The Singapore National Precision Medicine Strategy.
Journal
Nature genetics
ISSN: 1546-1718
Titre abrégé: Nat Genet
Pays: United States
ID NLM: 9216904
Informations de publication
Date de publication:
02 2023
02 2023
Historique:
received:
30
03
2022
accepted:
30
11
2022
pubmed:
20
1
2023
medline:
16
2
2023
entrez:
19
1
2023
Statut:
ppublish
Résumé
Precision medicine promises to transform healthcare for groups and individuals through early disease detection, refining diagnoses and tailoring treatments. Analysis of large-scale genomic-phenotypic databases is a critical enabler of precision medicine. Although Asia is home to 60% of the world's population, many Asian ancestries are under-represented in existing databases, leading to missed opportunities for new discoveries, particularly for diseases most relevant for these populations. The Singapore National Precision Medicine initiative is a whole-of-government 10-year initiative aiming to generate precision medicine data of up to one million individuals, integrating genomic, lifestyle, health, social and environmental data. Beyond technologies, routine adoption of precision medicine in clinical practice requires social, ethical, legal and regulatory barriers to be addressed. Identifying driver use cases in which precision medicine results in standardized changes to clinical workflows or improvements in population health, coupled with health economic analysis to demonstrate value-based healthcare, is a vital prerequisite for responsible health system adoption.
Identifiants
pubmed: 36658435
doi: 10.1038/s41588-022-01274-x
pii: 10.1038/s41588-022-01274-x
doi:
Types de publication
Journal Article
Review
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
178-186Investigateurs
Rob M Van Dam
(RM)
Yik Ying Teo
(YY)
Marie Loh
(M)
Paul Eillot
(P)
Eng Sing Lee
(ES)
Joanne Ngeow
(J)
Elio Riboli
(E)
Rinkoo Dalan
(R)
Irfahan Kassam
(I)
Lakshmi Narayanan Lakshmanan
(LN)
Tock Han Lim
(TH)
Hong Kiat Ng
(HK)
Theresia Mina
(T)
Darwin Tay
(D)
Charumathi Sabanayagam
(C)
Yih Chung Tham
(YC)
Tyler Rim
(T)
Tin Aung
(T)
Miao Ling Chee
(ML)
Hengtong Li
(H)
Miao Li Chee
(ML)
Khung Keong Yeo
(KK)
Stuart Alexander Cook
(SA)
Chee Jian Pua
(CJ)
Chengxi Yang
(C)
Yap Seng Chong
(YS)
Johan Gunnar Eriksson
(JG)
Kok Hian Tan
(KH)
Fabian Yap
(F)
Chia Wei Lim
(CW)
Pi Kuang Tsai
(PK)
Wen Jie Chew
(WJ)
Wey Ching Sim
(WC)
Li-Xian Grace Toh
(LG)
Clarabelle Bitong Lin
(CB)
Yee Yen Sia
(YY)
Tat Hung Koh
(TH)
Wee Yang Meah
(WY)
Joanna Hui Juan Tan
(JHJ)
Justin Jeyakani
(J)
Jack Ow
(J)
Shimin Ang
(S)
Ashar J Malik
(AJ)
Dimitar Kenanov
(D)
Informations de copyright
© 2023. Springer Nature America, Inc.
Références
Shapiro, M. D., Tavori, H. & Fazio, S. PCSK9: from basic science discoveries to clinical trials. Circ. Res. 122, 1420–1438 (2018).
doi: 10.1161/CIRCRESAHA.118.311227
pubmed: 29748367
pmcid: 5976255
Sahin, U., Karikó, K. & Türeci, Ö. mRNA-based therapeutics—developing a new class of drugs. Nat. Rev. Drug Discov. 13, 759–780 (2014).
doi: 10.1038/nrd4278
pubmed: 25233993
Global Spending on Health: a World in Transition (WHO, 2019).
Global Health Estimates 2019: Life Expectancy, 2000–2019 (WHO, 2020).
Schroeder, S. A. Shattuck Lecture. We can do better—improving the health of the American people. N. Engl. J. Med. 357, 1221–1228 (2007).
doi: 10.1056/NEJMsa073350
pubmed: 17881753
Roden, D. M. et al. Pharmacogenomics. Lancet 394, 521–532 (2019).
doi: 10.1016/S0140-6736(19)31276-0
pubmed: 31395440
pmcid: 6707519
Le, D. T. et al. PD-1 blockade in tumors with mismatch-repair deficiency. N. Engl. J. Med. 372, 2509–2520 (2015).
doi: 10.1056/NEJMoa1500596
pubmed: 26028255
pmcid: 4481136
Olstad, D. L. & McIntyre, L. Reconceptualising precision public health. BMJ Open 9, e030279 (2019).
doi: 10.1136/bmjopen-2019-030279
pubmed: 31519678
pmcid: 6747655
Middleton, P. G. et al. Elexacaftor–tezacaftor–ivacaftor for cystic fibrosis with a single Phe508del allele. N. Engl. J. Med. 381, 1809–1819 (2019).
doi: 10.1056/NEJMoa1908639
pubmed: 31697873
pmcid: 7282384
Cully, M. Target validation: genetic information adds supporting weight. Nat. Rev. Drug Discov. 14, 525 (2015).
doi: 10.1038/nrd4692
pubmed: 26228753
King, E. A., Davis, J. W. & Degner, J. F. Are drug targets with genetic support twice as likely to be approved? Revised estimates of the impact of genetic support for drug mechanisms on the probability of drug approval. PLoS Genet. 15, e1008489 (2019).
doi: 10.1371/journal.pgen.1008489
pubmed: 31830040
pmcid: 6907751
Ochoa D. et al. Human genetics evidence supports two-thirds of the 2021 FDA-approved drugs. Nat. Rev. Drug Discov. 21, 551 (2022).
Stark, Z. et al. Integrating genomics into healthcare: a global responsibility. Am. J. Hum. Genet. 104, 13–20 (2019).
doi: 10.1016/j.ajhg.2018.11.014
pubmed: 30609404
pmcid: 6323624
The All of Us Research Program Investigators et al. The ‘All of Us’ Research Program. N. Engl. J. Med. 381, 668–676 (2019).
Turnbull, C. et al. The 100 000 Genomes Project: bringing whole genome sequencing to the NHS. BMJ 361, k1687 (2018).
doi: 10.1136/bmj.k1687
pubmed: 29691228
Stark, Z. et al. Australian genomics: a federated model for integrating genomics into healthcare. Am. J. Hum. Genet. 105, 7–14 (2019).
doi: 10.1016/j.ajhg.2019.06.003
pubmed: 31271757
pmcid: 6612707
Cohn, E. G., Henderson, G. E. & Appelbaum, P. S. Distributive justice, diversity, and inclusion in precision medicine: what will success look like? Genet. Med. 19, 157–159 (2017).
Caffrey, M. Disparities in cancer care: has precision medicine widened the gap? AJMC (22 April 2021).
Sirugo, G., Williams, S. M. & Tishkoff, S. A. The missing diversity in human genetic studies. Cell 177, 26–31 (2019).
doi: 10.1016/j.cell.2019.02.048
pubmed: 30901543
pmcid: 7380073
Taliun, D. et al. Sequencing of 53,831 diverse genomes from the NHLBI TOPMed Program. Nature 590, 290–299 (2021).
doi: 10.1038/s41586-021-03205-y
pubmed: 33568819
pmcid: 7875770
Karczewski, K. J. et al. The mutational constraint spectrum quantified from variation in 141,456 humans. Nature 581, 434–443 (2020).
doi: 10.1038/s41586-020-2308-7
pubmed: 32461654
pmcid: 7334197
Manrai, A. K. et al. Genetic misdiagnoses and the potential for health disparities. N. Engl. J. Med. 375, 655–665 (2016).
doi: 10.1056/NEJMsa1507092
pubmed: 27532831
pmcid: 5292722
Martin, A. R. et al. Clinical use of current polygenic risk scores may exacerbate health disparities. Nat. Genet. 51, 584–591 (2019).
doi: 10.1038/s41588-019-0379-x
pubmed: 30926966
pmcid: 6563838
Martin, A. R. et al. Human demographic history impacts genetic risk prediction across diverse populations. Am. J. Hum. Genet. 100, 635–649 (2017).
doi: 10.1016/j.ajhg.2017.03.004
pubmed: 28366442
pmcid: 5384097
Privé, F. et al. Portability of 245 polygenic scores when derived from the UK Biobank and applied to 9 ancestry groups from the same cohort. Am. J. Hum. Genet. 109, 12–23 (2022).
doi: 10.1016/j.ajhg.2021.11.008
pubmed: 34995502
pmcid: 8764121
Graham, S. E. et al. The power of genetic diversity in genome-wide association studies of lipids. Nature 600, 675–679 (2021).
doi: 10.1038/s41586-021-04064-3
pubmed: 34887591
pmcid: 8730582
Atkinson, E. G. et al. Tractor uses local ancestry to enable the inclusion of admixed individuals in GWAS and to boost power. Nat. Genet. 53, 195–204 (2021).
doi: 10.1038/s41588-020-00766-y
pubmed: 33462486
pmcid: 7867648
Mahajan, A. et al. Trans-ancestry genetic study of type 2 diabetes highlights the power of diverse populations for discovery and translation. Nat. Genet. 54, 560–572 (2022).
Chambers, J. C. et al. Epigenome-wide association of DNA methylation markers in peripheral blood from Indian Asians and Europeans with incident type 2 diabetes: a nested case–control study. Lancet Diabetes Endocrinol. 3, 526–534 (2015).
Chandalia, M. et al. Insulin resistance and body fat distribution in South Asian men compared to Caucasian men. PLoS ONE 2, e812 (2007).
doi: 10.1371/journal.pone.0000812
pubmed: 17726542
pmcid: 1950568
Wu, D. et al. Genetic admixture in the culturally unique Peranakan Chinese population in Southeast Asia. Mol. Biol. Evol. 38, 4463–4474 (2021).
doi: 10.1093/molbev/msab187
pubmed: 34152401
pmcid: 8476152
Population Trends, 2021 (Singapore Department of Statistics, 2021).
Government Health Expenditure and Healthcare Financing (Singapore Ministry of Health, 2018).
GBD 2017 Disease and Injury Incidence and Prevalence Collaborators Global, regional, and national incidence, prevalence, and years lived with disability for 354 diseases and injuries for 195 countries and territories, 1990–2017: a systematic analysis for the Global Burden of Disease Study 2017. Lancet 392, 1789–1858 (2018).
doi: 10.1016/S0140-6736(18)32279-7
Huang, K. K. et al. Genomic and epigenomic profiling of high-risk intestinal metaplasia reveals molecular determinants of progression to gastric cancer. Cancer Cell 33, 137–150 (2018).
doi: 10.1016/j.ccell.2017.11.018
pubmed: 29290541
Metspalu, A., Köhler, F., Laschinski, G., Ganten, D. & Roots, I. The Estonian Genome Project in the context of European genome research. Dtsch. Med. Wochenschr. 129, S25–S28 (2004).
pubmed: 15133739
Chen, J. et al. Genomic landscape of lung adenocarcinoma in East Asians. Nat. Genet. 52, 177–186 (2020).
doi: 10.1038/s41588-019-0569-6
pubmed: 32015526
Bylstra, Y. et al. Implementation of genomics in medical practice to deliver precision medicine for an Asian population. NPJ Genom. Med. 4, 12 (2019).
doi: 10.1038/s41525-019-0085-8
pubmed: 31231544
pmcid: 6555782
McGuire, A. L. et al. The road ahead in genetics and genomics. Nat. Rev. Genet. 21, 581–596 (2020).
doi: 10.1038/s41576-020-0272-6
pubmed: 32839576
pmcid: 7444682
Life Insurance Association (LIA) Moratorium on Genetic Testing and Insurance (Singapore Ministry of Health, 2021).
Lysaght, T. et al. Trust and trade-offs in sharing data for precision medicine: a national survey of Singapore. J. Pers. Med. 11, 921 (2021).
Lysaght, T. et al. ‘Who is watching the watchdog?’: ethical perspectives of sharing health-related data for precision medicine in Singapore. BMC Med. Ethics 21, 118 (2020).
doi: 10.1186/s12910-020-00561-8
pubmed: 33213433
pmcid: 7678103
Ong, S., Ling, J., Ballantyne, A., Lysaght, T. & Xafis, V. Perceptions of ‘precision’ and ‘personalised’ medicine in Singapore and associated ethical issues. Asian Bioeth. Rev. 13, 179–194 (2021).
doi: 10.1007/s41649-021-00165-3
pubmed: 33959200
pmcid: 8079483
Majithia, S. et al. Cohort profile: the Singapore Epidemiology of Eye Diseases study (SEED). Int. J. Epidemiol. 50, 41–52 (2021).
doi: 10.1093/ije/dyaa238
pubmed: 33393587
Tan, K. et al. Cohort profile: the Singapore Multi-Ethnic Cohort (MEC) study. Int. J. Epidemiol. 47, 699–699j (2018).
doi: 10.1093/ije/dyy014
pubmed: 29452397
Soh, S. et al. Cohort profile: Growing Up in Singapore Towards healthy Outcomes (GUSTO) birth cohort study. Int. J. Epidemiol. 43, 1401–1409 (2014).
doi: 10.1093/ije/dyt125
pubmed: 23912809
Yap, J. et al. Harnessing technology and molecular analysis to understand the development of cardiovascular diseases in Asia: a prospective cohort study (SingHEART). BMC Cardiovasc. Disord. 19, 259 (2019).
doi: 10.1186/s12872-019-1248-3
pubmed: 31752689
pmcid: 6873552
Van der Auwera G. A. & O'Connor, B. D. Genomics in the Cloud: Using Docker, GATK, and WDL in Terra 1st edn (O’Reilly Media, 2020).
Poplin, R. et al. Scaling accurate genetic variant discovery to tens of thousands of samples. Preprint at bioRxiv https://doi.org/10.1101/201178 (2018).
McLaren, W. et al. The Ensembl Variant Effect Predictor. Genome Biol. 17, 122 (2016).
doi: 10.1186/s13059-016-0974-4
pubmed: 27268795
pmcid: 4893825
Landrum, M. J. et al. ClinVar: improving access to variant interpretations and supporting evidence. Nucleic Acids Res. 46, D1062–D1067 (2018).
Code of Practice for Key Office Holders Under the Healthcare Services Act (Singapore Ministry of Health, 2019).
Doyle, D. L. et al. 2013 review and update of the genetic counseling practice based competencies by a task force of the accreditation council for genetic counseling. J. Genet. Couns. 25, 868–879 (2016).
doi: 10.1007/s10897-016-9984-3
pubmed: 27333894
Christenhusz, G. M., Devriendt, K. & Dierickx, K. To tell or not to tell? A systematic review of ethical reflections on incidental findings arising in genetics contexts. Eur. J. Hum. Genet. 21, 248–255 (2013).
doi: 10.1038/ejhg.2012.130
pubmed: 22739341
Green, R. C. et al. ACMG recommendations for reporting of incidental findings in clinical exome and genome sequencing. Genet. Med. 15, 565–574 (2013).
doi: 10.1038/gim.2013.73
pubmed: 23788249
pmcid: 3727274
eMERGE Clinical Annotation Working Group. Frequency of genomic secondary findings among 21,915 eMERGE network participants. Genet. Med. 22, 1470–1477 (2020).
Kuo, C. W. et al. Frequency and spectrum of actionable pathogenic secondary findings in Taiwanese exomes. Mol. Genet. Genomic Med. 8, e1455 (2020).
doi: 10.1002/mgg3.1455
pubmed: 32794656
pmcid: 7549563
Chen, W. et al. Secondary findings in 421 whole exome-sequenced Chinese children. Hum. Genomics 12, 42 (2018).
doi: 10.1186/s40246-018-0174-2
pubmed: 30217213
pmcid: 6137878
Van Hout, C. V. et al. Exome sequencing and characterization of 49,960 individuals in the UK Biobank. Nature 586, 749–756 (2020).
doi: 10.1038/s41586-020-2853-0
pubmed: 33087929
pmcid: 7759458
Cohen, J. T., Goodell, S. & Neumann, P. J. The cost savings and cost-effectiveness of clinical preventive care. In Synthesis Project (Robert Wood Johnson Foundation, 2009).
Hatz, M. H., Schremser, K. & Rogowski, W. H. Is individualized medicine more cost-effective? A systematic review. Pharmacoeconomics 32, 443–455 (2014).
doi: 10.1007/s40273-014-0143-0
pubmed: 24574059
Kasztura, M., Richard, A., Bempong, N. E., Loncar, D. & Flahault, A. Cost-effectiveness of precision medicine: a scoping review. Int. J. Public Health 64, 1261–1271 (2019).
doi: 10.1007/s00038-019-01298-x
pubmed: 31650223