclones and identified spatially segregated subclones with distinct differentiation levels and expression of genes associated with drug resistance and survival.

Understanding tissue development and intra-tissue evolution requires the ability to trace clones in intact tissues coupled with high-plex molecular profiling preserving spatial context. However, current lineage tracing tools are incompatible with spatial omics. Here, we present SUMMIT (Spatially Unveiling Mitochondrial Mutations In Tissues), a spatially-resolved lineage tracing technology that integrates gene expression profiling with mitochondrial mutation-based clone identification. Unlike synthetic lineage recording methods, SUMMIT relies only on endogenous mutations and thus can be applied to human tissues. To address the compositional mixing of cell types within spatial spots, SUMMIT includes a rigorous statistical framework to confidently assign variants to specific cell subpopulations and achieves high power for spatially localized clones by pooling information across neighboring spots. We validated SUMMIT using a controlled model in which we mixed two cancer cell lines in a mouse tumor, then demonstrated it on multiple human tissues including Barrett’s esophagus, gastric cardia, small bowel, and colorectal cancer. Across these samples, we distinguished between global mutations and mutations marking locally restricted clones. The coupled transcriptomic data allowed us to characterize the cell type composition within each clone and delineate their spatial configuration. This integrated approach provides a framework to understand spatially-defined clonal evolution in preserved human tissue.