Accurate Identification of Subclones in Tumor Genomes

Abstract

Understanding intra-tumor heterogeneity is critical for studying tumorigenesis and designing personalized treatments. To decompose the mixed cell population in a tumor, subclones are inferred computationally based on variant allele frequency (VAF) from bulk sequencing data. In this study, we showed that sequencing depth, mean VAF, and variance of VAF of a subclone are confounded. Without considering this effect, current methods require deep-sequencing data (>300x depth) to reliably infer subclones. Here we present a novel algorithm that incorporates depth-variance and mean-variance dependencies in a clustering error model and successfully identifies subclones in tumors sequenced at depths of as low as 30x. We implemented the algorithm as a model-based adaptive grouping of subclones (MAGOS) method. Analyses of computer simulated data and empirical sequencing data showed that MAGOS outperformed existing methods on minimum sequencing depth, decomposition accuracy, and computation efficiency. The most prominent improvements were observed in analyzing tumors sequenced at depths between 30x and 200x, while the performance was comparable between MAGOS and existing methods on deeply sequenced tumors. MAGOS supports analysis of single nucleotide variants and copy number variants from a single sample or multiple samples of a tumor. We applied MAGOS to whole-exome data of late-stage liver cancers and discovered that high subclone count in a tumor was a significant risk factor of poor prognosis. Lastly, our analysis suggested that sequencing multiple samples of the same tumor at standard depth is more cost-effective and robust for subclone characterization than deep sequencing a single sample. MAGOS is available at github (https://github.com/liliulab/magos).

Genome-wide screening and functional validation of methylation barriers near promoters

“…The presence of both cancer driver genes and non-driver genes in dmPIRs is consistent with the selective advantage hypothesis, which posits that aberrant DNA methylations appear randomly throughout the genome and are subject to somatic evolutionary selection during tumor development ( 56 ). While positive selection promotes alterations conferring growth advantages, such as hypermethylated promoters of tumor suppressor genes, frequency of certain neutral alterations may also drift higher by chance ( 57 , 58 ). Based on this hypothesis, a large fraction of the dmPIR-tagged genes in our study may have captured growth-neutral methylation changes.…”

A mouse-specific model to detect genes under selection in tumors

“…Tumorigenesis is an evolutionary process, in which selectively advantageous mutations accumulate in cancer cells, leading to uncontrolled cell growth and tumor formation (25,26).…”

Bioinformatics Illustrations Decoded by ChatGPT: The Good, The Bad, and The Ugly

“…For the “Clonal evolution” case study, somatic mutations in a pair of primary and recurrent tumors from an MM patient (ID: 1201) in the MMRF-CoMMpass study were sourced from the GDC data portal 22 . Clonal and subclonal mutations were identified using the MAGOS method 29 . Relative prevalence of (sub)clones and their evolutionary relationships were inferred using the ClonEvol method 30 .…”