VHL and clear cell Renal Cell Carcinoma. Expression profiling reveals cell of origin. Renal Cell Carcinoma (RCC) Title. VHL syndrome hallmark cancer:

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1 VHL and clear cell Renal Cell Carcinoma Gene expression profiles in renal cell carcinoma October 23, 2014 VHL syndrome hallmark cancer: Clear cell renal cell carcinoma (ccrcc) VHL mutations also a hallmark of sporadic ccrcc Title VHL mutation HIF stabilization and gene expression changes associated with the hypoxia response. W. Kimryn Rathmell, MD, PhD Renal Cell Carcinoma (RCC) Expression profiling reveals cell of origin Originates in the renal cortex Most common solid lesion occurring in the kidney (80 85% of all primary renal neoplasms) Diseased Kidney 3 Cancer Genome Atlas Consortium, Nature,

2 VHL/HIF Regulatory Pathway GLUT1 PDK LDHA Glycolysis HIF1α HIFβHIF1α BNIP3 Apoptosis HIF1α PDGF FLT1 VEGF Angiogenesis MMP2 CXCR4 HIF2α Invasion Metastasis HIFβHIF2α HIF2α CCD1 TGFα OCT4 Proliferation Dedifferentiation Outline Gene expression profiles in clear cell renal cell carcinomas. Validating a prognostic signature in RCC. Exploring gene expression profiles in VHL syndrome RCC tumors. Sporadic VHL mutant tumors express HIF1 and HIF2, or HIF2 alone. Gene expression patterns show HIF specific variability HIF1 drives glycolytic genes, mtor pathway HIF2 drives genes involved in cell cycle and DNA damage. Overlap in angiogenesis and motility targets. Gordan et al, Cancer Cell, 2008 Gordan et al, Cancer Cell,

3 Using gene expression to identify tumor subgroups. cca and ccb K=3, K=4 still fall into two groups cca (classical angiogenic), ccb (bad) Validation in a historical dataset 3

4 Extent of Disease at Diagnosis Most cancers of the kidney and renal pelvis are diagnosed when the disease is still localized to the primary site Metastatic Spread 20% Loco-regional Spread 19% Unknown 5% Localized Disease 56% Determining Prognosis: Anatomic Extent of Disease Most consistent factor used to determine RCC prognosis Probability of Survival year Cancer-specific Survival Based on TNM Stage Log Rank P Value<.001 Stage IV (N=318) Stage I (N=185) Stage II (N=57) Stage III (N=83) Months of Postsurgery TNM Stage 5-year Cancerspecific Survival Stage I 91 ± 2.5% Stage II 74 ± 6.9% Stage III 67 ± 6.1% Stage IV 32 ± 3.2% National Cancer Institute. SEER Stat Fact Sheets. Available at: Accessed August 28, Reprinted with permission from Tsui KH, et al. J Urol. 2000;163: cca/ccb predicts for cancer specific and overall survival outcomes Validation dataset reveals a small distinct tumor set Brannon, et al, Eur Urol,

5 Cluster 3 are highly divergent from cca and ccb in metabolic genes. VHL mutants in both cca and ccb Brannon, et al, Eur Urol, 2012 Brannon, et al, Eur Urol, 2012 Developing a clinic tool: ClearCode34 95 clear cell tumors Prognostic value of ClearCode34 evaluated in TCGA LAD and ConsensusCluster analysis Set aside arrays with non-concordant assignments 72 arrays (microarray standard set) (69 tumors, 3 replicates) 43 cca arrays (42 tumors, 1 replicate) 29 ccb arrays (27 tumors, 2 replicates) Prediction Analysis for Microarrays (PAM) Predictive biomarkers: ClearCode34 Recurrence-free Survival cca ccb (n=205) (n=175) No. of events Median RFS, months HR, 2.3; 95% CI, 1.6 to 3.3; P=4.3e Time cca ccb Cancer-Specific Survival cca ccb (n=205) (n=175) No. of events Median CSS, months HR, 2.9; 95% CI, 1.6 to 5.6; P= cca ccb 5

6 Prognostic value of ClearCode34 validated in UNC cohort Prognostic value of ClearCode34 validated in TCGA Recurrence-Free Survival cca ccb cca ccb (n=69) (n=88) No. of events Median RFS, months HR, 2.1; 95% CI, 1.3 to 3.4; P=.001 Cancer-Specific Survival cca ccb (n=69) (n=88) No. of events 7 25 Median CSS, months HR, 3.0; 95% CI, 1.3 to 7.0; P=.005 cca ccb Abbreviation: HR, hazard ratio Subtype cca was used as reference in univariate and multivariate analysis. $ Stage I was used as reference in univariate and multivariate analysis. Stage was encoded as an ordinal variable with three levels. Grade 1 and 2 were combined and used as reference in univariate and multivariate analysis. Grade was encoded as an ordinal variable with three levels. Integrated prognostic models can evaluate risk outcomes RCC Algorithms for cancer-specific survival Group Risk Score Low Intermediate High >1.5 Recurrence-free Survival Low Risk (N=98) Intermediate Risk (N=140) High Risk (N=28) Log-rank P=3.04e-09 Time Cancer-Specific Survival Low Risk Intermediate Risk High Risk Log-rank P=2.03e-08 UCLA Integrated Staging System (UISS) 6

7 ClearCode34 Model outperforms established algorithms C-index i i C - C n - d n ex d ex UISS ClearCode34 UISS Model SSIGN ClearCode34 SSIGN ClearCode34 Model Model Change in in chi-squared statistic C h ange C h ange i n C i n h i C -squared h i -squared S t a S t is t a t ic t i s t i c * * * CC34Model->UISS UI->Model Model->SS SS->Model CC34Model->SSIGN ClearCode34 Summary ClearCode34 can accurately classify ccrcc tumors Prognostic value of cca/ccb classification validated in a TCGA and UNC cohort Integrated model for recurrence-free and cancerspecific survival constructed using ccrcc subtypes and traditional clinical variables stage and grade. This classifier adds value to predicting cancerspecific survival above and beyond established algorithms. How do we know this is meaningful for VHL patients? *VHL disease tumors and sporadic ccrccs (the majority VHL mutated) are not distinguishable by gene expression. TCGA includes 14 tumors from VHL patients: 2 class comparison reveals NO significantly different upregulated genes, 209 significantly downregulated. VHL cases in the TCGA A mixture of cca and ccb TCGA Sample ID ClearCode34 Status Grade Size (cm) T Stage N Stage M StatusStage KIRC TCGA A3 xxxx cca G2 3 T1a NX M0 Stage I KIRC TCGA A3 xxxx cca G3 4.9 T1b N0 M0 Stage I KIRC TCGA A3 xxxx ccb G2 4 T1a N0 M0 Stage I KIRC TCGA AK xxxx G2 5.5 T1 N0 M0 Stage I KIRC TCGA AK xxxx cca G2 12 T3b N0 M0 Stage III KIRC TCGA AK xxxx cca G2 7.5 T2 N0 M0 Stage II KIRC TCGA AK xxxx G3 6.5 T3b N1 M0 Stage III KIRC TCGA AK xxxx ccb G3 11 T2 NX M0 Stage II KIRC TCGA AK xxxx cca G2 7 T1b NX M0 Stage I KIRC TCGA AK xxxx G2 9 T2 N0 M1 Stage IV KIRC TCGA AK xxxx G2 6 T1b NX M0 Stage I KIRC TCGA AK xxxx ccb G3 5.5 T3a NX M0 Stage III KIRC TCGA AK xxxx cca G2 4 T1a NX M0 Stage I KIRC TCGA AK xxxx cca G3 9 T2 N0 M0 Stage II 7

8 Summary The hypoxia gene expression signature dominates VHL mutated RCC, but many other pathways emerge. The expression profile matches ccrcc to the early proximal tubule. Tumor expression profiles can reveal relevant biology and aid in disease prognosis. Rathmell Lab Kate Hacker, PhD Alex Arreola, PhD Samira Brooks Zufan Debebe, PhD Sneha Sundaram, PhD Catherine Fahey Adam Sendor Rathmell Lab Past Members Rose Brannon, PhD Oishee Sen Shufen Chen, MD, PhD Lance Cowey, MD Caroline Martz Lee, MD, PhD Tricia Wright, PhD Neal Rasmussen, PhD Translational Pathology Laboratory Genomics Core, Tissue Procurement Facility Acknowledgments Rutgers Gyan Bhanot, PhD Anupama Reddy, PhD Joel Parker, PhD UNC Biomedical Research Imaging Center Weili Lin, PhD Amir Khandani, MD Julia Fielding, MD The Cancer Genome Atlas Particularly: Chad Creighton, Marston Linehan, Richard Gibbs, Kenna Shaw Clinical TCGA Partiularly: James Hsieh, Ari Hakimi, Toni Chouieri Funding: AACR INNOVATOR Award, NIH (TCGA), NIH (K24), V Foundation for Cancer Research 8