Jason Tetro
What causes cancer? It’s a question that has been asked for millennia (Sudhakar, 2009) yet no concrete answer has been found. In the 4th Century BCE, Hippocrates believed the answer lied in the presence of “black bile,” which to him was one of the four major humors in the body. Over the centuries, other theories evolved such as alterations in the lymphatic system, chronic irritation, trauma, and the presence of parasites. It wasn’t until the 1970s when molecular analyses discovered the presence of oncogenes and tumor suppressors. Yet, even the discovery of these two factors could not allow for proper prediction of cancer onset.
Today, a more evidence-based search has developed thanks in part to the ability to sequence the human genome. A closer inspection of our genetic code has revealed cancer usually does not arise from a single cause. Instead, a combination of different alterations in the genome – known as mutations – leads to a tipping point in which the cell becomes cancerous.
Some of these changes are known to have a greater impact on cancer onset. Collectively, they are known as driver mutations (Raphael, Dobson, Oesper, & Vandin, 2014). Driver mutations can come in almost any form. Some are singular changes in the genomic sequence, known as single-nucleotide variants. Others happen to be complex rearrangements of large genomic segments better known as structural variants. Then there is the inadvertent duplication or deletion of genome segments, referred to as copy number variation (Hastings, Lupski, Rosenberg, & Ira, 2009).
While researchers have closed the knowledge gap in terms of the cause of cancer, they also have inadvertently made the landscape more complex. Investigations into the human genome have led to large databases such as The Cancer Genome Atlas and The International Cancer Genome Consortium. Tens of thousands of human genomic sequences are held in these repositories with well over a million different mutations saved. This demonstration of “Big Data” may seem impressive, however, without some type of rigorous analysis in place the likelihood of finding a “cause” presents an unyielding task.
Last month, a group of Chinese researchers revealed they were up to the task and published a fascinating article in Frontiers in Genetics (Chen et al., 2018). They took a closer look at driver mutations in the hopes of finding a prime suspect. Their analyses revealed that one particular process in the cell seemed to have a large contribution to the onset of cancer. It’s known as lysine modification and based on the results, changes to this vital process could be an early warning sign for cancer.
Lysine modification, as the name implies, is a process whereby a protein is altered through the attachment of a molecule to one of the protein’s lysine amino acids, a process known as covalent bonding. Several different types of modifications are known to occur here such as:
- Ubiquitylation (also called ubiquitination) (Dwane, Gallagher, Ni Chonghaile, & O’Connor, 2017), which involves the protein in a variety of different functions;
- Acetylation (Yang & Seto, 2008), which can open the protein for interaction with other proteins;
- Methylation (Lanouette, Mongeon, Figeys, & Couture, 2014), to prevent association with other proteins;
- Glycation (Ansari, Moinuddin, & Ali, 2011) to create an autoantigen and;
- SUMOylation (Wilkinson & Henley, 2010), which serves to alter the protein’s function during stress.
Knowing lysine modification is a key component to normal cell function, the authors hypothesized that any changes in this process due to mutation could drive a cell towards cancer. But to prove this, they needed to perform some serious computer simulations (Figure 1). They had over 100,000 possible lysine modification sites on well over 13,000 proteins to analyze. They also had well over a million possible cancer-influencing mutations to consider. The task was not going to be easy but they believed it would be well worth the effort.
During the first round of analysis, the team tried to match mutations to lysine modification to one of twelve different cancers. The end result was a reduction in the number of possible sites by a third. This number, 68,401, was still rather unseemly in terms of looking for a possible cause however it was still progress.
The next stage of the process involved looking at protein associations with cancer in terms of driver mutations. Of the over 13,000 possible protein candidates, only 473 had higher amounts of mutation. To find out which ones contributed to cancer, the team consulted the Catalogue of Somatic Mutations in Cancer. As the name implies, this database houses a list of all the protein mutations known to have an association with cancer. This step drastically reduced the number down to 45 although twenty of these had much lower rates of mutation. That left the team with a final number of 25 possible proteins.
With a more manageable number at hand, the team performed the last step of the exercise. They tried to find out which cellular processes were affected by these mutations. Considering the cancer is a phenomenon in which cells tend to grow out of control, there was little surprise when these proteins were found to be involved mainly in the cell’s life cycle, including mitosis and, perhaps more importantly, programmed cell death. In essence, the mutations led to a change in the way the cell lives, reproduces, and dies.
Putting all the information together, the team concluded lysine mutations are significant drivers of cancer onset and progression through a change in the cell cycle leading to overgrowth and tumor formation. Although they could not explain how these mutations occurred, the results could be useful in developing both diagnostic as well as predictive testing. In this light, cancer – or possibly the risk for the disease – can be discovered earlier leading to more timely and appropriate treatment.
Although this study is not a smoking gun in terms of finding a cause for cancer, the results do bring us one step closer to understanding how cancer happens. The data can also shed more light on the importance of searching through Big Data to come up with unique and useful discoveries. In light of the rather unhelpful historical assumptions regarding the cause, this study reveals we may one day find the answer to that millennia-old question.
Jason Tetro is better known for his work in microbiology and immunology. However, over the last three decades in which he has been involved in scientific research, he has seen a significant overlap with cancer research, particularly in the approaches used to increase our understanding of the mechanisms belying tumorigenesis. He hopes to share is multidisciplinary perspective and relay his fascination for the discoveries that may one day lead to a cure.
References
Ansari, N. A., Moinuddin, & Ali, R. (2011). Glycated lysine residues: a marker for non-enzymatic protein glycation in age-related diseases. Dis Markers, 30(6), 317-324. doi:10.3233/dma-2011-0791
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3825483/
Chen, L., Miao, Y., Liu, M., Zeng, Y., Gao, Z., Peng, D., . . . Ren, J. (2018). Pan-Cancer Analysis Reveals the Functional Importance of Protein Lysine Modification in Cancer Development. Front Genet, 9, 254. doi:10.3389/fgene.2018.00254
https://www.frontiersin.org/articles/10.3389/fgene.2018.00254/full
Dwane, L., Gallagher, W. M., Ni Chonghaile, T., & O’Connor, D. P. (2017). The Emerging Role of Non-traditional Ubiquitination in Oncogenic Pathways. J Biol Chem, 292(9), 3543-3551. doi:10.1074/jbc.R116.755694
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5339741/
Hastings, P. J., Lupski, J. R., Rosenberg, S. M., & Ira, G. (2009). Mechanisms of change in gene copy number. Nat Rev Genet, 10(8), 551-564. doi:10.1038/nrg2593
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2864001/
Lanouette, S., Mongeon, V., Figeys, D., & Couture, J. F. (2014). The functional diversity of protein lysine methylation. Mol Syst Biol, 10, 724. doi:10.1002/msb.134974
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4023394/
Raphael, B. J., Dobson, J. R., Oesper, L., & Vandin, F. (2014). Identifying driver mutations in sequenced cancer genomes: computational approaches to enable precision medicine. Genome Medicine, 6(1), 5. doi:10.1186/gm524
https://genomemedicine.biomedcentral.com/articles/10.1186/gm524
Sudhakar, A. (2009). History of Cancer, Ancient and Modern Treatment Methods. J Cancer Sci Ther, 1(2), 1-4. doi:10.4172/1948-5956.100000e2
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2927383/
Wilkinson, K. A., & Henley, J. M. (2010). Mechanisms, regulation and consequences of protein SUMOylation. Biochem J, 428(2), 133-145. doi:10.1042/bj20100158
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3310159/
Yang, X. J., & Seto, E. (2008). Lysine acetylation: codified crosstalk with other posttranslational modifications. Mol Cell, 31(4), 449-461. doi:10.1016/j.molcel.2008.07.002
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2551738/
Featured Image Credits
Squamous cell carcinoma (cancer) cells from a human mouth. Modified from original image by Weining Zhong
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