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

Abstract

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 platformindependent 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.

Somatic Copy Number Alterations and Associated Genes in Clear-Cell Renal-Cell Carcinoma in Brazilian Patients

“…Altogether, it is essential to consider that changes in CNAs could not affect gene expression levels due to transcriptional adaptative mechanisms. A recent study used a new method, known as transcriptional adaptation to CNA profiling (TACNA), and found a strong correlation of expression levels changes by CNAs with mean methylation from TCGA datasets [ 47 ]. These findings suggest that dysregulation of the epigenome could be one of the underlying mechanisms driving the translational response to CNAs, and further studies could help to understand the impact of the results regarding tumor suppressors involved in the chromatin modification mechanisms.…”

Leukoplakia and Immunology: New Chemoprevention Landscapes?

“…On the other hand, Castagnola et al found that only OPMDs on the tongue mucosa showed a major frequency of aneuploidy confronted to OPMDs on buccal mucosa [73], which highlights the possibility that only OPMDs limited to tongue mucosa may benefit from immunotherapy treatment. Moreover, Bhattacharya et al found four genes associated with decreased CD8+ T-cell number in the microenvironment of tumor when transcriptionally affected by copy number alterations [74]. It is known that a large number of CD8+ T cells is associated with major response rates to immune checkpoint inhibitors [75,76].…”

Robust metabolic transcriptional components in 34,494 patient-derived samples and cell lines

“…Firstly, we used c-ICA to segregate the average expression patterns of complex biopsies into statistically independent components (Bhattacharya et al, 2020;Biton et al, 2014;Kong et al, 2008). Metabolic processes relevant to cancer biology can be challenging to detect when their transcriptional footprints (TFs) are subtle and concealed by more pronounced TFs from other biological processes and experimental artifacts.…”

“…Previously, we collected gene expression data from four databases: the Gene Expression Omnibus (GEO dataset, n = 21,592), The Cancer Genome Atlas (TCGA dataset, n = 10,817), the Cancer Cell Line Encyclopedia (CCLE dataset, n = 1,067), and the Genomics of Drug Sensitivity in Cancer (GDSC dataset, n = 1,018) ( Figure 1A), totaling 34,494 samples (Bhattacharya et al, 2020). Of this total, 28,200 expression profiles originated from patient-derived complex tissue cancer biopsies, 4,209 from complex tissue biopsies of non-cancerous tissue, and 2,085 from cell lines.…”

“…A detailed description of the methods are available in the Supplementary Note. The methods for data acquisition, preprocessing of the four datasets GEO, TCGA, GDSC, and CCLE and c-ICA was published previously (Bhattacharya et al, 2020). These methods are summarized below.…”