Discovery and Validation of Prognostic Genomic Based Signatures in High Risk Bladder Cancer Following Cystectomy

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1 Discovery and Validation of Prognostic Genomic Based Signatures in High Risk Bladder Cancer Following Cystectomy Anirban P. Mitra, M.D., Ph.D. Center for Personalized Medicine University of Southern California

2 UROTHELIAL TUMORIGENESIS AND OUTCOMES Mitra et al, JCO 2006 Mitra et al, Nat Rev Urol 2010

3 MOLECULAR ALTERATIONS Mitra et al, Curr Opin Urol 2012

4 MOLECULAR ALTERATIONS Mitra et al, Annu Rev Pathol 2009

5 PROFILING STRATEGIES PATHWAY SPECIFIC APPROACH GLOBAL APPROACH Small, finite panel Cost effective Faster clinical implementation ADVANTAGES Unbiased genomic interrogation High throughput Discovery potential Limited marker interrogation Medium to low throughput Selection based on prior studies DISADVANTAGES Larger panel size Potential for false discovery Expensive, specialized Mitra et al, Cancer Mets Rev 2009

6 PREDICTING NODAL METASTASIS Mitra et al, BMC Cancer 2006

7 PREDICTING NODAL METASTASIS Gene Usage Frequencies Predicted N+ by GP Predicted N by GP Using KDR, MAP2K6, ICAM1 Pathologically N+ Pathologically N Accuracy: 81% Sensitivity: 70% Specificity: 86% PPV: 70% NPV: 86% Mitra et al, BMC Cancer 2006

8 PREDICTING DISEASE SPECIFIC SURVIVAL Using JUN, MAP2K6, STAT3, ICAM1 External validation p < n RR (95% CI) p Favorable expression 3 of 4 genes (Reference) of 4 genes ( ) Mitra et al, JCO 2009

9 PROGNOSTIC VALUE OF PATIENT VARIABLES Categorization of smoking intensity Combination of smoking intensity and marker alterations is most prognostic Mitra et al, Cancer 2013

10 GENOMIC LANDSCAPE OF BLADDER CANCER TCGA, Nature 2014

11 WHOLE TRANSCRIPTOME PROGNOSTIC PROFILING STUDY OBJECTIVE Develop and validate a genomic signature that can predict risk of post cystectomy bladder cancer recurrence. PATIENTS AND METHODS 225 patients with pt2 T4aN0M0 or ptanyn1 3M0 urothelial carcinoma of the bladder without neoadjuvant chemotherapy. o 2 years follow up (if no recurrence) Primary tumors from cystectomy specimens as archival formalin fixed, paraffin embedded tissues. Whole transcriptome profiling by Affymetrix Human Exon 1.0 ST GeneChips. Features most predictive for recurrence identified (n=15). o Combined in a random forest machine learning algorithm to generate a signature

12 STUDY DESIGN DISCOVERY SET (n=133) Age Gender pt, pn LVI Clinical Classifier (CC) G CC VALIDATION SET (n=66) No neoadj chemo Radical cystectomy o 34 pt2n0m0 o 54 pt3 4aN0M0 o 45 ptanyn+m0 Median FU 9.3 yr 1.4x10 6 feature oligonucleotide microarray 15 feature Genomic Classifier (GC) [score 0 1] No neoadj chemo Radical cystectomy o 15 pt2n0m0 o 26 pt3 4aN0M0 o 25 ptanyn+m0 Median FU 10.8 yr G IBCNC IBCNC

13 CLASSIFIER PERFORMANCE DISCOVERY SET GC Feature Associated Gene 1. FOXO6 2. HSD17B7 3. ARID4B 4. ENAH 5. MAP4K3 6. MARCH7 7. MECOM 8. LRBA 9. MUT 10. CRCP 11. SYPL1 12. ARFGEF1 13. EHF 14. METTL7A 15. PPP1R12A Performance on Discovery Set

14 GC PERFORMANCE VALIDATION SET Locked classifier blindly applied to validation set

15 CLASSIFIER PERFORMANCE VALIDATION SET Adding GC to clinical nomograms improved their performance Risk stratification based on G CC scores p < GC+ IBCNC IBCNC: 0.73 G-IBCNC: 0.82 GC+ CC CC: 0.78 G-CC: 0.86

16 GC PERFORMANCE VALIDATION SET Multivariable associations with risk of bladder cancer recurrence Performance in LN patient subset p = RR of Recurrence HR p GC Age <70 yrs Male Gender Caucasian Race Tumor Stage pt pt pt4a Nodal Status N LVI Adj Chemo administered CC GC p = 0.004

17 GC GENES ASSOCIATED BIOLOGICAL FUNCTIONS GC genes and first degree partners extracted using interactome database Associated biological processes mapped GC components are biologically relevant

18 EXTERNAL VALIDATION OF GC Total number of patients = 341 p = p < TCGA, Nature 2014 Kim et al, Mol Cancer 2010 p < p = Als et al, Clin Cancer Res 2007 Riester et al, Clin Cancer Res 2012

19 FUTURE DIRECTIONS Non invasive bladder cancer cell sampling Urine OR Microfilter captured cells TURBT Response to neoadjuvant chemo GC Response to adjuvant chemo Birkhahn, Mitra et al, Eur J Cancer 2013 Other molecular correlates (e.g. TP53 mutations in cystectomy specimens) [SWOG 4B951]

20 CONCLUSIONS Global and pathway specific approaches may be used to profile the bladder cancer transcriptome for predictors of post cystectomy outcomes. We document the discovery and validation of a 15 marker genomic classifier (GC) comprising of biologically relevant transcripts, which was validated on external datasets. The integrated genomic clinical classifier (G CC) shows improved prognostic performance over clinical models alone. The classifier may help guide clinical decision making for adjuvant chemotherapy administration after further prospective validation.

21 ACKNOWLEDGEMENTS Prognostic Profiling Whole Transcriptome Peter C. Black, MD (UBC) Siamak Daneshmand, MD (USC) Eila C. Skinner, MD (Stanford) Timothy J. Triche, MD, PhD (USC) Elai Davicioni, PhD (GenomeDx Biosciences) Lucia L. Lam, BS (GenomeDx Biosciences) Christine Buerki, PhD, & team (GenomeDx) Funding Prognostic Profiling Other Projects Richard J. Cote, MD, FRCPath, FCAP (Univ. of Miami) Donald G. Skinner, MD (USC) Susan Groshen, PhD (USC) Victoria K. Cortessis, PhD (USC) Frederic M. Waldman, MD, PhD (UCSF) William P. Worzel, MS (Everist Genomics) TP53 Mutation Assessment MVAC Trial Seth P. Lerner, MD (Baylor) Walter M. Stadler, MD (Univ. of Chicago) Gary Steinberg, MD, FACS (Univ. of Chicago) Derek Raghavan, MD, PhD (Levine Cancer Institute) Nanotechnology Bladder Cancer Detection Yu Chong Tai, PhD (Caltech) Marc Birkhahn, MD (Urologie am Ring)