Contribution of genomics and genetics to personalised medicine

In the context of ‘Neurology beyond Big Data’, the overarching theme of the EAN 2023 Congress, Prof. Nicholas Wood (University College London, UK) gave a plenary talk on the direct ways that genomics and genetics can contribute to personalised medicine. He sees a bright future for “omic-delivered” healthcare but big data will have to assist in integrating these masses of data and to turn them to good use to establish individual risk profiles, identify biomarkers, and design new drugs.

Prof. Wood's primary research interest is the genetics of neurological diseases, with an emphasis on Parkinson’s disease (PD) and the ataxias. “We can now get a whole-genome sequencing test for about the price of an MRI scan and extremely fast,” he said, adding that it is currently impossible to interpret this data in a sophisticated way, though that is changing [1]. Prof. Wood shared an example of rapid sequencing-based diagnosis of a rare disease (related to thiamine metabolism) in a paediatric patient, who could be treated and discharged from the hospital fully healthy within 24 hours. “There are about 7,000 rare genetic conditions, and over half of them are neurological. So, all neurologists need to be aware of the progress being made in this area.”

Most diseases are multifactorial and much more complex to treat than the example above. Therefore, polygenic risk scores (PRS) can be determined, which compile genome-wide significant variants and quantify the cumulative effect of genetic risk in a patient. Since the lifetime risk of PD, for example, is low, clinical application on a day-to-day basis is not (yet) feasible, but PRS might be used to identify high-risk patients, which is of value when validating biomarkers or when stratifying people to participate in clinical trials.

Prof. Wood stressed that 1 of the problems with genomic science that needs solving is the use of mainly White European-based data. “This has a clinical and a moral impact. We should sequence the whole genome of different populations all around the world to enrich the data.”

Genetic and genomic insights can also be used to design and drive therapies and biomarkers. Over the last 25 years, genetics has provided a list of previously unknown molecules to target ; for example, tens of molecules robustly associated with PD. As a result, the design of drugs has also improved, using different processes as targets, like protein misfolding/homeostasis, inflammation, synaptic loss, or mitochondrial dysfunction. “With all the limitations of animal models, we need to start using the human as the model of the disease,” Prof. Wood argued.

He took PD as an example to explain in more detail how therapies may be developed both for rare, single-gene hereditary forms of the disease and for much more common, polygenic low-risk variants. Alpha-synuclein (SNCA) is a pathogenic hallmark of all synucleinopathies, including PD. Specific mutations in the SNCA gene or the leucine-rich repeat kinase 2 (LRRK2) gene, amongst others, cause rare monogenetic forms of PD, but these 2 genes are also common hits in genome-wide association studies. You can isolate cells of patients carrying these mutations to better understand the pathway of SNCA-induced toxicity (formation of oligomeric aggregates, calcium dysregulation, mitochondrial dysfunction, lysosomal dysfunction, upregulated autophagy, and cell death). “Once you have found the 'route in' to what is going wrong, you can try and fix it. If you find something that influences SNCA or LRRK2 biology or biochemistry, there is a better than 50% chance that it plays a role in rare familial PD and sporadic forms of the disease.” Clinical trials of monoclonal antibodies against SNCA have not yet been successful in PD, but antisense oligonucleotides have been shown to abolish SNCA-induced toxicity in midbrain dopaminergic neurons of animal models [2–4].

So, what is next for “omic-delivered” healthcare: genomics, transcriptomics, proteomics, metabolomics, etc.? Prof. Wood sees a very complex task ahead, namely the integration of all this data. “I don’t think human minds can handle this,” he concluded. “It is going to be algorithm-based, and I think we will see a lot of activity from AI and machine learning.”

1. Wood NW. What is the difference between personalized medicine and precision medicine? EAN 2023 Annual Meeting, 1–4 July, Budapest, Hungary.
2. Pagano G, et al. N Engl J Med. 2022 Aug 4;387(5):421-432.
3. Lang AE, et al. N Engl J Med. 2022 Aug 4;387(5):408-420.
4. Sucunza D, et al. Int J Mol Sci. 2021 May 1;22(9):4825.

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Posted on 7 September, 20237 September, 2023