Face it. Despite our best intentions, bad stuff happens on occasion, as seen in the medical field. In hopes of a positive outcome, a patient undergoes treatment, but has a negative reaction. In the pursuit of “first do no harm,” more safeguards are put into place to minimize the chances of a negative outcome. As technology improves, however, we may soon have an equivalent to the bubble wrap suit, particularly in the development of new medical treatments.
The Food and Drug Administration (FDA) – the federal gatekeeper for new drugs – has established numerous criteria that drug candidates must meet before being sold in the U.S. marketplace (FDA, 2017a). After testing the drug candidate in animals, the clinical evaluation begins. The first phase involves looking at how the drug candidate is processed in a few healthy people and figuring out common side effects. If things look good and the drug is considered to be not too toxic, the investigation focuses on both the safety and the effectiveness of the drug candidate for treating the disease/condition in a large number of people. If all goes well with the testing and review, the FDA decides whether to approve the drug. Further monitoring of drug safety still happens after the approval, but success depends on medical professionals and insurance companies (FDA, 2017b; FDA, 2018).
Who foots the bill for all the testing done during the clinical phases? Well, usually the company or other parties who wishes to have the drug approved. You can imagine that this process is not cheap. It is the worst nightmare imaginable when something goes horribly wrong, particularly in later stages. Despite all the safety precautions taken, this nightmare becomes reality more often than anyone would like to see.
The unfathomable sadly materialized in the clinical testing of Pfizer’s drug candidate torcetrapib, designed to treat high cholesterol (Berenson, 2006). The safety barriers put in place failed. The initial biomarkers selected to monitor the drug recipients did not work to alert doctors that something was about to go horrifically wrong. Pfizer lost about a billion dollars in the development of the drug, and sons and daughters lost their lives (Williams et al., 2018).
In a retrospective study, researchers applied a new proteomic technology (SOMAscan platform) to blood samples collected from participants of the failed study (Williams et al., 2018). The data from this proteomic investigation illuminated unknown deep physiological effects of the experimental drug that were also quite pervasive, and which probably would not have been picked up by technologies available at the time the clinical trial was conducted. The effects were especially prevalent in the bodily functions of immunity and inflammation. The insights provided by the SOMAscan proteomic analysis offered probable explanations for the deaths attributed to taking the experimental drug.
Although the SOMAscan technology was not available at that time, the odds are very good that the problems with torcetrapib would have surfaced early enough to make a difference. It is important to note that during the clinical trials, clinicians used instead the best available measurement, the Framingham risk score (a score to assess the likelihood of having heart problems), to monitor the patients, and observed a decreased risk of heart problems for the experimental drug users (Williams et al., 2018). In the retrospective SOMAscan study, the proteomic data showed the opposite result; the experimental drug users were actually at an elevated risk (Williams et al., 2018). This is not the first-time proteomics beat the Framingham risk score, a gold standard for assessing cardiovascular event risks. In an earlier and separate retrospective study, the proteomic assessment using the SOMAscan platform outperformed the Framingham risk score in determining the patients most likely to suffer a heart attack (Ganz et al., 2016).
While it is too late to help those who suffered from the unexpected effects of torcetrapib, we now have an opportunity to spare others from unintended harm. Applying the proteomic bubble wrap during clinical trials may indeed prevent unnecessary deaths. Also, the SOMAscan platform may better identify patients who would benefit the most from the experimental drug or who should avoid it all costs. Already, we know that the inclusion of biomarkers in the stratification of patients to receive experimental treatment can improve the success rate for FDA approval (Wong, Siah, & Lo, 2018). However, we need to use every tool at our disposal to monitor the right things at the right time, to avoid the kind of outcomes seen with the torcetrapib trial.
Berenson, A. (2006, December 4). End of Drug Trial Is a Big Loss for Pfizer. The New York Times. Retrieved from http://www.nytimes.com/2006/12/04/health/04pfizer.html.
Food and Drug Administration. (2017a, November 24). The FDA’s Drug Review Process: Ensuring Drugs Are Safe and Effective. Retrieved on March 20, 2018 at https://www.fda.gov/Drugs/ResourcesForYou/Consumers/ucm143534.htm
Food and Drug Administration. (2017b, November 17). FDA’s Sentinel Initiative – Background. Retreived on March 20, 2018 at https://www.fda.gov/Safety/FDAsSentinelInitiative/ucm149340.htm
Food and Drug Administration. (2018, February 21). Questions and Answers on FDA’s Adverse Event Reporting System (FAERS). Retrieved on March 20, 2018 at https://www.fda.gov/Drugs/GuidanceComplianceRegulatoryInformation/Surveillance/AdverseDrugEffects/default.htm.
Ganz, P., Heidecker, B., Hveem, K., Jonasson, C., Kato, S., Segal, M. R., . . . Williams, S. A. (2016). Development and Validation of a Protein-Based Risk Score for Cardiovascular Outcomes Among Patients With Stable Coronary Heart Disease. JAMA, 315(23), 2532-2541. doi:10.1001/jama.2016.5951
Williams, S. A., Murthy, A. C., DeLisle, R. K., Hyde, C., Malarstig, A., Ostroff, R., . . . Ganz, P. (2018). Improving Assessment of Drug Safety Through Proteomics: Early Detection and Mechanistic Characterization of the Unforeseen Harmful Effects of Torcetrapib. Circulation, 137(10), 999-1010. doi:10.1161/CIRCULATIONAHA.117.028213
Wong, C. H., Siah, K. W., & Lo, A. W. (2018). Estimation of clinical trial success rates and related parameters. Biostatistics. doi:10.1093/biostatistics/kxx069
It is simple. It is non-invasive. Yet, it is not without risk. I am referring to imaging technologies, such as X-rays and computed tomography (CT) scans, that expose bodies to radiation to “see” under the skin.
What’s the risk with radiation? Well, the radiation can alter/damage DNA. Unfortunately, damaged DNA does not lead to one developing super powers despite what the comics might say. But damaged DNA can lead to cancer (ACS, 2015). And the risk only increases with repeated rounds of radiation exposure.
It’s true that the amount of radiation exposure from a simple X-ray of an extremity (e.g., an arm) is equivalent to about 3 hours of environmental radiation exposure for the typical adult (Radiological Society of North America, 2018). However, if you are subjected to many X-rays or other imaging procedures that use way more radiation over the course of your lifetime, the added radiation exposure quickly adds up. As you might expect, this extra exposure increases your cancer risk but not your Spidey senses.
What can be done to mitigate the risk? Cutting down on the number of X-rays would help. One superhero may swoop in to do just that: Researchers are pioneering the use of a simple blood sample to figure out if bones are healing.
Current monitoring of broken bones uses highly variable metrics and lacks universal standards. Researchers from Boston University set out to determine if a standardized lab test built on blood analysis could take the place of all these other approaches (Hussein et al., 2017). Using a mouse model, the researchers collected many samples and searched for different types of biomarkers. They noted that hundreds of proteins changed dramatically during the course of the bone-healing process. Optimistically, they noted that protein measurement-based blood tests may prove especially promising in monitoring human bone repair.
In a serendipitous and almost concurrent moment, an international group of researchers led by scientists from the Shriners Hospitals for Children and the Oregon Health and Science University in Portland happened upon related findings (Coghlan et al., 2017). These researchers set out to bring order to the field of monitoring how quickly children grow, which currently yields variable results, particularly in very young children. In their search, the researchers identified that the circulating levels of the protein “noncollagenous 1 domain of type X collagen” (CXM) tracked well with growth rate (specifically, the rate of bone growth). For young patients suffering from growth disorders, having a more precise way of monitoring growth rates proves crucial to gauge appropriate response to medical treatment. As for the moment of serendipity, the researchers also noted that the levels of CXM rise during the healing of broken bones.
We will likely never be able to eliminate imaging based on X-rays: It is just too useful. We may, however, be able to reduce our need for them with improved diagnostic technologies for particular uses, like monitoring bone healing. Not only will new approaches like blood analysis inevitably reduce the amount of radiation exposure, but it could be easier on the pocket book too: Just think of how many superhero comic books could be purchased for the cost of a CT scan or a standard X-ray!
American Cancer Society (ACS) medical and editorial content team. (2015, February 24). Do x-rays and gamma rays cause cancer? Retrieved from https://www.cancer.org/cancer/cancer-causes/radiation-exposure/x-rays-gamma-rays/do-xrays-and-gamma-rays-cause-cancer.html
Coghlan, R. F., Oberdorf, J. A., Sienko, S., Aiona, M. D., Boston, B. A., Connelly, K. J., . . . Horton, W. A. (2017). A degradation fragment of type X collagen is a real-time marker for bone growth velocity. Sci Transl Med, 9(419). doi:10.1126/scitranslmed.aan4669
Hussein, A. I., Mancini, C., Lybrand, K. E., Cooke, M. E., Matheny, H. E., Hogue, B. L., . . . Gerstenfeld, L. C. (2017). Serum proteomic assessment of the progression of fracture healing. J Orthop Res. doi:10.1002/jor.23754
Radiological Society of North America, Inc. (accessed February 8, 2018) Radiation Dose in X-Ray and CT Exams. Retrieved from https://www.radiologyinfo.org/en/info.cfm?pg=safety-xray
Tiny toes. Tiny fingers. Tiny diapers. The word “tiny” almost perfectly describes infants born very prematurely. Yet, it fails to properly describe the battle for survival these tiny warriors endure. And the more premature the baby, the harder the fight. Can recent advances in medicine help boost the odds in the warriors’ favor?
Upon entering the world, preemies struggle to breathe with underdeveloped lungs. Medical intervention in the form of oxygen therapy or mechanical ventilation can help, but many babies receiving such treatments often develop bronchopulmonary dysplasia (BPD) (Baker, Abman, & Mourani, 2014). This disease is characterized by problems with the development of the veins and arteries that feed into and out of lungs and air sacs. Not too surprisingly, BPD can give rise to more conditions, such as pulmonary vascular disease (PVD) or pulmonary hypertension, which in turn significantly increase infant mortality. Diagnosing these conditions can be difficult, but a better understanding of their molecular underpinnings may help identify the tiny warriors at greatest risk early enough to intervene successfully.
Researchers are answering the call to battle. Using an arsenal of scientific methodologies (including the proteomic analysis of blood samples from premature infants), one group found that current medical interventions can cause a decrease in levels of a critical protein called “platelet-derived growth factor receptor a” (PDGF-Ra) (Oak et al., 2017). Decreasing the amount of PDGF-Ra experimentally in animal models leads to the manifestation of traits similar to those seen in BPD-afflicted infants. The same researchers also found that adding back PDGF-Ra could rescue the observed consequences of the simulated medical intervention, suggesting a new way to attack the diseases that arise from standard treatments.
In another answer to the call-of-action, a different group used proteomics to better understand how PVD arises (Wagner et al., 2018). In the results, it was not too surprising to see proteins associated with the PDGF signalling network on the list of potential biomarkers. Surprisingly, some of the other biomarker candidates suggest new signaling pathways may be involved in the onset of PVD. Although exciting, future work is needed not only to confirm, but also expand upon these early suggestive findings.
With each answer to the call to battle, we are moving ever closer to improving the odds for the tiny warriors. The day may soon come when these tiny ones no longer struggle for breath or suffer the unintended consequences of the medical community’s life-saving interventions. When it does come, it will truly be a happy victory.
Baker, C. D., Abman, S. H., & Mourani, P. M. (2014). Pulmonary Hypertension in Preterm Infants with Bronchopulmonary Dysplasia. Pediatr Allergy Immunol Pulmonol, 27(1), 8-16. doi:10.1089/ped.2013.0323
Oak, P., Pritzke, T., Thiel, I., Koschlig, M., Mous, D. S., Windhorst, A., . . . Hilgendorff, A. (2017). Attenuated PDGF signaling drives alveolar and microvascular defects in neonatal chronic lung disease. EMBO Mol Med, 9(11), 1504-1520. doi:10.15252/emmm.201607308
Wagner, B. D., Babinec, A. E., Carpenter, C., Gonzalez, S., O’Brien, G., Rollock, K., . . . Abman, S. H. (2018). Proteomic Profiles Associated with Early Echocardiogram Evidence of Pulmonary Vascular Disease in Preterm Infants. Am J Respir Crit Care Med, 197(3), 394-397. doi:10.1164/rccm.201703-0654LE
“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