PhyliCS: a Python library to explore scCNA data and quantify spatial tumor heterogeneity

• Published in: BMC bioinformatics Vol. 22; no. 1; pp. 1 - 21
• Main Authors: Montemurro, Marilisa, Grassi, Elena, Pizzino, Carmelo Gabriele, Bertotti, Andrea, Ficarra, Elisa, Urgese, Gianvito
• Format: Journal Article
• Language: English
• Published: London BioMed Central 03.07.2021; BioMed Central Ltd; Springer Nature B.V; BMC
• Subjects: Algorithms; Bioinformatics; Biomedical and Life Sciences; Cancer; Cancer evolution; Clones; Clustering; Comparative genomics; Computational Biology/Bioinformatics; Computer Appl. in Life Sciences; Copy number; Copy number variations; Data mining; Development and progression; DNA; DNA sequencing; Gene expression; Gene mutations; Genetic aspects; Heterogeneity; Intra-tumor heterogeneity; Life Sciences; Methods; Microarrays; Mutation; Nucleotide sequencing; Oncology, Experimental; Phylogenetics; Python; Python (Programming language); Regions; Single-cell sequencing; Software; Spatial data; Spatial distribution; Spatial heterogeneity; Subpopulations; Tumors; Italy; Single-cell sequencing; Algorithms; Clones; DNA; Cancer evolution; Intra-tumor heterogeneity
• ISSN: 1471-2105; 1471-2105
• DOI: 10.1186/s12859-021-04277-3

Background Tumors are composed by a number of cancer cell subpopulations (subclones), characterized by a distinguishable set of mutations. This phenomenon, known as intra-tumor heterogeneity (ITH), may be studied using Copy Number Aberrations (CNAs). Nowadays ITH can be assessed at the highest possible resolution using single-cell DNA (scDNA) sequencing technology. Additionally, single-cell CNA (scCNA) profiles from multiple samples of the same tumor can in principle be exploited to study the spatial distribution of subclones within a tumor mass. However, since the technology required to generate large scDNA sequencing datasets is relatively recent, dedicated analytical approaches are still lacking. Results We present PhyliCS, the first tool which exploits scCNA data from multiple samples from the same tumor to estimate whether the different clones of a tumor are well mixed or spatially separated. Starting from the CNA data produced with third party instruments, it computes a score, the Spatial Heterogeneity score, aimed at distinguishing spatially intermixed cell populations from spatially segregated ones. Additionally, it provides functionalities to facilitate scDNA analysis, such as feature selection and dimensionality reduction methods, visualization tools and a flexible clustering module. Conclusions PhyliCS represents a valuable instrument to explore the extent of spatial heterogeneity in multi-regional tumour sampling, exploiting the potential of scCNA data.