vhl and clear cell renal cell carcinoma...renal cell carcinoma (rcc) • originates in the renal...
TRANSCRIPT
11/7/2014
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October 23, 2014
Title
Gene expression profiles in renal cell carcinoma
W. Kimryn Rathmell, MD, PhD
VHL and clear cell Renal Cell Carcinoma
• VHL syndrome hallmark cancer:– Clear cell renal cell carcinoma (ccRCC)
• VHL mutations also a hallmark of sporadic ccRCC
• VHL mutationHIF stabilization and gene expression changes associated with the hypoxia response.
Renal Cell Carcinoma (RCC)• Originates in the renal cortex• Most common solid lesion occurring in the kidney (80‐85% of all primary renal neoplasms)
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Diseased Kidney
Expression profiling reveals cell of origin
Cancer Genome Atlas Consortium, Nature, 2013
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VHL/HIF Regulatory PathwayHIF2α
HIF2α
HIFβ
HIF1α
HIF1α
HIF1α
HIFβ
VEGF
PDGF CCD1GLUT1
HIF2α
PDK
LDHAFLT1 TGFα
OCT4
Glycolysis
BNIP3
ApoptosisAngiogenesis Invasion
Metastasis
MMP2
CXCR4
De-differentiation
Proliferation
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.
Gordan et al, Cancer Cell, 2008
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
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Using gene expression to identify tumor subgroups. ccA and ccB
Brannon, et al, Genes and Cancer, 2010
K=3, K=4 still fall into two groups
Brannon, et al, Genes and Cancer, 2010
ccA (classical angiogenic), ccB (bad)
Brannon, et al, Genes and Cancer, 2010
Validation in a historical dataset
Brannon, et al, Genes and Cancer, 2010
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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
13National Cancer Institute. SEER Stat Fact Sheets. Available at: http://seer.cancer.gov/statfacts/html/kidrp.html. Accessed August 28, 2008.
Loco-regional Spread 19%
Localized Disease 56%
Metastatic Spread 20%
Unknown 5%
Determining Prognosis: Anatomic Extent of Disease• Most consistent factor used to determine RCC prognosis
14Reprinted with permission from Tsui KH, et al. J Urol. 2000;163:1090-1095.
5-year Cancer-specific Survival Based on TNM Stage
TNM Stage
5-year Cancer-specific Survival
Stage I 91 ± 2.5%
Stage II 74 ± 6.9%
Stage III 67 ± 6.1%Stage IV 32 ± 3.2%
12 24 36 48 60 72 84 96 108 120 132
1.00
.75
.50
.25
0
Log Rank P Value<.001 Stage IV(N=318)
Stage III(N=83)
Stage II (N=57)
Stage I (N=185)
Months of Postsurgery
Prob
abili
ty o
f Sur
viva
l
ccA/ccB predicts for cancer specific and overall survival outcomes
Brannon, et al, Genes and Cancer, 2010
Validation dataset reveals a small distinct tumor set
Brannon, et al, Eur Urol, 2012
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Cluster 3 are highly divergent from ccAand ccB in metabolic genes.
Brannon, et al, Eur Urol, 2012
VHL mutants in both ccA and ccB
Brannon, et al, Eur Urol, 2012
Developing a clinic tool: ClearCode34
LAD and ConsensusCluster analysisSet aside arrays with non-concordant assignments
43 ccA arrays(42 tumors, 1 replicate)
72 arrays (microarray standard set)(69 tumors, 3 replicates)
29 ccB arrays(27 tumors, 2 replicates)
Prediction Analysis for Microarrays (PAM)
95 clear cell tumors
Predictive biomarkers:ClearCode34
Brooks, et al, Eur Urol, 2014
Prognostic value of ClearCode34 evaluated in TCGA
0 20 40 60 80 100
0.0
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Time (months)
0 20 40 60 80 100
0.0
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Time (months)0 20 40 60 80 100
ccA ccB(n=205) (n=175)
No. of events 50 75Median RFS, months 91 53HR, 2.3; 95% CI, 1.6 to 3.3; P=4.3e-060.
0 0
.2
0.4
0.6
0.
8 1
.0 ccAccB
0 20 40 60 80 100
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4 0
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Can
cer-S
peci
fic S
urvi
val
(pro
babi
lity)
ccAccB
Time (months) Time (months)
Rec
urre
nce-
free
Sur
viva
l (p
roba
bilit
y)ccA ccB
(n=205) (n=175)No. of events 14 29Median CSS, months -- --HR, 2.9; 95% CI, 1.6 to 5.6; P=0.0005
Brooks, et al, Eur Urol, 2014
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Prognostic value of ClearCode34 validated in UNC cohort
0 50 100 150 200
0.0
0.2
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0.6
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1.0
Time (months)
0 50 100 150 200
0.0
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1.0
Time (months)
Can
cer-S
peci
fic S
urvi
val
(pro
babi
lity)
0.0
0.2
0.
4 0
.6
0.8
1.0
Time (months)0 50 100 150 200
ccAccB
ccA ccB (n=69) (n=88)
No. of events 7 25Median CSS, months -- 151HR, 3.0; 95% CI, 1.3 to 7.0; P=.005R
ecur
renc
e-Fr
ee S
urvi
val
(pro
babi
lity)
0.0
0.2
0.
4 0
.6
0.8
1.0
ccAccB
ccA ccB (n=69) (n=88)
No. of events 26 59Median RFS, months 88 52HR, 2.1; 95% CI, 1.3 to 3.4; P=.001
0 50 100 150 200Time (months)
Brooks, et al, Eur Urol, 2014
Prognostic value of ClearCode34 validated in TCGA
Abbreviation: HR, hazard ratioSubtype 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.
Brooks, et al, Eur Urol, 2014
Integrated prognostic models can evaluate risk outcomes
Group Risk Score Low 0-0.5Intermediate 0.5-1.5High >1.5
0 50 100 150 200
0.0
0.2
0.4
0.6
0.8
1.0
Time (months)
Rec
urre
nce-
free
Sur
viva
l (p
roba
bilit
y)0.
0 0
.2
0.4
0
.6
0.8
1.
0
0 50 100 150 200Time (months)
Log-rank P=3.04e-09
Low Risk (N=98)Intermediate Risk (N=140)High Risk (N=28)
0 50 100 150 200
0.0
0.2
0.4
0.6
0.8
1.0
Time (months)
Log-rank P=2.03e-08
Low Risk Intermediate RiskHigh Risk
0.0
0.2
0
.4
0.6
0
.8
1.0
Time (months)
Can
cer-S
peci
fic S
urvi
val
(pro
babi
lity)
0 50 100 150 200
Brooks, et al, Eur Urol, 2014
RCC Algorithms for cancer-specific survival
UCLA Integrated Staging System (UISS)
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ClearCode34 Model outperforms established algorithms
UISS ClearCode34 Model
C-in
dex
0.50
0.55
0.60
0.65
UISS ClearCode34 Model
.50
.5
5
.6
0
.6
5C
-inde
x
CC34Model->UISS
Cha
nge
in C
hi-s
quar
ed S
tatis
tic0
24
68
100
2
4
6
8
10
C
hang
e in
chi
-squ
ared
sta
tistic
Model->UI UI->Model7.3
1.9
2.5
6.7*
*CC34Model->SSIGN
Cha
nge
in C
hi-s
quar
ed S
tatis
tic0
510
1520
2530
35
SSIGN ClearCode34 Model
C-in
dex
0.50
0.55
0.60
0.65
0.70
SSIGN ClearCode34 Model
.50
.5
5
.
60
.65
.70
C-in
dex
0
5
10
15
2
0
25
30
35
Cha
nge
in c
hi-s
quar
ed s
tatis
tic
26.9
3.2
15.9
14.2*
*Model->SS SS->Model
*
Brooks, et al, Eur Urol, 2014
ClearCode34 Summary
• ClearCode34 can accurately classify ccRCCtumors
• Prognostic value of ccA/ccB classification validated in a TCGA and UNC cohort
• Integrated model for recurrence-free and cancer-specific survival constructed using ccRCC subtypes and traditional clinical variables stage and grade.
• This classifier adds value to predicting cancer-specific 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 upregulatedgenes, 209 significantly downregulated.
VHL cases in the TCGA‐A mixture of ccA and ccB
TCGA Sample ID ClearCode34 Status GradeSize (cm) T Stage N Stage M StatusStage
KIRC TCGA‐A3‐xxxx ccA G2 3 T1a NX M0 Stage IKIRC TCGA‐A3‐xxxx ccA G3 4.9 T1b N0 M0 Stage IKIRC TCGA‐A3‐xxxx ccB G2 4 T1a N0 M0 Stage IKIRC TCGA‐AK‐xxxx G2 5.5 T1 N0 M0 Stage IKIRC TCGA‐AK‐xxxx ccA G2 12 T3b N0 M0 Stage IIIKIRC TCGA‐AK‐xxxx ccA G2 7.5 T2 N0 M0 Stage IIKIRC TCGA‐AK‐xxxx G3 6.5 T3b N1 M0 Stage IIIKIRC TCGA‐AK‐xxxx ccB G3 11 T2 NX M0 Stage IIKIRC TCGA‐AK‐xxxx ccA G2 7 T1b NX M0 Stage IKIRC TCGA‐AK‐xxxx G2 9 T2 N0 M1 Stage IVKIRC TCGA‐AK‐xxxx G2 6 T1b NX M0 Stage IKIRC TCGA‐AK‐xxxx ccB G3 5.5 T3a NX M0 Stage IIIKIRC TCGA‐AK‐xxxx ccA G2 4 T1a NX M0 Stage IKIRC TCGA‐AK‐xxxx ccA G3 9 T2 N0 M0 Stage II
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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 .
AcknowledgmentsRathmell LabKate Hacker, PhDAlex Arreola, PhDSamira BrooksZufan Debebe, PhDSneha Sundaram, PhDCatherine FaheyAdam Sendor
Rathmell Lab Past MembersRose Brannon, PhDOishee SenShufen Chen, MD, PhDLance Cowey, MDCaroline Martz Lee, MD, PhDTricia Wright, PhDNeal Rasmussen, PhD
Translational Pathology LaboratoryGenomics Core, Tissue Procurement Facility
RutgersGyan Bhanot, PhDAnupama Reddy, PhD
Joel Parker, PhD
UNC Biomedical Research Imaging CenterWeili Lin, PhDAmir Khandani, MDJulia Fielding, MD
The Cancer Genome AtlasParticularly: Chad Creighton, Marston Linehan, Richard Gibbs, Kenna Shaw
Clinical TCGAPartiularly: James Hsieh, Ari Hakimi, Toni Chouieri
Funding: AACR INNOVATOR Award, NIH (TCGA), NIH (K24), V Foundation for Cancer Research