Advances in single cell RNA sequencing (scRNAseq) technologies uncovered an unexpected complexity in solid tumors, underlining the relevance of intratumor heterogeneity for cancer progression and therapeutic resistance. Heterogeneity in the mutational composition of cancer cells is well captured by tumor phylogenies, which demonstrate how distinct cell populations evolve, and, e.g. develop metastatic potential or resistance to specific treatments. Unfortunately, because of their low read coverage per cell, mutation calls that can be made from scRNAseq data are very sparse and noisy. Additionally, available tumor phylogeny reconstruction methods cannot computationally handle a large number of cells and mutations present in typical scRNAseq datasets. Finally, there are no principled methods to assess distinct subclones observed in inferred tumor phylogenies and the genomic alterations that seed them. Here we present Trisicell, a computational toolkit for scalable tumor phylogeny reconstruction and evaluation from scRNAseq as well as single cell genome or exome sequencing data. Trisicell allows the identification of reliable subtrees of a tumor phylogeny, offering the ability to focus on the most important subclones and the genomic alterations that are associated with subclonal proliferation. We comprehensively assessed Trisicell on a melanoma model by comparing the phylogeny it builds using scRNAseq data, to those using matching bulk whole exome (bWES) and transcriptome (bWTS) sequencing data from clonal sublines derived from single cells. Our results demonstrate that tumor phylogenies based on mutation calls from scRNAseq data can be robustly inferred and evaluated by Trisicell. We also applied Trisicell to reconstruct and evaluate the phylogeny it builds using scRNAseq data from melanomas of the same mouse model after treatment with immune checkpoint blockade (ICB). After integratively analyzing our cell-specific mutation calls with their expression profiles, we observed that each subclone with a distinct set of novel somatic mutations is strongly associated with a distinct developmental status. Moreover, each subclone had developed a specific ICB-resistance mechanism. These results demonstrate that Trisicell can robustly utilize scRNAseq data to delineate intratumoral heterogeneity and tumor evolution.
SITH: an R package for visualizing and analyzing a spatial model of intratumor heterogeneity
