Precision Medicine

  • Posted on: Wed, 11/25/2015 - 21:00
  • By: OCHIS

No two people are truly alike and it is fine to be different, because healthcare can now be customized with precision medicine. Here, we discuss the past, present and future of precision medicine.

Why Precision Medicine?

Precision medicine is a model that tailors medical decisions and practices to individual patients through taking into account differences in genes, environments and lifestyles [1]. The term was first introduced in 2011 by national research council in the US [2], and now become a preferred term for what used to be called personalized medicine.

Traditional medicine has been focused on treating the "average" patients—the "one-size-fits-all" approach, however, may be effective to some patients but not others. Precision medicine has fundamentally different aims: 1) discovering unique characteristics in specific patients, including genetic and environmental factors, and 2) designing accurate prevention and treatment strategies that target on the individual abnormalities. Thanks to affordable genome sequencing technology, a human genome now can be sequenced with $1000 (down from $400 million 15 years ago) [3], and treatments targeting on specific genetic alterations have become a reality.

The concept of precision medicine is not new—it has been used in cancer care and already saved lives. Patients with melanomas, leukemias, breast, lung, and colorectal cancers, now routinely undergo molecular testing, which enables physicians to select treatments that improve chances of survival and reduce exposure to adverse effects [4]. In 2010, Emily Whiteheads, a five-year-old acute lymphoblastic leukemia patient, was cured like a miracle after her T-cells were genetically reprogrammed to recognize and attack cancer cells [5]. During the past several decades, patients of breast cancer, leukemia, cystic fibrosis and other diseases have been benefitted from tailored treatments.

Current Stage of Precision Medicine

In January 2015, President Obama announced a $215 million research in genomic mapping and precision medicine, known as Precision Medicine Initiative, with the mission to work toward the development of individualized treatments [6,7]. As a major piece of the plan, the National Institutes of Health (NIH) along with other stakeholders will launch a research cohort of over 1 million volunteer patients. Participates will contribute various sources of data, such as patient’s genes, metabolites, microorganisms, lifestyles and medical records. The cohort will fulfill the first and crucial step of collecting data for precision medicine researches. The data will later be accessible to scientists from multiple disciplines to inspire insights and evaluate novel approaches [6].

The abundance and complexity of patient-level data suggest the significant role of computational approaches and big data analysis techniques in translating the biomedical observations into better survival among patients. Both academia and industry have made efforts to discover specific disease causes and identify individualized treatments from data.

IBM Watson and 23andMe are two leading companies that leverage the genetic sequencing technology and the big data approach to enhance precision medicine. IBM Watson Health recently announced that they have collaborated with 14 leading cancer institutes to select therapies based on individual tumor’s genetic fingerprints. More excitingly, Watson can do it in a minute with a strong cognitive computing system, which applies a combination of machine learning, data mining, and natural language processing techniques in automatically translating DNA and relevant literature to evidence-based discoveries [8]. On the other hand, 23andMe also made significant contributions in bringing precision medicine directly to the public: they built one of the world’s largest databases of individual genetic information and collaborated with Pfizer in launching a personalized medicine initiative to cure lupus and inflammatory bowel disease [9].

In addition, the Cancer Genome Atlas (TCGA) program of National Cancer Institute (NCI) has integrated diverse cancer genomic data, such as DNA and RNA sequencing data, copy number variations, and microarray data [10]. Using these data, they have identified genetic alterations and somatic mutations that characterize different tumor types and redefined cancer classifications. Scientists have also used their genomic data to promote the development of targeted cancer drugs [10]. On the other hand, the NCI-Molecular Analysis (NCI-Match) Trial has opened in August 2015, aiming to determine whether treating cancers according to genetic abnormalities will show effectiveness [11].

Challenges for Precision Medicine

Despite current achievements and efforts, we are still facing challenges to realize the promise of precision medicine. First, human diseases are far too complex to ever be described with total precision. DNA sequence, environmental exposures, epigenetic factors and others are all playing roles. In fact, the more we know about a disease, the more questions arise. Another major issue, as many scientists in this field have criticized, is that we need to translate existing biomedical discoveries into effective disease treatments. For instance, sickle cell disease was found to be caused by a single amino acid mutation E6V (Glutamic acid to Valine at 6 position) in haemoglobin in 1949. These days, the finding of this very precise target is described in almost every biology and biochemistry textbook, but sickle cell disease still only has one definitive treatment--bone marrow transplant, which is too expensive and dangerous to even remotely qualify as a public health success [12]. In the cancer field, the mutations of Ras, a powerful cancer driver, are found in several most aggressive and deadly cancers, including up to 25% of lung tumors and 90% of pancreatic tumors. However, this protein has been considered as “undruggable” for a long time, and FDA-approved Ras inhibitors do not exist until now [13]. As more scientists and physicians realize these problems, many efforts are being made to develop new therapeutics, such as the CRISPR technique for sickle cell disease [14], and fragment-based approach and covalent inhibitors for targeting Ras [15,16].

The Future of Precision Medicine

After years of preparation of science, technology and funding, now it has become the right moment for precision medicine. The next generation of scientists will be encouraged to develop creative new approaches for detecting, quantifying, and analyzing a broad range of biomedical information. As Dr. Francis Collins, the director of NIH stated in an interview, successes in precision oncology and pharmacogenomics (subfields of precision medicine) will be expected in the near future, and long-term benefits will show in the years down the road [17]. In addition, with the application of cutting-edge technology, such as big data, mobile Internet and the Internet of Things, today’s precision medicine may become tomorrow’s public health.

In March 2015, China also initiated the precision medicine research and development through the Ministry of Science and Technology, and announced 60 billion yuan ($9.45 billion) investments [18]. Despite the obstacles including privacy regulation, policy making and scientific problems, biologists, physicians, data scientists, and technology developers in China are provided with huge opportunities to invent innovative methodologies for the ultimate goal of delivering the right healthcare strategies to the right people at the right time.













[12] Precision Medicine: Too Much Precision, Not Enough Medicine?

[13] Ledford H. (2015) Cancer: The Ras renaissance. Nature. 520(7547):278-80.


[15] Sun Q, Burke JP, Phan J, Burns MC, Olejniczak ET, Waterson AG, Lee T, Rossanese OW, Fesik SW. (2012) Discovery of small molecules that bind to K-Ras and inhibit Sos-mediated activation. Angew Chem Int Ed Engl. 51(25):6140-3.

[16] Ostrem JM, Peters U, Sos ML, Wells JA, Shokat KM. (2013) K-Ras(G12C) inhibitors allosterically control GTP affinity and effector interactions. Nature. 503(7477):548-51.