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Accelerating the benefits of genomics worldwide

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Grand Challenges in Genomics
A Joint NHGRI and Wellcome Trust Strategic Meeting
25 and 26 February 2019
https://www.wellcomeevents.org/WELLCOME/media/uploaded/EVWELLCOME/event_661/Draft_agenda_for_WT_December_2018.pdf
Join lecture: Nicky Mulder, Han Brunner and Joaquin Dopazo

Published in: Health & Medicine
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Accelerating the benefits of genomics worldwide

  1. 1. Accelerating the Benefits of Genomics Worldwide Nicky Mulder Han Brunner Joaquin Dopazo
  2. 2. Areas of health ecosystem, role of genomics Treatment Screening /surveillance Diagnostics, risk assessment Genomics/omics Microbiome Environmental exposuresmHealth Biorepositories DatabasesHealth systems Data Infrastructure Tools Genetic tests Omics facilities Analysis tools Interpretation tools Clinical facilities Pathogen outbreak & response Risk stratification Drug discovery
  3. 3. Other applications of genomics • Agriculture (>1.2 million species of plants & animals) • Animal and plant health • Food security • Aquaculture • Biodiversity (Earth biogenome project) • Bio-products • …..
  4. 4. General requirements for exploiting genomics data (for health) • Consent from participants • Research perspective • Reference datasets -need background information on the healthy/normal state • Access to enough samples • Access to other data • Good phenotyping • Data management and analysis tools • Clinical perspective • Adequate screening and diagnostic tools (array versus WGS) • Access to latest research in the field • Evidence for genotype-phenotype link • Evidence of clinical actionability • Resources and skills for data analysis: training in genomics, genomic medicine, clinical data interpretation, data governance and ethics, genetic counselling
  5. 5. Technical requirements for genomics data • Reference datasets and (meta)databases • Mobile device data collection and integration • Integration of genomic data with EHRs • Clinical decision support tools • Data must be well curated, including provenance • Data must be harmonized or standardized • Data storage facilities • Data transfer facilities • Data submission (& utilization) facilities • Authentication and authorization • Training in all data related skills
  6. 6. Limits to sharing and reuse of genomics data • Clinical data: • Privacy • Fear of it getting into hands of the wrong people (medical insurance) • Human genetic data: • Anonymized, but risk of identification • History of vulnerable populations and exploitation • Pathogen data • Potential for discovery or commercialization? • Research perspective for data reuse: • Recognizing the contribution of researchers who generated the data • Maximizing the timely availability of research data • Ensuring responsible secondary use of data • Robust data sharing model with implementation strategy for data access and transfer, data access agreements and MoUs
  7. 7. Barriers to accelerating benefits of genomics and data sharing in Africa (LMICS) • Data infrastructure challenges: • Data transfer and storage • Data processing, analysis and interpretation • Data curation and submission, skills for access • Previous exploitation –fear of loss of scientific discovery • Conservative ethics review system that considers specific consent to be sacred • Resource limitations (tools and skills) for analysing and exploiting data • Sample sizes (budget) • Researchers & clinicians usually don’t budget for data • Meta data is not well curated, data quality and accuracy is not a high priority for clinicians and some researchers • Insufficient training in data management and analysis • Intellectual property rights management • Capacity for innovation and translation (practically)
  8. 8. Technical solutions for global use of genomics data • Improved resources for safe, responsible sharing of data (is local repository recognised?) • Data compression formats • Tiered access to data: • Complete data files available under controlled access (e.g. EGA, dbGAP) • Pooled summary data available under restricted access -EGA or locally managed website for registered users? • Minimum data available with no restrictions, e.g. Beacons –need extensions • Is it findable? Metadata available for searching in catalogues • Authentication and authorization • Federated data analysis tools (though still some limitations if can’t move raw data) • New tools • New genotyping arrays –screening versus discovery • Reference graphs • Variant calling • Meta analysis
  9. 9. Future perspective • Embrace the 4th Industrial Revolution: fusing physical, digital and biological • Big data available, use AI to turn it into information then knowledge (clinical decision support, biomarker discovery, drug repositioning) • Is priority data generation or harmonizing and using what we have already so that new data can be more rapidly interpreted? • Move to functional/validation studies • Look at the bigger picture (genotype + epigenetics + phenotype + environment + microbiome + other omics) • Improve the interface between the data generator, data analyst and end user (e.g. clinician) ->interpreters and interpretation tools • Go global • population (& pathogen) migration and admixture • Population comparisons (common versus novel but relevant) • Lift as you rise!
  10. 10. The future of Genomics in the clinical space Han G. Brunner Grand Challenges London February 2019
  11. 11. Let’s prove that Genomics diagnostics pays for itself Now is the time to study the environment Saturation Genome Editing to understand the coding genome Towards Universal Genomic NIPT Preconception Carrier screening for consanguinous couples How much Genomic quality control of IVF embryos? From trait to state?
  12. 12. Genomics diagnostics pays for itself
  13. 13. Genomics diagnostics pays for itself
  14. 14. Genomics diagnostics pays for itself But it needs more proof from Health Technology Assessment
  15. 15. Now is the time to study the environment It often takes more than just bad habits to have a disease Alpha 1 antitrypsin deficiency + smoking = emphysema PGD deficiency + fava beans = hemolysis Pharmacogenetics DYPD deficiency + 5-FU = liver failure
  16. 16. Now is the time to study the environment Job Verdonschot unpublished
  17. 17. Now is the time to study the environment It may take more than just bad habits to have a disease Alpha 1 antitrypsin eficiency + smoking = emphysema PGD deficiency + fava beans = hemolysis Pharmacogenetics DYPD deficiency + 5-FU = liver failure Titin mutation + Environment = Cardiomyopathy
  18. 18. We need Saturation Genome Editing to understand the human coding genome There is not enough observational evidence to guide diagnostics
  19. 19. We need Saturation Genome Editing to understand the human coding genome There is not enough observational evidence to guide diagnostics Every clinical exome generates ~25 new UNIQUE variants ~ 25% of missense variants in disease genes are Variants of Unknown Significance
  20. 20. We need Saturation Genome Editing to understand the human coding genome There is not enough observational evidence to guide diagnostics
  21. 21. >60% of SEVERE HANDICAP is by new mutations Gilissen et al. Nature, 2014 No diagnosis De novo SNVs De novo SVsInherited recessive 2% De novo 60%
  22. 22. We cannot prevent de novo mutations An inconvenient truth
  23. 23. So should we offer prenatal testing to everyone? We cannot prevent de novo mutations Non invasive prenatal testing NIPT Why offer this for Down syndrome (risk 1/1000) and not for other forms of ID (risk 5/1000)? Towards universal NIPT by Genome
  24. 24. Preconception Carrier Testing is efficacious for consanguinous couples And the risks are high
  25. 25. Preconception Carrier Testing is efficacious for consanguinous couples And the risks are high
  26. 26. Preconception exome test in consanguinous couples Duo exome sequencing 1.000 genes For severe recessive disease ! ?? ?
  27. 27. Preconception Carrier Testing is efficacious for consanguinous couples And the risks are high
  28. 28. Preconception Carrier Testing is efficacious for consanguinous couples And the risks are high 7/22 couples tested prospectively are at 25% riskCOL7A1 ZMPSTE24
  29. 29. Preimplantation Aneuploidy Screening is often offered commercially to optimize IVF succes
  30. 30. What if PGS included a Polygenic Score for Educational Attainment?
  31. 31. What if PGS included not just aneuploidy screening but also a Polygenic Score for Educational Attainment? Does this constitute an ethical problem?
  32. 32. Genomics is moving from Trait to State
  33. 33. Can Medical Genomics move from Trait to State? Can we think of other things than cancer that we would want to catch early?? Genomics in the clinical space
  34. 34. Let’s prove that Genomics diagnostics pays for itself Now is the time to study the environment Saturation Genome Editing to understand the coding genome Towards Universal Genomic NIPT Preconception Carrier screening for consanguinous couples How much Genomic quality control of IVF embryos? From trait to state?
  35. 35. The future of Genomics in the clinical space Han G. Brunner Grand Challenges London February 2019
  36. 36. Personalized medicine: current scenario Intuitive Based on trial and error Identification of probabilistic patterns Decisions and actions based on knowledge Intuitive Medicine Empirical Medicine Precision Medicine Today Tomorrow Degree of personalization Data generation Knowledge generation Future challenges in the way in which we generate: • Data • Information • Knowledge and the way in which we store and manage them Clinical use of data
  37. 37. Personalized medicine First tier: use of patient genomic data for precision diagnosis (typically RDs) and treatment recommendation (cancer). Extensively implemented in hospitals Requires information on gene to phenotype association Second tier: use clinical data (eHR) along with genomic data for preventive medicine and biomarker discovery. Andalusian Population Health Database, with over 12M people since 2001. Aim: converting the whole Public Health System (SAS) into a huge prospective clinical study (GDPR compliance within SAS)
  38. 38. Transition to models that integrate omic and clinical data … … Genome Clinia Clinical study • Treatment of genomic data for research purposes (GDPR) • Principle of use of minimal personal data • Data pseudoanonimization • Each study requires of a specific genomic and clinical data collection into an external database • Serious security concerns (genomic + clinical data outside the hospital) • Static clinical data (e.g. if a control becomes a case the external DB will not be updated) • Limited genomic data reuse for purposes different from the original study. … … Genome Clinic …. Study1 ….. Studyn Query engine • Clinical data dynamically associated to genomic data • Possibility of many clinical studies by reanalyzing genomic data under diverse perspectives (with no extra investment) • Growing genomic DB with increasing study possibilities • The whole health system becomes a enormous potential prospective clinical study Today’s knowledge generation Possibilities in systems with universal eHR Possibly the largest database ever created with detailed clinical data, storing information on 12.083.681 patients since 2001
  39. 39. Possible future models for large-scale data sharing … Study1 Risk. Data encryption Genomic Clinic … Risk …. Study1 ….. Studyn Federated External repository
  40. 40. Data generation 1$ genome? • DNA sequencing prices will soon be comparable to any other conventional test. • Actually, they are already, if the whole treatment is considered Panel WES WGS Obstacles: Lack of information about most of the findings
  41. 41. Generation of information: rare diseases and cancer About 6000 rare disease over 80% with genetic cause In less than 5 years most of the rare variation will be known Today 1-3 new therapies enter in hospitals every Q In less than 5 years WES and WGS will increase therapeutic options for patients RD & cancer are genetic diseases with strong penetrance
  42. 42. Generation of information: the reality of cancer and complex diseases beyond biomarkers • Conventional single-gene biomarkers have a demonstrated clinical utility. However, their success is purely probabilistic, often modest and frequently lack any mechanistic anchoring to the fundamental cellular processes responsible for the disease or therapeutic response. Modular nature of genetic diseases: Causative genes for the same or phenotypically similar diseases may generally reside in the same biological module. More sophisticated biomarkers (mechanistic models of cell activity) need to be considered. • Complex diseases: complex genetics plus the strong role or the environment: other omics need to be considered (transcriptomics, methylomics, metabolomics, human microbiome…) Mechanistic models will play a major role as dynamic biomarkers. Will predict the effect of interventions. Environment constantly influences cell behaviour causing changes in epigenome, transcriptome, metabolome and microbiome, that must be dynamically interpreted in the context of the genomic profile and related to phenotypes: multi- omic data integration Currently, only one third of the genome can be modelled
  43. 43. Knowledge generation (AI in medicine) Topol, 2019, Nat. Med. Variables Samples Variables Samples Curse of dimensionality Learning biological knowledge from the data is currently quite complex. New methods for feature selection, dimensionality reduction, multi- view learning and network learning need to be developed. Optimal ML scenario
  44. 44. Precision systems medicine Intuitive Based on trial and error Identification of probabilistic patterns Decisions and actions based on knowledge Intuitive Medicine Empirical Medicine Systems Medicine Today Tomorrow Degree of personalization The real disruption will come with the leap from empirical medicine, based on pattern identification to Systems medicine, based on mechanistic biological knowledge. Mechanistic models of cells, organs, etc. will allow even the management of new, yet unseen pathologic scenarios.

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