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11
Prostate Cancer Recurrence Risk Assessment and the Role of Genomic
Profiling and Somatic Mutational Analysis
Charles J Ryan, MDProfessor of Clinical Medicine and Urology
Helen Diller Family Comprehensive Cancer CenterUniversity of California, San Francisco
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
• 43% have 1st repeat biopsy
• 44% have a 2nd repeat biopsy
• 43% have a 3rd repeat 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–
779.
Cycle of Follow
Up & Anxiety
Elevated PSA
Prostate Biopsy
NegativePatholog
y Results
ConfirmMDx
ConfirmMDx detects a field effect or halo associated with the presence of cancer at the DNA level.
Epigenetic Field Effect
Biopsy
Cancer
• 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
Henrique R, et al., Epigenetic Heterogeneity of High-Grade Prostatic Intraepithelial Neoplasia: Clues for Clonal Progression in Prostate Carcinogenesis, Mol Cancer Res 2006;4:1-8
Multivariate Analysis of Known Risk Factors and Assay Performance
Age (0.51)
HGPIN (0.5)
Suspicious DRE
(0.3)
PSA < or > 10
(0.18)
Atypical Cells (0.011)
ConfirmMDx (<0.0001)
0
0.5
1
1.5
2
2.5
3
3.5
Odds Ratios of Clinical Risk Factors
(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
9
• Goal: inform physician-patient decisions about optimal initial treatment approach and timing
• Numerous existing instruments– D’Amico / AUA risk groups– >120 nomograms– UCSF-CAPRA
Active surveillanceEarly local therapyMultimodal therapySystemic therapy
Risk Adapted Treatment
Risk Assessment: D’Amico / AUA
D’Amico et al. JAMA 1998; 280:969
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
New tool must improve on a reference standard
Kattan et al. JNCI 1998; 90:766
Validation Studies1. Graefen et al. JCO 2002; 20:32062. Graefen et al. Urol Oncol 2002; 7:1413. Bianco et al. J Urol 2003; 170:734. Greene et al. J Urol 2004; 171:22555. Zhao et al. Urology 2008; 78:396
Shariat et al. JCO 2008; 26:1526
C-index 0.71 --> 0.88
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
Needle biopsy -> Death
Prostatectomy Relapse
Prolaris - Advancement
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
Cellular Organization
FLNCGSN
GSTM2 TPM2
Stromal Response
BGNCOL1A1SFRP4
ProliferationTPX2
Androgen Signaling
AZGP1 FAM13C
KLK2SRD5A2
ReferenceARF1ATP5ECLTCGPS1PGK1
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
• 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
Prostate BiopsyTURPProstatectomy
Klein et. al. ASCO GU 2011; Klein et. al. ASCO 2012.
• Prospectively-designed independent validation study in contemporary, early-stage patients
• Pre-specified, analytically validated GPS assay performed on needle biopsy specimens
• Primary endpoint of adverse pathology to address concerns regarding understaging and biopsy undersampling for grade
UCSF Clinical Validation StudyBiopsy Specimens (n=395) Adverse Pathology at RP
GPS Validation:Prediction of Adverse Pathology
Prostatectomy Study (Cleveland Clinic)Two tumor foci per patient (n=441)
Clinical Recurrence, PCSS, Adverse Pathology at RP
Prostate Cancer Technical Feasibility
Assay Finalization and Analytical Validation17-Gene GPS Assay
Biopsy Study (Cleveland Clinic)Biopsy specimens (n=167)
Adverse Pathology at RP
GPS Prediction of Grade And Stage
OddsRatio 95% CI LR Chi-
Square P-value
• Binary univariate logistic regression• 20 GPS units analogous to comparison of top vs. bottom
quartiles of patients
Prediction of High Grade Disease
GPS per 20 units 2.48 (1.60, 3.85)
16.78 <0.001Prediction of pT3
GPS per 20 units 2.20 (1.46, 3.31)
14.44 <0.001
Cooperberg et al, AUA 2013
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
1-3/1-3 0
1-3/4-5 1 Age <50 0
4-5/1-5 3 >50 1
Sum points from each variable for 0-10 score
Cooperberg et al. J Urol 2005; 173:1938
The UCSF-CAPRA Score to predict PCSM
% of biopsycores positive
Gleason(primary/secondary)
Capra Score and GC are Correlated
Multivariable Performance of GPS
Model VariableOdds
Ratio95% CI P-value
1 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
2 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=4.4Biopsy1/12 Gleason 3+3=61/12 Gleason 3+4 =710/12 cores negativeWanted 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:_MethylationConfirmMDx
Rules out – NO PCRules in – Need subsequent Bx
Positive Biopsy Prolaris
Oncotype DX
Death from PC
Recurrence, PCSS
Post Prostatectomy
Decipher
Prolaris
Risk of Metastasis
Biochemical Recurrence
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
Profile of Distinct and Emerging Clinical States.
CRPC
ASI or ARTTherapy
Primary Resistance(Non-
response)
Response
Acquired Resistance:
(compensatory /adaptive)
Death Non-PC Cause
Resistance with Phenotypic Change:e.g. Neuro-endocrine
ASI= Androgen Synthesis InhibitorART = AR Targeted Therapy
Ryan Proc GU ASCO 2013
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
ResistanceEnzalutamide Resistance
Unknowns:Effect on subsequent AR-
targeted rxMarker-guided therapy
Small EJ AACR Prostate Meeting, San Diego 2014
PARADIGM Integrative Analysis(Josh Stuart, UCSC)
• 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
Multimodal Data Pathway Modelof Cancer
mCRPC Tumors
Inferred Activities
1) Adaptive Pathways
2) Unbiased Analysis
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 experimentsCompare pathway analysis across discrete, clinically
dichotomized groups:Abiraterone naïve vs resistantEnzalutamide naïve vs resistantPrimary Resistance vs Acquired ResistanceEnzalutamide vs Abiraterone resistanceLiver vs non-liverAggressive variant vs conventional
Pathway Analysis
Differentially expressed genes + connections
Small EJ AACR Prostate Meeting, San Diego 2014
4040
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.
Conclusion