Computational validation of clonal and subclonal copy number alterations from bulk tumour sequencing

Abstract

Cancer is a global health issue that places enormous demands on healthcare systems. Basic research, the development of targeted treatments, and the utility of DNA sequencing in clinical settings, have been significantly improved with the introduction of whole genome sequencing. However the broad applications of this technology come with complications. To date there has been very little standardisation in how data quality is assessed, leading to inconsistencies in analyses and disparate conclusions. Manual checking and complex consensus calling strategies often do not scale to large sample numbers, which leads to procedural bottlenecks. To address this issue, we present a quality control method that integrates point mutations, copy numbers, and other metrics into a single quantitative score. We demonstrate its power on 1,065 whole-genomes from a large-scale pan-cancer cohort, and on multi-region data of two colorectal cancer patients. We highlight how our approach significantly improves the generation of cancer mutation data, providing visualisations for cross-referencing with other analyses. Our approach is fully automated, designed to work downstream of any bioinformatic pipeline, and can automatise tool parameterization paving the way for fast computational assessment of data quality in the era of whole genome sequencing.

Clinical application of tumour-in-normal contamination assessment from whole genome sequencing

“…The SNV-only analysis (i.e. not incorporating CNA data) requires that the tumour genome does not harbour a high number of CNAs as otherwise the value of VAF is cofounded by copy number variations (Househam et al 2021).…”

“…By incorporating CNAs in the logic, TINC normalises the observed VAFs in the tumour sample for chromosome copy number and therefore is resilient to confounding effects of tumour aneuploidy. Incorporation of copy number data is only performed for tumours for which the most extensive copy number state is one of 1:0 (loss of heterozygosity, LOH), 1:1 (heterozygous diploid), 2:0 (copy-neutral LOH), 2:1 (triploid) or 2:2 (tetraploid genome-doubled), representing the majority of copy number states observed for cancer genomes (Househam et al 2021). For such cases, only SNVs residing within regions of the most prevalent copy number state are used in TIN estimation.…”

“…When TINC is run with CNA data this test is not required since we are already filtering mutations by the tumour’s most prevalent karyotype. This choice allows control for CNAs that confound the VAF distribution (Househam et al 2021).…”

Multi-omic Analysis of Familial Adenomatous Polyposis Reveals Molecular Pathways and Polyclonal Spreading Associated with Early Tumorigenesis

“…To further investigate the phylogenetic relationship between samples harboring identical mutations, we performed ancestral sequence reconstruction (ASR) to determine the likely locations of the mutations on the phylogenetic trees: private mutations on terminal branches and shared mutations on internal branches. We estimated the cancer cell fraction (CCF) for each mutation, thus accounting for differences in sample purity and ploidy 23 . Mutations present in mucosal tissues as well as many benign and dysplastic samples tended to have low CCF values, re ective of multiple subclonal populations that had not xed in the population (Figure 3D, Supplemental Figure 3C,G,J,M,P).…”

“…Cancer cell fraction (CCF) was calculated using the R software CNAqc with default settings 23 . To characterize mutations as "clonal" or "subclonal", we rst create 2 pseudo variables:…”