Data sharing to advance gene-targeted therapies in rare diseases.

Recent advancements in gene-targeted therapies have highlighted the critical role data sharing plays in successful translational drug development for people with rare diseases. To scale these efforts, we need to systematize these sharing principles, creating opportunities for more rapid, efficient, and scalable drug discovery/testing including long-term and transparent assessment of clinical safety and efficacy. A number of challenges will need to be addressed, including the logistical difficulties of studying rare diseases affecting individuals who may be scattered across the globe, scientific, technical, regulatory, and ethical complexities of data collection, and harmonization and integration across multiple platforms and contexts. The NCATS/NIH Gene-Targeted Therapies: Early Diagnosis and Equitable Delivery meeting series held during June 2021 included data sharing models that address these issues and framed discussions of areas that require improvement. This article describes these discussions and provides a series of considerations for future data sharing.

Whole-genome sequencing holds the key to the success of gene-targeted therapies.

Rare genetic disorders affect as many as 3%-5% of all babies born. Approximately 10,000 such disorders have been identified or hypothesized to exist. Treatment is supportive except in a limited number of instances where specific therapies exist. Development of new therapies has been hampered by at least two major factors: difficulty in diagnosing diseases early enough to enable treatment before irreversible damage occurs, and the high cost of developing new drugs and getting them approved by regulatory agencies. Whole-genome sequencing (WGS) techniques have become exponentially less expensive and more rapid since the beginning of the human genome project, such that return of clinical data can now be achieved in days rather than years and at a cost that is comparable to other less expansive genetic testing. Thus, it is likely that WGS will ultimately become a mainstream, first-tier NBS technique at least for those disorders without appropriate high-throughput functional tests. However, there are likely to be several steps in the evolution to this end. The clinical implications of these advances are profound but highlight the bottlenecks in drug development that still limit transition to treatments. This article summarizes discussions arising from a recent National Institute of Health conference on nucleic acid therapy, with a focus on the impact of WGS in the identification of diagnosis and treatment of rare genetic disorders.

Scaling genetic resources: New paradigms for diagnosis and treatment of rare genetic disease.

Development of genetic tests for rare genetic diseases has traditionally focused on individual diseases. Similarly, development of new therapies occurred one disease at a time. With >10,000 rare genetic diseases, this approach is not feasible. Diagnosis of genetic disorders has already transcended old paradigms as whole exome and genome sequencing have allowed expedient interrogation of all relevant genes in a single test. The growth of newborn screening has allowed identification of diseases in presymptomatic babies. Similarly, the ability to develop therapies is rapidly expanding due to technologies that leverage platform technology that address multiple diseases. However, movement from the basic science laboratory to clinical trials is still hampered by a regulatory system rooted in traditional trial design, requiring a fresh assessment of safe ways to obtain approval for new drugs. Ultimately, the number of nucleic acid-based therapies will challenge the ability of clinics focused on rare diseases to deliver them safely with appropriate evaluation and long-term follow-up. This manuscript summarizes discussions arising from a recent National Institutes of Health conference on nucleic acid therapy, with a focus on scaling technologies for diagnosis of rare disorders and provision of therapies across the age and disease spectrum.

A data-driven architecture using natural language processing to improve phenotyping efficiency and accelerate genetic diagnoses of rare disorders.

Effective genetic diagnosis requires the correlation of genetic variant data with detailed phenotypic information. However, manual encoding of clinical data into machine-readable forms is laborious and subject to observer bias. Natural language processing (NLP) of electronic health records has great potential to enhance reproducibility at scale but suffers from idiosyncrasies in physician notes and other medical records. We developed methods to optimize NLP outputs for automated diagnosis. We filtered NLP-extracted Human Phenotype Ontology (HPO) terms to more closely resemble manually extracted terms and identified filter parameters across a three-dimensional space for optimal gene prioritization. We then developed a tiered pipeline that reduces manual effort by prioritizing smaller subsets of genes to consider for genetic diagnosis. Our filtering pipeline enabled NLP-based extraction of HPO terms to serve as a sufficient replacement for manual extraction in 92% of prospectively evaluated cases. In 75% of cases, the correct causal gene was ranked higher with our applied filters than without any filters. We describe a framework that can maximize the utility of NLP-based phenotype extraction for gene prioritization and diagnosis. The framework is implemented within a cloud-based modular architecture that can be deployed across health and research institutions.