Identification and utilization of copy number information for correcting Hi-C contact map of cancer cell lines

Abstract

Background Hi-C and its variant techniques have been developed to capture the spatial organization of chromatin. Normalization of Hi-C contact map is essential for accurate modeling and interpretation of high-throughput chromatin conformation capture (3C) experiments. Hi-C correction tools were originally developed to normalize systematic biases of karyotypically normal cell lines. However, a vast majority of available Hi-C datasets are derived from cancer cell lines that carry multi-level DNA copy number variations (CNVs). CNV regions display over- or under-representation of interaction frequencies compared to CN-neutral regions. Therefore, it is necessary to remove CNV-driven bias from chromatin interaction data of cancer cell lines to generate a euploid-equivalent contact map. Results We developed the HiCNAtra framework to compute high-resolution CNV profiles from Hi-C or 3C-seq data of cancer cell lines and to correct chromatin contact maps from systematic biases including CNV-associated bias. First, we introduce a novel ‘entire-fragment’ counting method for better estimation of the read depth (RD) signal from Hi-C reads that recapitulates the whole-genome sequencing (WGS)-derived coverage signal. Second, HiCNAtra employs a multimodal-based hierarchical CNV calling approach, which outperformed OneD and HiNT tools, to accurately identify CNVs of cancer cell lines. Third, incorporating CNV information with other systematic biases, HiCNAtra simultaneously estimates the contribution of each bias and explicitly corrects the interaction matrix using Poisson regression. HiCNAtra normalization abolishes CNV-induced artifacts from the contact map generating a heatmap with homogeneous signal. When benchmarked against OneD, CAIC, and ICE methods using MCF7 cancer cell line, HiCNAtra-corrected heatmap achieves the least 1D signal variation without deforming the inherent chromatin interaction signal. Additionally, HiCNAtra-corrected contact frequencies have minimum correlations with each of the systematic bias sources compared to OneD’s explicit method. Visual inspection of CNV profiles and contact maps of cancer cell lines reveals that HiCNAtra is the most robust Hi-C correction tool for ameliorating CNV-induced bias. Conclusions HiCNAtra is a Hi-C-based computational tool that provides an analytical and visualization framework for DNA copy number profiling and chromatin contact map correction of karyotypically abnormal cell lines. HiCNAtra is an open-source software implemented in MATLAB and is available at https://github.com/AISKhalil/HiCNAtra.

Analysis of Aneuploidy Spectrum From Whole-Genome Sequencing Provides Rapid Assessment of Clonal Variation Within Established Cancer Cell Lines

“…Similarly, reads from Hi-C experiments can be effectively used for computing RD signal. 25 - 28 Therefore, the free access of NGS reads from hundreds of cancer cell lines has paved the way for easy sharing as well as integrative analyses of data. However, caution should be employed before integrating different datasets generated using the same cell line in different labs in the light of widespread concerns about genetic drift in cultured cell lines over time.…”

Tracing cancer evolution and heterogeneity using Hi-C

“…Several computational methods have been developed to analyze Hi-C data to infer copy number variation 30,31,32,33,34,35 . Some of these methods developed foundational techniques for identifying and annotating interchromosomal translocations as well 36,37,38 .…”

“…Notably, Hi-C has been optimized for formalin-fixed, paraffin-embedded (FFPE) tumor samples as Fix-C 29 , allowing these methods to be applied to archived patient samples throughout cancer progression. Several computational methods have been developed to analyze Hi-C data to infer copy number variation 30,31,32,33,34,35 . Some of these methods developed foundational techniques for identifying and annotating interchromosomal translocations as well 36,37,38 .…”

Improving Hi-C contact matrices using genome graphs

“…Given that read alignment is always the initial step in Hi-C analysis, errors at this stage can proliferate, leading to inaccuracies throughout the downstream analyses. To rectify the Hi-C analysis of cancer cell lines, substantial efforts have been made to develop algorithms to identify structural variations and rearrange the cancer genomes from Hi-C data, sometimes with the help of other data types such as whole genome sequencing to enhance accuracy and precision [Wang et al, 2020, Schöpflin et al, 2022, Khalil et al, 2020, Wang et al, 2021] of Hi-C analysis of cancer cell lines. However, these steps still rely on the linear reference genome.…”