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How Big Data Powers Precision Medicine

Biotechnology on the health care front is in the midst of a mobile revolution: Massive datasets from genetic testing are being collected and merged to produce new insights into diseases and treatments. The promise (and stakes) these technologies represent cannot be overstated. And when it comes to impact on human life, there will always be many challenges and even more questions.

How Does Precision Medicine Work?

The best way to characterize precision medicine is seeing it in action. A patient, “Anna”, receives a CT scan that shows a tumor. Here is the high-level sequence of events that follow:

  • Anna’s doctor performs a biopsy to remove cells from the tumor and sends the sample to a biotech lab.
  • The lab identifies the tumor’s DNA (genetic material) to report back.
  • The biotech company uploads this information into an AI analytical program, together with Anna’s age, sex, and other relevant information (e.g. environmental factors, specific exposures, etc.)
  • The AI analytics engine sorts through millions of data sets, finding examples or trends from other patients whose disease DNA, age, sex, and health information are similar to Anna’s.
  • The algorithm parses out specific treatments, most effective for patients like Anna.
  • Anna’s doctor reviews the feedback for actionable insights and develops a treatment plan.
  • The plan forward will be based on the precise molecular makeup of Anna’s tumor, rather than an average adult with her general condition.
  • Her treatment outcome gets uploaded into the database so its accuracy continues to improve.

How Does Precision Medicine Differ from Clinical Trials?

Traditionally, clinical trials serve as the primary medical approach to evaluate treatment effectiveness. But only 3% of cancer patients participate in such trials. Before the advent of big data, medical treatments had to be devised on the basis of the average patient.

Historically, many studies focused only on men, and symptoms (and treatments) considered “average” were not always applicable to women. Similarly, the fact that research subjects were overwhelmingly white meant that results were not necessarily accurate for members of other racial and ethnic groups. In 2015, only 6% of participants in federally-funded clinical trials were Black and Latino, while these groups accounted for 30% of the U.S. population.

Precision medicine looks to break this cycle and center around a very familiar focus in the world of emerging technology: Personalization.

How Can Big Data Result in More Individualized Treatment?

Giant quantities (petabytes) of data from electronic health records is stored in the cloud, and health information platforms analyze that data. When huge streams of data flow through data mining algorithms, they segment into tiny highly precise groups. These groups may be separated according to an almost unlimited number of indexing factors. In addition to age, ethnicity, gender, health condition, they can also be separated by environment, lifestyle, patient genetics, and — most importantly for cancer treatment — genomic tumor testing.

Integrating observational data into the mix allows for more precise coverage of patient variables, needs, and responses. These insights translate into granular, personalized medical treatment. Information regarding consistency of effectiveness for patients resembling the current case can inform doctors more directly. Oncology utilizes these advances in a practical way.

What Other Benefits Could Precision Medicine Offer?

The potential of genomic medicine is vastly undefined, but it can give patients and doctors access far-ranging information. In addition to revealing a person’s likelihood of acquiring a specific disease, it can also predict the impact of a lifestyle change or the patient’s predisposition to a particular treatment.

Furthermore, unique drugs can be tailor-made to target the underlying genetic defect that gave rise to the patient’s tumor. Genentech “now imagines what it calls “cancer vaccines,” tailored not just to broad subtypes of people but to the unique signature of a person’s tumor,” according to the MIT Technology Review.

How Does Medical Big Data Translate into Big Business?

Like many disruptive technologies, precision medicine startups are bypassing traditional institutional structures. Whereas in the past, patients couldn’t just send their own biological material to a testing lab, many of today’s startups offer this exact service. The removal of gatekeepers means individual patients can simply purchase a home kit. With kits such as Helix’s Mayo Clinic GeneGuide patients put a bit of saliva into sample containers. In return, they receive a report on their risks for 15 different health problems, together with the option to purchase a half-hour consultation with a genetic counselor.

Businesses like TestYourCancer.com, by contrast, rely on physicians to collect and submit samples of blood, bone marrow or other tissue. After receiving the sample, the company separates DNA from the biological material and assigns it a genomic profile. Adding further patient information, such as where in the body the problem is located, it is possible to choose specific treatments that are already proven effective in very similar scenarios. San Francisco’s Freenome is running its own clinical studies, to detect changes in gene expression, immune system activity, and proteins associated with cancer.

Meanwhile, established companies that began as consumer genealogy resources are also moving to enter the precision medicine market. In the Spring of 2018, 23andMe signed an agreement with GlaxoSmithKline to collaborate for the next four years on exploring new medicines based on human genetics. Eighty percent of 23andMe’s customer base (5 million and growing) have agreed to participate in research, allowing the discovery of finely targeted treatments.

What are the Patient Challenges?

Although today’s data management capabilities are unprecedented, it is still a challenge to analyze, integrate, store and interpret the masses of genetic data, and then funnel information to clinicians in real time. When consumers receive genetic reports directly, there is always a question around access to professional assistance understanding their options.

Furthermore, patient privacy considerations continue to be top-of-mind. The benefits of precision medication must be available to patients, while still protecting their personal health information. Life insurers and long-term care insurers are in the process of developing rules around when they can set rates based on a client’s genetic test results. Medical information in a person’s employment file, derived from their application for worker’s compensation or paid time off, is generally not considered Protected Health Information, and is not covered by HIPAA rules.

How Widespread is Precision Medicine?

For most people, precision medicine is still an unfamiliar concept. But it’s been around for a few years. A few notable facts:

  • The Obama White House announced its Precision Medicine Initiative four years ago, in 2015.
  • Organizations like Frontline Genomics promote genomic research by connecting industry, researchers, government entities and practitioners.
  • The publicly available Cancer Genome Atlas has molecularly characterized 20,000 primary cancers.
  • The market for gene testing is expected to exceed $7 Billion USD by the end of 2020, according to Global Industry Analysts Inc.

Building from the Essential Codebase

Increasingly, biology and the origins of disease represent a “code,” which can be mastered in the same way as any digital language. “You don’t just read the code of biology, but you can also write, or design, with it, ” according to the blog for venture fund Andreesen Horowitz, which has invested $650 million in biotech in the past three years. Designing with biological code suggests a future in which our relationships with our health care providers–and our own bodies–will be profoundly evolved.

 

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