Tumoroscope: a probabilistic model for mapping cancer clones in tumor tissues

Abstract

Spatial and genomic heterogeneity of tumors is the key for cancer progression, treatment, and survival. However, a technology for direct mapping the clones in the tumor tissue based on point mutations is lacking. Here, we propose Tumoroscope, the first probabilistic model that accurately infers cancer clones and their high-resolution localization by integrating pathological images, whole exome sequencing, and spatial transcriptomics data. In contrast to previous methods, Tumoroscope explicitly addresses the problem of deconvoluting the proportions of clones in spatial transcriptomics spots. Applied to a reference prostate cancer dataset and a newly generated breast cancer dataset, Tumoroscope reveals spatial patterns of clone colocalization and mutual exclusion in sub-areas of the tumor tissue. We further infer clone-specific gene expression levels and the most highly expressed genes for each clone. In summary, Tumoroscope enables an integrated study of the spatial, genomic, and phenotypic organization of tumors.

CaClust: linking genotype to transcriptional heterogeneity of follicular lymphoma using BCR and exomic variants

“…Indeed, innovations in this area emerged only very recently and are not commercially available [18][19][20]. To address this, computational methods were proposed that probabilistically match genomic alterations between bulk DNA sequencing and single cell RNA sequencing (scRNA-seq) or spatial transcriptomics data [15,[21][22][23][24][25]. In particular, our approach, CACTUS was previously applied to FL data by clustering cells by BCR sequences and performing cluster-to-clone assignment by mutation matching, benefiting from BCR information in this task [15].…”