Learn How Big Data Technologies and Platforms are Imperative for Precision Medicine Initiative

“You can match a blood transfusion to a blood type – that was an important discovery. What if matching a cancer cure to our genetic code was just as easy, just as standard? What if figuring out the right dose of medicine was as simple as taking our temperature?”

With these lines, Obama, the former President of the United States, launched the Precision Medicine Initiative on January 30, 2015. The initiative also known as personalized medicine emphasizes the need to have tailor healthcare for each individual, considering individual differences in genes, environments and lifestyles. Today, when a person is diagnosed with cancer, he/she usually receives the same treatment as another patient with the same type and stage of cancer. However, doctors recently discovered that different people responded differently to the same treatment, without deciphering the reason.

The Promise of Precision Medicine

After decades of research, scientists now understand that patients’ tumors undergo genetic mutation that causes cancer cells to grow and spread. They have also learned that the changes that occur in one person’s cancer may not occur in others, who have similar type of cancer. And, the same cancer-causing changes may be found in different types of cancers. The hope of precision medicine is that one day treatment will be customized to suit the genetic changes in each person’s cancer. Scientists see a future when genetic tests will help decide on the treatments a patient’s tumor is most likely to respond to, sparing the patient from futile treatments.

With its promise of delivering the Right Therapy, to the Right Person, at the Right time, precision medicine is based on the foundation that every disease is unique whether it lies in the same organ of two different individuals or two different organs of the same individual. Hence, we need a unique strategy to cure a specific disease that is different from the rest. Another perspective to this is that, with access to genomic information of 1 million Americans, a single disease can be segregated into at least 50 unique diseases and on the other hand, 50 different diseases can be so similar that they can be combined as one disease.

Leveraging Innovation to Transform Lives

Discovering cures in ways that we have never seen before, the precision medicine initiative was much welcomed by researchers and health institutions, across the globe. With each passing month, we experienced a major leap towards this initiative showing positive results. Some of the stories worth highlighting are as follows:

Ivacaftor: Much applauded by the experts in the healthcare domain, the drug ‘Ivacaftor’ eased the symptoms of cystic fibrosis in a definite subset of individuals. Besides, the drug ‘Gleevec’ was welcomed as an angel by a segment of leukemic individuals with a very explicit mutation in their tumors. Following these success stories, a large international study, partly funded by National Institutes of Health (NIH) recently carried out an exercise wherein they analyzed data collected over many years that contains vast troves of genomic and clinical information from more than 50,000 people with and without diabetes. The study eventually provided clarifications that anti-diabetes therapies producing a specific gene that lowers glucose levels, called GLP1R, are unlikely to increase the risk of cardiovascular disease. The pharmaceutical industry has always been made cognizant of the fact that people with type 2 diabetes are at increased risk for heart attacks, stroke, and other forms of cardiovascular diseases. Food and Drug Administration(FDA) on numerous occasions has recommended that drug developers take special care to showcase that potential drugs to treat diabetes do not have an adverse effect on the cardiovascular system. The study indicated that the anti-diabetes therapies, GLP1R, might even provide some protection against cardio vascular diseases.

In a nutshell, precision medicine can assist researchers and health providers in deducing what line of treatment will be most effective in a scenario, whether there is a need for a surgery and most importantly, the drug dosage to be prescribed. There are arguments that the genomic information can be leveraged by researchers in predicting when complex diseases such as cancer, alzheimer’s syndrome, etc. may afflict an individual and treatment may be provided at a much earlier stage.

Data Opportunities are Big

The restructuring of health data with higher weightage on the genomic skeleton of an individual, is a big IT opportunity with major emphasis on ‘Big Data’. The mission of the multi-million government-funded projects makes way for ‘Big Data’ players to venture into the healthcare realm and prove an important point; IT as a backbone to support major business transformations.

As mentioned by the US national coordinator for HIT Karen DeSalvo, MD. “This strong foundation of health information technology makes it possible to bring to the bedside, personalized treatment through precision medicine.” Gradually, many information technology platforms are accessible to the healthcare people that stores and analyzes vast amount of health data collected from millions of patients, thereby allowing the physicians and others across the care continuum to make faster and more effective decisions.

Furthermore, as the data been generated in healthcare is highly complex and intricate, it becomes the driving force to develop Big Data for health. Volume, velocity, variety, veracity, variability, and value are the “Vs” of Big Data informatics that enables the care managers to unfold all the mysteries of the healthcare and world.

One such early Big Data initiative is American Society of Clinical Oncology’s (ASCO) CancerLinQ that intends to transform Cancer Care. CancerLinQ is working towards improving patient outcomes through the generation of new knowledge, based on real-world patients. They are also working on learning tools that aid in the application of that knowledge to patient care. When complete, it will seamlessly and securely aggregate and analyze data from Electronic Health Records (EHRs) and other sources in order to provide three things;

• Clinical decision support to help physicians choose the right therapy, at the right time for each patient;
• Rapid, quality feedback to allow providers to compare their care against guidelines and against the care of their peers
• Analytical tools that will help improve care by uncovering hidden patterns in patient characteristics, treatments, and outcomes.

Mentioned below are some of the key areas wherein Big Data Support can improve the care performance and quality assurance by manifolds:

Repurposing of prevailing drugs to more clinically valuable application is a major conversation the industry is having. Although this is not a new concept, with the availability and accessibility of vast genomic information through various Big Data platforms, the clinicians can make better decisions about when, why and how to repurpose a given drug.

The use of predictive platforms that can recognize the patterns in the patient data and interpret results has, in fact, prevented the outbreak of many epidemics. Also, these Predictive Data Analytics has reduced the number of reporting of chronic conditions by detecting them at a much earlier age. For example, the EHR has been successfully mined for post-market surveillance of medications and improved pharmacovigilance.

Sensing, Tracking and/or Predicting Major Outbreaks: Another major area of application of Big Data analytics is in sensing, tracking and/or predicting the major outbreaks by decoding the messages available on social media.
As a matter of fact, approximately 20% of patients with chronic healthcare conditions such as diabetes, cardiovascular disease, and cancer, go online to actively seek others and share experiences of related conditions on various platforms such as Facebook, Twitter, LinkedIn, etc.

One such case wherein Big Data has been proved quite beneficial is when *Odlum et al. demonstrated that analyzing tweet activity around Ebola Virus Detection (EVD) captured progressive increases in the number of tweets discussing EVD case identification in Nigeria occurring at least three days prior to the news alert and seven days before the Official Centre for Disease Control warnings.

Big Data technologies and platforms have the true potential to transform the entire healthcare world and assist Precision Medicine Initiative in achieving its mission. Having said that, we need to manage the risk of data privacy, security, governance and ownership that comes with the whole package.

Initiative still in its Embryonic Stage

Although precision medicine is one such step that met with equivocal accomplishment and support from all over the world, the advanced efforts to customization of disease-treatment processes led to debates on the impact and outreach. There were deliberations on whether the million-dollar plan has the potential to revolutionize the way diseases have been treated. For instance, Nigel Paneth, a pediatrician and epidemiologist at Michigan State University once commented that “These latter innovations [e.g., Ivacaftor] are part of many small-step improvements in [cystic fibrosis] management that have increased survival rates dramatically in the past two decades. They cost a fraction of what the [high-tech] drugs cost, and they work for every patient.”

To encapsulate, the precision medicine initiative is still in its budding stage and we still have a long way to go. On one hand, researchers from different parts of the world are trying their best to come up with cost-effective drugs in shortest possible time. Whereas, on the other hand, studies for bringing the scattered genomic information – not only from the various EHR systems but also from the various wearable devices into a single database, are continuously gaining focus.

Once we go beyond the privacy and security concerns and set this entire initiative to work, there will be no count of how many lives would be saved or significantly improved.

References:

M.Odlum, S.Yoon. What can we learn about the Ebola outbreak from tweets? Am.J.Infect Control 2015, 43(6);563-571

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Posted by :
Vijayalakshmi Raj
Vijayalakshmira@hexaware.com