Refinement of computational identification of somatic copy number alterations using DNA methylation microarrays illustrated in cancers of unknown primary

Abstract

High-throughput genomic technologies are increasingly used in personalized cancer medicine. However, computational tools to maximize the use of scarce tissues combining distinct molecular layers are needed. Here we present a refined strategy, based on the R-package 'conumee', to better predict somatic copy number alterations (SCNA) from deoxyribonucleic acid (DNA) methylation arrays. Our approach, termed hereafter as 'conumee-KCN', improves SCNA prediction by incorporating tumor purity and dynamic thresholding. We trained our algorithm using paired DNA methylation and SNP Array 6.0 data from The Cancer Genome Atlas samples and confirmed its performance in cancer cell lines. Most importantly, the application of our approach in cancers of unknown primary identified amplified potentially actionable targets that were experimentally validated by Fluorescence in situ hybridization and immunostaining, reaching 100% specificity and 93.3% sensitivity.

Keywords: DNA methylation; actionable target identification; cancers of unknown primary; gene amplification; somatic copy number alterations.

Figure 1

Computational prediction of SCNA from DNA methylation arrays using conumee-K_CN. (A) Workflow for the developed strategy to refine SCNA detection, ‘conumee-K_CN’, trained over 442 primary tumor samples from TCGA, across 18 cancer types, with matched genotyping (SNP6 array) and DNA methylation array (450K) data. Our thresholding strategy refines conumee outputs and quantitatively calls SCNA from 450K arrays by considering tumor purity ρ (RF_Purity) and rigorous estimation of copy-number-state (CN)-dependent constants K_CN. ^*A list of 94 genes frequently amplified or deleted in cancer was used as reference to define the copy number states. Thus, by using calibrated K_CN’s and considering tumor purity, intra-sample variability and copy-number-state-dependent noise, thresholds for each CN can be estimated for each 450K profiled sample to accurately identify SCNA. (B) Benchmarking of our strategy (conumee-K_CN) against conumee (fixed threshold of 0.3), cnAnalysis450k and ChAMP in an independent, validation set consisting of 151 TCGA samples, with matched genotyping (SNP6 array) and DNA methylation array (450K) data. True positive (TP) and false-positive (FP) rates of 450K-derived calls versus SNP6-derived calls (ASCAT) for amplifications are depicted, showing the improved performance of our approach. (C) TP and FP rates of conumee-K_CN versus ASCAT in the TCGA validation set for the three amplification copy number states (Amp10, Amp and Gain). (D) Representative examples of gene amplifications in two samples from the TCGA validation cohort. Thresholds estimated by conumee-K_CN for Amp and Amp10 are depicted (dotted grey lines). TP = #𝑇𝑟u𝑒 p𝑜𝑠𝑖𝑡𝑖v𝑒𝑠/#𝑇𝑟u𝑒 p𝑜𝑠𝑖𝑡𝑖v𝑒𝑠 + #𝐹𝑎l𝑠𝑒 𝑁𝑒g𝑎𝑡𝑖v𝑒𝑠; FP = #𝐹𝑎l𝑠𝑒 p𝑜𝑠𝑖𝑡𝑖v𝑒𝑠/#𝐹𝑎l𝑠𝑒 p𝑜𝑠𝑖𝑡𝑖v𝑒𝑠 + #𝑇𝑟u𝑒 𝑁𝑒g𝑎𝑡𝑖v𝑒𝑠.

Figure 2

Epigenomic-based computational prediction of SCNA in CUP using conumee-K_CN. (A) Workflow for the detection of SCNA in CUPs using conumee-K_CN and further identification of gene amplifications with clinical relevance. (B) Experimental validation of conumee-K_CN predicted SCNA in CUP patient samples. Representative images of copy number state validation by FISH and protein expression by IHC are shown for three well-recognized oncogenes: MYC (c-MYC), CCND1 (Cyclin D1) and ERBB2 (HER2). ^*Orange/green break-apart FISH probes were used.