Inferring Allele-Specific Copy Number Aberrations and Tumor Phylogeography from Spatially Resolved Transcriptomics

Cong Ma, Metin Balaban, Jingxian Liu, Siqi Chen, Li Ding, Benjamin J. Raphael

Abstract: A key challenge in cancer research is to reconstruct the somatic evolution within a tumor over time and across space. Spatially resolved transcriptomics (SRT) measures gene expression at thousands of spatial locations in a tumor, but does not directly reveal genetic aberrations. We introduce CalicoST, an algorithm to simultaneously infer allele-specific copy number aberrations (CNAs) and a spatial model of tumor evolution from SRT of tumor slices. By modeling CNA-induced perturbations in both total and allele-specific gene expression, CalicoST identifies important types of CNAs - including copy-neutral loss of heterozygosity (CNLOH) and mirrored subclonal CNAs- that are invisible to total copy number analysis. CalicoST achieves high accuracy by modeling both correlations in space with a Hidden Markov Random Field and across genomic segments with a Hidden Markov Model.