Methodological breakthroughs over the past four decades have repeatedly revolutionized transcriptome profiling. Using RNA sequencing (RNA-seq), it has now become possible to sequence and quantify the transcriptional outputs of individual cells or thousands of samples. These transcriptomes provide a link between cellular phenotypes and their molecular underpinnings, such as mutations. In the context of cancer, this link represents an opportunity to dissect the complexity and heterogeneity of tumours and to discover new biomarkers or therapeutic strategies. Here, we review the rationale, methodology and translational impact of transcriptome profiling in cancer.
A historical timeline of transcriptomics
Illustrated is the lockstep development of experimental and computational aspects of transcriptomics. Advances in the experimental protocols for the high-throughput profiling of RNA necessitate the development of databases to catalogue the results and trigger curation efforts to define reference transcriptomes. However, these endeavours depend on the development of accurate and scalable computational methods to search, quantify and assemble RNA molecules. Within each field, the most influential, seminal or unique references were selected.
Transcriptomics is the large-scale study of RNA molecules by use of high-throughput techniques. It examines the abundance and makeup of a cell’s transcriptome. In contrast to DNA, which is largely identical across all cells of an organism, the actively transcribed RNA is highly dynamic, reflecting the diversity of cell types, cellular states and regulatory mechanisms. Because a transcriptome profile can be regarded as a signature or snapshot of the underlying cell state, the experimental profiling of samples and specimens can provide insights into their unique biology.