A Pan-Cancer Transcriptome Analysis Reveals Pervasive Regulation through Tumor-Associated Alternative Promoters

Abstract

Most human protein-coding genes are regulated by multiple, distinct promoters, suggesting that the choice of promoter is as important as its level of transcriptional activity. While the role of promoters as driver elements in cancer has been recognized, the contribution of alternative promoters to regulation of the cancer transcriptome remains largely unexplored. Here we show that active promoters can be identified using RNA-Seq data, enabling the analysis of promoter activity in more than 1,000 cancer samples with matched whole genome sequencing data. We find that alternative promoters are a major contributor to tissue-specific regulation of isoform expression and that alternative promoters are frequently deregulated in cancer, affecting known cancer-genes and novel candidates. Noncoding passenger mutations are enriched at promoters of genes with lower regulatory complexity, whereas noncoding driver mutations occur at genes with multiple promoters, often affecting the promoter that shows the highest level of activity. Together our study demonstrates that the landscape of active promoters shapes the cancer transcriptome, opening many opportunities to further explore the interplay of regulatory mechanism and noncoding somatic mutations with transcriptional aberrations in cancer.

Antisense RNAs Influence Promoter Usage of Their Counterpart Sense Genes in Cancer

“…Most human protein-coding genes have multiple promoters which direct transcription of promoter-specific gene isoforms. The choice of promoter has been shown to be a major influence on the cancer transcriptome (16,17), as these promoters are deregulated across tissues and cancer types, affecting cancer-related genes. When these promoters are differentially activated within a particular gene and derive isoform-specific protein variants with distinct functions, it is much more informative and accurate to use promoter activity than gene expression for survival analysis (17).…”

“…The choice of promoter has been shown to be a major influence on the cancer transcriptome (16,17), as these promoters are deregulated across tissues and cancer types, affecting cancer-related genes. When these promoters are differentially activated within a particular gene and derive isoform-specific protein variants with distinct functions, it is much more informative and accurate to use promoter activity than gene expression for survival analysis (17). Given the proximity of ncNATs to sense transcripts, ncNATs are potential modulators of promoter selection and activity of their counterpart sense genes.…”

Genetic effects on promoter usage are highly context-specific and contribute to complex traits

“…In addition to splicing, genetic variants can also alter transcript sequence by regulating promoter and 3ʹ end usage, which we refer to collectively hereafter as transcript usage QTLs (tuQTLs). Alternative transcript start and end sites underlie most transcript differences between tissues (Pal et al, 2011; Reyes and Huber, 2018), they are dynamically regulated in response to cellular stimuli (Alasoo et al, 2015; Richards et al, 2017) and they are also frequently dysregulated in cancer (Demircioğlu et al, 2017; Lee et al, 2018). Moreover, experimental procedures designed to capture either 5ʹ or 3ʹ ends of transcripts have identified disease-relevant genetic variants that regulate promoter or 3ʹ end usage (Garieri et al, 2017; Zhernakova et al, 2013).…”

Direct analysis of ribosome targeting illuminates thousand-fold regulation of translation initiation

“…Notably, yeast transcript isoform analysis across different environmental conditions identified more than 1200 genes with regulated alternative 5′-UTRs 21 and alternative TSS selection contributes more to mammalian mRNA isoform diversity than alternative splicing in some tissues 22 . Tumor-specific changes in promoter usage have also been observed in dozens of different cancer types 48 . DART will be broadly useful to determine how sequence differences between alternative 5′-UTRs affect protein production, which is critical for predicting the functional output of normal or pathological transcriptional regulation that changes the type of mRNA produced as well as the number of mRNA molecules.…”