“Space vomit” is a truly scientific idiom. At least, it was in the lab. We used this queasy term to describe rendered 3D images of nucleic acids that resembled more the product of an astronaut’s upset stomach than the structure of a molecule. The data were just too noisy and, ultimately, useless for extracting any meaningful insight.
Worse, even when we knew that we had space vomit on our hands, we were still tempted to try and make sense of it. Maybe, one more tweak to the algorithm or input file would suddenly transform the mess into something meaningful? Having given into the temptation too many times, I know first-hand that this kind of salvage moment VERY RARELY happens. And, I think, this is a lesson that many precision medicine disciples could benefit from learning as well.
Okay, what does space vomit have to do with precision medicine? Precision medicine typically conjures the image of using genetic testing to indicate the best medical treatment or lifestyle choices for people (Marcon, Bieber, & Caulfield, 2018). But we have to be honest: in the majority of cases, deducing a medical or lifestyle choice from a genome sequence is akin to trying to make spatial sense of space vomit: It is just too messy.
Indeed, our genomes are incredibly noisy and contain too much “stuff” for us to effectively make sense of it, at least at the moment. Only ~1% of our DNA codes for proteins (the molecules responsible for almost every function in our bodies) (Zhao, 2012). The rest holds not only the instructions for building the protein, but also the instructions for when to use the protein and the instructions for controlling the protein’s activity. In addition to carrying relevant information, the genome can also include DNA from random sources, such as viruses, transposons, bacteria, etc. (Crisp, Boschetti, Perry, Tunnacliffe, & Micklem, 2015; Soucy, Huang, & Gogarten, 2015). And these are just a few of the numerous complicated variations that our genomes carry.
The noise/complexity problem only gets worse beyond the sequence itself. A person’s DNA gets replicated over and over during the course of a lifetime, and the machinery responsible for this task occasionally makes mistakes (Harris & Nielsen, 2014), which may get passed to the next generation. Some of these mistakes, or mutations, may be expected to give rise to some horrible disease, but even having a “bad” mutation is not a guarantee that the bearer will show clinical symptoms of the disease (Chen et al., 2016)!
Despite the uncertainty, many researchers (and even a growing number of companies) are deeply invested in linking genomic mistakes to various traits and medical problems. But trying to associate complex traits to changes in the genetic code is difficult at best. Recently, a call-to-action has been raised for changing how this is being done (Boyle, Li, & Pritchard, 2017).
Aside from trying to extract insights from the genome, another problem exists: the actual readout of the genetic material. Different entities, such as business and research institutions, usually have different protocols for generating data and algorithms for figuring out the DNA sequence from the data. Now, it may seem that no matter what, the same sequence should be generated. Right? Recently, a JAMA Oncology article described yet another instance of discrepancies when it comes to DNA sequencing (Torga & Pienta, 2017). In this article, researchers sent samples from the same set of patients to two different companies with Clinical Laboratory Improvement Amendments (CLIA) certified labs (i.e., a way of vetting companies that sell diagnostic tests) and got back different sequences for the majority of the patients. So which company provided the “correct” DNA sequence? This is a great question; especially when the choice of medical care made by providers presumably hinges on having a “correct” sequence.
And this brings us back to space vomit: There are instances when knowing the correct DNA sequence can be beneficial in the treatment of cancer, such as melanoma and some types of lung cancer (Harris, 2018). But these occurrences may be the exception rather than the rule, though they might be what cause people to give into the temptation of continuing to hammer away at noisy, complex, and potentially incorrect data to find the magical answer for medical treatments or lifestyle choices. It is reassuring to know that awareness is growing about the limitations of the DNA sequence for determining better medical intervention (Harris, 2018). Perhaps the precision medicine field will now turn their attention to other, less space vomit-like data types that may more readily and easily lead to better medical or lifestyle choices.
Boyle, E. A., Li, Y. I., & Pritchard, J. K. (2017). An Expanded View of Complex Traits: From Polygenic to Omnigenic. Cell, 169(7), 1177-1186. doi:10.1016/j.cell.2017.05.038
Chen, R., Shi, L., Hakenberg, J., Naughton, B., Sklar, P., Zhang, J., . . . Friend, S. H. (2016). Analysis of 589,306 genomes identifies individuals resilient to severe Mendelian childhood diseases. Nat Biotechnol, 34(5), 531-538. doi:10.1038/nbt.3514
Crisp, A., Boschetti, C., Perry, M., Tunnacliffe, A., & Micklem, G. (2015). Expression of multiple horizontally acquired genes is a hallmark of both vertebrate and invertebrate genomes. Genome Biol, 16, 50. doi:10.1186/s13059-015-0607-3
Harris, K., & Nielsen, R. (2014). Error-prone polymerase activity causes multinucleotide mutations in humans. Genome Res, 24(9), 1445-1454. doi:10.1101/gr.170696.113
Harris, R. (2018, January 15). For Now, Sequencing Cancer Tumors Holds More Promise Than Proof. NPR. Retrieved from https://www.npr.org/sections/health-shots/2018/01/15/572940706/for-now-sequencing-cancer-tumors-holds-more-promise-than-proof.
Marcon, A. R., Bieber, M., & Caulfield, T. (2018). Representing a “revolution”: how the popular press has portrayed personalized medicine. Genet Med. doi:10.1038/gim.2017.217
Soucy, S. M., Huang, J., & Gogarten, J. P. (2015). Horizontal gene transfer: building the web of life. Nat Rev Genet, 16(8), 472-482. doi:10.1038/nrg3962
Torga, G., & Pienta, K. J. (2017). Patient-Paired Sample Congruence Between 2 Commercial Liquid Biopsy Tests. JAMA Oncol. doi:10.1001/jamaoncol.2017.4027
Zhao, R. (2012, November 8). ENCODE: Deciphering Function in the Human Genome. Retrieved from https://www.genome.gov/27551473/genome-advance-of-the-month-encode-deciphering-function-in-the-human-genome/.
Achoo…sniffle sniffle…cough…hack…These sounds echoing throughout the office herald the arrival of yet another cold/flu season. Yay! But before you convince yourself to work from behind the barricades of your home, remember that we do have a wonderful defense: our immune system. When it works properly, everything is right with the world. However, it can also go very wrong.
The scientific literature is peppered with examples of how our immune system defender can turn on us, such as in the case of rheumatoid arthritis. In your joints, the synovium provides critical lubrication, akin to the oil needed to keep the engine parts running smoothly in cars (Mayo Clinic, 2017a). In rheumatoid arthritis, the immune system starts attacking this tissue, causing unnecessary inflammation where it does not belong. Without treatment, the inflammation persists, the pain worsens and the joints become disfigured and even inoperable.
New research is highlighting many additional, even surprising instances in which our immune system ally can seem to turn on us. Trisomy 21, the inclusion of an additional chromosome 21, leads to Down Syndrome (DS) and its wide spectrum of complications (Mayo Clinic, 2017b). To understand how an extra chromosome can lead to such a diverse set of complications, researchers measured changes in blood proteins to try to understand what is happening at a deeper level. From their findings, they learned that the extra 21st chromosome can lead to “profound” protein changes in the immune system (Sullivan et al., 2017). Interestingly, they saw dramatic increases in DS individuals of the inflammatory TNF-a signaling pathway (a target for some rheumatoid arthritis medications) (Rau, 2002; Sullivan et al., 2017), in addition to many other immune system-related proteins. More work is needed to pinpoint how these protein changes can affect various DS complications. However, it is tantalizing to think that medications aimed at treating rheumatoid arthritis may also potentially benefit DS individuals in some way.
Another surprising instance of our friend the immune system causing chaos may be premature labor. Researchers noted that a mother-to-be’s immune system changes during pregnancy to allow the fetus to grow without being attacked (Aghaeepour et al., 2017). When they investigated blood samples from pregnant women, they mapped out a clear-cut timeline of changes to the immune system. If the strict schedule is not followed, the researchers hypothesize it could be the prelude to premature labor. If this hypothesis plays out, it could yield beneficial diagnostics that could warn doctors and the mom-to-be ahead of time and result in an increase in happier birth stories.
It really does seem that a legitimate way to describe our immune system is to call it our greatest frenemy. (Hopefully, this does not bring back any painful middle school memories.) With continuing advances in diagnostics and therapeutics, we might be able to determine when the relationship turns adversarial, and take the necessary steps to mend that break. Hopefully, the friendship is mended before we need our ally the most. Goodness…I just heard another cough!
Aghaeepour, N., Ganio, E. A., McIlwain, D., Tsai, A. S., Tingle, M., Van Gassen, S., . . . Gaudilliere, B. (2017). An immune clock of human pregnancy. Sci Immunol, 2(15). doi:10.1126/sciimmunol.aan2946
Mayo Clinic. (2017a). Rheumatoid arthritis – Symptoms and causes. Retrieved on December 8, 2017 at https://www.mayoclinic.org/diseases-conditions/rheumatoid-arthritis/symptoms-causes/syc-20353648.
Mayo Clinic. (2017b). Down syndrome – Symptoms and causes. Retrieved on December 8, 2017 at https://www.mayoclinic.org/diseases-conditions/down-syndrome/symptoms-causes/syc-20355977
Rau, R. (2002). Adalimumab (a fully human anti-tumour necrosis factor alpha monoclonal antibody) in the treatment of active rheumatoid arthritis: the initial results of five trials. Ann Rheum Dis, 61 Suppl 2, ii70-73.
Sullivan, K. D., Evans, D., Pandey, A., Hraha, T. H., Smith, K. P., Markham, N., . . . Blumenthal, T. (2017). Trisomy 21 causes changes in the circulating proteome indicative of chronic autoinflammation. Sci Rep, 7(1), 14818. doi:10.1038/s41598-017-13858-3
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.
Fessenden, M. (2017). Protein maps chart the causes of disease. Nature, 549(7671), 293-295. doi:10.1038/549293a
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
News Flash! The wedding between Big Foot and The Loch Ness Monster to be the event of the century! It’s going to be huuuuge! A-list celebrities plan on attending…
Now, is that a true news item or a false one? Before you hit the buzzer and say it is false, consider for a second that it just might be true. People can and do give themselves or their children unique monikers (Big Foot Smith is not outside of belief). The point is, we do not know many things for certain, but we can deduce validity — at least in part — from the source of the info. However, it can be a difficult task even for professional seekers of truth, e.g., scientists.
Traditionally, scientists turn to scholarly journals for valid information. In the olden days, researchers would dive into the bowels of the library to find the journal issue carrying a sought-after article, or send an undergrad to fetch it. Not anymore. Today, a few internet clicks and POOF! Millions of hits in mere seconds. Though it has become easier to access information, it can sometimes be more difficult to discern the legitimacy of the information or the source.
A great example of discerning legitimacy comes in the form of so-called “predatory journals.” What is a predatory journal? It is a journal that exists solely for profit making rather than disseminating knowledge. For a fee, the predatory journal publishes pretty much anything thrown at them, without thorough review of the findings. It can be difficult to spot these predators for they go by titles that sound legit, and can (and do) fool even the most senior of scientists (Cobey, 2017).
How can scientists (and the rest of us) evade these noxious predators? Confirming that a journal is listed in the PubMed database, which has banned some predatory journals, is one strategy (Deprez and Chen, 2017). Other databases such as Journal Citation Reports or Directory of Open Access Journals might be useful (Moher et al., 2017). Looking at impact factors, questioning librarians, or seeing if the journal has any characteristics that have been attributed to a predatory journal (Moher et al., 2017) may also help get to the valid data/knowledge.
However, predatory journals are not the only sources of suspect information. Digital apps and other software can crunch away data and offer something “insightful.” Yet, these data and the resulting findings are not always useable or accurate. For instance, the software that converts experimental data into a DNA sequence has about a 50% reproducibility rate (Keshavan, 2017). Think about it. Your genetic test results might be different if the samples were re-analyzed. If these results were used to decide medical treatment, the treatment might be inappropriate 50% of the time! To rein in the variability seen in DNA sequencing, the FDA is taking action. Though still short of implementing regulations, the FDA beseeches the sequencing companies to scrutinize their software and improve it (Keshavan, 2017).
So, how are consumers using results from genetic tests? Recently, a survey showed how much consumers actually used the knowledge acquired from commercial tests (Barton, 2017). Consumers can be told of their risk for cancer based on genetic tests that look for single point mutations. These tests did not greatly sway health-related behaviors in either a negative direction or positive direction for many of the participants (except in the case for prostate cancer tests) (Barton, 2017). On the bright side, it would appear that the news is not causing everyone (except for those with worrisome test scores for prostate cancer) to rush and get potentially unnecessary diagnostic tests done, which can carry their own set of problems.
Where do we go from here? With the sheer crush of data available it seems like it is almost impossible avoid predators and wring credible and reproducible information/knowledge from the internet or companies. Yet, it is not. We need to just take the time to carefully scrutinize the information/ knowledge, investigate the publishing practices of journals, query how a company validates their results, put the claims in the context of other knowledge, etc. This is not always easy because we do not always have the time to do this well, and still read entertaining stories on the internet, such as the Big Foot and Loch Ness Monster nuptials.
Barton, M. K. (2017). Health behaviors not significantly changed by direct-to-consumer genetic testing. CA Cancer J Clin, 67(3), 175-176. doi:10.3322/caac.21368
Cobey, K. (2017). Illegitimate journals scam even senior scientists. Nature, 549(7670), 7. doi:10.1038/549007a
Deprez E. and Chen C. (2017, August 29). Medical journals have a fake news problem. Bloomberg. Retrieved from https://www.bloomberg.com/news/features/2017-08-29/medical-journals-have-a-fake-news-problem.
Keshavan, M. (2017, August 1). FDA pushes to bring order to the chaotic world of DNA sequencing. Statnews. Retrieved from https://www.statnews.com/2017/08/01/fda-dna-sequencing/
Moher, D., Shamseer, L., Cobey, K. D., Lalu, M. M., Galipeau, J., Avey, M. T., . . . Ziai, H. (2017). Stop this waste of people, animals and money. Nature, 549(7670), 23-25. doi:10.1038/549023a
By guest blogger: N. T. Feles
I am new to this whole blogging gig, but excited for the opportunity. You see, I come from a long line of writers. Though many members in my family choose to pen their thoughts using clay as a medium, my distant cousin — who wrote an influential physics paper (Hetherington & Willard, 1975) — and I have chosen a different media: keyboards.
What does one blog about? I guess I could just say what is on my mind. Lately, I have been fascinated by how pseudoscience dictates courses of action that can have a profound impact on health. I would really love to know why people are turning away from science and embracing the absurd.
Pawing through the internet, I see products, services and news stories that leave me speechless. For instance, people listening to Hollywood types and forgoing lifesaving vaccinations for fear of developing neurological problems, an urban myth that has been disproven by science. Promotion (and presumed purchases) of gemstone eggs that can promote health. Crystal cleanses that can remove toxins from your body. (I thought this was the job of your kidneys and liver?) Supplements made from plant extracts touted as being able to restore hormone balance, etc.
Know what these situations remind me of? The era before government regulation, when people could hawk bogus treatments and make outlandish claims about their curative effects. Thank goodness for regulations. Now, we have a set of standards to ensure that approved medications are safe and can do what the manufacturers’ say. If only we had this with vitamin and supplement markets, which are still not regulated and where untested claims are still being made and believed.
I know humans can be smart and make good decisions. So, why do they fall for these hokey claims? I am neither a psychiatrist nor a psychologist, but I can guess. As I clawed through the literature, I happened upon an article that explains the power that celebrities hold. Some of the reasons are obvious, such as celebrities being the leaders of our cultural herd and many people wanting to emulate them (Not I. I am not a herd animal.) (Hoffman & Tan, 2013). But the authors also dive into rationale that made my furry chin drop. Why? Apparently, people think if a “trustworthy” celebrity is successful (i.e., paid millions or received a tiny golden statue in the film industry awards ceremony), then it means that person will automatically be successful in other ventures such as medicine, a phenomenon known as the halo effect (Hoffman & Tan, 2013).
Somehow, I do not see someone who got a tiny golden statue for playing some famous person’s love interest getting anywhere near me with a scalpel and anesthesia! Unless, of course, that person received years of practical training from a credited medical school. Which I doubt they did. Anyway, the article is eye opening and worth reading and sending to others.
So, what can be done to get people to listen more to competent professional experts instead of celebrities who deem the next unfortunate animal to be the “it” pet or preach bad medical advice? This is a hard one. The easiest thing to do would be to tell them that they are wrong for following a celebrity’s advice on a medical thing. Surprisingly, this is likely to backfire and make the person further believe the fallible medical advice (Shermer, 2017).
In an altruistic universe, celebrities would be very mindful that with their great powers of influence, comes great responsibility. They would be sure to promote sound medical advice that helps their fans and not just someone’s pocket books. It is reassuring that some celebrities do realize this and do promote the correct medical information (Hoffman & Tan, 2013). We just need more celebrities to do it.
In that same universe, perhaps celebrities would be selected for their wisdom, education or humanitarian endeavors. I do not know if someone overheard me, but a recent commercial provided a glimpse into this alternative reality. The commercial featured Mildred Dresselhaus, a notable scientist, as an A-list celebrity. People clamored to hear her talks, named their children after her, asked for her autograph, etc. How neat would it be it were not fictional? I wonder if Dr. Dresselhaus would have promoted better medical advice?
Well, I am tired of standing on my soap box and about to miss out on my 20 hours of sleep. This blogging thing was fun, but are you going to take my word about what was said? I am not a puppy wielding celebrity, but a cat named Noodle. Then again, I do know how to persuade humans to heed what I say: the science backs me up (McComb, Taylor, Wilson, & Charlton, 2009).
Hetherington, J.H. & Willard, F. D. C. (1975). Two-, Three-, and Four-Atom Exchange Effects in bcc 3He. Phys. Rev. Lett. 35, 1442.
Hoffman, S.J. & Tan, C. (2013). Following celebrities’ medical advice: meta-narrative analysis. BMJ. 347:f7151 doi: 10.1136/bmj.f7151
McComb, K., Taylor, A. M., Wilson, C., & Charlton, B. D. (2009). The cry embedded within the purr. Curr Biol, 19(13), R507-508. doi:10.1016/j.cub.2009.05.033
Shermer, M. (2017, January). How to Convince Someone When Facts Fail. Scientific American. Retrieved from https://www.scientificamerican.com/article/how-to-convince-someone-when-facts-fail/
“Precision medicine” carries so much promise and engenders so much enthusiasm: medical care precisely assigned based on something that is measured about you uniquely. That sounds cool and so doable with today’s technology. Yet, we need to exercise caution in these early, heady days. If we do not, we will wind up overwhelmed, stuck on data that is not entirely useful, or attempt shortcuts that don’t improve medical care. As a result, the promise of precision medicine will not be realized. When it comes to our health, we will not be empowered. Let me explain.
Many things pertaining to health can be tracked/measured/tested on an almost daily basis or by the second: body mass index, calories consumed/burned, heart-rate, oxygen-levels, blood pressure, brain waves/activity, hours slept, exercise, diet, mutations, genes, proteins, RNA, cells, weight, height, respiration, body temperature, fertility status, glucose levels, sunlight exposure, electrolyte levels, pH of sweat/urine, numerous characteristics of blood, urine and fecal matter… Think about all the data being generated by this list that together describes you. And this is only the tip of the iceberg! This Mount Everest-size pile of information could very well (and does) overwhelm people who do not know what to do with it all, including our doctors (Standen, 2015).
Gorging at the data/ information buffet alone will not empower us to manage our health. Instead, we need to think critically about what we need know to answer a health question. Here is a case in point. Already, it is becoming clearer that genetics alone cannot be used to foresee susceptibility to diseases (refer to previous blogs). The groupie following is waning for the mantra that unlocking our genetic code will improve our understanding of disease and will revolutionize the way we think and approach healthcare (Joyner, 2016). Although genomics can provide beneficial information relevant to patient care, it is not successful in all cases. As an example, let us examine warfarin (a blood thinning drug that can be broken down at different rates in patients). Two genes were identified that contributed to warfarin metabolism (Drew, 2016). When patients were given the proper dose based on their genetics, the results showed no improvement in the patients’ response (Drew, 2016). Drats!
On the bright side, we are getting closer to living the precision medicine promise. From these experiences, we are gaining wisdom (i.e., a deeper understanding about the application of information (Rowley, 2007)). However, this is taking a lot of time. What if we could use technology to speed up the process? Would that help empower us sooner? Again, nope! Recently, Watson (IBM’s artificial intelligence) was fed a monstrous amount of material and expected to recommend cancer treatments to doctors (Ross and Swetlitz, 2017). Well, the supercomputer floundered and recommended treatments that would not have necessarily helped the patients (Ross and Swetlitz, 2017). What happened? Well, it is reported that the imported material had been biased by those who fed it to Watson (Ross and Swetlitz, 2017).
So how do we realize the promise of precision medicine? Until Watson (or some other nifty artificial intelligence) advances to the point of making sense and infers something unbiased and insightful from the big-heap-of-data/knowledge for us, we must focus and be sure to collect the right data that will be meaningful for an intended purpose. We should avoid at all costs the temptation to binge at the data/information buffet or continue trying to get a failing idea to work. As Eric Topol, a famed cardiologist and advocate for precision medicine, put it best, “We need to go beyond ‘big’ and go deep” (Dusneck, 2017). By thinking critically about what data we need to answer a health question, we can be empowered. Precision medicine may then become reality.
Drew, L. (2016). Pharmacogenetics: The right drug for you. Nature, 537(7619), S60-62. doi:10.1038/537S60a
Dusneck, J. (2017, May 25) Cardiologist Eric Topol on why we need to map the human body and “go deep” with big data. Scope. Retrieved from http://scopeblog.stanford.edu/2017/05/25/cardiologist-eric-topol-on-why-we-need-to-map-the-human-body-and-go-deep-with-big-data/.
Joyner, M. J. (2016). Precision Medicine, Cardiovascular Disease and Hunting Elephants. Prog Cardiovasc Dis, 58(6), 651-660. doi:10.1016/j.pcad.2016.02.004
Ross, C. and Swetlitz, I. (2017, September 5) IBM pitched its Watson supercomputer as a revolution in cancer care. It’s nowhere close. Stat. Retrieved from https://www.statnews.com/2017/09/05/watson-ibm-cancer/.
Rowley, J. (2007). The wisdom hierarchy: representations of the DIKW hierarchy. Journal of Information Science, 33 (2), 163–180.
Standen, A. (2015, January 19) Sure You Can Track Your Health Data, But Can Your Doctor Use It? NPR. Retrieve from http://www.npr.org/sections/health-shots/2015/01/19/377486437/sure-you-can-track-your-health-data-but-can-your-doctor-use-it.