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Data Sharing, Diversity Key to Accelerating Precision Medicine


– Enhanced data sharing and increased diversity will help accelerate precision medicine efforts and establish a more equitable healthcare industry, according to a new commentary published in Cell.

In the commentary, Francis S. Collins, MD, PhD, Director of NIH and Joshua C. Denny, MD, MS, CEO of the All of Us Research Program, noted that the healthcare industry is already beginning to realize the promise of data-driven transformation.

“Researchers are routinely using healthcare data for discovery, identifying genomic underpinnings of cancer and many other common and rare diseases, introducing transformative molecularly targeted therapies, and leveraging massive computational capabilities with new machine learning methods. We are beginning to see the fruits of these efforts,” the authors stated.

“There is perhaps no more poignant example than the response to the COVID-19 pandemic. Genomics and molecular technologies were key in identifying the etiologic agent, developing diagnostics and treatments, and creating vaccine candidates. At the same time, COVID-19 has highlighted the need for precision medicine to move further and faster.” 

In order to accelerate equitable precision medicine efforts, Collins and Denny stated that the industry will need to maximize the potential of big data resources.

An open science approach is emerging, which will allow researchers from around the world to access data from national cohorts like the UK Biobank and the All of Us Research Program. However, healthcare leaders will have to take additional steps to enable widespread data sharing, Collins and Denny said.

“The next step is clear: make it easier for researchers to merge data from multiple cohorts. Currently, this requires painstaking manual phenotype adjudication and building large consortia including experts from each cohort. Fortunately, there are efforts underway to improve this process,” the authors wrote.

“Groups such as the Global Alliance for Genomics and Health are working to develop and to coordinate common data models and file formats to facilitate collaboration and interoperability. In recognition of the need for better collaboration, the International Hundred Thousand Plus Cohort Consortium has brought together more than 100 cohorts in 43 countries comprising more than 50 million participants.”

In addition to enhancing data sharing efforts, improving diversity and inclusion in healthcare research will also speed the development of equitable precision medicine.

The authors noted that less than three percent of the participants in published, genome-wide association studies are of African, Hispanic, or Latin American ancestries, while 86 percent of clinical trial participants are white.

This lack of diversity could exacerbate existing health disparities, as well as prevent researchers from making discoveries that could benefit all patient populations.

“With a growing depth of data, we have an opportunity to replace adjustments for race and ethnicity with more specific measures. In particular, ‘race’ conflates a plethora of social, cultural, political, geographic, and biologic factors together and can perpetuate systemic racism,” said Collins and Denny.

“Routine collection of social determinants of health in both research and clinical care in combination with more precise measures of environmental influences, habits, and genetic ancestry can provide more rational, etiology-based adjustments and yield better risk stratifications and treatments.”

As the industry works to promote diversity and inclusivity in research efforts, Collins and Denny pointed out that healthcare should also aim to increase the diversity of the biomedical research workforce.

“A more diverse workforce—in culture, ancestry, beliefs, scientific backgrounds, and methodological approaches—brings increased understanding, innovation, trust, and cultural sensitivity; is more likely to pursue questions relevant to different audiences; and ultimately delivers better research,” the authors stated.

Collins and Denny also emphasized the role big data and artificial intelligence will likely play in the advancement of precision medicine. The technology has transformed areas ranging from language translation to image interpretation, and holds great potential for speeding the development of personalized therapies.

However, the team pointed out that the use of data analytics and AI in healthcare has been limited by the lack of readily available large, commonly structured datasets.

“Looking forward, biomedical datasets will become increasingly ready for analyses. The growth of clinical data (including image, narrative, and real-time monitoring data), molecular technologies (genomics principal among them), and the availability of devices and wearables to provide high-resolution data streams will dramatically expand the availability of detailed phenotype and environmental data not previously available at this scale,” the authors said.

“Applications of machine learning approaches could result in new taxonomies of disease through genomic, phenomic, and environmental predictors.”

The ongoing pandemic has highlighted the need for healthcare research to change in order to serve communities nationwide – especially communities of underserved populations.

“In this time of COVID-19, science has been the answer to an existential medical threat. Yet we are reminded that many of the benefits of medicine’s advancement have not always been available to all. Biomedical approaches, computation algorithms, and the availability of high-resolution data will dramatically increase over the next decade,” Collins and Denny concluded.

“Implementation of a bold plan to collaborate internationally, to engage diverse populations of participants and scientists, to deeply measure our populations, to make clinical and research data broadly available, and to implement this knowledge in clinical practice—in a true learning healthcare system—will allow us to achieve the vision of precision medicine for all populations.”

This content was originally published here.

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