Robust Inference of Copy Number Variations in Spatial Transcriptomics

Music City Center 

Intratumor heterogeneity (ITH), a hallmark of cancer, is characterized by genetically distinct clusters of cells, or clones, that are spatially organized within a tumor. Copy-number variation (CNV), one of the key drivers of ITH, affects genomic segments by altering the underlying number of chromosomes. Spatial transcriptomics (ST), measuring RNA expression simultaneously from thousands of tissue-locations, offers a unique opportunity to identify the CNV architecture and spatial organization of the cancer-clones. We introduce a robust framework, integrating gene expression, spatial coordinates, and SNPs from ST samples, to identify segments with somatic CNVs and their allele-specific copy-number profiles. Our framework employs a Gaussian mixture model to capture spatially correlated expression patterns and a mixture of Binomial distributions to model the allele counts. Using datasets across multiple ST platforms, we first assessed the quality and signal-to-noise ratio in the SNPs to ensure reliable allele-specific inference. We then demonstrated that the proposed model had superior yet robust performance in discovering CNVs from the malignant region of ST tumor samples.

Copy-number variations

Spatial transcriptomics in cancer biology

Intratumor heterogeneity

Multimodal data integration 

Biometrics Section