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Prostate Cancer Recurrence Risk
Assessment and the Role of Genomic
Profiling and Somatic Mutational Analysis
Charles J Ryan, MD
Professor of Clinical Medicine and Urology
Helen Diller Family Comprehensive Cancer Center
University of California, San Francisco
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Biomarker Analysis in Prostate Ca:
Potential Uses
• Whom to biopsy
• Whom to Re-Biopsy
• Whom to treat or not to treat
• Outcome on therapy in metastatic disease (CRPC)
– Prognosis
– Prediction
2
Biomarker Analysis in Prostate Ca:
Potential Uses
• Whom to biopsy- what is the risk of cancer?
– PSA
– PHI
– Capra
– PCA3
3
Biomarker Analysis in Prostate Ca:
Potential Uses
•
• Whom to Re-Biopsy
•
•
4
Methylation Field Effect: Application to
False Biopsy
Challenge with current
methods:
• Standard of care for biopsy =12
cores
• The needle may miss cancer
• Pathologists can only interpret
what is seen on the slide
Biopsy
Cancer
A biopsy procedure samples less than 1% of the entire
gland
1.Taneja et al.: The American Urological Association (AUA) Optimal Techniques of Prostate Biopsy and Specimen Handling. 2013.
2. Shen et al.: Three-Dimensional Sonography With Needle Tracking - Role in Diagnosis and Treatment of Prostate Cancer. J. Ultrasound Med. 2008; Jun;
27(6): 895-905.
Fear of Undetected Cancer Leads to
High Rate of Repeat Biopsy
Cycle of
Follow
Up
& Anxiety
• 43% have 1st repeat biopsy
• 44% have a 2nd repeat
biopsy
• 43% have a 3rd repeat
biopsy
Negative
Patholog
y
Results
Elevated
PSA
Prostate
Biopsy
Approximately 700,000 repeat biopsies annually.
1) Welch HG et al: Detection of Prostate Cancer via Biopsy in the Medicare SEER Population During the PSA Era. J Natl Cancer Inst 2007;99: 1395 – 400.
2) Pinsky PF et al: Repeat Prostate Biopsy in the Prostate, Lung, Colorectal and Ovarian Cancer Screening Trial. BJU International 99, no. 4 (April 2007): 775–
ConfirmMDx
ConfirmMDx detects a field effect or halo associated
with the presence of cancer at the DNA level.
• This epigenetic “halo” around a
cancer lesion can be present
despite having a normal
appearance under the
microscope.
• Residual tissues from previous
negative biopsy are tested to help
rule-out cancer.
Halo
Cancer
Biopsy
Epigenetic Field
Effect
Henrique R, et al., Epigenetic Heterogeneity of High-Grade Prostatic Intraepithelial Neoplasia: Clues for Clonal Progression in Prostate Carcinogenesis, Mol Cancer Res
Multivariate Analysis of Known Risk Factors
and Assay Performance
Odds Ratios of Clinical Risk Factors
3.5
3
2.5
2
1.5
1
0.5
0
Age
(0.51)
HGPIN
(0.5)
Suspicious
DRE
(0.3)
PSA < or >
10
(0.18)
Atypical
Cells (0.011) ConfirmMDx
(<0.0001)
(p-value)
ConfirmMDx applicable to all patients, compared to rare event with atypical
histology.
Stewart G, et al., Clinical Utility of an Epigenetic Assay to Detect Occult Prostate Cancer in Histopathologically Negative Biopsies: Results of the
MATLOC Study. JURO 2013. 189, 1110-1116
Biomarker Analysis in Prostate Ca:
Potential Uses
• Whom to treat or not to treat
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Risk Adapted Treatment
• Goal: inform physician-patient decisions about optimal
initial treatment approach and timing
Active surveillance
Early local therapy
Multimodal therapy
Systemic therapy
• Numerous existing instruments
– D’Amico / AUA risk groups
– >120 nomograms
– UCSF-CAPRA
Risk Assessment: D’Amico / AUA
Low
PSA ≤10, GS ≤6,
and stage T1-2a
Intermediate
PSA 10-20, GS 7,
or stage T2b
High
PSA >20, GS ≥8,
or stage T2c / T3a
D’Amico et al. JAMA 1998; 280:969
New tool must improve on a reference
standard
Validation Studies
1. Graefen et al. JCO 2002; 20:3206
2. Graefen et al. Urol Oncol 2002;
7:141
3. Bianco et al. J Urol 2003; 170:73
4. Greene et al. J Urol 2004; 171:2255
5. Zhao et al. Urology 2008; 78:396
Kattan et al. JNCI 1998; 90:766
C-index 0.71 --> 0.88
Shariat et al. JCO 2008; 26:1526
Many candidate assays
• Tissue: DNA CNV, RNA expression,
methylation, IHC/FISH
• Blood: miRNA, metabolic analytes, proteins
• Urine/EPS: RNA transcripts (post-DRE),
metabolic analytes
• Imaging: PET, MRSI
The Prolaris Assay
• Material = RNA expression
• 31 cell cycle progression (CCP) genes,
normalized to 15 housekeeper genes
• Score is expressed as average centered
expression of CCP genes relative to
housekeeper genes; negative scores = less
active CCP, positive scores = more active CCP
Cuzick J et al. Lancet Oncol 2011; 12:245
Well established and validated
method for quantifying the amount of
a gene of interest relative to a
reference sample after normalization
by housekeeper genes
Prolaris - Advancement
Prostatectomy  Relaps
Needle biopsy -> Death
CCP and CAPRA combined.
Cooperberg et al, JCO 31:1428, 2013
Watchful waiting cohort….10 yr risk
for death from PC
Oncotype DX Genomic Prostate Score (GPS)
 Quantitative 17-gene RT-
PCR assay on manually
microdissected tumor
tissue from needle biopsy
 Genes and biological
pathways predictive of
multiple endpoints, with
emphasis on clinical
recurrence
 Optimized for very small
tissue input: six 5 micron
sections of single needle
biopsy block with as
little as 1 mm tumor
length
Androgen Signaling
AZGP1
FAM13C
KLK2
SRD5A2
Cellular
Organization
FLNC
GSN
GSTM2
TPM2
Stromal Response
BGN
COL1A1
SFRP4
Proliferation
TPX2
Reference
ARF1
ATP5E
CLTC
GPS1
PGK1
GPS =
0.735*Stromal Response group
-0.352*Androgen Signaling group
+0.095*Proliferation group
-0.368*Cellular Organization group
Scaled between 0 and 100
GPS Test Development:
Two Major Challenges Addressed
Prostatectomy
TURP
Prostate
Biopsy
•
Biopsy under-sampling and
tumor heterogeneity:
Identified genes that predict
clinical outcome in both
dominant and highest grade
regions
•
Very small biopsy tumor
volumes: Developed
standardized quantitative
methods for reliable gene
expression measurement
in prostate needle biopsies
Klein et. al. ASCO GU 2011; Klein et. al. ASCO 2012.
GPS Validation:
Prediction of Adverse Pathology
Prostate Cancer Technical Feasibility
Prostatectomy Study (Cleveland Clinic)
Two tumor foci per patient (n=441)
Clinical Recurrence, PCSS, Adverse Pathology at RP
Biopsy Study (Cleveland Clinic)
Biopsy specimens (n=167)
Adverse Pathology at RP
•
Prospectively-designed independent validation
study in contemporary, early-stage patients
Assay Finalization and Analytical Validation
17-Gene
GPS Assay
Pre-specified,
analytically validated GPS assay
•
performed on needle biopsy specimens
UCSF Clinical Validation Study
• Primary endpoint of adverse pathology to address
Biopsy Specimens (n=395)
concerns regarding understaging and biopsy
Adverse Pathology at RP
undersampling for grade
GPS Prediction of Grade And Stage
• Binary univariate logistic regression
• 20 GPS units analogous to comparison of top vs. bottom quartiles of
patients
Odds
Ratio
95% CI
LR ChiSquare
P-value
Prediction of High Grade Disease
GPS per 20 units
2.48
(1.60, 3.85)
16.78
<0.001
Prediction of pT3
GPS per 20 units
2.20
(1.46, 3.31)
14.44
<0.001
Cooperberg et al, AUA 2013
The UCSF-CAPRA Score to predict PCSM
Variable
Level
Points
Variable
Level
Points
PSA
≤6
0
T-stage
T1/T2
0
6.1-10
1
T3a
1
10.1-20
2
20.1-30
3
<34%
0
>30
4
>34%
1
Gleason
1-3/1-3
0
(primary/
secondary)
1-3/4-5
1
<50
0
4-5/1-5
3
>50
1
% of biopsy
cores
positive
Age
Sum points from each variable for 0-10 score
Cooperberg et al. J Urol 2005; 173:1938
Capra Score and GC are Correlated
Multivariable Performance of GPS
Model
1
2
Variable
Odds Ratio
95% CI
P-value
GPS (per 20 units)
1.85
(1.23, 2.81)
0.003
Age (continuous)
1.05
(1.01, 1.09)
0.004
PSA (continuous)
1.11
(1.04, 1.18)
0.002
Clinical Stage T2 vs. T1
1.57
(0.98, 2.51)
0.059
Biopsy Gleason Score (7 v. 6)
1.70
(1.00, 2.88)
0.050
GPS (per 20 units)
2.13
(1.44, 3.16)
<0.001
CAPRA
1.58
(1.24, 2.02)
<0.001
Cooperberg et al, AUA 2013
70 yo
PSA=4.4
Biopsy
1/12 Gleason 3+3=6
1/12 Gleason 3+4 =7
10/12 cores negative
Wanted active surveillance….
Decipher: Risk of Metastases post RP
• Decipher is a 22-gene genomic classifier, with
genes chosen purely by statistical selection to
predict metastasis among high-risk RP patients at
Mayo, no pathway analysis (includes non-coding
genes, 3 unknowns)
• Rather than RT-PCR on established gene set,
clinical assay is run using Affy Human Exon 1.0ST
GeneChip (1.4M probe sets interrogating 5.5M
features of whole exome)
• Decipher score is calculated, but an enormous
trove of data is kept in the databank for ongoing /
future discovery
Erho et al., PLoS ONE 8:e66855, 2013
Condition
Test
Readout
Negative Biopsy
MDXHealth:
_Methylation
ConfirmMDx
Rules out – NO PC
Rules in – Need
subsequent Bx
Positive Biopsy
Prolaris
Death from PC
Oncotype DX
Recurrence, PCSS
Decipher
Risk of Metastasis
Prolaris
Biochemical
Recurrence
Post
Prostatectomy
Biomarker testing has multiple
clinical uses in localized disease.
Crawford and Shore
What about CRPC?
• Candidate Biomarkers
1. AR status
2. TMPRSS-ERG
3. Androgens
4. Clinical Factors
31
mCRPC Pre-Chemotherapy Nomogram
mCRPC Tissue Collection and Analysis
Ryan Proc GU ASCO 2013
Profile of Distinct and Emerging Clinical States.
Primary
Resistance(Nonresponse)
ASI or ART
Therapy
Response
Resistance with
Phenotypic Change:
e.g. Neuro-endocrine
Acquired
Resistance:
(compensatory
/adaptive)
Death Non-PC Cause
CRPC
ASI= Androgen Synthesis Inhibitor
ART = AR Targeted Therapy
CRPC:
Sample Mutational Screen
AR Amplification
(Reported to Patients)
AR Amplification by FISH ( n = 33)
Abiraterone naive
10/13 (77%)
Abiraterone resistant 3/14 (21%)
Analysis Pending:
Primary vs Secondary Resistance
Enzalutamide Resistance
Unknowns:
Effect on subsequent AR-targeted rx
Marker-guided therapy
Small EJ AACR Prostate Meeting, San Diego 2014
PARADIGM Integrative Analysis
(Josh Stuart, UCSC)
mCRPC
Tumors
Multimodal Data
Pathway Model
of Cancer
Inferred
Activities
1) Adaptive
Pathways
1) Unbiased
Analysis
•
•
•
•
Integrate data for pathway-based PARADIGM analysis
Focused analysis to assess Adaptive Pathway activity in each sample
Inferred activities reflect neighborhood of influence around a pathway.
Unbiased analysis will identify additional pathways
Vaske et al Bioinformatics (2010); TCGA Network, Nature 2011; Heiser et al PNAS
2011
Pathway Analysis
Goals:
Unbiased analysis across all patients (n = 300)
Biomarker and therapeutic applications.
Interim (Subset) Analyses - Caveat Emptor!
Hypothesis-generating experiments
Compare pathway analysis across discrete, clinically
dichotomized groups:
Abiraterone naïve vs resistant
Enzalutamide naïve vs resistant
Primary Resistance vs Acquired Resistance
Enzalutamide vs Abiraterone resistance
Liver vs non-liver
Aggressive variant vs conventional
Pathway Analysis
Differentially expressed
genes + connections
Small EJ AACR Prostate Meeting, San Diego 2014
Conclusion
1. Genomics is coming to prostate cancer
2. For localized disease it is here as a prognostic
tool.
3. It has not yet become a predictive tool linked to
treatment (like Oncotype Dx Breast)
4. There is no evidence (yet) that outcomes in
advanced prostate cancer are better when a
“personalized” or risk adapted approach is utilized.
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