CACTUS: integrating clonal architecture with genomic clustering and transcriptome profiling of single tumor cells

Abstract

Background Drawing genotype-to-phenotype maps in tumors is of paramount importance for understanding tumor heterogeneity. Assignment of single cells to their tumor clones of origin can be approached by matching the genotypes of the clones to the mutations found in RNA sequencing of the cells. The confidence of the cell-to-clone mapping can be increased by accounting for additional measurements. Follicular lymphoma, a malignancy of mature B cells that continuously acquire mutations in parallel in the exome and in B cell receptor loci, presents a unique opportunity to join exome-derived mutations with B cell receptor sequences as independent sources of evidence for clonal evolution. Methods Here, we propose CACTUS, a probabilistic model that leverages the information from an independent genomic clustering of cells and exploits the scarce single cell RNA sequencing data to map single cells to given imperfect genotypes of tumor clones. Results We apply CACTUS to two follicular lymphoma patient samples, integrating three measurements: whole exome, single-cell RNA, and B cell receptor sequencing. CACTUS outperforms a predecessor model by confidently assigning cells and B cell receptor-based clusters to the tumor clones. Conclusions The integration of independent measurements increases model certainty and is the key to improving model performance in the challenging task of charting the genotype-to-phenotype maps in tumors. CACTUS opens the avenue to study the functional implications of tumor heterogeneity, and origins of resistance to targeted therapies. CACTUS is written in R and source code, along with all supporting files, are available on GitHub (https://github.com/LUMC/CACTUS).

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

“…There are several approaches for clonal deconvolution of bulk DNA-seq data, reconstructing the clone genotypes, the frequencies of the clones, and their phylogenetic relationships [6][7][8][9][10][11]. More recently, several methods for identifying clonal evolution from mutations found in scDNA-seq [12] or from combined bulk and scDNA-seq [13][14][15] were proposed. Despite recent technological advances [16], scDNA-seq remains much more laborious, more inaccurate, and less affordable than the highly established bulk DNA-seq [17].…”

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

“…In addition to acquisition of mutations in BCR loci, aberrant somatic hypermutation in non-BCR loci causes accumulation of somatic variants that can be clonal (early events, present in all clones) or subclonal (later events, specific to a subset of clones) [14]. Under the assumption that acquisition of a potential oncogenic subclonal mutation occurs alongside BCR diversification, BCRs can serve as markers in the study of clonal evolution in FL tumours [15]. At the same time, FL cells may display transcriptional heterogeneity, with different transcriptional subpopulations displaying varying drug responses [16].…”

“…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].…”

“…To this end, we propose CaClust, a nonparametric Bayesian extension of CACTUS [15], which is able to find a confident cell-to-clone mapping, improve estimation of clone genotypes, and infer genotypes for single cells. CaClust identifies the number of BCR clusters from the data and models probabilistic profiles of BCR sequences characteristic of every cluster.…”

Semi-deconvolution of bulk and single-cell RNA-seq data with application to metastatic progression in breast cancer

“…It accomplishes this by developing a hybrid algorithm to infer genomics and clonal frequencies in both bulk and single-cell samples, using single-cell data in some samples as partial references for bulk data in others. In this regard, it follows a strategy of mixed bulk and single-cell data previously applied in tumor evolutionary studies primarily with single-cell and bulk DNA-seq ( Lei et al , 2020 ; Malikic et al , 2019a , b ; Salehi et al , 2017 ) or using bulk DNA-seq to guide interpretation of single-cell RNA-seq data ( McCarthy et al , 2020 ; Shafighi et al , 2021 ). Bulk and single-cell RNA-seq has been previously combined in bMIND ( Wang et al , 2021 ), although with a different goal of using paired data from single samples to better reconstruct cell type profiles.…”