Inferring tumor absolute copy number and clonal substructure from single-cell chromatin accessibility

Area of Science:

• Genomics
• Computational Biology
• Cancer Research

Background:

• Accurate absolute copy number inference from single-cell chromatin accessibility (scATAC-seq) is challenging.
• This limitation obscures the distinction between genetic and epigenetically driven oncogenic dependencies.

Purpose of the Study:

• Introduce TeaCNV, a computational framework for reconstructing clonal absolute copy number profiles and tumor clonal architectures from scATAC-seq data.
• Enable copy number profiling and clonal deconvolution from epigenomic assays without matched DNA baselines.

Main Methods:

• TeaCNV reconstructs absolute copy number profiles and tumor clonal architectures from scATAC-seq data.
• Validated in silico and against bulk whole-genome sequencing in renal cell carcinomas.
• Applied to six cancer types: renal, breast, pancreatic, head and neck, colorectal, and ovarian.

Main Results:

• TeaCNV resolved subclonal absolute copy number profiles with <10% error and detected copy number variations (CNVs) with 98.6% accuracy.
• Outperformed existing methods in copy number variation detection and profiling.
• Delineated polyclonal architectures and revealed CNV-driven chromatin accessibility patterns in key driver genes (AKT2, ZNF217, SOX2).

Conclusions:

• TeaCNV bridges critical gaps in studying oncogenic dependencies and genotype-phenotype relationships at single-cell resolution.
• Enables precise analysis of tumor heterogeneity and evolution using epigenomic data.
• Advances the understanding of cancer driver genes and their regulatory mechanisms.