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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 1 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 9 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. 40 40