Challenges and Promises of Translational Informatics
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The reduction in the cost of genomic sequencing and the expanded utility of next generation sequencing (NGS) technology for expression, methylation and single cell analysis has led to the widespread adoption of these techniques by translational researchers. With it, has come a new set of challenges from data management to integrating diverse data types to leveraging the molecular and cellular insights these techniques provide into the clinic. Translational informatics, which focuses on the use, analysis and management of molecular and clinical data, has become an ever-important aspect of translational research. In this blog, Fan Fan, Vice President of Product Management at DNAnexus, a company focused on biomedical informatics and data management, discusses trends in translational informatics and how it has begun to revolutionize healthcare.
An Introduction to Translational Informatics
Translational research has been around for a long time. At a high level, translational research takes what we learn from basic research and applies it to problems related to real-world challenges. For healthcare, translational research seeks to bring together domain-specific scientific knowledge in genetics, cell biology, statistics, and other scientific disciplines, with disease information and clinical care in order to yield actionable insights that lead to better patient outcomes, often through the development of new drugs, new therapies or new standards of care. Recently developed genomics techniques, fueled by next-generation sequencing (NGS), and the lower cost of obtaining genomic data with other molecular and cellular level information have revolutionized approaches to translational research and advanced the need for computational analysis.
Translational informatics is essentially the usage of bioinformatics and statistical approaches to explore, synthesize, combine and interpret data that comes from laboratory research, clinical study, electronic health records, imaging data, etc. into useful insights. A common theme we hear from our customers is “we are drowning in data, but starving for insights that will lead to new therapies.” In order to gain knowledge from massive amounts of combined clinical, genomics, proteomics and other -omics data, research teams need a robust informatics strategy to interrogate the data for targets discovery, as well as biomarkers of disease progression and therapy response. We believe the best approach for this is one that leverages data science combined with massive computing power to manage, analyze, and make sense of all aspects of data.
New Applications of Translational Informatics
Genomics and translational informatics has shown its potential to advance drug development and clinical applications. We are seeing a growing number of new therapeutics that have emerged that can target specific mutations in a patient. In the past, many drugs failed clinical trials, due to mixed responses from the patients. Many trials now employ NGS sequencing-based approaches to expand their data coverage, searching for molecular signatures (i.e. biomarkers) that can identify high and low responders, which allows them to improve their clinical trial study design as well as the designs of the drugs they develop. Ultimately this helps companies de-risk drug discovery by more quickly finding a match between promising therapeutics, a patient population, and potential companion diagnostic tests. This is beneficial for both patients and companies, shortening clinical trials and bringing effective drugs to market faster. A great example of this is Regeneron’s collaboration with Geisinger Health System. They examined genetic variants in three genes that are associated with familial hypercholesterolemia (FH) from 50,726 volunteers. They compared the diagnosis rate of using genetic information vs standard clinical criteria, and found that using standard clinical criteria alone would have missed nearly 75% of the cases, indicating potential underdiagnosis and undertreatment in routine care, and demonstrated the value of incorporating sequencing-based genomics approaches.
This also highlights the need for collaboration in research. We are seeing an increase in customer requests for ways to share data, whether it be across hallways or international borders. It is no longer possible to successfully operate in a single location, as many researchers are distributed globally and there is a surge in public-private research programs (for example, UK Biobank). This reflects an awareness within the research community that only through collaboration will the industry overcome significant challenges around drug development and clinical applications.
Using Data Effectively in Translational Research
Translational informatics is, ultimately, a big data problem. The volume of the data is very large due to both the number of samples and the amount of data generated per sample. With the advent of cost-effective sequencing, what in the past might have been just a handful of genomic panels run on a small clinical study has skyrocketed into huge datasets such as the UK Biobank, Genomics England, or in the US the All of Us Program -- all of which will produce whole exome or whole genome data on subject numbers from 100,000 to 1,000,000. This unprecedented scale and volume requires new tools and methodologies. No longer will a laptop suffice for storing and analyzing large amounts of data from recent biomarker, sequencing and genomic studies. Furthermore, the focus on privacy and security means that researchers must be guardians and protectors of the data and requires strict guidelines and processes. At DNAnexus we’ve always taken a proactive approach, providing a comprehensive security and compliance framework, including standards that meet GDPR, FedRAMP Moderate, GxP, and more.
A significant amount of time in the drug discovery journey is spent creating data analysis workflows that employ multiple approaches and pipelines. Researchers need a flexible platform where they can build a collection of best-in-class workflows and deploy their own proprietary and open-source tools, working together to accelerate science.
Leveraging translational informatics is not without its challenges. One of those challenges revolves around data integration. In order to accelerate medical discovery, the integration of genomic and phenotypic data needs to happen on a massive scale. A great example of this is a project we’ve been involved in with Regeneron Pharmaceuticals. They have built one of the world’s largest genetic databases, pairing sequenced exomes with electronic health records (EHRs) of nearly 500,000 people and plan to sequence many, many more. They combine phenotypic information from EHRs with exome data to mine for mutations that increase or reduce the risk of specific diseases. Regeneron engaged with DNAnexus to provide the technology backbone that supports their data analysis effort and enables their research partners to securely share and manage genetic and clinical data and tools at scale. All paving the way for novel scientific insights.
Translational Research and Personalized Medicine
Translational research can help us make medicine more precise and personalized. We are seeing new classes of drugs that are more specifically targeted to the molecular profile of oncology patients’ tumors. Whereas in the past there was a one-size-fits-all approach to prescribing medications, precision medicine is personalizing patient treatments. Some examples of recent new drug approvals are Keytruda -- the first cancer treatment for any solid tumor with a specific genetic marker, Kymriah -- a CAR T-cell therapy approved to treat certain children and young adults with B-cell acute lymphoblastic leukemia, or Onpattro -- the first-of-its kind targeted RNA-based therapy to treat a rare disease. All of these drugs leveraged an understanding of genomics to target specific mutations.
These two areas, translational research and precision medicine, can effectively work in tandem where insights provided by translational research can help to inform the treatment decisions made for individual patients. This is an exciting time to be in the precision medicine field, DNAnexus is committed to innovation in this area and empowering forward-thinking translational researchers as they take aim at improving outcomes for patients.
About the author: Fan Fan, Vice President Product Management, at DNAnexus, works closely with prospects, customers, and the DNAnexus team to drive product vision and strategy. Fan joined DNAnexus from Adobe, where she served as Principal of Product Management responsible for cloud-based document productivity services. Prior to Adobe, she led Product Management at Model N, directly engaging with some of the world's largest