Putting Genomic Data To Good Use

Assistant Professor Adrian Teo and his colleagues at the National University of Singapore’s Yong Loo Lin School of Medicine believe in the power of localised translation research in support of Singapore’s precision medicine agenda.

In April 2022, nine-month-old Teddi Shaw was diagnosed with a rare and deadly metabolic disorder: metachromatic leukodystrophy, or MLD. Caused by genetic mutations which gradually damage the protective layers of nerve cells in the brain and nervous system, it seemed that like others born with the disease, Teddi would face seizures, the loss of her senses and mobility, as well as a far shorter life than most infants.

However, just ten months later, the UK’s University College London announced that Teddi was disease-free, thanks to a revolutionary gene therapy known as atidarsagene autotemcel. The faulty genes causing Teddi’s MLD had been corrected by inserting functional copies of those genes into Teddi’s stem cells. Her mother declared her a “walking, running chatterbox”, with no signs of disease.

The success of innovative treatments like Teddi’s relies on robust precision medicine programmes like Singapore’s National Precision Medicine Programme (NPM), launched in 2017. Designed to accelerate biomedical research and improve health outcomes, the programme began by generating an extensive genomic database among Singaporeans in the SG10K_Health project.

Now as the NPM programme enters its second phase, researchers like Assistant Professor Adrian Teo, Principal Investigator at A*STAR’s Institute of Molecular and Cell Biology, are working closely with researchers at the National University of Singapore (NUS)’s Yong Loo Lin School of Medicine to charge onward. In support of the NPM, the Yong Loo Lin School of Medicine has launched the NUS Precision Medicine Translational Research Program (TRP), of which Teo and colleagues are a part, to drive precision medicine for the future.

Genomic insights: from bench to clinic

The TRP brings together a multidisciplinary team of experts, including clinician scientists, geneticists, proteomics scientists and bioinformaticians, to facilitate the pipeline required for precision medicine programmes.

In this regard, the TRP has three main objectives. The first is to move from “maps to mechanisms,” or to use human genetics to better understand and treat diseases. The second is to create human models of disease which can shed light on physiological changes related to genetic variants of interest. The third, and perhaps the most ambitious, is to modify the natural history of disease through novel therapies that can help revolutionise medicine.

Teo shares that his work revolves around translating genetic and omics data into clinical applications, identifying and validating novel therapeutic targets to fully realise the NPM’s goals. For example, biomarkers identified by omics approaches could provide a way to predict certain diseases at the population level.

To this end, the TRP is strategically designed around two significant areas: a central multi-omics programme which generates data from various omics techniques and genetic analyses, and a complementary mechanistic design arm which focuses on understanding the molecular mechanisms of various diseases, including genes and proteins differentially expressed in Asian populations.

“Many precision medicine programmes are initially focused on sequencing and omics data collection. At the TRP, we are focused on studying the functions of genes, proteins, and metabolites that are altered by common genetic variants that cause disease in Asians,” Teo shared.

The team’s initial area of focus is diabetes and metabolic diseases. “This is a strong yet niche area at NUS compared to the Singapore NPM programme’s broader aspects,” explained Teo.

Roadblocks to precision medicine

Writing in a perspective article published in Frontiers in Digital Health in 2022, Teo and other collaborators identified several challenges to precision medicine programmes in Singapore and globally. These include ethical issues about trust, personal identification and security of participants’ data, which can impede large-scale data collection. To protect participant identity and privacy, the researchers suggest it is important to provide data de-identification and anonymisation.

However, where participant recruitment is more targeted and linked to clinical care, data anonymisation may not always be feasible. In these cases, artificial intelligence (AI) and machine learning advances may provide a way to ensure patient privacy via data encryption.

Another important factor for programmes like the NPM is continued access to more East Asian data. “For precision medicine to be applied effectively to stratify East Asian populations, large amounts of East Asian genomic and phenome data are still needed to enable a robust dataset,” explained Teo.

Beyond that, the standardisation of laboratory methodologies, validation of biological assays, data analysis approaches and data pipelines also present other pertinent challenges for the long-term success of national precision medicine programmes.

For Teo and the team at the TRP, these challenges can be overcome. According to Teo, it is first important to scale TRP’s data capabilities to make meaningful contributions to match the large national efforts. “We must balance this with the limited capacity to conduct the mechanistic work,” he added.

References:

1 Kong, D., Yu, H., Sim, X., White, K., Tai, E.S., et al. Multidisciplinary Effort to Drive Precision-Medicine for the Future. Front. Digit. Health 4:845405 (2022).