Technology to Enable the Clinical Genomics Revolution Session 100, February 13, 2019 Kate Birch, Data & Technology Program Manager, Melbourne Genomics Health Alliance and CSIRO 1 Conflict of Interest Kate Birch, BSc(Hons) MIS CHIA, has no real or apparent conflicts of interest to report. 2 Agenda • From clinical genetics to clinical genomics • Melbourne Genomics Health Alliance • In clinical practice, is genomics better than standard care? • Technology to enable genomics • Patients views on data sharing 3 Learning Objectives • Recognize the need for whole-of-system change in the implementation of clinical genomics • Demonstrate evidence for the utility and economics of genomics as a front line test • Distinguish the different requirements for the implementation of technology in emerging versus established areas of clinical practice • Contrast the differences in data sharing preferences and concerns between patient populations and well populations 4 From genetics to genomics • Genetics scrutinizes the functioning and composition of the single gene where as • Genomics addresses all genes and their inter relationships in order to identify their combined influence on the growth and development of the organism World Health Organisation 5 Cost of sequencing a human genome 6 Phimister et al., NEJM 366: 757-9, 2012 From genetics to genomics Integration with microbiome, proteomics, metaboloimics... Whole genome Whole exome Large panels Small panels Single gene 7 From genetics to genomics Integration with microbiome, proteomics, metaboloimics... Whole genome Whole exome Large panels Small panels Single gene 8 An analogy….. Shred them. Read each piece and reconstruct the story. Find the typos. 1000 copies of War and Peace = a single genome Do they change the meaning of the sentence? 9 Research Clinical care 10 10 We know what we are doing… it will just happen But it typically takes 17 years!! Half of Evidence Based Practices ever reach widespread clinical use Morris et al 2011 11 Slide by Dr Stephanie Best 12 Only 2% of US genomic data is used for clinical care Erik Jylling - Executive Vice President Health Politics at Danish Regions 12 Challenges to Overcome • Lack of evidence of effectiveness • Lack of funding for clinical testing • Unprepared workforce • Concerns about ethical and legal issues • Inadequate infrastructure • Fragmentation and silos • Lack of agreement about investment and priorities Manolio et al, 2015 13 14 Challenge: Create whole of system change 14 The Australian Health Care System Complex and fragmented Health service funding and responsibilities Australia’s Health 2014, AIHW 15 Slide by Australian Genomics 16 Melbourne Genomics Health Alliance 16 17 The Melbourne Genomic Health Alliance set out to make Victoria a world leader in the translation and use of clinical genomics in healthcare 17 Genomic medicine in healthcare The benefits for patients and the community Faster diagnosis Targeted therapy Better manage Improved prognosis “Precision Medicine” disease risk and preventative care 18 Approach 19 Head-to-head comparison in the ‘real world’ First line test Right patient, right test, right time A test of last resort X 20 21 In clinical practice, is genomics better than usual care? Yes, often. 21 Flagships: evidence to guide the use of genomics 2014-2015 2016-2018 2017-2019 • AML • Complex care • Controlling superbugs • Childhood syndromes • Congenital deafness • Bone marrow failure • Focal epilepsy • Dilated cardiomyopathy • Complex neurological and • Hereditary colorectal • Immunology neurodegenerative diseases cancer • Advanced solid cancers • Genetic kidney disease • Hereditary neuropathy • Advanced lymphoma (non- Hodgkin) • Perinatal autopsy Right way Acceptable Equitable Right patient Right test Right Effective time 22 Evaluation questions Data collection Impact of test on: • Clinical & cost data • Diagnostic yield & clinical management • CRF and chart review • Health outcomes (health system perspective) • Hospital costings • Family outcomes • Medicare & Pharmaceutical Benefit Scheme • Policy and process • Participant measures • Reanalysis of data • Surveys at baseline (pre-result) and post • Data sharing for research result • Additional (secondary) findings • Interviews and focus groups • Singletons vs Trios • Health professionals • Pre-test counselling • Participants • Adoption • Other • How can appropriate clinician decision- • Recorded consultations making be supported • Genetic counsellor notes 23 More diagnoses than usual Childhood Focal Hereditary Syndromes Epilepsy Colorectal Ca. n=80 n=40 n=35 Diagnostic Rate Singleton 58% 13% 14% WES Usual care 14% 0% 14% Stark et al. Perucca et al. 2016 2017 24 Melbourne Genomics study: an Internationally-Recognised Real-World Case-Study “It is a game changer, both in terms of patient outcomes and economic sustainability.” Backgroun • Childhood syndromes patients (101 Take- When Exome Seq was used: d children). Home • Five times more patients were • Royal Children’s Hospital Melbourne. Messages diagnosed. • Patients received both: • The cost per patient was reduced by A. Traditional diagnostic tests. 75%. B. Exome Test. • Four times more patients had improved care Five times the diagnosis at oneNB. No patients quarter required novel expensive treatments. the cost Implication Exomes as a diagnostic test: NumbersExperimental finding Traditional Exome s • Provides spectacular diagnostic Diagnostics power. • Significantly reduces average patient Rate of diagnosis 11% 55% costs. • Improves critical management care. Average cost per $27,040 $6,003 patient Patients with improved 4% 16% 25 care outcomes Most cost effective early in patient care 26 Reproductive outcomes Recurrence risk 50% 25% 25% 25% <1% <1% <1% <1% <1% Diagnosis group N=48 Reproductive PGD PND PND PND PND - - - - intervention Recurrence risk ~10% ~10% No diagnosis group N=32 Reproductive TOP intervention 27 28 Technology to enable clinical genomic 29 Access to Genomic Information Develop and implement a single set of standards, policies and procedures to support a common infrastructure for the management and use of genomic data by stakeholders in Victoria. 30 Ideal end state in an unconstrained environment Policy & Process People Technology 7. Identity & Access Management 1. Standardised policy and processes for data 8. Clinical Tools 9. Diagnostic Tools 10. Patient Tools 5. Staff to management & access Electronic manage the Clinician (data governance) Orders and Analysis Consent data Knowledge Results (Pipeline) Tools Results Clinical Decision Support Curation Tools Tools Education 2. Standardised policy & processes for patient consent 6. Staff to 11. Data Access Tools manage the technology 12. Master 13. Genomic Data Repository 3. Standardised policy and Patient Index processes for test ordering & reporting 14. Data Integration LIMS 4. Change control process EMR Public variant (genomic (clinical data) curation data sequencing data) 31 Traditional Software Selection Configuration Requirements Software Selection andIt should definitely be black. Post- and Analysis Evaluation Purchase Purchase It should definitelyCustomisation be green. Colour is unimportant. Business User System ? Requirements Requirements Requirements Commercial and Organisational Last week it needed toView be to future Contractual Requirements black, but now it absolutelyRequirements Requirements must be green. 32 33 Diagnostic Tools Approach LOVD EOI Cpipe Prototype Future Workshop learnings needs s SeqLine Pilot r Prototype Define Requirements Procure • New software • What we’ve learnt we • Rigorous • Existing software need? • In depth • Open source tool • What we can see we’ll • Hands on • Supported the first need soon? Alliance funded • What we predict we’ll tests need in the future? 34 35 GenoVic is unique, but not new GenoVic 36 A clinical system for genomics Providing end-to-end modular cloud services for multiple laboratories entity & Access Management 9. Diagnostic Tools 10. Patient Tools Analysis Consent (Pipeline) Tools Results Curation Tools Education 11. Data Access Tools 13. Genomic Data Repository 14. Data Integration Investigation LIMS Public variant (genomic curation data 37 sequencing data) Patient’s views on data sharing 38 39 39 40 40 Melbourne41 Genomics Health Alliance | Document Name Here 41 Research by… Principal funder Executive Director Alliance members A/Prof Clara Gaff Penny Gleason Dr Christine Walker Evaluation Team Dr Melissa Martyn Dr Emily Forbes Anaita Kanga- Pariaba Nessie Mupfeki Clinical team Elly Lynch Genetic Counsellors Sophie Beck 42 GenoVic team Clinical consent for data sharing • No choice: ‘Anonymised’ data is shared Those who did not agree were significantly more likely to be parents • Opt in: to share re-identifiable dataconsenting for children p=0.001 98% agree 43 Survey respondents – Administered after genetic counselling, before results received • 87% response rate • 31 days median time from testing consent to survey return – Characteristics • No significant differences to larger cohort 44 Recall is high • Vast majority of patients accurately recall their data sharing decision • Vast majority of patients understand anonymised data may be shared 45 Information about data sharing was satisfactory • Majority received enough information • Most had no remaining concerns about data sharing 46 Genomics Privacy How difficult do you think it would be for Ease of someone to be identified from their identification stored genome sequence? & Level of concern if How concerned would you be if identified