Transcript Document

Molecular diagnosis of cancer:
Making it a reality
Peter Johnson
On behalf of the CR UK
Stratified Medicine Programme
The march of technology…
Single genes
Gene panels
Whole exomes
Whole genomes
Plus expression profiling, copy number variation, epigenetics,
miRNA profiles…
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
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
The Stratified Medicine Programme pilot study combines
service delivery and research components
Central data
repository (ECRiC)
Research
infrastructure
Service
delivery
component
Current gene list and technology
Tumour
Colorectal carcinoma
Genes of interest
KRAS
BRAF
NRAS
PIK3CA
TP53 mutation
Breast carcinoma
PIK3CA
TP53
BRAF
PTEN mutation
+ PTEN LOH by microsatellite analysis
Prostate carcinoma
PTEN
BRAF mutation
TMPRSS2-ERG fusion by FISH (moving to rt-PCR)
+ PTEN LOH by microsatellite analysis
Lung carcinoma
EGFR
KRAS
BRAF mutation
ALK rearrangement by FISH
Ovarian carcinoma
Malignant
melanoma
TP53
PTEN
PIK3CA
BRAF mutation
+ PTEN LOH by microsatellite analysis
BRAF
KIT
NRAS
PIK3CA
mutation
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
7229 patients consented and 5237 samples sent for testing
Target for samples sent
Patients consented
Consented minus patient drop out
Samples sent for testing
Results returned
Datasets sent to ECRIC
10000
Oct-12 Nov-12 Dec-12
9000
Monthly
recruitment
673
708
455
Monthly
samples
sent
384
400
635*
Monthly
results
returned
518
340
451
Paired
samples
63
(1.5%)
8000
7000
6000
5000
68
69
(1.5%) (1.3%)
4000
3000
2000
1000
0
8
Analysis by tumour type
2000
1800
1600
1400
1200
Target for samples sent
Consented
1000
Sent for testing
Tested
800
600
400
200
0
Breast
Colorectal
Lung
Melanoma
Ovary
Prostate
9
Programme whole gene test failure rates - Year 2
% Whole gene test
70%
60%
50%
40%
30%
20%
10%
0%
17%
4%
7%
6%
9%
6%
8%
4%
4%
3%
2%
10
6%
6%
Results from the first 1000 cases
Proportion of tumours with sequence variation by type
Data analysis: Breast cancer
260 sequence abnormalities
detected in samples from 246
patients
Data analysis: Colorectal cancer
Data analysis: Lung cancer
NOS = not otherwise specified
Pennell N A JOP 2012;8:34s-37s
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
Challenges of obtaining the SM Programme
patient dataset from routine clinical data
• Data reflects the patient pathway with a multitude of one-tomany 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
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
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