Assessment of the normal cell contamination impact on tumour sample analysed with SNP arrays: The signal confusion nightmare

Abstract Recent advances in high-throughput technologies enable a more comprehensive interpretation of the tumour evolution through the study of the intra-tumour heterogeneity. Several algorithms, however, often relies on the use of models that described the top of the iceberg regarding the stromal contamination of the samples, making diagnosis difficult to assess. Indeed, such as radio wave receivers, tools to analyse high-throughput technologies data, are used to enable the discrimination between multiple signals differing in frequencies. However, such tools often look at the average frequency more than distinct signals, leading to analyse a confused signal. This confusion could dramatically lead to a mis–interpretation of the real data, especially during the diagnosis as it relies on the choice of a unique scenario among many others. Here, we describe how this signal confusion occurs in the most classical DNA microarray analysis of tumours and we provide statistics to determine how many other possible scenario can lead the same signals, in order to improve the robustness of pigeon hole logic based analysis. Based on simulations, where a unique tumour population was diluted by an increasing gradient of normal cells, we underline the causes and consequences of such signal confusion for up to five allelic copies. Despite the removal of all technical biaises and background noise, we show how the signal confusion remains systematically present in the commonly used DNA microarray analysis, especially for the genotypes AAAAB, AAAB and AAB for copy numbers 5, 4 and 3 respectively, as well as their symmetric combinations for the B allele.