1. Peter Johnson On behalf of the CR UK Stratified Medicine Programme Molecular diagnosis of cancer: Making it a reality

2. The march of technology… Single genes Gene panels Whole exomes Whole genomes Plus expression profiling, copy number variation, epigenetics, miRNA profiles…

3. EGFR experience shows gradual test adoption even when funding is agreed Source: 12 months experience of EGFR testing in the UK, R. Butler, AstraZeneca et al, 2010

4. Background to the Programme SOMATIC MUTATION TESTING FOR PREDICTION OF TREATMENT RESPONSE IN PATIENTS WITH SOLID TUMOURS: – Already happening and demand predicted to increase – Funding not well established and access variable across the UK – Published data from quality assurance schemes suggest issues with the reproducibility and accuracy – Needs to work in formalin-fixed, paraffin embedded tissue – Needs to take account of multiple potential targets in each tumour – Little consensus on who to test, how to test, what to test or how to interpret and report the results

5. Service delivery component Research infrastructure The Stratified Medicine Programme pilot study combines service delivery and research components Central data repository (ECRiC)

6. Current gene list and technology

7. Stratified Medicine Programme Patient Dataset Derived from the core NHS Cancer Outcomes and Services Dataset (COSD): – New NHS information standard; mandation and stepwise implementation from January 2013 – Linked to modernisation of the cancer registries with creation of a unified National Cancer Registration Service in England – Sections for demographics, diagnosis, imaging, pathology, treatment and outcomes data – Aim for automated data collection and extraction as far as possible

8. 8 Oct-12Nov-12Dec-12 Monthly recruitment 673708455 Monthly samples sent 384400635* Monthly results returned 518340451 Paired samples 63 (1.5%) 68 (1.5%) 69 (1.3%)

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11. Results from the first 1000 cases Proportion of tumours with sequence variation by type

12. Data analysis: Breast cancer 260 sequence abnormalities detected in samples from 246 patients

13. Data analysis: Colorectal cancer

14. Data analysis: Lung cancer Pennell N A JOP 2012;8:34s-37s NOS = not otherwise specified

15. Findings from the Stratified Cancer Medicine programme General acceptability to patients: over 98% consent Key role of clinical teams for each tumour type Critical role of pathology department in managing tissue samples Impact of mass testing in technology hubs Value of QA system Highly variable sample quality, with impact on test failure rates Complexities of NHS IT. XML messaging protocols Automated data extraction not yet reliable 15

16. Challenges of obtaining the SM Programme patient dataset from routine clinical data Data reflects the patient pathway with a multitude of one-to- many relationships: repeating identifiers required in submitted data to ensure linkage in database Difficulty in sourcing some data items e.g. histopathology data, co-morbidity scoring, performance status Lack of inter-operability between information technology systems used in histopathology and other parts of the electronic patient record Use of Read codes and different versions of SNOMED Cross-border data issues: problems sourcing some demographic details

17. Future Priorities Improve completeness, linkage and standardization of submitted data Defining minimum sample requirements Optimising test turnaround times Moving towards routine commissioning of tests of proven utility Development of multiplex technology for parallel analysis of many genes Expansion of early phase therapy portfolio to exploit the molecular findings

18. Acknowledgements The patients who have consented to take part in the programme Lead investigators and teams at the clinical and technology hubs Stratified Medicine Programme team at CR-UK HQ Dr Jem Rashbass and team, National Cancer Registration Service Funding partners AstraZeneca and Pfizer