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Winning the Translational Race:
Making Good Choices in Biomarker Assay
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Science Webinar Series
Winning the Translational Race:
Making Good Choices in Biomarker Assay
Development for the Clinic
9 October 2013
Brought to you by the Science/AAAS Custom Publishing Office
Participating Experts:
Richard Kennedy M.D., Ph.D.
Almac Diagnostics
Craigavon, UK
James A. Timmons, Ph.D.
Loughborough University
Leicestershire, UK
Sponsored by:
Best Practice in Biomarker Development
Richard Kennedy
VP and Diagnostic Lab Director, Almac Diagnostics
McClay Professor of Medical Oncology , Queen’s University Belfast
Consultant Medical Oncologist, Northern Ireland Cancer Centre
Clinical Biomarkers
Risk Assessment
Is patient at risk of disease? BRCA1/2, MSH2 mutation
Disease Markers
Is disease present? PSA, b-HCG
Prognostic
Is treatment needed? Oncotype Dx, Gleason score
Predictive
Which treatment? ER, HER2, KRAS mutation, BCR-ABL
Pharmacogenomic
Is the treatment safe?DPD, UGT, TMPT
Pharmacodynamic
What is the correct dose? Ki67, H2AX, S6K1, CE- MRI
Physiological
Is the patient fit for treatment? FBP, U+E, LFTs
Kennedy, Harkin, Salto-Tellez and Johnston et al, Oxford Textbook of Oncology 2013, In print
Clinical Biomarkers
Risk Assessment
Is patient at risk of disease? BRCA1/2, MSH2 mutation
Disease Markers
Is disease present? PSA, b-HCG
Prognostic
Is treatment needed? Oncotype Dx, Gleason score
Predictive
Which treatment? ER, HER2, KRAS mutation, BCR-ABL
Pharmacogenomic
Is the treatment safe?DPD, UGT, TMPT
Pharmacodynamic
What is the correct dose? Ki67, H2AX, S6K1, CE- MRI
Physiological
Is the patient fit for treatment? FBP, U+E, LFTs
Kennedy, Harkin, Salto-Tellez and Johnston et al, Oxford Textbook of Oncology 2013, In print
Predictive Biomarkers
• Predict benefit from a specific therapy
• Over 15,000 manuscripts reporting predictive
biomarkers in cancer
• Few have made an impact on clinical practice
What to Consider
Discovery and Development
•
•
•
•
•
•
What does it measure? Quantitative/Qualitative
Preclinical/retrospective/prospective discovery
Technology/reagents/lab effects
Centre effects
Population effects
Convoluting factors
Validation
• Analytical validation
• Clinical validation
What to Consider
Discovery and Development
•
•
•
•
•
•
What does it measure? Quantitative/Qualitative
Preclinical/retrospective/prospective discovery
Technology/reagents/lab effects
Centre effects
Population effects
Convoluting factors
Validation
• Analytical validation
• Clinical validation
Biomarker Discovery
Compare
samples to
identify
distinguishing
features
Biomarker Quantitative
Or Qualitative
Material
• Disease biopsy
• Blood/plasma
• Skin/hair bulb
• Mucosa
• Radiological
Technology
• Immunohistochemistry
• Mutation specific or Q-PCR
• DNA-Microarray
• Next generation sequencing
• Mass spectrometry
• MRI/USS/CT scan/PET
Biomarker
• Protein expression/modification
• mRNA expression
• miRNA expression
• DNA mutation/methylation
• Metabolite
• Radiological measurement
Two Major Types of Predictive Biomarker
Qualitative
• Mutation / no mutation (KRAS / BRAF / p53)
• Expression / no expression (c-KIT)
Quantitative
•
•
•
•
Score based
Positive or negative result depends on a score
IHC for estrogen receptor
Q-PCR / DNA microarray multigene signatures (OncotypeDx,
Mammoprint)
What to Consider
Discovery and Development
•
•
•
•
•
•
What does it measure? Quantitative/Qualitative
Preclinical/retrospective/prospective discovery
Technology/reagents/lab effects
Centre effects
Population effects
Convoluting factors
Validation
• Analytical validation
• Clinical validation
Biomarker Discovery Strategies
1. Preclinical Model Systems
2. Retrospective Archived Tissue
3. Prospective Discovery
Biomarker Discovery Strategies
1. Preclinical Model Systems
2. Retrospective Archived Tissue
3. Prospective Discovery
Preclinical Biomarker Discovery
• Human cell lines and animal models
• Advantages:
• Early in drug trial process
• Allows behavior of drug to be modeled in specific
molecular contexts
• Disadvantages:
• Different physiology
• Can be little genetic variation between animals
• No immune system in xenografts
• No tumour stroma in cell culture
• No reliable cut-off for quantitative assays
Biomarker for SRC Inhibitor
Isogenic Cell Line and Xenograft Data
7 gene classifier for SRC activity
Test on independent cell lines
Almac Diagnostics
AACR 2012
Biomarker Discovery Strategies
1. Preclinical Model Systems
2. Retrospective Archived Tissue
3. Prospective Discovery
Retrospective Biomarker Discovery
• Use archived tissue from tumour banks
• Advantages:
• Relevant human material
• Full clinical annotation including outcome is often available
• Large numbers may be available - clustering analysis
• Can set population distribution based cut-off for
quantitative assays
• Disadvantages:
• Unlikely to be possible for novel therapies entering trials
• Tissue may not have been collected appropriately
• Archived tissue can degrade over time
Example: Biomarker for Angiogenic Agents
300 High grade
serous ovarian
samples
Angiogenesis
63 gene microarray assay for non-angiogenesis
Retrospectively validate in ICON7 Bevacizumab
In Ovarian cancer study
Gourley C., Michie C, Keating K, Gavigan A, DeHaroS, Hill L, Harkin DP, Kennedy RD ASCO 2011
Biomarker Discovery Strategies
1. Preclinical Model Systems
2. Retrospective Archived Tissue
3. Prospective Discovery
Predictive Biomarker Discovery
• Analysis of tissue from responding and non-responding
patients on a clinical study
• Advantages:
• Material is relevant to the drug in question
• Disadvantages:
• New drug may be given in combination with other
•
therapies, difficult to develop specific biomarker
Can require large numbers of patients - needs adequate
numbers of responding and non responding patients
Simple Biomarker Discovery Trial Design
Patient Enrolment
Sample Biomarker Studies
New Treatment
Responders
Non-Responders
Biomarker Generation
• If predicted response rate is 10% in unselected population will need 500
•
people to get 50 responder samples!
Adaptive trial designs may help reduce numbers.
What to Consider
Discovery and Development
•
•
•
•
•
•
What does it measure? Quantitative/Qualitative
Preclinical/retrospective/prospective discovery
Technology/reagents/lab effects
Centre effects
Population effects
Convoluting factors
Validation
• Analytical validation
• Clinical validation
Technology
Material Type
• Storage and shipping (fresh/archived)
• Patient safety/comfort (biopsy/resection/blood)
Technology
• Quality (CE marked, GMP)
• Maintenance (calibration and scheduled servicing)
• Practicality (cost, turnaround time, ease of use)
• Longevity (will it be obsolete soon?)
Reagents
• Many laboratory reagents are “research use only” (RUO)
• Can be considerable variation in performance batch to
batch
• Biomarker may become “batch dependent”
• Ideally use GMP reagents, batch tested
• Can consider pooled batches if RUO only available
Batch Effects
Almac Diagnostics 2010
Lab Operator Effects
• Biomarkers discovered by a single lab operator may only
work for that individual
• Modified lab protocols
• Very experienced in a particular assay
• Adhere to strict standard operating procedures
• Randomize samples between several operators during
discovery phase
Lab Operator Effects
Operator 1
Operator 2
Almac Diagnostics 2010
What to Consider
Discovery and Development
•
•
•
•
•
•
What does it measure? Quantitative/Qualitative
Preclinical/retrospective/prospective discovery
Technology/reagents/lab effects
Centre effects
Population effects
Convoluting factors
Validation
• Analytical validation
• Clinical validation
Clinical Centre Effects
• Biomarkers discovered from a single centre may not be
applicable elsewhere
• Specific surgical approaches
• Specific specimen fixation protocols
• Ideally use material and clinical data representing
response/non-response from multiple centres
Centre Effects
Kennedy et al J Clin Oncol. 2011 Dec 10;29(35):4620-6
What to Consider
Discovery and Development
•
•
•
•
•
•
What does it measure? Quantitative/Qualitative
Preclinical/retrospective/prospective discovery
Technology/reagents/lab effects
Centre effects
Population effects
Convoluting factors
Validation
• Analytical validation
• Clinical validation
Population Effects
• Important to ensure that the population used for
biomarker discovery is relevant to the population in
which it will be applied
• E.g.
• Afro Caribbean variations in prostate or breast
cancer biology
• Asian variations in lung cancer biology
What to Consider
Discovery and Development
•
•
•
•
•
•
What does it measure? Quantitative/Qualitative
Preclinical/retrospective/prospective discovery
Technology/reagents/lab effects
Centre effects
Population effects
Convoluting factors
Validation
• Analytical validation
• Clinical validation
Balancing for Convoluting Factors
Positive and negative discovery samples must be balanced for
potential confounding factors such as:
•
•
•
•
•
•
Gender
Age
Ethnicity
Other medication/smoking
Other medical conditions
Known prognostic/predictive factors
• Tumour Stage
• Tumour Grade
• Lymphocyte infiltrate
Failure to do this may result in a biomarker for the wrong thing
What to Consider
Discovery and Development
•
•
•
•
•
•
What does it measure? Quantitative/Qualitative
Preclinical/retrospective/prospective discovery
Technology/reagents/lab effects
Centre effects
Population effects
Convoluting factors
Validation
• Analytical validation
• Clinical validation
Biomarker Validation
1. Regulatory Landscape
2. Analytical Validation
3. Clinical Validation
Regulatory Landscape
• Different levels of regulatory approval exist
• This choice is influenced by the type of biomarker and
intended use and risk to patients
• Companion diagnostics most stringent
• Relevant bodies are
• CLIA (Clinical Laboratory Improvement Amendment act)
• FDA (Food and Drug Administration) PMA route for companion
•
diagnostics
EMA (European Medicines Agency)
• Needs to be considered at the start of research
Biomarker Validation
1. Regulatory Landscape
2. Analytical Validation
3. Clinical Validation
Analytical Validation
• Precision
• Accuracy
Analytical Validation
• Precision
• Accuracy
Precision
• Measure of biomarker repeatability
• Loss of precision can occur due to:
• Inherent variability in technology (IHC for phosphoproteins, plasma protein measurement)
• Variability in reagents, equipment or technique
• Normal/Stromal/malignant cellular content
Effects
Effects of Macrodissection on Precision of a q-PCR-based Biomarker
Full Biopsy Material
Macrodissected Biopsy Material
Almac Diagnostics 2013
Analytical Validation
• Precision
• Accuracy
Accuracy
• A measure of how close the result is to the known truth
• Truth may be a:
• Result from a reference lab
• Gold standard technology
• Can be affected by:
• Site / type of biopsy - tumour heterogeneity
• Incorrect sample fixation or lab technique
• further treatment since diagnostic biopsy taken
Gene Expression Differences
Gene Expression Differences Between Original Diagnostic
Tissue and Recurrent Disease
• Series of ovarian cancers analysed pre-chemotherapy and on recurrence
• 486 genes >2 fold differentially expressed p<0.005
Biomarker Validation
1. Regulatory Landscape
2. Analytical Validation
3. Clinical Validation
Clinical Validation
• Absolute requirement for a companion diagnostic to be used
for drug selection in regular clinical practice
• Needs strategy agreed with regulatory authority prior to
study
• Simple biomarker validation
• Complex biomarker validation
• Must show that the biomarker can adequately stratify
patients (sensitivity, specificity, hazard ratio)
Simple Biomarker Validation Study
Patients Enrol to Study
Biomarker
Predicted Responder
Predicted Non-Responder
Randomise
Not on Trial
Experimental
Drug
Standard
Therapy
Compare outcome
Mandrekar and Sargent J Clin Oncol 2009 27(24):4027-34
Complex Biomarker Validation Study
Patients Enrol to Study
Biomarker
Predicted Responder
Predicted Non-Responder
Randomise
Randomize
Receive
Experimental
Drug
Receive
Standard
Therapy
Receive
Experimental
Drug
Calculate Sensitivity / Specificity etc.
Receive
Standard
Treatment
Conclusions
What to consider for a predictive biomarker to be used in
the clinic:
• Discovery and Development
• Correct discovery dataset
• Correct technology and reagents
• Not convoluted by other known factors
• Validation
• Regulatory requirements depending on use
• Analytical: precision and accuracy
• Clinical: sensitivity, specificity etc
Science Webinar Series
Winning the Translational Race:
Making Good Choices in Biomarker Assay
Development for the Clinic
9 October 2013
Brought to you by the Science/AAAS Custom Publishing Office
Participating Experts:
Richard Kennedy M.D., Ph.D.
Almac Diagnostics
Craigavon, UK
James A. Timmons, Ph.D.
Loughborough University
Leicestershire, UK
Sponsored by:
Winning the Translational Race:
Why so-called “hypothesis free”
technologies may be the best
Professor James A Timmons PhD
Chair in Systems Biology, Loughborough
University, UK
Lets start at the beginning – why do we need biomarkers?
Caleb Parry
(1755-1822)
“It is much more important to know what kind of
patient has a disease, than to know what kind of
disease a patient has”
My background
20 yr of Personalized medicine
• PhD in Physiology and Metabolism, 80 articles and patents
• 8yr pharmaceutical industry experience profiling drug-targets and
developing pharmaco-dynamic markers
• 10 yr genomics and bioinformatics experience focused on
personalized medicine
Main interest: producing tools so that we can move (way) beyond
the current over-reliance on epidemiological correlation and preclinical models
70
50
65
45
60
40
55
35
30
50
25
45
20
40
15
35
10
30
5
0
25
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
Annual R&D Expenditure ($ billion)
NCE's launched per year
The drugs don’t work (or the research model doesn’t)
Drug benefits in humans are not
easily predicted by the mouse
00
Year
Year
The pharmaceutical sector has
spent $400 BILLION in two
decades on a research model
that fails – lets not repeat this
mistake
Graph source: PriceWaterhouseCoopers
Personalized medicine based on
translational research done in
humans aims to solve this
problem
Major life-style changes do not protect your heart when
applied as a 1-size fits all approach
Intervention worked – the key biomarkers
changed e.g. less diabetes, higher fitness
and less fat 10yr later (on average)
https://www.lookaheadtrial.org/public/home.cfm
•
ZERO improvement in heartattack, angina or stroke
(n=5,145)
Did Look Ahead fail because exercise fails in some people or is the
correlation between exercise and cardiovascular health not causal?
FITNESS GAINS
MUSCLE GAINS
INSULIN SENSITIVITY
How much exercise should YOU do to get the
most/any benefit?
Can we predict the outcome of lifestyle interventions in humans?
Case Study 1: aerobic fitness
How do we do molecular translation research directly in humans?
DNA
DNA Sequence variation e.g. GWAS
TRANSCRIPTION
RNA
[RNA] variation
TRANSLATION
PROTEIN/METABOLITE
[P/M] variation
POST-TRANSLATION
Physiology
Timmons et al US09/56057 2009
Clinical variation
Methodology for OMIC diagnostic discovery and validation
DISCOVER
Sample Size
~50-100
Sample Size
~50-100
Pre-intervention [Gene]
vs response
Predictor discovery
Pre-intervention [Gene]
vs response
Predictor Validation
RNA
Predictor
US09/56057 & GB2474618
VALIDATE
VALIDATE x 2+
Sample Size
~500
Candidate
genomics
Targeted
DNA SNP
biomarkers
Affymetrix Gene-Chips to measure clinical sample* [RNA]
*
*
** *
11µm
11µm
Millions of identical
probes / feature
* We can use tiny micro-samples e.g. size of a few grains of rice
Predicting aerobic fitness changes in humans
6-week training period
(100% supervised, 100% compliance)
Single small muscle
biopsy and blood
sample
Baseline determinations of VO2max – test-retest within 5%
24 subjects cycled 4 days/week (45min@ 72% of aerobic capacity)
Genomic, biochemical and physiology submax and max measures
Can we predict the ‘low’ responders from the baseline RNA profile?
Timmons et al 2005 FASEB J and Keller et al 2011 JAP
RNA classifier produced/validated to predict gains in aerobic adaptation
Gene predictor score
(n=24, cc=0.76, p<0.00001)
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
% increase in aerobic capacity
Baseline fitness does not predict improvement (r2=0.04)
We predict aerobic fitness changes from RNA profile (r2=0.76)
Timmons et al US09/56057 2009
Group 2 (young, athletic)
N=17, cc=0.6, p=0.0001
Can we predict the future health?
Case Study 2: healthy ageing
How do we build a diagnostic for ‘successful ageing’ ?
Cross-sectional – discovery
Healthy
Young
(25y)
1992
Human muscle
Classification
Healthy
Old
(65y)
Longitudinal 20y follow-up - application
Classifer
RNA
(70y)
2012
Medical
Status
(~90y)
Human muscle
Key - Physiologically phenotyped
drug-free snap-frozen human clinical
samples – not biobank ‘junk’
Muscle samples on Affymetrix ‘RNA’ Gene-chips
~54,000 transcripts per sample
Q. Is the induction of this
signature beneficial ?
Computational scheme for building prototype
Loop samples in and out of the
1st data-set to avoid over-fitting
Rank RNA predictive
performance during each loop
and take the best performing
probe-sets forward to a final
classifier list
Throw-away your 1st data-set!!
You then need >2 independent
data sets to validate the
classifier performance
Timmons JA and Knudsen S
STEP 1: Build the first accurate healthy muscle ‘ageing’ signature
(n=300 subjects)
%
100
100
96
87
70
60
LA
SS
IF
IE
R
S
K
R
A
U
E
PP
A
O
FF
M
H
TR
A
N
Y
B
ER
D
PB
A
M
TR
A
IN
IN
G
EL
L¥
*
50
C
Kazmi et al 2013 under review
85
80
None are regulated by
exercise or common
disease
Is this program active in
other tissues?
96
90
90
C
We measure 146 RNA
(0.7%) and can muscle
age with 93% accuracy
STEP 2: Demonstrate healthy ‘ageing’ signature works in brain
Fig 2
91
90
84
84
81
80
72
70
60
50
SU
R
PE
IO
F
G
L
EX
G
S
U
T
C
EN
T
R
IN
H
L
A
C
O
T
R
C
R
YR
O
*
AIN
BR
R
A
LE
EN
MU
SC
ST
Can this multi-tissue
molecular program predict
future health?
93
PO
There appears to be a
common age-program
100
Percentage correct classifications
The same 146 RNA can tell
age of human brain up to
91% accuracy
U
MP
A
PO
IP
H
Post-mortem ‘healthy’ human brain (n=200)
Kazmi et al 2013 under review
S
The first valid diagnostic of human ageing
 The new diagnostic is highly
sensitive and specific
 Majority of 146 genes have not
previously linked to ageing
(most molecular ageing data is
mouse, worm & fly)
 Is this new ‘healthy ageing’
diagnostic useful?
Is our ‘healthy ageing signature’ prognostic ?
Longitudinal 20y follow-up - application
Human muscle
Human brain
1990
Classification
Classifer
RNA
(69y)
2011
Medical
Status
(~90y)
Hypothesis: induction of the ‘age
signature’ is protective
Longitudinal study: 108 healthy Swedes - baseline & 20yr follow-up
Muscle tissue sample from men in 1992 (passport age=69y) and
measure only the 146 ‘age-genes’ RNA levels
Subject ranked 1st (low median gene score) - had failed to
activate the ‘healthy age’ program
Subject ranked 108th (high median gene score) - had strongly
activated the ‘healthy age’ program
A 4-fold range in gene-score was seen despite all men being 69y
old (& not related to life-style or fitness)
Kazmi et al 2013 under review
We have produce a robust biomarker of
health tissue age – we don’t know the
underlying biology but we can relate the
biomarker to disease and death
Kazmi et al 2013 under review
0.25
0.20
0.15
0.10
0.05
Probabilityover
of death 20yr (%)
Chance of death
A higher gene-score at 69y meant a lower
risk of death after 20yrs (p=0.049)
0.00
A lower gene-score at 69y = a greater
decline in renal function after 12yrs
(p<0.001)
0.30
Induction of ‘healthy ageing RNA profile’ reduces death
P=0.05
40
60
80
100
ageing score
RNA Gene expression
score
What should the future look like?
Optimising life-style advice to combat disease and ageing
Joe
Fitness response
Drug response
Muscle response
BP response
AGE Profile
Appetite response
Jill
Ian
A single clinical sample & multiple
diagnostics will provide details on how
you respond to treatment – only then
can we offer advice that works for YOU
Conclusions
•
RNA provides better promise than DNA – as variance in
expression integrates genomic, epigenomics and environmental
influences
•
Proteomics is far behind in cost & reproducibility and protein
abundance may never be a good functional surrogate
•
Technical and cost barriers currently prevent wide-spread RNA
diagnostics being used routinely in clinical medicine – but this will
change
•
A good biomarker is characterized by its technical performance –
not by how much we ‘think’ we understand its link to the disease.
Science Webinar Series
Winning the Translational Race:
Making Good Choices in Biomarker Assay
Development for the Clinic
9 October 2013
Brought to you by the Science/AAAS Custom Publishing Office
Participating Experts:
Richard Kennedy M.D., Ph.D.
Almac Diagnostics
Craigavon, UK
To submit your questions,
type them into the text box
and click
.
James A. Timmons, Ph.D.
Loughborough University
Leicestershire, UK
Sponsored by:
Science Webinar Series
Winning the Translational Race:
Making Good Choices in Biomarker Assay
Development for the Clinic
9 October 2013
Brought to you by the Science/AAAS Custom Publishing Office
Look out for more webinars in the series at:
webinar.sciencemag.org
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