Personalized Therapy for Prostate Cancer due to Genetic Testings

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1 Personalized Therapy for Prostate Cancer due to Genetic Testings Stephen J. Freedland, MD Professor of Urology Director, Center for Integrated Research on Cancer and Lifestyle Cedars-Sinai Medical Center Los Angeles, CA USA

2 Disclosures Myriad Research collaboration GenomeDx Research collaboration Speaker

3 Disclosures Myriad Research collaboration GenomeDx Research collaboration Speaker

4 Outline Why personalized testing? What is genetics? Prolaris OncotypeDx Decipher Conclusions

5 Why Personalized Testing Allows the right patient to receive the right treatment at the right time Men with aggressive disease receive aggressive therapy (multimodal therapy) Men with low-risk disease receive less aggressive therapy (active surveillance) Ultimate goal is: Reduce side effects Improved outcomes

6 Current State of the Art PSA, stage, and Gleason classic prostate cancer triad Low-risk, intermediate-risk, high-risk NCCN (and others) adoption and updates Very low-risk, low-risk, intermediate-risk, high-risk, very highrisk Multivariable nomograms or tables Partin tables, CAPRA, Kattan, Stephenson, etc. All have value All predict recurrence and distant events (metastasis, prostate cancer death) All are good but far from perfect

7 Current State of the Art

8 Goal: Personalized Medicine cancer Personalized Medicine Genetics/ Genomics

9 What is Genetics? Genomics? According to Wikipedia: Genomics refers to the study of the genome. A genome is the genetic material of an organism In common terms, genomics is the analysis of the DNA material that make us unique The genome is made up of 3 billion base pairs We all differ by <1% These differences determine how we look, think, act These differences can also affect tumor growth

10 Genetics is Complex Traditionally: DNA à mrna à protein Now: DNA à mirrna or lncrna or mrna à protein, act on other RNA, alter DNA function

11 How to Measure Genetics? PCR for specific mrna Measures a small number of genes very accurately Gene expression chips Measure many genes RNA seq Sequence all RNA Used more and more in the research setting Whole exosome RNA Sequence all transcribed RNAs

12 How to Measure Genetics (con t)? SNPs Single nucleotide polymorphisms Can be done one at a time or using a large chip Copy number alterations/variations Often using chips Whole genome sequencing Rarely done cost is high, but coming down NOTE: Most commercial genetic tests for cancer using PCR costs are lower

13 Molecular Tests Available Who to biopsy? When to rebiopsy? Who to treat? Follow on therapy? PSA PCA3

14 Prolaris FFPE tissue 31- proliferation gene qrt-pcr assay Cell cycle progression (CCP) score Uses 15 housekeeping genes Within a tumor, faster growth means more cells will be in S phase at any given time Thus, more S phase = higher CCP score Higher CCP = faster growing cancer

15 Prolaris Originally developed in men treated conservatively Strongly predicted prostate cancer death Now been validated for men at the time biopsy undergoing surgery, radiation, or active surveillance to predict prostate cancer death Can be used after radical prostatectomy On the Prolaris website, they list 10 supporting publications Higher scores = worse outcome

16 OncotypeDx (GPS) FFPE biopsy tissue 17-gene RT-qPCR assay Genes come from multiple different biological pathways Developed for predicting risk of adverse final pathology on men who are candidates for active surveillance Gleason >4+3, extraprostatic extension Only a single validation study to date Did correlate with metastases, but only 5 cases

17 What is Decipher ARCHIVED FFPE TISSUE Long term follow-up available GENETIC MATERIAL Measuring activity of genes GENECHIP TECHNOLOGY Genome-wide analysis GENOME ANALYSIS Cancer progression gene signature Clinical-grade expression assay CLIA certified - Analyzes 1.4 million genomic markers - Includes coverage of non-coding RNA

18 Decipher after Surgery Initially developed among 545 men treated with radical prostatectomy at Mayo Median follow-up 16.9 years Predicted metastases with AUC = 0.75 Outperformed all other prognostic measures Identified cut-points of: <0.4 à low-risk à intermediate risk >0.6 à high-risk Erho et al. PLOS ONE 2013

19 Decipher after Surgery Response to Radiation Freedland et al. European Urology, in press

20 Decipher after Surgery Timing of Salvage Radiation 188 patients with pt3 and/or SM+, who received post-rp RT Primary endpoint: metastasis Adjuvant RT = pre-xrt PSA <0.2 Salvage RT = pre-xrt PSA >0.2 ng/ml Den et al. Journal of Clinical Oncology 2015; 33:

21 Decipher high risk men benefit from earlier radiotherapy C Cumulative Incidence of Metastasis (%) Salvage RT Adjuvant RT Decipher Genomic low- risk P =.788 D Cumulative Incidence of Metastasis (%) Decipher Genomic high- risk Salvage RT Adjuvant RT P = GC Score Time After RT (years) HR No. at risk (Adjuvant vs. Salvage RT) Time After RT (years) 95% CI p value High-Risk Low-Risk % reduction in metastasis risk for Decipher high-risk patients treated with adjuvant vs. salvage RT Den et al. Journal of Clinical Oncology 2015; 33:

22 Decipher on Biopsy Decipher has been shown to predict metastases for both surgically and radiation treated patients Klein et al, Urology, 2016 Nguyen et al, Prostate Cancer and Prostatic Diseases, in press

23 Genomic Classifier: Beyond Prognosis Genomic Classifier (Commercial Test) that evaluates 22 markers to predict metastasis 1.4 million expression biomarkers Includes analysis of all known genes with every genomic classifier test run GenomeDx shares 1.4m expression biomarker data with physician-scientists across the nation to create new tests to predict sensitivity/resistance to: Hormone Radiation Chemotherapy

24 Decipher GRID TM : Research use only (RUO) genomics information Largest Urology RNA expression database (10x bigger than NCI) Approaches that use polymerase chain reaction (PCR) cannot provide GRID Number of patient expression profiles 10,000 TCGA 498 Growing at more than 1 patient/hour

25 Beyond Prognostication Provides detailed information on many biologically relevant pathways Constantly being updated

26 Conclusions: We are approaching a modern era of personalized medicine New tests available New ones being developed constantly All predict clinical outcomes with greater accuracy than clinical features In my opinion, Decipher is the best studied and most accurate Importantly, it provide greater information than can ever be learned from a few genes alone In the right setting, genomic testing can be very helpful