ClevRvis: visualization techniques for clonal evolution. (11th April 2023)

• Record Type: Journal Article
• Authors: Sandmann, Sarah; Inserte, Clara; Varghese, Julian
• Is Part Of: GigaScience. Volume 12(2023)
• Publication Date: 2023-04-11
• Subjects: clonal evolution -- tumor development -- visualization -- cancer cell fraction -- biallelic events -- therapy effect; Information storage and retrieval systems -- Research -- Periodicals; Biology -- Research -- Periodicals; Medical sciences -- Research -- Periodicals; Database management -- Periodicals; 570.285
• DOI: 10.1093/gigascience/giad020
• Languages: English
• ISSNs: 2047-217X

Abstract

Abstract: Background: A thorough analysis of clonal evolution commonly requires integration of diverse sources of data (e.g., karyotyping, next-generation sequencing, and clinical information). Subsequent to actual reconstruction of clonal evolution, detailed analysis and interpretation of the results are essential. Often, however, only few tumor samples per patient are available. Thus, information on clonal development and therapy effect may be incomplete. Furthermore, analysis of biallelic events—considered of high relevance with respect to disease course—can commonly only be realized by time-consuming analysis of the raw results and even raw sequencing data. Results: We developed clevRvis, an R/Bioconductor package providing an extensive set of visualization techniques for clonal evolution. In addition to common approaches for visualization, clevRvis offers a unique option for allele-aware representation: plaice plots. Biallelic events may be visualized and inspected at a glance. Analyzing 4 public datasets, we show that plaice plots help to gain new insights into tumor development and investigate hypotheses on disease progression and therapy resistance. In addition to a graphical user interface, automatic phylogeny-aware color coding of the plots, and an approach to explore alternative trees, clevRvis provides 2 algorithms for fully automatic time point interpolation and therapy effect estimation. Analyzing 2 public datasets, we show that both approaches allow for valid approximation of a tumor's development in between measured time points. Conclusions: clevRvis represents a novel option for user-friendly analysis of clonal evolution, contributing to gaining new insights into tumor development.