Accurate Inference of Tumor Purity and Absolute Copy Numbers From High-Throughput Sequencing Data

Abstract

Inference of absolute copy numbers in tumor genomes is one of the key points in the study of tumor genesis. However, the mixture of tumor and normal cells poses a big challenge to this task. Accurate estimation of tumor purity (i.e., the fraction of tumor cells) is a necessary step to solve this problem. In this paper, we propose a new approach, AITAC, to accurately infer tumor purity and absolute copy numbers in a tumor sample by using high-throughput sequencing (HTS) data. In contrast to many existing algorithms for estimating tumor purity, which usually rely on pre-detected mutation genotypes (heterogeneity and homogeneity), AITAC just requires read depths (RDs) observed at the regions with copy number losses. AITAC creates a non-linear model to correlate tumor purity, observed and expected RDs. It adopts an exhaustive search strategy to scan tumor purity in a wide range, and chooses the tumor purity that minimizes the deviation between observed RDs and expected ones as the optimal solution. We apply the proposed approach to both simulation and real sequencing data sets and demonstrate its performance by comparing with two classical approaches. AITAC is freely available at https://github.com/BDanalysis/aitac and can be expected to become a useful approach for researchers to analyze copy numbers in cancer genome.

Comprehensive Integrative Analysis Reveals the Association of KLF4 with Macrophage Infiltration and Polarization in Lung Cancer Microenvironment

“…Tumor purity is defined as the percentage of cancer cells in solid tumors, which may act as a prognostic factor indicator or predictor of chemotherapy benefit. It is an important tool in analyzing the patient's condition in clinical practices as contamination of normal cells in tumor tissues may subsequently hamper genomic analysis [58,59]. Moreover, cancers with high genomic instability will possess more genomic diversity leading to the formation of more neoantigens and greater infiltration of immune cells [60,61].…”

Molecular characterization of lung squamous cell carcinoma tumors reveals therapeutically relevant alterations

“…The total number of non-synonymous somatic mutations within the coding region were extracted for calculation of tumor mutation burden across each tumor. The percentage of tumor cells in all the samples were computed from the exome sequencing data using the tool AITAC [ 48 ] ( https://github.com/BDanalysis/aitac ). Signature analysis on the exome sequencing data was performed using the R/Bioconductor package MutationalPattens [ 49 ] ( http://bioconductor.org/packages/MutationalPatterns ).…”

Obtaining spatially resolved tumor purity maps using deep multiple instance learning in a pan-cancer study

“…More importantly, there exists inter-observer variability between pathologists' estimates. 6,18 Tumor purity can also be inferred from different types of genomic data, such as somatic copy number [19][20][21][22][23][24][25] and mutations, [26][27][28][29][30][31] gene expression data, [32][33][34][35] and DNA methylation data. [36][37][38][39] The tumor purity obtained from these methods will be referred to as genomic tumor purity in this study.…”