Transcript Slide 1

Cancer Clinical Trials Unit Scotland
A NCRI Accredited Cancer Trials Unit
Welcome & Overview
Professor David Cameron
Clinical Cancer Research Champion for Scotland
Edinburgh Cancer Research Centre
Cancer Clinical Trials Unit Scotland
A NCRI Accredited Cancer Trials Unit
Detect Cancer Early Programme
Ms Nicola Barnstaple
Programme Manager, Detect Cancer Early Programme
Scottish Government
Key challenges in Scotland
• Increasing cancer incidence – predicted 35,000
cases per year in 2020
• Ageing population -proportion of over-75s up
25% by 2023
• Impact of health inequality - mortality rates from
cancer in the 10% most deprived areas are
around 1.5 times those in the 10% least deprived
areas
• Survival for some cancer types is lower in
Scotland than in other European countries
Scotland: age-standardised incidence and mortality
rates (EASRs), by SIMD 2009 deprivation quintile
Scotland: age-standardised cancer incidence and mortality rates
(EASRs), by SIMD 2009 deprivation quintile
All cancers
600.0
Breast
160.0
Incidence
Mortality
120.0
400.0
100.0
EASR
EASR
500.0
300.0
Incidence
140.0
Mortality
80.0
60.0
40.0
200.0
20.0
100.0
-
5=Least
deprived
5=Least
deprived
4
3
2
1=Most
deprived
4
Colorectal
Incidence
70.0
Mortality
2
1=Most
deprived
Lung
120.0
Incidence
Mortality
100.0
60.0
80.0
EASR
50.0
EASR
3
40.0
60.0
30.0
40.0
20.0
20.0
10.0
-
5=Least
deprived
4
3
2
1=Most
deprived
5=Least
deprived
4
3
2
1=Most
deprived
Cancer staging by deprivation
2010/2011
(baseline)
Stage 1
Stage 2
Stage 3
Stage 4
Not known
1=most deprived
19.8%
21.4%
19.3%
30.2%
9.3%
2
21.8%
24.1%
18.6%
26.4%
9.2%
3
22.6%
26.1%
18.0%
23.7%
9.6%
4
25.0%
27.5%
16.2%
22.9%
8.3%
5=least deprived
27.2%
28.0%
15.6%
21.1%
8.0%
Stage 1
Stage 2
Stage 3
Stage 4
2011/2012
Not known
1=most deprived
20.3%
22.1%
18.6%
30.9%
8.1%
2
23.0%
23.6%
18.8%
27.0%
7.6%
3
23.5%
25.9%
17.5%
25.6%
7.4%
4
26.9%
26.7%
16.5%
23.1%
6.9%
5=least deprived
27.5%
27.1%
16.4%
21.8%
7.1%
Key challenges
• Increasing cancer incidence – predicted
35,000 cases per year in 2020
• Ageing population -proportion of over-75s
up 25% by 2023
• Impact of health inequality - mortality rates
from cancer in the 10% most deprived
areas are around 1.5 times those in the
10% least deprived areas
• Survival for some cancer types is lower in
Scotland than in other European countries
Five-year age-adjusted relative survival (%) with 95% confidence intervals for
adults diagnosed during 2000-2007, by selected country and cancer site/type
90.0
80.0
70.0
Relative survival (%)
60.0
50.0
40.0
30.0
20.0
10.0
0.0
Skin Melanoma
UK, Scotland
UK,England
Norway
Finland
Denmark
Lung
UK, Scotland
UK,England
Norway
Type of cancer / Country
Finland
Rectum
Denmark
UK, Scotland
UK,England
Norway
Finland
Denmark
Colon
UK, Scotland
UK,England
Norway
Finland
Denmark
UK, Scotland
UK,England
Norway
Finland
Denmark
Stomach
Five-year age-adjusted relative survival (%) with 95% confidence intervals for
adults diagnosed during 2000-2007, by selected country and cancer site/type
100.0
90.0
80.0
Relative survival (%)
70.0
60.0
50.0
40.0
30.0
20.0
10.0
0.0
UK, Scotland
UK,England
Norway
Finland
Denmark
Kidney
UK, Scotland
UK,England
Norway
Type of cancer / Country
Finland
Prostate
Denmark
UK, Scotland
UK,England
Norway
Finland
Denmark
Ovary
UK, Scotland
UK,England
Norway
Finland
Denmark
UK, Scotland
UK,England
Norway
Finland
Denmark
Breast (Woman)
Non-Hodgkin lymphoma
Source: Lancet Oncol 2014; 15(1): 23-34
Colorectal cancer diagnosed 1995-99. Five year relative survival vs survival
conditional on surviving at least one year
80
70
60
% surviving
50
Denmark
England
Scotland
Finland
Norway
Sweden
40
30
20
10
0
5-year Relative survival
Conditional survival
So what is the explanation for this
apparent excess of early mortality?
•Unfavourable stage distribution due to
delays and/or tumour biology?
and/or
•Poor general health/lifestyle factors laid
down over decades?
Source: Lancet Oncol 2014; 15(1): 2-3
Source: BMJ 2010; 341: c5133
Eurocare 5 findings
• Survival from major epithelial cancers seems to be lower
in Scotland (and the UK) compared to all of the Nordic
countries except Denmark
• The excess risk of death seems to occur early on and is
more apparent in oldest age groups
• Some evidence suggests that, on average, UK patients
may be presenting with more advanced disease at
diagnosis
• But we don’t know for sure whether this is due to later
presentation, later referral, delays in diagnosis or
staging, or more aggressive disease
• We know that lifestyle factors can influence survival, but
we don’t really know to what extent, if any, this
contributes to European survival variations
• The reasons for reported survival differences seem most
likely to be multifactorial
Cancer: approx 6% total NHS
spend
Cancer services: estimated activity and costs: Scotland 2007/08
Acute
Services
episodes
Geriatric
long stay
episodes
Out
patient
services
Pharmaceutical GMS
items
visits
dispensed
Activity
188,141
517
144,624
1,153,614
195,363
Cost
£390M
£8.5M
£25M
£46M
£6.3M
Costs
• Projected 65% increase in costs of
treating cancer by 2021
• For colon cancer: treatment in Stage 1
costs £3131 and treatment in stage 4
costs £12519
DCE HEAT Target
• to achieve a 25% increase in the proportion of
breast, colorectal and lung cancers (combined)
diagnosed at stage 1 by December 2015 when
compared to the 2010 and 2011 (combined)
baseline (23% → 29%).
Social Marketing
Primary Care
• Review of Scottish Referral Guidelines for
Suspected Cancer
• New sGMS contract initiative for bowel
screening
• Primary Care education sessions
• Improvements in e-Health, eRAT
• Development of practice profiles for
cancer
Evaluation – key points
•
•
•
•
•
Data on cancer diagnoses not yet available
4.7% increase in cancers diagnosed at Stage I (2012/13 compared to
baseline)
Priming Campaign - just under half (48%) of all respondents feel more
confident about approaching their GP with signs or symptoms which could
possibly be cancer
Breast Campaign – 50% increase in attendances at GP for breast
symptoms
Bowel Campaign- increases in requests for replacement kits and calls to
screening helpline, increase in screening programme participation (56.1%
from 54.9%)
•
•
Lung Campaign - Significant improvement in relation to key campaign
message of importance of getting cough checked
Other measures of success – emergency admissions, ICBP, TCT, other
studies
DCE Next Steps
• Consolidation
• Breast Screening Campaign
• Updated bowel and lung cancer
campaigns
• Interim Evaluation
• Consideration of new tumour groups
Early diagnosis is important
•
•
•
•
relations with patients and families
RCGP/Patient Safety Agency report
best chance for long-term survival.
still well enough to tolerate disease modifying
treatments
• emergency diagnoses don’t do as well
• more time to manage symptoms
• allows more to join clinical trials
Scottish Cancer Taskforce
•
•
•
•
DCE
Treatment capacity
TCAT
QPI
Acknowledgements
EUROCARE-5 slides mostly reproduced from the Lancet
Oncology papers
Data are also now available to download from the EUROCARE
website: http://www.eurocare.it/
Cancer Clinical Trials Unit Scotland
A NCRI Accredited Cancer Trials Unit
Development and Validation of a Radiosensitivity
Signature for Breast Cancer
Dr Felix Feng
Assistant Professor of Radiation Oncology
University of Michigan
A collaboration between the University of
Michigan (Drs. Feng, Speers, and Pierce)
and PFS Genomics
Background
>6000 women with BCS and node-negative disease
EBCTCG, Lancet 2005;366:20872106
Can we identify these patient
populations currently?
Not well from current clinicopathologic data.
Additionally, there are conflicting findings from studies
assessing association between intrinsic molecular
subtypes and radiosensitivity.
There is a clear need to develop and validate molecular
signatures to predict who will benefit from
intensification or omission of radiotherapy.
Hypothesis:
•
Gene expression profiling data from breast
cancer cell lines coupled with intrinsic
radiosensitivity information can be used to
identify a radiosensitivity signature
•
This signature can be used to identify patients in
these two disparate groups and predict likelihood
of recurrence after adjuvant RT treatment in
early stage patients
Human Breast Cancer Cell Lines
Clonogenic survival assay performed on 21
BCC lines to determine intrinsic
radiosensitivity
10 basal, 8 luminal, 3 HER2/neu cell lines
Affymetrix Microarray profiling focused on RT related gene
Spearman’s correlation methodexpression
with RT sensitivity as a continuous variable
147 genes significantly associated with RT sensitivity (80 + correlated, 67 - correlated)
Gene enrichment analysis of positively and negatively
associated radiation resistant genes
Significant gene enrichment for genes involved in cell cycle arrest and DNA damage response
Expression validation (RNA and protein)
Functional validation
Training of Signature in Human Breast Cancer Datasets with Recurrence Data
343 pts with early stage, LN- IDC treated with BCS and RT (no adjuvant systemic
chemo) with LRF survival data-Random Forest Modeling
Clinical Validation in Human Breast Cancer Datasets with Recurrence Data
184 pts with early stage, LN- IDC treated with BCS and RT (no adjuvant
systemic chemo) with LRFS data
Resistant (SF >50% )
Basal - 4
Luminal- 2
HER2 - 1
Moderately Resistant (SF 49-39%
Basal - 3
Luminal -3
HER2 - 0
Sensitive (SF <39%)
Basal - 3
Luminal - 3
HER2 - 2
P-value: NS
• For each gene (~43,000 probe sets) calculate a correlation coefficient between
expression values and SF 2Gy value as a continuous variable
4.0
.80
Surviving Fraction after 2
Gy
RAD51A
P
.70
3.5
• Identify genes that
are positively or
negatively correlated
with clonogenic
survival with a Pvalue <0.05 and a
FDR of < 1%
3.0
.60
2.5
.50
.40
2.0
.30
1.5
1.0
.20
0
R: -0.92
P value:
<0.001
FDR: <0.001
1
2
3
Normalized
Gene
Expression
Expression
• Use unsupervised
hierarchical
clustering to evaluate
the identified gene
4 lists 5
Basal B
Basal A
Luminal
HER2
67 genes
increased in
radioresistant
cell lines
147 Genes
Correlated
with
Radiation
Sensitivity
80 Genes
decreased in
radioresistant
cell lines
-2.0
0
2.0
Training of the Signature in Clinical
Dataset
343 patients treated with BCS who received adjuvant radiation therapy:
Patient characteristics:
• 343 patients with mostly pT1 or pT2 tumors
• All patients managed surgically with BCS
• 215 patients with LN- disease, 128 patients with LN+ disease
• 77% ER + ; 23% ER• Median follow-up was 6.7 years (range, 0.05 to 18.3)
• 25% (119 patients) with locoregional recurrence events
• 141 patients received systemic therapy (110 received
chemotherapy, 8 received hormonal therapy, 23 received both)
Servant, Clin Can Res. 2012;18:17041715.
Training of the Signature in Clinical
Dataset
• Genes identified used to train a Random Forest Model
• Prognostic value of each gene calculated comparing
expression values from recurrent vs. non-recurrent patients
• Performance evaluated on each subset of genes using out
of bag (OOB) error rate
• Best performing gene signature was selected and locked
for cross-validation and external validation
Uni- and Multivariate Analysis in CrossValidation Clinical Dataset- Local
Recurrence
Validation of Signature in Clinical
Dataset
295 patients treated with BCS or mastectomy who received adjuvant
radiation therapy without neo- or adjuvant chemotherapy:
Patient characteristics:
• 295 patients with pT1 or pT2 tumors
• 55% (161 patients) with BCS and 45% (134 patients) with mastectomy, all
with axillary LN dissection
• LN negative (clinically)
• 51% (151 patients) LN-negative; 49% (144 patients) LN-positive
• Age < 53 yo
• 90 patients received chemotherapy, 20 patients received hormonal
therapy; 20 patients with both
• 1 patient treated with combined chemo +hormonal therapy
• 77% (226 patients) ER + ; 23% (69 patients) ER• Median follow-up was 6.7 years (minimum follow-up was 5 years)
van de Vijver, N Engl J Med. 2002 Dec
19;347(25):1999-2009.
Sensitivity for recurrence: 85%
Negative Predictive Value: 97%
Log-rank P-value <0.001
Hazard Ratio: 6.1 (95% CI 4.48-
Uni- and Multivariate Analysis in CrossValidation Clinical Dataset- Local
Recurrence
Rate of distant recurrence as a continuous function of the Recurrence
Score®. The continuous function was generated using a piecewise log hazard ratio
model. The dashed curves indicate the 95% CI and the rug plot (x-axis) shows the
Recurrence Score for individual patients in the study. from Paik et al NEJM 2004
Rate of local-recurrence as a continuous function of the Radiation Signature
Score from the random forest model prediction. The continuous function was
generated using a Cox stepwise logistic regression model. The dashed curves
indicate the 95% CI
Conclusions
• Genes associated with intrinsic radiation resistance or
sensitivity can be identified by combining gene expression
data and clonogenic survival data from human breast cancer
cell lines
• Intrinsic radiation sensitivity is independent of breast cancer
subtype
• Radiation signature development identifies genes with novel
association to radiation resistance
• This signature predicts likelihood of response to adjuvant
radiotherapy and may be useful in identifying patients who
may require treatment intensification
Selecting a platform
• Easiest options include
• qPCR array (like Oncotype)
• Nanostring platform (like Prosigna)
• focused microarray (like Mammaprint)
• However, these options don’t allow for
• Assessment of multiple signatures (particularly
relevant for tissues from valuable phase III studies)
• Flexibility in signature refinement
• Discovery
• Thus, we decided to go with a clinical-grade highdensity array (one of the highest-throughput assays
that can be run on formalin-fixed tissue)
Precision genomic technology
Human Exon Arrays as a Discovery and Validation
Platform
ARCHIVED
FFPE TISSUE
GENETIC
MATERIAL
GENECHIP
TECHNOLOGY
GENOME
ANALYSIS
Long term followup available
Measuring activity
of genes
Genome-wide
analysis
Cancer progression
gene signature
• Uses archived FFPE tissues (success with up to 25 year old samples)
• Clinical-grade expression assay – CLIA certified lab
• Robust technology and comprehensive and in-depth data analysis
Abdueva et al., Journal of Molecular Diagnostics 2010, Vergara et al., Frontiers in Genetics 2011, Erho et al., Journal of Oncology 2012
58
Human Exon Array:
Derived from ENCODE RNA expression data
• 5 million features on array
• 1.4 million RNA transcripts
• 0.2 million mRNA exons
• 0.2 million intronic/anti-sense transcripts
• ~ 1 million non-coding RNA transcripts!
Publications using this array technology in prostate cancer
Initial reports of the Decipher signature in different cohorts
• Erho N et al. Discovery and validation of a prostate cancer genomic
Mayo
classifier that predicts early metastasis following radical prostatectomy.
PLoS One. 2013 Jun 24;8(6):e66855.
• Karnes RJ et al. Validation of a genomic classifier that predicts
Mayo
metastasis following radical prostatectomy in an at risk patient
population. J Urol. 2013 Dec;190(6):2047-53.
• Klein EA et al. A genomic classifier improves prediction of metastatic
disease within 5 years after surgery in node-negative high-risk prostate
Cleveland
cancer patients managed by radical prostatectomy without adjuvant
Clinic
therapy. Eur Urol. 2014. In press.
• Den RB et al. Genomic prostate cancer classifier predicts biochemical
failure and metastases in patients after postoperative radiation therapy.
TJU
Int J Radiat Oncol Biol Phys. 2014 Aug 1;89(5):1038-46
• Additional cohorts being assessed from the University of Michigan,
Johns Hopkins, NYU, Moffitt, and the Radiation Therapy Oncology
Group (RTOG)
GenomeDx Biosciences Confidential
21/07/2015
60
Publications using this array technology in prostate cancer
Secondary analyses of the datasets
• Prensner JR et al. RNA biomarkers associated with metastatic
Michigan progression in prostate cancer: A multi-institutional high-throughput
analysis of SChLAP1. Lancet Oncology 2014. Accepted and in press.
• Den RB et al. A genomic classifier identifies men with adverse
pathology after radical prostatectomy who benefit from adjuvant
TJU
radiation therapy. Journal of Clinical Oncology 2014. Accepted and in
press.
• Cooperberg MR et al. Combined Value of Validated Clinical and
Genomic Risk Stratification Tools for Predicting Prostate Cancer
UCSF
Mortality in a High-risk Prostatectomy Cohort. Eur Urol. 2014. Accepted
and in press.
• Ross AE et al. A genomic classifier predicting metastatic disease
progression in men with biochemical recurrence after prostatectomy.
Hopkins
Prostate Cancer Prostatic Dis. 2014 Mar;17(1):64-9
• Additional paper from the University of Michigan on age-related
biological changes in tumors
GenomeDx Biosciences Confidential
21/07/2015
61
Publications using this array technology in prostate cancer
Clinical utility studies
• Badani K et al. Impact of a genomic classifier of metastatic risk on
Columbia postoperative treatment recommendations for prostate cancer patients:
a report from the DECIDE study group. Oncotarget. 2013 Apr;4(4):600-9
• Badani KK et al. Effect of a genomic classifier test on clinical practice
Columbia decisions for patients with high-risk prostate cancer after surgery. BJU
Int. 2014. In press.
• Nguyen PL et al. Impact of a genomic classifier of metastatic risk on
post-prostatectomy treatment recommendations by radiation oncologists
and urologists. Urology 2014. In press.
Harvard/
Michigan
GenomeDx Biosciences Confidential
21/07/2015
62
Publications using this array technology in prostate cancer
Validation of laboratory biology studies
• Prensner JR et al. The long noncoding RNA SChLAP1 promotes
Michigan aggressive prostate cancer and antagonizes the SWI/SNF complex.
Nature Genetics 2013 Nov;45(11):1392-8.
• Prensner JR et al. The IncRNAs PCGEM1 and PRNCR1 are not
Michigan implicated in castration resistant prostate cancer. Oncotarget. 2014 Mar
30;5(6):1434-8.
• Hurley PJ et al. Secreted protein, acidic and rich in cysteine-like 1
(SPARCL1) is down regulated in aggressive prostate cancers and is
Hopkins
prognostic for poor clinical outcome. Proc Natl Acad Sci U S A. 2012
Sep 11;109(37):14977-82.
• Additional studies submitted to JNCI (Hopkins), European Urology
(Michigan), IJROBP (Michigan)
GenomeDx Biosciences Confidential
21/07/2015
63
Discovery of SChLAP1 (a long noncoding RNA) as the top
gene associated with metastatic progression in prostate cancer
Prensner et al, Nature Genetics, 2013; Prensner et al, Lancet Oncology (accepted), 2014
64
FFPE samples profiled using Exon arrays
Tumor Type
Prostate
Bladder
Sarcoma
Thyroid
Breast
Pancreas
Kidney
n
3,200
300
240
120
72
58
20
Erho, N., et al. Discovery and validation of a prostate cancer genomic
classifier that predicts early metastasis following radical prostatectomy.
PLoS One. 2013 Jun 24;8(6):e66855. doi:
10.1371/journal.pone.0066855. Print 2013.
Erho, N. et al. Transcriptome-wide detection of differentially expressed
coding and non-coding transcripts and their clinical significance in
prostate cancer. J Oncol. 2012;2012:541353. Epub 2012 Aug 16.
Abdueva D, et al. Quantitative expression profiling in formalin-fixed
paraffin-embedded samples by affymetrix microarrays. J Mol Diag
2010;12:409-17.
Karnes, R.J. et al. Validation of a genomic classifier that predicts
metastasis following radical prostatectomy in an at risk patient
.
population. J Urol. 2013 Dec;190(6):2047-53.
Mitra, A.P., et al. Discovery and validation of a novel expression
signature for recurrence in high-risk bladder cancer post-cystectomy. J
NCI accepted April 2014
Presner, J., et al. The long noncoding RNA SChLAP1 promotes
aggressive prostate cancer and antagonizes the SWI/SNF complex.
Nat Genetics 2013 45(11):1392-8.
Wiseman, S.M. et al. Whole-transcriptome profiling of thyroid nodules
identifies expression-based signatures for accurate thyroid cancer
diagnosis. J Clin Endocrinol Metab 2013 98(10):4072-9
Knudsen, E.S. Progression of ductal carcinoma in situ to invasive
breast cancer is associated with gene expression programs of EMT
and myoepithelia.2012 Breast Cancer Res Treat. 133(3):1009-24.
65
Exon arrays used to examine laser capture microdissected*
stromal and epithelial cells from DCIS and IBC
Knudsen, E.S. Progression of ductal carcinoma in situ to invasive breast
cancer is associated with gene expression programs of EMT and
myoepithelia.2012 Breast Cancer Res Treat. 133(3):1009-24.
*LCM peformed on FFPE specimens
Exon arrays profiled using an input of 50 ng of RNA
66
RNA Extraction using the GenomeDx protocol
• RNA extraction from formalin-fixed, paraffin-embedded
specimens follows a procedure over 3 days to first digest,
then extract, then isolate and purify RNA for expression
analysis.
• All conducted in a CLIA-certified laboratory
• Optimized for both blocks or slides
• Is now semi-automated
RTOG 96-01 RNA Yields/Purity
(~20 year old blocks)
The RNA extraction/exon array approach is now being
used for samples from the following RTOG trials:
• 96-01
• 92-02
• 94-08
• 94-13
• 99-02
• 99-10
• 01-26
Conclusions
• A reliable molecular tool is needed to personalize
radiotherapy for early stage breast cancer patients
• We have developed a signature for radiation
intensification
• We have a platform that allows for validation of existing
signatures and development of new ones
• Thanks to Ian Kunkler, David Cameron, and John Bartlett,
we have established a collaboration that aims to apply this
platform to randomized clinical trial samples