Joint Clustering of Single-Cell Sequencing and Fluorescence In Situ Hybridization Data for Reconstructing Clonal Heterogeneity in Cancers

• Published in: Journal of computational biology Vol. 28; no. 11; p. 1035
• Main Authors: Fu, Xuecong, Lei, Haoyun, Tao, Yifeng, Heselmeyer-Haddad, Kerstin, Torres, Irianna, Dean, Michael, Ried, Thomas, Schwartz, Russell
• Format: Journal Article
• Language: English
• Published: United States 01-11-2021
• Subjects: Anaphase; Aneuploidy; Brain Neoplasms - genetics; Clonal Evolution; Cluster Analysis; Computational Biology - methods; Evolution, Molecular; Glioblastoma - genetics; Humans; In Situ Hybridization, Fluorescence - methods; Markov Chains; Monte Carlo Method; Phylogeny; Sequence Analysis, RNA; Single-Cell Analysis - methods; single-cell genomics; cancer; clonal evolution; sequencing
• ISSN: 1557-8666
• DOI: 10.1089/cmb.2021.0255

Aneuploidy and whole genome duplication (WGD) events are common features of cancers associated with poor outcomes, but the ways they influence trajectories of clonal evolution are poorly understood. Phylogenetic methods for reconstructing clonal evolution from genomic data have proven a powerful tool for understanding how clonal evolution occurs in the process of cancer progression, but extant methods so far have limited the ability to resolve tumor evolution via ploidy changes. This limitation exists in part because single-cell DNA-sequencing (scSeq), which has been crucial to developing detailed profiles of clonal evolution, has difficulty in resolving ploidy changes and WGD. Multiplex interphase fluorescence in situ hybridization (miFISH) provides a more unambiguous signal of single-cell ploidy changes but it is limited to profiling small numbers of single markers. Here, we develop a joint clustering method to combine these two data sources with the goal of better resolving ploidy changes in tumor evolution. We develop a probabilistic framework to maximize the probability of latent variables given the pre-clustered datasets, which we optimize via Markov chain Monte Carlo sampling combined with linear regression. We validate the method by using simulated data derived from a glioblastoma (GBM) case profiled by both scSeq and miFISH. We further apply the method to two GBM cases with scSeq and miFISH data by reconstructing a phylogenetic tree from the joint clustering results, demonstrating their synergistic value in understanding how focal copy number changes and WGD events can collectively contribute to tumor progression.