Accurate tumor clonal structures require single‐cell analysis. Issue 1 (8th September 2022)

• Record Type: Journal Article
• Authors: Su, Xianbin; Bai, Shihao; Xie, Gangcai; Shi, Yi; Zhao, Linan; Yang, Guoliang; Tian, Futong; He, Kun‐Yan; Wang, Lan; Li, Xiaolin; Long, Qi; Han, Ze‐Guang
• Is Part Of: Annals of the New York Academy of Sciences. Volume 1517:Issue 1(2022)
• Page Start: 213
• Page End: 224
• Publication Date: 2022-09-08
• Subjects: clonal structure -- genetic heterogeneity -- single‐cell analysis -- tumor evolution -- variant allele frequency; Medical sciences -- Periodicals; Medicine -- Periodicals; Science -- Periodicals; 610
• DOI: 10.1111/nyas.14897
• Languages: English
• ISSNs: 0077-8923

Abstract

Abstract: Tumor clonal structure is closely related to future progression, which has been mainly investigated as mutation abundance clustering in bulk samples. With relatively limited studies at single‐cell resolution, a systematic comparison of the two approaches is still lacking. Here, using bulk and single‐cell mutational data from the liver and colorectal cancers, we checked whether co‐mutations determined by single‐cell analysis had corresponding bulk variant allele frequency (VAF) peaks. While bulk analysis suggested the absence of subclonal peaks and, possibly, neutral evolution in some cases, the single‐cell analysis identified coexisting subclones. The overlaps of bulk VAF ranges for co‐mutations from different subclones made it difficult to separate them. Complex subclonal structures and dynamic evolution could be hidden under the seemingly clonal neutral pattern at the bulk level, suggesting single‐cell analysis is necessary to avoid underestimation of tumor heterogeneity. Abstract : In this study, we demonstrated that bulk‐level analyses may be ill‐suited for revealing tumor clonal structure due to differences between mutation cluster and tumor subclone. The absence of subclonal mutation clusters does not necessarily support clonal neutral evolution, and tumor clonal structure and evolution history can be better revealed by single‐cell analysis.