“It is important to analyze transcriptome and genome data together because some aberrations in the transcriptome originate from aberrations in the DNA copy number,” says Peter J. Park, Ph.D., associate professor of pediatrics at Harvard Medical School.
As an example, Dr. Park and colleagues recently analyzed the involvement of transposable elements in human malignancies. Transposable elements, which abound in the human genome, were associated in previous reports with tumor development, but a comprehensive study was lacking.
In somatic genomes, their activity is normally suppressed epigenetically and at post-transcriptional levels, but the disruption of these mechanisms during malignancies is thought to facilitate their retrotransposition, a process in which these elements are copied and then inserted into new sites in the genome. These insertions can then disrupt the normal function of the genome.
“Whole-genome sequencing data offers an unprecedented opportunity for characterizing transposable elements, but they have not been studied in great detail because it is very difficult to work with repetitive sequences,” explains Dr. Park. Genomic reads from whole-genome sequencing data containing transposable elements have often been disregarded due to difficulties in assigning them to specific chromosomal regions.
“We developed a computational pipeline to analyze these reads and compared tumor and normal genomes from the same patient,” says Dr. Park. This analysis, performed in dozens of cancer samples, allowed identification of many somatic insertions of transposable elements at a single-nucleotide resolution, including a colorectal sample in which more than 100 such elements were found.
“We have several reasons to think that these insertions are biologically important. One of them is that they are not randomly inserted into the genome, but appear to target genes that are also frequently mutated in cancer,” explains Dr. Park. After correlating the insertion sites with DNA methylation and gene expression data, Dr. Park and colleagues found that genes affected by insertions were, on average, downregulated, supporting their hypothesis.
“We wanted to more rationally examine the link between disease-related microRNAs and cancer transcriptomes,” says Hiroshi I. Suzuki, M.D., Ph.D., project assistant professor of molecular pathology at the University of Tokyo. While microRNAs impact protein levels primarily by destabilizing their target mRNA molecules, it has been challenging to test this in dynamic biological systems that contain multiple miRNA molecules whose levels fluctuate.
“Understanding this interaction helps us analyze the cancer transcriptome,” explains Dr. Suzuki. By taking advantage of two analytical pipelines, GSEA (Gene Set Enrichment Analysis) and FAME (Functional Assessment of miRNAs via Enrichment), Dr. Suzuki and colleagues developed a new approach, GFA (GSEA-FAME Analysis), which allows microRNA activities to be predicted from mRNA expression data, including microRNA perturbation experiments, and provides the proof of concept for mRNA destabilization by microRNAs in the disease transcriptomes.
By using GFA to mine the multidimensional data from The Cancer Genome Atlas (TCGA), Dr. Suzuki and colleagues identified several microRNAs that can serve as robust prognostic markers for cancer survival.
“Many previous datasets used either microRNA profiling or mRNA profiling, but in our analysis, we showed that it is the combined microRNA and mRNA profiling. This provides exceptional opportunities to identify and develop robust biomarkers,” says Dr. Suzuki.
Looking at the Big Picture
“Historically, investigators have frequently focused on specific genes or pathways but are now realizing that the whole transcriptome, as opposed to single genes, may be involved in any response,” says Hua Lu, M.D., Ph.D., professor and chair of biochemistry and molecular biology at Tulane University School of Medicine.
Researchers in Dr. Lu’s lab recently described and characterized inauhzin, a small molecule that activates and stabilizes p53 by increasing its acetylation and, as a result, suppresses tumor growth. Microarray analyses combined with RT-qPCR performed by Dr. Lu and colleagues revealed that the induction of p53 target genes occurs at a much larger scale than previously thought.
Over 320 genes were overexpressed at least 2.3-fold, and over 260 genes were downregulated at least twofold by inauhzin, in a p53-dependent manner. “This finding provided opportunities to see more genes that are involved, globally, in the p53 response, and to obtain a much better image than by examining a single pathway,” says Dr. Lu.
This strategy also unveiled multiple genes that are regulated by inauhzin in a p53-independent manner. “This is what should be done when examining a drug, to understand the response, because a number of classical drugs have been revealed to have additional targets,” explains Dr. Lu.
Reaping the benefits of technological advances, transcriptomics is catalyzing the emergence of new paradigms in molecular and clinical oncology. The increasing focus on surveying global cellular perturbations, and the integration of the data with other systems-level approaches, including genomics and proteomics, define new conceptual frameworks.
These interdependent technological and research developments are paving the path toward the time when the systematic analysis of cancer transcriptomes, still in its infancy, will become a routine part of clinical medicine.