CLImAT: accurate detection of copy number alteration and loss of heterozygosity in impure and aneuploid tumor samples using whole-genome sequencing data

Abstract

Motivation: Whole-genome sequencing of tumor samples has been demonstrated as an efficient approach for comprehensive analysis of genomic aberrations in cancer genome. Critical issues such as tumor impurity and aneuploidy, GC-content and mappability bias have been reported to complicate identification of copy number alteration and loss of heterozygosity in complex tumor samples. Therefore, efficient computational methods are required to address these issues.

Results: We introduce CLImAT (CNA and LOH Assessment in Impure and Aneuploid Tumors), a bioinformatics tool for identification of genomic aberrations from tumor samples using whole-genome sequencing data. Without requiring a matched normal sample, CLImAT takes integrated analysis of read depth and allelic frequency and provides extensive data processing procedures including GC-content and mappability correction of read depth and quantile normalization of B-allele frequency. CLImAT accurately identifies copy number alteration and loss of heterozygosity even for highly impure tumor samples with aneuploidy. We evaluate CLImAT on both simulated and real DNA sequencing data to demonstrate its ability to infer tumor impurity and ploidy and identify genomic aberrations in complex tumor samples.

Availability and implementation: The CLImAT software package can be freely downloaded at http://bioinformatics.ustc.edu.cn/CLImAT/.

Fig. 1.

Estimated tumor impurity and ACN of simulated samples. (A) Tumor impurity estimated by ABSOLUTE and CLImAT for samples at 60× coverage. 2p: diploid samples, 3p: triploid samples, 4p: tetraploid samples. (B) ACNs estimated by ABSOLUTE and CLImAT for simulated samples. Each bar shows the mean and standard deviation of estimated ACNs obtained from 10 samples with tumor impurity ranging from 0 to 0.9

Fig. 2.

LOH detection performance of FREEC, SNVMix and CLImAT on unpaired simulated data. (A) Results for diploid samples. (B) Results for triploid samples. (C) Results for tetraploid samples

Fig. 3.

LOH detection performance for primary TNBC samples. LOH detected by ASCAT from Affymetrix SNP6.0 arrays is used as ground truth

Fig. 4.

Result comparison of CLImAT and ASCAT for TNBC sample 1. BAF is presented by five different aberration states: homozygous deletion (HOMD), hemizygous deletion (HEMD), heterozygous (HET), copy neutral LOH (NLOH) and amplified LOH (ALOH). LRR/RD is presented by homozygous deletion (HOMD), hemizygous deletion (HEMD), neutral (NEUT) and amplification (AMP)