Stephen Harlin
The Harlin Center for Genomic Medicine, USA
Title: Implementing Genomics in a Precision Medicine Practice
Biography
Biography: Stephen Harlin
Abstract
A tremendous opportunity exists for implementing genomics into clinical practice. However, there is little research on genomics-based practice protocols or clinical decision support systems (CDSS’s) that incorporate genomic information technologies at the point-of-care.
We developed a CDSS whose user interface combines raw DNA sequence variant data and gut microbial biomarkers to enable a high level, holistic interpretation of the holobiome. Our goal was to individualize therapeutic and preventive interventions, based on insights gleaned from two ‘omics’ data sources and advanced laboratory biomarkers for oxidative stress and inflammation.
While several studies have shown that polygenic risk analysis accurately predicts the individual risk of developing a chronic disease, our model attempts to extend risk stratification to identify molecularly defined subsets of individuals. Specifically, our CDSS clusters polymorphisms in multiple genetic loci that contribute to the pathogenesis of many diseases: glucose and lipid dysregulation, endothelial dysfunction, mitochondrial stress, deficiencies in DNA repair capacity, circadian disruption, maladaptive emotion regulation, and a dysbiotic gut microbiome.
We believe that intelligent decision support tools are crucial to the integration of heterogenous inputs from ‘omic’ technologies and evidence-based clinical regimens. Towards that end, we have designed an application that allows for easy, reliable, and rapid assessment of genotype-phenotype relationships. Output is relevant and intelligible to both physicians and patients. In short, our vision of a genomic CDSS fully embraces the concept of the clinical holobiome.
Finally, we present a descriptive case study of a 58-year-old woman with a history of thrice recurrent bladder cancer. The presentation demonstrates how genetic loci, likely representing disease mechanistic pathways, can be paired with microbiota datasets and interpreted in the context of other known risk factors. This comprehensive approach provides new prospects for disease management and prevention.