The American cartoonist and inventor Rube Goldberg was best known for his series of cartoons featuring absurdly intricate contraptions designed to perform mundane tasks. The humor comes from the apparent simplicity of the task: Why not just take the egg into one’s own hands and crack it open? However, Rube Goldberg was onto something: We humans are living Rube Goldberg machines. That simple act of cracking open an egg requires an inordinately complex sequence of events to occur within our bodies.
We know that such a simple act requires exquisite coordination between body and brain. However, this interaction is just the surface. If we probe deeper (to the molecular level), we can see an orchestra of DNA, RNA, and proteins working in harmony to carry out the egg-breaking. When everything works in a harmonious balance, we are fine. When discord arises, disease often results. By probing the different players of the molecular biology trilogy, unique understandings about the disease can be gleaned and harnessed for the implementation of precision medicine.
Yet, we must be cautious about which molecules we monitor for precision medicine because the realization of our own inherent complexity holds especially true in the doctor’s office. Take cancer treatment as an example. Not only are cancer genomes highly variable (Tomasetti, Vogelstein, & Parmigiani, 2013; Vogelstein et al., 2013), but cancers can be affected by numerous molecular pathways (Loeb & Loeb, 2000). As a result, successful treatments for one type of cancer do not always work efficiently for other cancers — or even other tumors of the same type of cancer!! — even though they share the same mutations (Kobayashi & Mitsudomi, 2016; Kopetz et al., 2010; Prahallad et al., 2012).
To develop medicines with greater precision, we certainly should tap into the data geyser born from the omics revolution. Before tapping in, however, we need to determine just what information we really need and how to put it together. This knowledge makes the path clearer for harnessing the wealth of data to make the vision of precision medicine a reality.
Historically, research fixated on specific pathways or individual proteins, but this approach has nearly maxed out the potential benefits regarding our understanding or providing new treatments for cancer (Sapiezynski, Taratula, Rodriguez-Rodriguez, & Minko, 2016). For the next generation of medicines/treatments, we will need to look at how numerous pathways influence one another and how they may differ among individuals. Already, this realization has birthed yet another omics, known as interactomics.
What in the world is interactomics? In essence, it’s about looking at how all the proteins interact with one another and how the interactions change in real-time in response to cues from the environment, etc. (Fessenden, 2017). It’s akin to playing the “Six Degrees of Kevin Bacon” game, but with proteins. For many researchers, interactomics could be a powerful tool for precisely understanding how a faulty protein can cause problems in other molecular pathways, which can give rise to diseases (Fessenden, 2017).
Looking at the protein version of the Kevin Bacon game is another reminder of our biological Rube Goldberg machines’ complexity. It is also a wonderful step to a deeper and sounder understanding of the body’s mechanical workings, which could be a boon for precision medicine. To properly tackle the ginormous challenge of generating a sounder understanding, however, will take a massively coordinated effort of the pharmaceutical industry, research community, and medical community.
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Kobayashi, Y., & Mitsudomi, T. (2016). Not all epidermal growth factor receptor mutations in lung cancer are created equal: Perspectives for individualized treatment strategy. Cancer Sci, 107(9), 1179-1186. doi:10.1111/cas.12996
Kopetz, S., Desai, J., Chan, E., Hecht, J. R., O’Dwyer, P. J., Lee, R. J., . . . Saltz, L. (2010). PLX4032 in metastatic colorectal cancer patients with mutant BRAF tumors. Journal of Clinical Oncology, 28(15_suppl), 3534-3534. doi:10.1200/jco.2010.28.15_suppl.3534
Loeb, K. R., & Loeb, L. A. (2000). Significance of multiple mutations in cancer. Carcinogenesis, 21(3), 379-385.
Prahallad, A., Sun, C., Huang, S., Di Nicolantonio, F., Salazar, R., Zecchin, D., . . . Bernards, R. (2012). Unresponsiveness of colon cancer to BRAF(V600E) inhibition through feedback activation of EGFR. Nature, 483(7387), 100-103. doi:10.1038/nature10868
Sapiezynski, J., Taratula, O., Rodriguez-Rodriguez, L., & Minko, T. (2016). Precision targeted therapy of ovarian cancer. J Control Release, 243, 250-268. doi:10.1016/j.jconrel.2016.10.014
Tomasetti, C., Vogelstein, B., & Parmigiani, G. (2013). Half or more of the somatic mutations in cancers of self-renewing tissues originate prior to tumor initiation. Proc Natl Acad Sci U S A, 110(6), 1999-2004. doi:10.1073/pnas.1221068110
Vogelstein, B., Papadopoulos, N., Velculescu, V. E., Zhou, S., Diaz, L. A., Jr., & Kinzler, K. W. (2013). Cancer genome landscapes. Science, 339(6127), 1546-1558. doi:10.1126/science.1235122