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The Theme - Precision Medicine Revolution

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(The University of Chicago - Alvin Wei-Cheng Wong)

 

Smart, Precision and Preventive Medicine in the Era of Big Data


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The availability of the human genome, large amounts of data on individual genetic variations, environmental interactions, influence of lifestyle, and cutting-edge tools and technologies for big-data analysis have led to the age of personalized and precision medicine. Imagine receiving a full diagnosis from a simple blood test, or the ability to tailor the perfect treatment to each person's own genetic makeup, taking into account an individual patient's specific susceptibilities to side effects. What if we could apply a patient’s genetic information to gain insights into the genetic variations of diseases and expedite drug development, to create more precise therapies? This is the goal of precision medicine. Welcome to the world of precision medicine. That sounds futuristic, like science fiction, or a cool exhibit, something that promises great things for tomorrow, something that’s not here yet. But this is different. Precision medicine is already here. We have revolutionary new knowledge which has given us new targets for a smarter, more scientific approach that is helping all patients - but particularly those who need it most.

What is precision medicine? Precision approach to medicine includes an individual’s genetic profile to guide decisions made in regard to the prevention, better diagnoses, earlier interventions, more-efficient drug therapies, customized treatment plans. It is transforming the way diseases like cancer are treated by tailoring medical care to the person’s genetic makeup, or the genetic profile of an individual’s tumour. In precision medicine, the focus is on identifying the approaches that will be effective for patients based on genetics as well as environmental and lifestyle factors. 

According to the National Institutes of Health (NIH), precision medicine is "an emerging approach for disease treatment and prevention that takes into account individual variability in genes, environment, and lifestyle for each person." This approach will allow doctors and researchers to predict more accurately which treatment and prevention strategies for a particular disease will work in which groups of people. It is in contrast to a "one-size-fits-all" approach, in which disease treatment and prevention strategies are developed for the average person, with less consideration for the differences between individuals. Hospitals, networks and the federal government can use precision medicine to drive expenses out of the system, better understand disease and prevent people from getting sick in the first place. So we like to think of precision medicine as the right treatment for the right patient at the right time for the right cost.

Most often today, you go to your doctor with your symptoms, and you get an evaluation, maybe have a few tests run.  If you are lucky, you’re on your way to a diagnosis and a path to feeling better. However, your treatment plan doesn’t have all that much to do with you specifically. It’s identical to what doctors would hand over to essentially anyone with the same condition. That’s because medicine as we know it revolves around “standards of care,” the best courses of prevention or treatment for the general population, or the average person on the street. With breast cancer, for example, those standards mean self-exams and mammograms after a set age and the usual chemotherapy to treat a tumor if one is found. If the first treatment doesn’t work, doctors and patients move on to the next one and the next. It’s trial and error, with life on the line.

Precision medicine is an approach that emphasizes the ways in which your disease risks are unique and different, just like your other, more obvious characteristics. Those disease risks are based on the predispositions written into your genome at birth, combined with your lifestyle and environment. In the case of cancer, the disease has its own genetic makeup, lending each tumor a unique character with unique tendencies and vulnerabilities. And perhaps there is, or soon will be, a drug or treatment or tailored combination of the two that will work better for you than it would for someone else. Precision medicine promises to increase patient-centered care, personalized patient-provider relationships, and treatments targeted to individual concerns and needs. However, such advantages come with certain ethical, legal and social controversies.

Precision medicine relies on an advanced DNA testing process known as next-generation sequencing (NGS). Today, developments in NGS and information technology have made precision medicine possible, with massive amounts of genetic, "omics," clinical, environmental, and lifestyle data now available. The tech­nology is still being developed, but NGS can map almost all of a person’s genome in a single run, making it much faster and cheaper than earlier methods. By detecting genetic markers for diseases, it can be used to inform prevention efforts, improve diagnoses, and suggest which treatments are likely to work best for each patient. The discovery of genetic biomarkers and their subsequent application in oncology research are paving the path toward a future in targeted therapy development, linking genes to disease states and prognosis and, ultimately, more effective treatments.

Beyond knowing the diseases, how to manage them is a huge task, a huge amount of information gathering. And today’s crop of electronic health records are not ready for that, In order to do precision medicine you need all the patient’s data in a common repository. Implementing precision medicine in a clinical setting requires seamless integration of data from clinical evaluations and biomedical investigations with genomics and other physiological profiling to characterize an individual patient’s disease progression. Implementing precision medicine practices in clinical settings requires coordinated efforts to integrate data from both healthy and disease states in individuals. The ideas behind precision medicine are not new, but the development of big data analytics, biobanking, new methods for categorizing and representing patients, and computational tools for analyzing large datasets have provided opportunities for more widespread use.

Precision medicine has the potential to profoundly improve the practice of medicine, but requires a health care workforce that understands the complexities of this field. However, the advances required will take time to implement. One important component of precision medicine is the use of an individual’s genomic information to offer targeted treatment, tailored to the individual. Genomics is a branch of molecular biology focused on studying all aspects of a genome, or the complete set of genes within a particular organism. Genomic technologies are generating an extraordinary amount of information. Genetics is already being used to direct clinical decision-making and its contribution is likely to increase. To accelerate these advances, fundamental changes are needed in the infrastructure and mechanisms for data collection, storage and sharing. This will create a continuously learning health-care system with seamless cycling between clinical care and research. Patients must be educated about the benefits of sharing data. The building blocks for such a system are already forming and they will accelerate the adoption of precision medicine.

Bioinformatics, the key to a precision medicine future, aims to store, organize, explore, extract, analyze, interpret, and utilize information from biological data. Translational bioinformatics is a rapidly emerging field of biomedical data sciences and informatics technologies that efficiently translate basic molecular, genetic, cellular, and clinical data into clinical products or health implications. Its focus is on applying informatics methodology to the increasing amount of biomedical and genomic data to formulate knowledge and medical tools, which can be utilized by scientists, clinicians, and patients. Furthermore, it involves applying biomedical research to improve human health through the use of computer-based information system. Translational bioinformatics employs data mining and analyzing biomedical informatics in order to generate clinical knowledge for application. Clinical knowledge includes finding similarities in patient populations, interpreting biological information to suggest therapy treatments and predict health outcomes.

The two fields, genomics and bioinformatics, have been facing enormous challenges and chances in the past three decades; even more, they, together with other emerging “omics” fields, will have to readjust their prospective as many precision medicine projects are going along to produce the long-promised big data. Development of high-throughput technologies, such as next generation sequencing, have also brought us into an “omics” era, where not only genomics, but transcriptomics, epigenomics, proteomics, metabolomics, and microbiomics data can be generated in real time, enabling scientists and clinicians to examine for the first time the effect of multi-omics on disease pathogenesis in unprecedented detail. The combined 'omics' information leads to a global profiling of health and disease, and provides new approaches for personalized health monitoring and preventative medicine.

Disease progression and drug response may vary significantly from patient to patient. Fortunately, the rapid development of high-throughput ‘omics’ technologies has allowed for the identification of potential biomarkers that may aid in the understanding of the heterogeneities in disease development and treatment outcomes. Integrated ‘omics’ investigations will be critical in piecing together targetable mechanisms of action for both drug development and monitoring of therapy in order to fully apply precision medicine to the clinic.

Someday, it should be possible for doctors to send individual cancer patients in for a genomic analysis and, based on the results, prescribe the drug they know will be the most effective. While the promise of this kind of personalized medicine is still distant, many researchers worldwide are working on it now, interpreting the genomes of individual cancer patients and searching for clues to how they will respond to various treatments. The field, which is known as cancer pharmacogenomics, is the study of how genes affect a person’s response to drugs. This relatively new field combines pharmacology (the science of drugs) and genomics (the study of genes and their functions) to develop effective, safe medications and doses that will be tailored to a person’s genetic makeup. 

Many drugs that are currently available are “one size fits all,” but they don’t work the same way for everyone. Whether used to explain a patient’s response or lack thereof to a treatment, or act as a predictive tool, precision medicine hopes to achieve better treatment outcomes, greater efficacy, minimization of the occurrence of drug toxicities and adverse drug reactions (ADRs). Adverse drug reactions are a significant cause of hospitalizations and deaths in the United States. With the knowledge gained from the human genome project, researchers are learning how inherited differences in genes affect the body’s response to medications. These genetic differences will be used to predict whether a medication will be effective for a particular person and to help prevent adverse drug reactions. The field of pharmacogenomics is still in its infancy. In the future, pharmacogenomics will allow the development of tailored drugs to treat a wide range of health problems, including cardiovascular disease, Alzheimer disease, cancer, HIV/AIDS, and asthma. 

Currently, there are two main barriers to greater implementation of precision medicine: High costs and technology limitations. Fortunately the cost of sequencing a genome continues to drop year-over-year. Advances in the speed, accuracy, and cost of next-generation gene sequencing making it possible for clinical labs to create thousands of new tests. Artificial Intelligence (AI) can speed up precision medicine. To tackle the vast amount of patient data that must be collected and analyzed, and to help cut down on costs many researchers are implementing machine learning techniques. Today, machine learning is playing an integral role in the evolution of the field of genomics. The potential for AI in precision medicine is big. 

The future of precision medicine will enable health care providers to tailor treatment and prevention strategies to people’s unique characteristics, including their genome sequence, microbiome composition, health history, lifestyle, and diet. To get there, we need to incorporate many different types of data, from metabolomics (the chemicals in the body at a certain point in time), the microbiome (the collection of microorganisms in or on the body), and data about the patient collected by health care providers and the patients themselves. Success will require that health data is portable, that it can be easily shared between providers, researchers, and most importantly, patients and research participants.

 

<updated by hhw: 8/3/17>

 

 

 

 

 

 
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