Estimation of cancer cell fractions and clone trees from multi-region sequencing of tumors.

Multi-region sequencing of solid tumors can improve our understanding of intratumor subclonal diversity and the evolutionary history of mutational events. Due to uncertainty in clonal composition and the multitude of possible ancestral relationships between clones, elucidating the most probable relationships from bulk tumor sequencing poses statistical and computational challenges. We developed a Bayesian hierarchical model called PICTograph to model uncertainty in assigning mutations to subclones, to enable posterior distributions of cancer cell fractions (CCFs) and to visualize the most probable ancestral relationships between subclones. Compared with available methods, PICTograph provided more consistent and accurate estimates of CCFs and improved tree inference over a range of simulated clonal diversity. Application of PICTograph to multi-region whole-exome sequencing of tumors from individuals with pancreatic cancer precursor lesions confirmed known early-occurring mutations and indicated substantial molecular diversity, including 6-12 distinct subclones and intra-sample mixing of subclones. Using ensemble-based visualizations, we highlight highly probable evolutionary relationships recovered in multiple models. PICTograph provides a useful approximation to evolutionary inference from cross-sectional multi-region sequencing, particularly for complex cases. https://github.com/KarchinLab/pictograph. The data underlying this article will be shared on reasonable request to the corresponding author. Supplementary data are available at Bioinformatics online. © The Author(s) 2022. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

Citation

Lily Zheng, Noushin Niknafs, Laura D Wood, Rachel Karchin, Robert B Scharpf. Estimation of cancer cell fractions and clone trees from multi-region sequencing of tumors. Bioinformatics (Oxford, England). 2022 Aug 02;38(15):3677-3683

Mesh Tags

PMID: 35642899

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