As an electrical engineer, Xiling Shen designed circuits for cell phones and optical devices, but he was really fascinated by the design of biological systems. In particular, he was puzzled at how cells could make robust decisions using suboptimal and highly variable components and how these processes go awry in diseases. Now, as a systems biologist at Cornell University, he is applying that curiosity to study how cancer may arise from the faulty decisions of microRNAs—short, single-stranded, non-coding RNA molecules that silence gene expression.
According to the authors, this robust decision-making process about cell fate is all-or-nothing. miR-34a converts noisy signaling inputs into a toggle switch, generating a binary Notch signal that triggers either self-renewal or differentiation. By contrast, the protein Numb regulates Notch levels in a continuously graded manner. As a result, the team found that miR-34a actually has a stronger influence on Notch regulation and cell-fate determination than Numb.
“That’s very puzzling, because our original thoughts were that microRNAs play a more modulating, fine-tuning role,” says Shen. “Our finding that this microRNA actually plays a dominant role in this, while the protein plays a more granular or minor role, goes against conventional thinking.”
Another study published this month in Nature suggests that some microRNAs act as modulators of mRNA–mRNA interactions rather than as on/off molecular switches (2). Using high-throughput sequencing and microarrays, Carlos Caldas of the Cancer Research UK Cambridge Institute and his collaborators analyzed microRNA and mRNA expression, along with DNA copy number, in a large number of human breast cancer tumors. They found that, at the whole-genome level, microRNAs primarily act by affecting the interactions between and mRNAs.
But these seemingly contrary results may actually reflect coexisting mechanisms, according to Jin Cheng, who studies the role of microRNAs in cancer at the H. Lee Moffitt Cancer Center and Research Institute. “People have identified different mechanisms—some microRNAs block translation, others promote mRNA degradation,” he says, adding that the mechanism depends on the particular microRNA.
For binary cell decisions, such as cell-fate choices and programmed cell death, microRNAs may act as a toggle switch. But for nuanced processes, such as metabolism, microRNAs may play a modulatory role. However, a given microRNA could wear both hats. Because microRNAs can have so many different mRNA targets, they are flexible in the functions they perform, and the nature of their role may depend more on the target than on the individual microRNA. For example, the microRNA may be capable of generating a sharper threshold response if its target has many binding sites or if the nucleotide sequences of the microRNA and its target are very similar.
Support for this idea comes in part from a 2011 Nature Genetics paper, which showed that microRNAs can act both as a switch and as a fine-tuner of gene expression (3). Alexander van Oudenaarden of the Massachusetts Institute of Technology and his collaborators used quantitative fluorescence microscopy and flow cytometry to measure gene expression in single cells in the presence and absence of microRNA regulation. They found significant variability across cells in the ability of microRNAs to repress their targets, and this variability was mostly washed out in the population average. Moreover, the sharpness of the switch from full microRNA repression to escape from repression depended on the strength of the interaction between the microRNA and its target.
Taken together, these studies reveal that discrepant results could arise from the use of different experimental techniques that take either a genome-wide approach or a hypothesis-driven approach. “What we generally measure nowadays are mass levels, which are essentially the average of the expression in a number of cells within the tumor,” explains Stefano Volinia of Ohio State University (OSU). “We need to improve the throughput and precision of cellular measurement of microRNA expression and localization. The tumor contains many cell types, not only cancer cells, but also normal stroma and immune cells.”
A Lifetime’s Work
Volinia and his colleague Carlo Croce of OSU may have hit upon the right combination of approaches to bring to the clinic. In a study published last month in the Proceedings of the National Academy of Sciences, the duo integrated mRNA, microRNA, and DNA methylation next-generation sequencing data from the Cancer Genome Atlas to identify signatures that could predict survival in patients with primary invasive ductal carcinoma, the most frequent type of breast cancer (4). This approach could help distinguish low-risk and high-risk patients better than other RNA predictors.
“The take-home message is that a hybrid gene signature has the strongest prognostic performance,” says Volinia. “We are now using a novel technique that is capable of testing microRNA and mRNA in the same test tube and at the same time. If our results are confirmed, the test could be used to choose the appropriate therapy in the context of personalized medicine.”
This type of approach could be most useful for aggressive types of cancer that currently lack effective therapies. Triple-negative breast cancer (TNBC), for instance, is characterized by the lack of expression of estrogen, progesterone, and HER2/neu receptors; therefore, patients with this disease do not respond to drugs targeting those receptors. Because TNBC is so heterogeneous, the ability to stratify patients into subgroups based on molecular signatures could help clinicians develop individualized treatment plans.
“I’ve been working on breast cancer for quite a long time, but from the point-of-view of proteins,” says Kay Huebner of Ohio State University Comprehensive Cancer Center (OSUCCC). “We thought that microRNAs might really be better at stratifying these breast cancers than some of the other methods.”
In a study published this February in PLoS ONE, Huebner, Pierluigi Gasparini of OSUCCC, and their collaborators analyzed microRNA and mRNA expression patterns to identify four molecular TNBC subgroups, as well as potential biomarkers that could predict patient survival (5). Patients with distinct microRNA and mRNA profiles might benefit from different types of drugs; those with a high-risk microRNA signature could benefit from more aggressive therapy. One of their new discoveries was a protective role for miR-155, which predicted overall patient survival.
But past studies have shown that miR-155 is associated with cancer growth and cell survival. For instance, Cheng and his collaborators reported in a study published this January in Oncogene that miR-155 promotes the growth of new blood vessels in breast cancer and is associated with poor prognosis, metastasis, and triple-negative tumors (6). “miR-155 is a key player in breast cancer metastasis,” says Cheng. “Clearly, our study indicates that miR-155 can be used as a therapeutic target to greatly improve personalized medicine for breast cancer.”
In his own studies, Volinia has confirmed that miR-155 is over-expressed in breast cancer and TNBC in particular, but he has not seen an association with the prognosis of patients with TNBC. The conflicting evidence about the role of miR-155 in breast cancer illustrates the complexity of the functions microRNAs perform, and how little is known about them. “Once you have one microRNA that you like, to understand everything that it does is almost a lifetime’s work,” says Huebner.
1. Bu, P., K.Y. Chen, J.H. Chen, L. Wang, J. Walters, Y.J. Shin, J.P. Goerger, J. Sun, M. Witherspoon, N. Rakhilin, et al. 2013. A microRNA miR-34a-regulated bimodal switch targets Notch in colon cancer stem cells. Cell Stem Cell. 12(5):602-15. doi: 10.1016/j.stem.2013.03.002.
2. Dvinge, H., A. Git, S. Gräf, M. Salmon-Divon, C. Curtis, A. Sottoriva, Y. Zhao, M. Hirst, J. Armisen, E.A. Miska, et al. 2013. The shaping and functional consequences of the microRNA landscape in breast cancer. Nature. 497(7449):378-82. doi: 10.1038/nature12108.
3. Mukherji, S., M.S. Ebert, G.X. Zheng, J.S. Tsang, P.A. Sharp, and A. van Oudenaarden. 2011. MicroRNAs can generate thresholds in target gene expression. Nat Genet 43(9):854-9. doi: 10.1038/ng.905.
4. Volinia, S., and C.M. Croce. 2013. Prognostic microRNA/mRNA signature from the integrated analysis of patients with invasive breast cancer. Proc Natl Acad Sci U S A. 110(18):7413-7. doi: 10.1073/pnas.1304977110.
5. Cascione, L., P. Gasparini, F. Lovat, S. Carasi, A. Pulvirenti, A. Ferro, H. Alder, G. He, A. Vecchione, C.M. Croce, C.L. Shapiro, and K. Huebner. 2013. Integrated microRNA and mRNA signatures associated with survival in triple negative breast cancer. PLoS One. 8(2):e55910. doi: 10.1371/journal.pone.0055910.
6. Kong, W., L. He, E.J. Richards, S. Challa, C.X. Xu, J. Permuth-Wey, J.M. Lancaster, D. Coppola, T.A. Sellers, J.Y. Djeu, and J.Q. Cheng. 2013. Upregulation of miRNA-155 promotes tumour angiogenesis by targeting VHL and is associated with poor prognosis and triple-negative breast cancer. Oncogene. doi: 10.1038/onc.2012.636.