Transcriptional effects of copy number alterations in a large set of human cancers

Copy number alterations (CNAs) can promote tumor progression by altering gene expression levels. Due to transcriptional adaptive mechanisms, however, CNAs do not always translate proportionally into altered expression levels. By reanalyzing >34,000 gene expression profiles, we reveal the degree of transcriptional adaptation to CNAs in a genome-wide fashion, which strongly associate with distinct biological processes. We then develop a platform-independent method—transcriptional adaptation to CNA profiling (TACNA profiling)—that extracts the transcriptional effects of CNAs from gene expression profiles without requiring paired CNA profiles. By applying TACNA profiling to >28,000 patient-derived tumor samples we define the landscape of transcriptional effects of CNAs. The utility of this landscape is demonstrated by the identification of four genes that are predicted to be involved in tumor immune evasion when transcriptionally affected by CNAs. In conclusion, we provide a novel tool to gain insight into how CNAs drive tumor behavior via altered expression levels. Copy number alterations (CNAs) can drive tumor progression in cancer by altering gene expression levels, but transcriptional adaption can skew CNA impact. Here, the authors present transcriptional adaptation to CNA (TACNA) profiling; a tool to extract the transcriptional effect of CNAs from expression data without requiring paired CNA profiles.