Implications for the Human Genome Project on the Management of Renal

Cell Carcinoma

Wade J Sexton MD Senior Member

Department of Genitourinary Oncology Moffitt Cancer Center

Tampa Florida

I have no disclosures related to this CME event

Is This an Indolent Tumor

Could we have predicted this event

Can We Predict Treatment Response Neoadjuvant TKI

Dec 2006 Mar 2007

bull Single institution open-label non-randomized ndash Biopsy proven clear cell RCC ndash cT2-T3b N0 M0 (all patients had cT3a tumors) ndash 24 patients

bull Neoadjuvant Axitinib ndash 5 mg BID with upward titration (10 mg BID) ndash 12 weeks continuous therapy (off 36 hours prior to

radical or partial nephrectomy)

Eur Urol (2014) httpdxdoiorg101016jeururo201401035

Karam JA et al Eur Urol (2014) httpdxdoiorg101016jeururo201401035

Partial Response Stable Disease

bull Response in 100 of tumors (23 patients) ndash Median reduction in diameter 283

bull Median 10 cm 69 cm ndash No progression while on therapy

PR in 1124 = 458

Can We Optimize Therapeutic Strategies IVC Tumor Thrombus

Karakiewicz P et al Eur Urol 2008 53(4)845ndash848

Level IV ndash Intra-atrial Level I-II - Infrahepatic

Sunitinib x 2 cycles

Pre-operative TKI IVC Tumor Thrombus

Karakiewicz P et al Eur Urol 2008 53(4)845ndash848

Is This an Indolent Tumor

Could we have predicted this event

Can We Predict Treatment Response Neoadjuvant TKI

Dec 2006 Mar 2007

bull Single institution open-label non-randomized ndash Biopsy proven clear cell RCC ndash cT2-T3b N0 M0 (all patients had cT3a tumors) ndash 24 patients

bull Neoadjuvant Axitinib ndash 5 mg BID with upward titration (10 mg BID) ndash 12 weeks continuous therapy (off 36 hours prior to

radical or partial nephrectomy)

Eur Urol (2014) httpdxdoiorg101016jeururo201401035

Karam JA et al Eur Urol (2014) httpdxdoiorg101016jeururo201401035

Partial Response Stable Disease

bull Response in 100 of tumors (23 patients) ndash Median reduction in diameter 283

bull Median 10 cm 69 cm ndash No progression while on therapy

PR in 1124 = 458

Can We Optimize Therapeutic Strategies IVC Tumor Thrombus

Karakiewicz P et al Eur Urol 2008 53(4)845ndash848

Level IV ndash Intra-atrial Level I-II - Infrahepatic

Sunitinib x 2 cycles

Pre-operative TKI IVC Tumor Thrombus

Karakiewicz P et al Eur Urol 2008 53(4)845ndash848

Could we have predicted this event

Can We Predict Treatment Response Neoadjuvant TKI

Dec 2006 Mar 2007

bull Single institution open-label non-randomized ndash Biopsy proven clear cell RCC ndash cT2-T3b N0 M0 (all patients had cT3a tumors) ndash 24 patients

bull Neoadjuvant Axitinib ndash 5 mg BID with upward titration (10 mg BID) ndash 12 weeks continuous therapy (off 36 hours prior to

radical or partial nephrectomy)

Eur Urol (2014) httpdxdoiorg101016jeururo201401035

Karam JA et al Eur Urol (2014) httpdxdoiorg101016jeururo201401035

Partial Response Stable Disease

bull Response in 100 of tumors (23 patients) ndash Median reduction in diameter 283

bull Median 10 cm 69 cm ndash No progression while on therapy

PR in 1124 = 458

Can We Optimize Therapeutic Strategies IVC Tumor Thrombus

Karakiewicz P et al Eur Urol 2008 53(4)845ndash848

Level IV ndash Intra-atrial Level I-II - Infrahepatic

Sunitinib x 2 cycles

Pre-operative TKI IVC Tumor Thrombus

Karakiewicz P et al Eur Urol 2008 53(4)845ndash848

Can We Predict Treatment Response Neoadjuvant TKI

Dec 2006 Mar 2007

bull Single institution open-label non-randomized ndash Biopsy proven clear cell RCC ndash cT2-T3b N0 M0 (all patients had cT3a tumors) ndash 24 patients

bull Neoadjuvant Axitinib ndash 5 mg BID with upward titration (10 mg BID) ndash 12 weeks continuous therapy (off 36 hours prior to

radical or partial nephrectomy)

Eur Urol (2014) httpdxdoiorg101016jeururo201401035

Karam JA et al Eur Urol (2014) httpdxdoiorg101016jeururo201401035

Partial Response Stable Disease

bull Response in 100 of tumors (23 patients) ndash Median reduction in diameter 283

bull Median 10 cm 69 cm ndash No progression while on therapy

PR in 1124 = 458

Can We Optimize Therapeutic Strategies IVC Tumor Thrombus

Karakiewicz P et al Eur Urol 2008 53(4)845ndash848

Level IV ndash Intra-atrial Level I-II - Infrahepatic

Sunitinib x 2 cycles

Pre-operative TKI IVC Tumor Thrombus

Karakiewicz P et al Eur Urol 2008 53(4)845ndash848

bull Single institution open-label non-randomized ndash Biopsy proven clear cell RCC ndash cT2-T3b N0 M0 (all patients had cT3a tumors) ndash 24 patients

bull Neoadjuvant Axitinib ndash 5 mg BID with upward titration (10 mg BID) ndash 12 weeks continuous therapy (off 36 hours prior to

radical or partial nephrectomy)

Eur Urol (2014) httpdxdoiorg101016jeururo201401035

Karam JA et al Eur Urol (2014) httpdxdoiorg101016jeururo201401035

Partial Response Stable Disease

bull Response in 100 of tumors (23 patients) ndash Median reduction in diameter 283

bull Median 10 cm 69 cm ndash No progression while on therapy

PR in 1124 = 458

Can We Optimize Therapeutic Strategies IVC Tumor Thrombus

Karakiewicz P et al Eur Urol 2008 53(4)845ndash848

Level IV ndash Intra-atrial Level I-II - Infrahepatic

Sunitinib x 2 cycles

Pre-operative TKI IVC Tumor Thrombus

Karakiewicz P et al Eur Urol 2008 53(4)845ndash848

Karam JA et al Eur Urol (2014) httpdxdoiorg101016jeururo201401035

Partial Response Stable Disease

bull Response in 100 of tumors (23 patients) ndash Median reduction in diameter 283

bull Median 10 cm 69 cm ndash No progression while on therapy

PR in 1124 = 458

Can We Optimize Therapeutic Strategies IVC Tumor Thrombus

Karakiewicz P et al Eur Urol 2008 53(4)845ndash848

Level IV ndash Intra-atrial Level I-II - Infrahepatic

Sunitinib x 2 cycles

Pre-operative TKI IVC Tumor Thrombus

Karakiewicz P et al Eur Urol 2008 53(4)845ndash848

Can We Optimize Therapeutic Strategies IVC Tumor Thrombus

Karakiewicz P et al Eur Urol 2008 53(4)845ndash848

Level IV ndash Intra-atrial Level I-II - Infrahepatic

Sunitinib x 2 cycles

Pre-operative TKI IVC Tumor Thrombus

Karakiewicz P et al Eur Urol 2008 53(4)845ndash848

Pre-operative TKI IVC Tumor Thrombus

Karakiewicz P et al Eur Urol 2008 53(4)845ndash848

Can We Optimize Systemic Therapies

March 2010 Feb 2012

Mar 2010 Feb 2012

Can We Optimize Systemic Therapies

March 2010 Feb 2012

Optimizing Systemic Therapies

There are Success Stories Using Current Decision Tools

But are we too often rolling the dice

Current Tools for Determining PrognosisTreatment for Kidney

Cancers

bull Post treatment prognosis ndash SSIGN ndash UCLA Integrated Staging System (UISS) ndash Karakiewicz Nomogram

bull Cytoreductive nephrectomy ndash Culp Criteria

bull Metastatic patients ndash Motzer criteria

Frank et al J Urol 20021682395-2400

SSIGN Score for Clear Cell RCCA

Overall Survival for RCCA UCLA Integrated Staging System (UISS)

Overall Survival for RCCA UCLA Integrated Staging System (UISS)

Overall Survival for RCCA UCLA Integrated Staging System (UISS)

NM at Diagnosis (n=468) M at Diagnosis (n=346)Low Intermediate High Low Intermediate High

No Patients 128 190 150 49 271 26Disease specific survival1 year 100 972 89 867 631 212 year 988 906 777 65 409 1053 year 949 877 637 556 314 04 year 931 855 609 371 228 05 year 911 804 547 32 195 0

Note Standard Error not shown

(Zisman et al JCO Dec 2002)

Karnofsky Performance Status bull 100 Normal no complaints no evidence of disease bull 90 Able to carry on normal activity minor signs or symptoms of disease bull 80 Normal activity with effort some signs or symptoms of disease bull 70 Cares for self unable to carry on normal activity or to do active work bull 60 Requires occasional assistance but is able to care for most of his

personal needs bull 50 Requires considerable assistance and frequent medical care bull 40 Disabled requires special care and assistance bull 30 Severely disabled hospital admission is indicated although death not

imminent bull 20 Very sick hospital admission necessary active supportive treatment

necessary bull 10 Moribund fatal processes progressing rapidly bull 0 Dead

Motzer Criteria

1 Low Karnoksky PS (le 70) 2 High LDH gt 15x normal 3 Low Hgb lt lower limit of normal 4 High corrected serum calcium gt 100 5 Absence of prior nephrectomy 6 Presence of liver mets 7 Increased alkaline phosphatase

Motzer RJ et al J Clin Oncol 2002 20289 (460 patients treated with IFN-alpha alone as initial therapy)

Motzer Criteria

bull Low risk ndash 0 risk factors median survival 30 months

bull Intermediate risk ndash 1-2 risk factors median survival 14 months

bull Poor risk ndash gt 3 risk factors median survival 5 months

bull Criteria developed during cytokine era

Motzer RJ et al J Clin Oncol 2002 20289

What do These Models Have in Common

bull Readily available patient characteristics bull Readily available tumor characteristics bull No tumor specific markers bull Is current practice a ldquopersonalized approachrdquo bull Still seems quite rudimentary in 2015 bull Can we do better

An Ongoing Struggle With Cancers hellipincluding kidney cancers

Traditional treatment model for many cancers (including RCCA) Like trying to fit a square peg into a round hole Tumors might appear the same but tumor biology response to therapy etc is very heterogeneous

hellipfrom the movie ldquoApollo 13rdquo

Renal Tumor Histology

Malignant Histology Frequency Origin Genetic Alteration

Clear cell 70-80 Proximal convoluted tubule 3p25 (VHL)

Papillary type I 5 Distal convoluted tubule 7q-31 (c-Met)

Papillary type II 10 Distal convoluted tubule 1q42 (FH)

Chromophobe 5 Distal convoluted tubule multiple (incl 17p)

Collecting duct lt1 Collecting ductMonosomy (16141522)

Medullary lt1 Collecting duct sickle cell

An ldquoexplosionrdquo of new information and strategies

The Cancer Genome Atlas (TCGA) bull TCGA project began in 2005 bull Primary aim

ndash Catalogue genetic mutations responsible for the development of cancer using high throughput genome sequencing techniques to improve our ability to diagnose treat and prevent cancer

bull Supervising bodies ndash National Cancer Institute ndash National Human Genome Research Institute

bull Funded by the US government

The Cancer Genome Atlas (TCGA) bull Initial focus towards three malignancies

ndash GBM Lung and Ovarian CA bull Goal characterize 20-25 tumors bull Up to 500 different patient tumor samples

ndash Obtained prior to adjuvant therapies bull Whole-genome and exome sequencing reveals

ndash Every tumor has different mutations ndash Mutations drive tumor biology

Comprehensive Molecular Characterization of Clear Cell RCCA

bull 346 Collaborators from numerous institutions bull Tumors from 446 patient assayed - at least one molecular

platform ndash RNA sequencing ndash DNA methylation arrays ndash miRNA sequencing ndash SNP arrays ndash Exome sequencing ndash Reverse phase protein arrays

bull Genetic changes underlying clear cell RCCA ndash alterations in genes (ie VHL) controlling cellular oxygen sensing ndash maintenance of chromatin states (ie PBRM1)

Nature 2013 Jul 4499(7456)43-9

Mar 2010 Feb 2012

Can We Optimize Systemic Therapies

March 2010 Feb 2012

Optimizing Systemic Therapies

There are Success Stories Using Current Decision Tools

But are we too often rolling the dice

Current Tools for Determining PrognosisTreatment for Kidney

Cancers

bull Post treatment prognosis ndash SSIGN ndash UCLA Integrated Staging System (UISS) ndash Karakiewicz Nomogram

bull Cytoreductive nephrectomy ndash Culp Criteria

bull Metastatic patients ndash Motzer criteria

Frank et al J Urol 20021682395-2400

SSIGN Score for Clear Cell RCCA

Overall Survival for RCCA UCLA Integrated Staging System (UISS)

Overall Survival for RCCA UCLA Integrated Staging System (UISS)

Overall Survival for RCCA UCLA Integrated Staging System (UISS)

NM at Diagnosis (n=468) M at Diagnosis (n=346)Low Intermediate High Low Intermediate High

No Patients 128 190 150 49 271 26Disease specific survival1 year 100 972 89 867 631 212 year 988 906 777 65 409 1053 year 949 877 637 556 314 04 year 931 855 609 371 228 05 year 911 804 547 32 195 0

Note Standard Error not shown

(Zisman et al JCO Dec 2002)

Karnofsky Performance Status bull 100 Normal no complaints no evidence of disease bull 90 Able to carry on normal activity minor signs or symptoms of disease bull 80 Normal activity with effort some signs or symptoms of disease bull 70 Cares for self unable to carry on normal activity or to do active work bull 60 Requires occasional assistance but is able to care for most of his

personal needs bull 50 Requires considerable assistance and frequent medical care bull 40 Disabled requires special care and assistance bull 30 Severely disabled hospital admission is indicated although death not

imminent bull 20 Very sick hospital admission necessary active supportive treatment

necessary bull 10 Moribund fatal processes progressing rapidly bull 0 Dead

Motzer Criteria

1 Low Karnoksky PS (le 70) 2 High LDH gt 15x normal 3 Low Hgb lt lower limit of normal 4 High corrected serum calcium gt 100 5 Absence of prior nephrectomy 6 Presence of liver mets 7 Increased alkaline phosphatase

Motzer RJ et al J Clin Oncol 2002 20289 (460 patients treated with IFN-alpha alone as initial therapy)

Motzer Criteria

bull Low risk ndash 0 risk factors median survival 30 months

bull Intermediate risk ndash 1-2 risk factors median survival 14 months

bull Poor risk ndash gt 3 risk factors median survival 5 months

bull Criteria developed during cytokine era

Motzer RJ et al J Clin Oncol 2002 20289

What do These Models Have in Common

bull Readily available patient characteristics bull Readily available tumor characteristics bull No tumor specific markers bull Is current practice a ldquopersonalized approachrdquo bull Still seems quite rudimentary in 2015 bull Can we do better

An Ongoing Struggle With Cancers hellipincluding kidney cancers

Traditional treatment model for many cancers (including RCCA) Like trying to fit a square peg into a round hole Tumors might appear the same but tumor biology response to therapy etc is very heterogeneous

hellipfrom the movie ldquoApollo 13rdquo

Renal Tumor Histology

Malignant Histology Frequency Origin Genetic Alteration

Clear cell 70-80 Proximal convoluted tubule 3p25 (VHL)

Papillary type I 5 Distal convoluted tubule 7q-31 (c-Met)

Papillary type II 10 Distal convoluted tubule 1q42 (FH)

Chromophobe 5 Distal convoluted tubule multiple (incl 17p)

Collecting duct lt1 Collecting ductMonosomy (16141522)

Medullary lt1 Collecting duct sickle cell

An ldquoexplosionrdquo of new information and strategies

The Cancer Genome Atlas (TCGA) bull TCGA project began in 2005 bull Primary aim

ndash Catalogue genetic mutations responsible for the development of cancer using high throughput genome sequencing techniques to improve our ability to diagnose treat and prevent cancer

bull Supervising bodies ndash National Cancer Institute ndash National Human Genome Research Institute

bull Funded by the US government

The Cancer Genome Atlas (TCGA) bull Initial focus towards three malignancies

ndash GBM Lung and Ovarian CA bull Goal characterize 20-25 tumors bull Up to 500 different patient tumor samples

ndash Obtained prior to adjuvant therapies bull Whole-genome and exome sequencing reveals

ndash Every tumor has different mutations ndash Mutations drive tumor biology

Comprehensive Molecular Characterization of Clear Cell RCCA

bull 346 Collaborators from numerous institutions bull Tumors from 446 patient assayed - at least one molecular

platform ndash RNA sequencing ndash DNA methylation arrays ndash miRNA sequencing ndash SNP arrays ndash Exome sequencing ndash Reverse phase protein arrays

bull Genetic changes underlying clear cell RCCA ndash alterations in genes (ie VHL) controlling cellular oxygen sensing ndash maintenance of chromatin states (ie PBRM1)

Nature 2013 Jul 4499(7456)43-9

March 2010 Feb 2012

Optimizing Systemic Therapies

There are Success Stories Using Current Decision Tools

But are we too often rolling the dice

Current Tools for Determining PrognosisTreatment for Kidney

Cancers

bull Post treatment prognosis ndash SSIGN ndash UCLA Integrated Staging System (UISS) ndash Karakiewicz Nomogram

bull Cytoreductive nephrectomy ndash Culp Criteria

bull Metastatic patients ndash Motzer criteria

Frank et al J Urol 20021682395-2400

SSIGN Score for Clear Cell RCCA

Overall Survival for RCCA UCLA Integrated Staging System (UISS)

Overall Survival for RCCA UCLA Integrated Staging System (UISS)

Overall Survival for RCCA UCLA Integrated Staging System (UISS)

NM at Diagnosis (n=468) M at Diagnosis (n=346)Low Intermediate High Low Intermediate High

No Patients 128 190 150 49 271 26Disease specific survival1 year 100 972 89 867 631 212 year 988 906 777 65 409 1053 year 949 877 637 556 314 04 year 931 855 609 371 228 05 year 911 804 547 32 195 0

Note Standard Error not shown

(Zisman et al JCO Dec 2002)

Karnofsky Performance Status bull 100 Normal no complaints no evidence of disease bull 90 Able to carry on normal activity minor signs or symptoms of disease bull 80 Normal activity with effort some signs or symptoms of disease bull 70 Cares for self unable to carry on normal activity or to do active work bull 60 Requires occasional assistance but is able to care for most of his

personal needs bull 50 Requires considerable assistance and frequent medical care bull 40 Disabled requires special care and assistance bull 30 Severely disabled hospital admission is indicated although death not

imminent bull 20 Very sick hospital admission necessary active supportive treatment

necessary bull 10 Moribund fatal processes progressing rapidly bull 0 Dead

Motzer Criteria

1 Low Karnoksky PS (le 70) 2 High LDH gt 15x normal 3 Low Hgb lt lower limit of normal 4 High corrected serum calcium gt 100 5 Absence of prior nephrectomy 6 Presence of liver mets 7 Increased alkaline phosphatase

Motzer RJ et al J Clin Oncol 2002 20289 (460 patients treated with IFN-alpha alone as initial therapy)

Motzer Criteria

bull Low risk ndash 0 risk factors median survival 30 months

bull Intermediate risk ndash 1-2 risk factors median survival 14 months

bull Poor risk ndash gt 3 risk factors median survival 5 months

bull Criteria developed during cytokine era

Motzer RJ et al J Clin Oncol 2002 20289

What do These Models Have in Common

bull Readily available patient characteristics bull Readily available tumor characteristics bull No tumor specific markers bull Is current practice a ldquopersonalized approachrdquo bull Still seems quite rudimentary in 2015 bull Can we do better

An Ongoing Struggle With Cancers hellipincluding kidney cancers

Traditional treatment model for many cancers (including RCCA) Like trying to fit a square peg into a round hole Tumors might appear the same but tumor biology response to therapy etc is very heterogeneous

hellipfrom the movie ldquoApollo 13rdquo

Renal Tumor Histology

Malignant Histology Frequency Origin Genetic Alteration

Clear cell 70-80 Proximal convoluted tubule 3p25 (VHL)

Papillary type I 5 Distal convoluted tubule 7q-31 (c-Met)

Papillary type II 10 Distal convoluted tubule 1q42 (FH)

Chromophobe 5 Distal convoluted tubule multiple (incl 17p)

Collecting duct lt1 Collecting ductMonosomy (16141522)

Medullary lt1 Collecting duct sickle cell

An ldquoexplosionrdquo of new information and strategies

The Cancer Genome Atlas (TCGA) bull TCGA project began in 2005 bull Primary aim

ndash Catalogue genetic mutations responsible for the development of cancer using high throughput genome sequencing techniques to improve our ability to diagnose treat and prevent cancer

bull Supervising bodies ndash National Cancer Institute ndash National Human Genome Research Institute

bull Funded by the US government

The Cancer Genome Atlas (TCGA) bull Initial focus towards three malignancies

ndash GBM Lung and Ovarian CA bull Goal characterize 20-25 tumors bull Up to 500 different patient tumor samples

ndash Obtained prior to adjuvant therapies bull Whole-genome and exome sequencing reveals

ndash Every tumor has different mutations ndash Mutations drive tumor biology

Comprehensive Molecular Characterization of Clear Cell RCCA

bull 346 Collaborators from numerous institutions bull Tumors from 446 patient assayed - at least one molecular

platform ndash RNA sequencing ndash DNA methylation arrays ndash miRNA sequencing ndash SNP arrays ndash Exome sequencing ndash Reverse phase protein arrays

bull Genetic changes underlying clear cell RCCA ndash alterations in genes (ie VHL) controlling cellular oxygen sensing ndash maintenance of chromatin states (ie PBRM1)

Nature 2013 Jul 4499(7456)43-9

There are Success Stories Using Current Decision Tools

But are we too often rolling the dice

Current Tools for Determining PrognosisTreatment for Kidney

Cancers

bull Post treatment prognosis ndash SSIGN ndash UCLA Integrated Staging System (UISS) ndash Karakiewicz Nomogram

bull Cytoreductive nephrectomy ndash Culp Criteria

bull Metastatic patients ndash Motzer criteria

Frank et al J Urol 20021682395-2400

SSIGN Score for Clear Cell RCCA

Overall Survival for RCCA UCLA Integrated Staging System (UISS)

Overall Survival for RCCA UCLA Integrated Staging System (UISS)

Overall Survival for RCCA UCLA Integrated Staging System (UISS)

NM at Diagnosis (n=468) M at Diagnosis (n=346)Low Intermediate High Low Intermediate High

No Patients 128 190 150 49 271 26Disease specific survival1 year 100 972 89 867 631 212 year 988 906 777 65 409 1053 year 949 877 637 556 314 04 year 931 855 609 371 228 05 year 911 804 547 32 195 0

Note Standard Error not shown

(Zisman et al JCO Dec 2002)

Karnofsky Performance Status bull 100 Normal no complaints no evidence of disease bull 90 Able to carry on normal activity minor signs or symptoms of disease bull 80 Normal activity with effort some signs or symptoms of disease bull 70 Cares for self unable to carry on normal activity or to do active work bull 60 Requires occasional assistance but is able to care for most of his

personal needs bull 50 Requires considerable assistance and frequent medical care bull 40 Disabled requires special care and assistance bull 30 Severely disabled hospital admission is indicated although death not

imminent bull 20 Very sick hospital admission necessary active supportive treatment

necessary bull 10 Moribund fatal processes progressing rapidly bull 0 Dead

Motzer Criteria

1 Low Karnoksky PS (le 70) 2 High LDH gt 15x normal 3 Low Hgb lt lower limit of normal 4 High corrected serum calcium gt 100 5 Absence of prior nephrectomy 6 Presence of liver mets 7 Increased alkaline phosphatase

Motzer RJ et al J Clin Oncol 2002 20289 (460 patients treated with IFN-alpha alone as initial therapy)

Motzer Criteria

bull Low risk ndash 0 risk factors median survival 30 months

bull Intermediate risk ndash 1-2 risk factors median survival 14 months

bull Poor risk ndash gt 3 risk factors median survival 5 months

bull Criteria developed during cytokine era

Motzer RJ et al J Clin Oncol 2002 20289

What do These Models Have in Common

bull Readily available patient characteristics bull Readily available tumor characteristics bull No tumor specific markers bull Is current practice a ldquopersonalized approachrdquo bull Still seems quite rudimentary in 2015 bull Can we do better

An Ongoing Struggle With Cancers hellipincluding kidney cancers

Traditional treatment model for many cancers (including RCCA) Like trying to fit a square peg into a round hole Tumors might appear the same but tumor biology response to therapy etc is very heterogeneous

hellipfrom the movie ldquoApollo 13rdquo

Renal Tumor Histology

Malignant Histology Frequency Origin Genetic Alteration

Clear cell 70-80 Proximal convoluted tubule 3p25 (VHL)

Papillary type I 5 Distal convoluted tubule 7q-31 (c-Met)

Papillary type II 10 Distal convoluted tubule 1q42 (FH)

Chromophobe 5 Distal convoluted tubule multiple (incl 17p)

Collecting duct lt1 Collecting ductMonosomy (16141522)

Medullary lt1 Collecting duct sickle cell

An ldquoexplosionrdquo of new information and strategies

The Cancer Genome Atlas (TCGA) bull TCGA project began in 2005 bull Primary aim

ndash Catalogue genetic mutations responsible for the development of cancer using high throughput genome sequencing techniques to improve our ability to diagnose treat and prevent cancer

bull Supervising bodies ndash National Cancer Institute ndash National Human Genome Research Institute

bull Funded by the US government

The Cancer Genome Atlas (TCGA) bull Initial focus towards three malignancies

ndash GBM Lung and Ovarian CA bull Goal characterize 20-25 tumors bull Up to 500 different patient tumor samples

ndash Obtained prior to adjuvant therapies bull Whole-genome and exome sequencing reveals

ndash Every tumor has different mutations ndash Mutations drive tumor biology

Comprehensive Molecular Characterization of Clear Cell RCCA

bull 346 Collaborators from numerous institutions bull Tumors from 446 patient assayed - at least one molecular

platform ndash RNA sequencing ndash DNA methylation arrays ndash miRNA sequencing ndash SNP arrays ndash Exome sequencing ndash Reverse phase protein arrays

bull Genetic changes underlying clear cell RCCA ndash alterations in genes (ie VHL) controlling cellular oxygen sensing ndash maintenance of chromatin states (ie PBRM1)

Nature 2013 Jul 4499(7456)43-9

Current Tools for Determining PrognosisTreatment for Kidney

Cancers

bull Post treatment prognosis ndash SSIGN ndash UCLA Integrated Staging System (UISS) ndash Karakiewicz Nomogram

bull Cytoreductive nephrectomy ndash Culp Criteria

bull Metastatic patients ndash Motzer criteria

Frank et al J Urol 20021682395-2400

SSIGN Score for Clear Cell RCCA

Overall Survival for RCCA UCLA Integrated Staging System (UISS)

Overall Survival for RCCA UCLA Integrated Staging System (UISS)

Overall Survival for RCCA UCLA Integrated Staging System (UISS)

NM at Diagnosis (n=468) M at Diagnosis (n=346)Low Intermediate High Low Intermediate High

No Patients 128 190 150 49 271 26Disease specific survival1 year 100 972 89 867 631 212 year 988 906 777 65 409 1053 year 949 877 637 556 314 04 year 931 855 609 371 228 05 year 911 804 547 32 195 0

Note Standard Error not shown

(Zisman et al JCO Dec 2002)

Karnofsky Performance Status bull 100 Normal no complaints no evidence of disease bull 90 Able to carry on normal activity minor signs or symptoms of disease bull 80 Normal activity with effort some signs or symptoms of disease bull 70 Cares for self unable to carry on normal activity or to do active work bull 60 Requires occasional assistance but is able to care for most of his

personal needs bull 50 Requires considerable assistance and frequent medical care bull 40 Disabled requires special care and assistance bull 30 Severely disabled hospital admission is indicated although death not

imminent bull 20 Very sick hospital admission necessary active supportive treatment

necessary bull 10 Moribund fatal processes progressing rapidly bull 0 Dead

Motzer Criteria

1 Low Karnoksky PS (le 70) 2 High LDH gt 15x normal 3 Low Hgb lt lower limit of normal 4 High corrected serum calcium gt 100 5 Absence of prior nephrectomy 6 Presence of liver mets 7 Increased alkaline phosphatase

Motzer RJ et al J Clin Oncol 2002 20289 (460 patients treated with IFN-alpha alone as initial therapy)

Motzer Criteria

bull Low risk ndash 0 risk factors median survival 30 months

bull Intermediate risk ndash 1-2 risk factors median survival 14 months

bull Poor risk ndash gt 3 risk factors median survival 5 months

bull Criteria developed during cytokine era

Motzer RJ et al J Clin Oncol 2002 20289

What do These Models Have in Common

bull Readily available patient characteristics bull Readily available tumor characteristics bull No tumor specific markers bull Is current practice a ldquopersonalized approachrdquo bull Still seems quite rudimentary in 2015 bull Can we do better

An Ongoing Struggle With Cancers hellipincluding kidney cancers

Traditional treatment model for many cancers (including RCCA) Like trying to fit a square peg into a round hole Tumors might appear the same but tumor biology response to therapy etc is very heterogeneous

hellipfrom the movie ldquoApollo 13rdquo

Renal Tumor Histology

Malignant Histology Frequency Origin Genetic Alteration

Clear cell 70-80 Proximal convoluted tubule 3p25 (VHL)

Papillary type I 5 Distal convoluted tubule 7q-31 (c-Met)

Papillary type II 10 Distal convoluted tubule 1q42 (FH)

Chromophobe 5 Distal convoluted tubule multiple (incl 17p)

Collecting duct lt1 Collecting ductMonosomy (16141522)

Medullary lt1 Collecting duct sickle cell

An ldquoexplosionrdquo of new information and strategies

The Cancer Genome Atlas (TCGA) bull TCGA project began in 2005 bull Primary aim

ndash Catalogue genetic mutations responsible for the development of cancer using high throughput genome sequencing techniques to improve our ability to diagnose treat and prevent cancer

bull Supervising bodies ndash National Cancer Institute ndash National Human Genome Research Institute

bull Funded by the US government

The Cancer Genome Atlas (TCGA) bull Initial focus towards three malignancies

ndash GBM Lung and Ovarian CA bull Goal characterize 20-25 tumors bull Up to 500 different patient tumor samples

ndash Obtained prior to adjuvant therapies bull Whole-genome and exome sequencing reveals

ndash Every tumor has different mutations ndash Mutations drive tumor biology

Comprehensive Molecular Characterization of Clear Cell RCCA

bull 346 Collaborators from numerous institutions bull Tumors from 446 patient assayed - at least one molecular

platform ndash RNA sequencing ndash DNA methylation arrays ndash miRNA sequencing ndash SNP arrays ndash Exome sequencing ndash Reverse phase protein arrays

bull Genetic changes underlying clear cell RCCA ndash alterations in genes (ie VHL) controlling cellular oxygen sensing ndash maintenance of chromatin states (ie PBRM1)

Nature 2013 Jul 4499(7456)43-9

Frank et al J Urol 20021682395-2400

SSIGN Score for Clear Cell RCCA

Overall Survival for RCCA UCLA Integrated Staging System (UISS)

Overall Survival for RCCA UCLA Integrated Staging System (UISS)

Overall Survival for RCCA UCLA Integrated Staging System (UISS)

NM at Diagnosis (n=468) M at Diagnosis (n=346)Low Intermediate High Low Intermediate High

No Patients 128 190 150 49 271 26Disease specific survival1 year 100 972 89 867 631 212 year 988 906 777 65 409 1053 year 949 877 637 556 314 04 year 931 855 609 371 228 05 year 911 804 547 32 195 0

Note Standard Error not shown

(Zisman et al JCO Dec 2002)

Karnofsky Performance Status bull 100 Normal no complaints no evidence of disease bull 90 Able to carry on normal activity minor signs or symptoms of disease bull 80 Normal activity with effort some signs or symptoms of disease bull 70 Cares for self unable to carry on normal activity or to do active work bull 60 Requires occasional assistance but is able to care for most of his

personal needs bull 50 Requires considerable assistance and frequent medical care bull 40 Disabled requires special care and assistance bull 30 Severely disabled hospital admission is indicated although death not

imminent bull 20 Very sick hospital admission necessary active supportive treatment

necessary bull 10 Moribund fatal processes progressing rapidly bull 0 Dead

Motzer Criteria

1 Low Karnoksky PS (le 70) 2 High LDH gt 15x normal 3 Low Hgb lt lower limit of normal 4 High corrected serum calcium gt 100 5 Absence of prior nephrectomy 6 Presence of liver mets 7 Increased alkaline phosphatase

Motzer RJ et al J Clin Oncol 2002 20289 (460 patients treated with IFN-alpha alone as initial therapy)

Motzer Criteria

bull Low risk ndash 0 risk factors median survival 30 months

bull Intermediate risk ndash 1-2 risk factors median survival 14 months

bull Poor risk ndash gt 3 risk factors median survival 5 months

bull Criteria developed during cytokine era

Motzer RJ et al J Clin Oncol 2002 20289

What do These Models Have in Common

bull Readily available patient characteristics bull Readily available tumor characteristics bull No tumor specific markers bull Is current practice a ldquopersonalized approachrdquo bull Still seems quite rudimentary in 2015 bull Can we do better

An Ongoing Struggle With Cancers hellipincluding kidney cancers

Traditional treatment model for many cancers (including RCCA) Like trying to fit a square peg into a round hole Tumors might appear the same but tumor biology response to therapy etc is very heterogeneous

hellipfrom the movie ldquoApollo 13rdquo

Renal Tumor Histology

Malignant Histology Frequency Origin Genetic Alteration

Clear cell 70-80 Proximal convoluted tubule 3p25 (VHL)

Papillary type I 5 Distal convoluted tubule 7q-31 (c-Met)

Papillary type II 10 Distal convoluted tubule 1q42 (FH)

Chromophobe 5 Distal convoluted tubule multiple (incl 17p)

Collecting duct lt1 Collecting ductMonosomy (16141522)

Medullary lt1 Collecting duct sickle cell

An ldquoexplosionrdquo of new information and strategies

The Cancer Genome Atlas (TCGA) bull TCGA project began in 2005 bull Primary aim

ndash Catalogue genetic mutations responsible for the development of cancer using high throughput genome sequencing techniques to improve our ability to diagnose treat and prevent cancer

bull Supervising bodies ndash National Cancer Institute ndash National Human Genome Research Institute

bull Funded by the US government

The Cancer Genome Atlas (TCGA) bull Initial focus towards three malignancies

ndash GBM Lung and Ovarian CA bull Goal characterize 20-25 tumors bull Up to 500 different patient tumor samples

ndash Obtained prior to adjuvant therapies bull Whole-genome and exome sequencing reveals

ndash Every tumor has different mutations ndash Mutations drive tumor biology

Comprehensive Molecular Characterization of Clear Cell RCCA

bull 346 Collaborators from numerous institutions bull Tumors from 446 patient assayed - at least one molecular

platform ndash RNA sequencing ndash DNA methylation arrays ndash miRNA sequencing ndash SNP arrays ndash Exome sequencing ndash Reverse phase protein arrays

bull Genetic changes underlying clear cell RCCA ndash alterations in genes (ie VHL) controlling cellular oxygen sensing ndash maintenance of chromatin states (ie PBRM1)

Nature 2013 Jul 4499(7456)43-9

Overall Survival for RCCA UCLA Integrated Staging System (UISS)

Overall Survival for RCCA UCLA Integrated Staging System (UISS)

Overall Survival for RCCA UCLA Integrated Staging System (UISS)

NM at Diagnosis (n=468) M at Diagnosis (n=346)Low Intermediate High Low Intermediate High

No Patients 128 190 150 49 271 26Disease specific survival1 year 100 972 89 867 631 212 year 988 906 777 65 409 1053 year 949 877 637 556 314 04 year 931 855 609 371 228 05 year 911 804 547 32 195 0

Note Standard Error not shown

(Zisman et al JCO Dec 2002)

Karnofsky Performance Status bull 100 Normal no complaints no evidence of disease bull 90 Able to carry on normal activity minor signs or symptoms of disease bull 80 Normal activity with effort some signs or symptoms of disease bull 70 Cares for self unable to carry on normal activity or to do active work bull 60 Requires occasional assistance but is able to care for most of his

personal needs bull 50 Requires considerable assistance and frequent medical care bull 40 Disabled requires special care and assistance bull 30 Severely disabled hospital admission is indicated although death not

imminent bull 20 Very sick hospital admission necessary active supportive treatment

necessary bull 10 Moribund fatal processes progressing rapidly bull 0 Dead

Motzer Criteria

1 Low Karnoksky PS (le 70) 2 High LDH gt 15x normal 3 Low Hgb lt lower limit of normal 4 High corrected serum calcium gt 100 5 Absence of prior nephrectomy 6 Presence of liver mets 7 Increased alkaline phosphatase

Motzer RJ et al J Clin Oncol 2002 20289 (460 patients treated with IFN-alpha alone as initial therapy)

Motzer Criteria

bull Low risk ndash 0 risk factors median survival 30 months

bull Intermediate risk ndash 1-2 risk factors median survival 14 months

bull Poor risk ndash gt 3 risk factors median survival 5 months

bull Criteria developed during cytokine era

Motzer RJ et al J Clin Oncol 2002 20289

What do These Models Have in Common

bull Readily available patient characteristics bull Readily available tumor characteristics bull No tumor specific markers bull Is current practice a ldquopersonalized approachrdquo bull Still seems quite rudimentary in 2015 bull Can we do better

An Ongoing Struggle With Cancers hellipincluding kidney cancers

Traditional treatment model for many cancers (including RCCA) Like trying to fit a square peg into a round hole Tumors might appear the same but tumor biology response to therapy etc is very heterogeneous

hellipfrom the movie ldquoApollo 13rdquo

Renal Tumor Histology

Malignant Histology Frequency Origin Genetic Alteration

Clear cell 70-80 Proximal convoluted tubule 3p25 (VHL)

Papillary type I 5 Distal convoluted tubule 7q-31 (c-Met)

Papillary type II 10 Distal convoluted tubule 1q42 (FH)

Chromophobe 5 Distal convoluted tubule multiple (incl 17p)

Collecting duct lt1 Collecting ductMonosomy (16141522)

Medullary lt1 Collecting duct sickle cell

An ldquoexplosionrdquo of new information and strategies

The Cancer Genome Atlas (TCGA) bull TCGA project began in 2005 bull Primary aim

ndash Catalogue genetic mutations responsible for the development of cancer using high throughput genome sequencing techniques to improve our ability to diagnose treat and prevent cancer

bull Supervising bodies ndash National Cancer Institute ndash National Human Genome Research Institute

bull Funded by the US government

The Cancer Genome Atlas (TCGA) bull Initial focus towards three malignancies

ndash GBM Lung and Ovarian CA bull Goal characterize 20-25 tumors bull Up to 500 different patient tumor samples

ndash Obtained prior to adjuvant therapies bull Whole-genome and exome sequencing reveals

ndash Every tumor has different mutations ndash Mutations drive tumor biology

Comprehensive Molecular Characterization of Clear Cell RCCA

bull 346 Collaborators from numerous institutions bull Tumors from 446 patient assayed - at least one molecular

platform ndash RNA sequencing ndash DNA methylation arrays ndash miRNA sequencing ndash SNP arrays ndash Exome sequencing ndash Reverse phase protein arrays

bull Genetic changes underlying clear cell RCCA ndash alterations in genes (ie VHL) controlling cellular oxygen sensing ndash maintenance of chromatin states (ie PBRM1)

Nature 2013 Jul 4499(7456)43-9

Overall Survival for RCCA UCLA Integrated Staging System (UISS)

Overall Survival for RCCA UCLA Integrated Staging System (UISS)

NM at Diagnosis (n=468) M at Diagnosis (n=346)Low Intermediate High Low Intermediate High

No Patients 128 190 150 49 271 26Disease specific survival1 year 100 972 89 867 631 212 year 988 906 777 65 409 1053 year 949 877 637 556 314 04 year 931 855 609 371 228 05 year 911 804 547 32 195 0

Note Standard Error not shown

(Zisman et al JCO Dec 2002)

Karnofsky Performance Status bull 100 Normal no complaints no evidence of disease bull 90 Able to carry on normal activity minor signs or symptoms of disease bull 80 Normal activity with effort some signs or symptoms of disease bull 70 Cares for self unable to carry on normal activity or to do active work bull 60 Requires occasional assistance but is able to care for most of his

personal needs bull 50 Requires considerable assistance and frequent medical care bull 40 Disabled requires special care and assistance bull 30 Severely disabled hospital admission is indicated although death not

imminent bull 20 Very sick hospital admission necessary active supportive treatment

necessary bull 10 Moribund fatal processes progressing rapidly bull 0 Dead

Motzer Criteria

1 Low Karnoksky PS (le 70) 2 High LDH gt 15x normal 3 Low Hgb lt lower limit of normal 4 High corrected serum calcium gt 100 5 Absence of prior nephrectomy 6 Presence of liver mets 7 Increased alkaline phosphatase

Motzer RJ et al J Clin Oncol 2002 20289 (460 patients treated with IFN-alpha alone as initial therapy)

Motzer Criteria

bull Low risk ndash 0 risk factors median survival 30 months

bull Intermediate risk ndash 1-2 risk factors median survival 14 months

bull Poor risk ndash gt 3 risk factors median survival 5 months

bull Criteria developed during cytokine era

Motzer RJ et al J Clin Oncol 2002 20289

What do These Models Have in Common

bull Readily available patient characteristics bull Readily available tumor characteristics bull No tumor specific markers bull Is current practice a ldquopersonalized approachrdquo bull Still seems quite rudimentary in 2015 bull Can we do better

An Ongoing Struggle With Cancers hellipincluding kidney cancers

Traditional treatment model for many cancers (including RCCA) Like trying to fit a square peg into a round hole Tumors might appear the same but tumor biology response to therapy etc is very heterogeneous

hellipfrom the movie ldquoApollo 13rdquo

Renal Tumor Histology

Malignant Histology Frequency Origin Genetic Alteration

Clear cell 70-80 Proximal convoluted tubule 3p25 (VHL)

Papillary type I 5 Distal convoluted tubule 7q-31 (c-Met)

Papillary type II 10 Distal convoluted tubule 1q42 (FH)

Chromophobe 5 Distal convoluted tubule multiple (incl 17p)

Collecting duct lt1 Collecting ductMonosomy (16141522)

Medullary lt1 Collecting duct sickle cell

An ldquoexplosionrdquo of new information and strategies

The Cancer Genome Atlas (TCGA) bull TCGA project began in 2005 bull Primary aim

ndash Catalogue genetic mutations responsible for the development of cancer using high throughput genome sequencing techniques to improve our ability to diagnose treat and prevent cancer

bull Supervising bodies ndash National Cancer Institute ndash National Human Genome Research Institute

bull Funded by the US government

The Cancer Genome Atlas (TCGA) bull Initial focus towards three malignancies

ndash GBM Lung and Ovarian CA bull Goal characterize 20-25 tumors bull Up to 500 different patient tumor samples

ndash Obtained prior to adjuvant therapies bull Whole-genome and exome sequencing reveals

ndash Every tumor has different mutations ndash Mutations drive tumor biology

Comprehensive Molecular Characterization of Clear Cell RCCA

bull 346 Collaborators from numerous institutions bull Tumors from 446 patient assayed - at least one molecular

platform ndash RNA sequencing ndash DNA methylation arrays ndash miRNA sequencing ndash SNP arrays ndash Exome sequencing ndash Reverse phase protein arrays

bull Genetic changes underlying clear cell RCCA ndash alterations in genes (ie VHL) controlling cellular oxygen sensing ndash maintenance of chromatin states (ie PBRM1)

Nature 2013 Jul 4499(7456)43-9

Overall Survival for RCCA UCLA Integrated Staging System (UISS)

NM at Diagnosis (n=468) M at Diagnosis (n=346)Low Intermediate High Low Intermediate High

No Patients 128 190 150 49 271 26Disease specific survival1 year 100 972 89 867 631 212 year 988 906 777 65 409 1053 year 949 877 637 556 314 04 year 931 855 609 371 228 05 year 911 804 547 32 195 0

Note Standard Error not shown

(Zisman et al JCO Dec 2002)

Karnofsky Performance Status bull 100 Normal no complaints no evidence of disease bull 90 Able to carry on normal activity minor signs or symptoms of disease bull 80 Normal activity with effort some signs or symptoms of disease bull 70 Cares for self unable to carry on normal activity or to do active work bull 60 Requires occasional assistance but is able to care for most of his

personal needs bull 50 Requires considerable assistance and frequent medical care bull 40 Disabled requires special care and assistance bull 30 Severely disabled hospital admission is indicated although death not

imminent bull 20 Very sick hospital admission necessary active supportive treatment

necessary bull 10 Moribund fatal processes progressing rapidly bull 0 Dead

Motzer Criteria

1 Low Karnoksky PS (le 70) 2 High LDH gt 15x normal 3 Low Hgb lt lower limit of normal 4 High corrected serum calcium gt 100 5 Absence of prior nephrectomy 6 Presence of liver mets 7 Increased alkaline phosphatase

Motzer RJ et al J Clin Oncol 2002 20289 (460 patients treated with IFN-alpha alone as initial therapy)

Motzer Criteria

bull Low risk ndash 0 risk factors median survival 30 months

bull Intermediate risk ndash 1-2 risk factors median survival 14 months

bull Poor risk ndash gt 3 risk factors median survival 5 months

bull Criteria developed during cytokine era

Motzer RJ et al J Clin Oncol 2002 20289

What do These Models Have in Common

bull Readily available patient characteristics bull Readily available tumor characteristics bull No tumor specific markers bull Is current practice a ldquopersonalized approachrdquo bull Still seems quite rudimentary in 2015 bull Can we do better

An Ongoing Struggle With Cancers hellipincluding kidney cancers

Traditional treatment model for many cancers (including RCCA) Like trying to fit a square peg into a round hole Tumors might appear the same but tumor biology response to therapy etc is very heterogeneous

hellipfrom the movie ldquoApollo 13rdquo

Renal Tumor Histology

Malignant Histology Frequency Origin Genetic Alteration

Clear cell 70-80 Proximal convoluted tubule 3p25 (VHL)

Papillary type I 5 Distal convoluted tubule 7q-31 (c-Met)

Papillary type II 10 Distal convoluted tubule 1q42 (FH)

Chromophobe 5 Distal convoluted tubule multiple (incl 17p)

Collecting duct lt1 Collecting ductMonosomy (16141522)

Medullary lt1 Collecting duct sickle cell

An ldquoexplosionrdquo of new information and strategies

The Cancer Genome Atlas (TCGA) bull TCGA project began in 2005 bull Primary aim

ndash Catalogue genetic mutations responsible for the development of cancer using high throughput genome sequencing techniques to improve our ability to diagnose treat and prevent cancer

bull Supervising bodies ndash National Cancer Institute ndash National Human Genome Research Institute

bull Funded by the US government

The Cancer Genome Atlas (TCGA) bull Initial focus towards three malignancies

ndash GBM Lung and Ovarian CA bull Goal characterize 20-25 tumors bull Up to 500 different patient tumor samples

ndash Obtained prior to adjuvant therapies bull Whole-genome and exome sequencing reveals

ndash Every tumor has different mutations ndash Mutations drive tumor biology

Comprehensive Molecular Characterization of Clear Cell RCCA

bull 346 Collaborators from numerous institutions bull Tumors from 446 patient assayed - at least one molecular

platform ndash RNA sequencing ndash DNA methylation arrays ndash miRNA sequencing ndash SNP arrays ndash Exome sequencing ndash Reverse phase protein arrays

bull Genetic changes underlying clear cell RCCA ndash alterations in genes (ie VHL) controlling cellular oxygen sensing ndash maintenance of chromatin states (ie PBRM1)

Nature 2013 Jul 4499(7456)43-9

Karnofsky Performance Status bull 100 Normal no complaints no evidence of disease bull 90 Able to carry on normal activity minor signs or symptoms of disease bull 80 Normal activity with effort some signs or symptoms of disease bull 70 Cares for self unable to carry on normal activity or to do active work bull 60 Requires occasional assistance but is able to care for most of his

personal needs bull 50 Requires considerable assistance and frequent medical care bull 40 Disabled requires special care and assistance bull 30 Severely disabled hospital admission is indicated although death not

imminent bull 20 Very sick hospital admission necessary active supportive treatment

necessary bull 10 Moribund fatal processes progressing rapidly bull 0 Dead

Motzer Criteria

1 Low Karnoksky PS (le 70) 2 High LDH gt 15x normal 3 Low Hgb lt lower limit of normal 4 High corrected serum calcium gt 100 5 Absence of prior nephrectomy 6 Presence of liver mets 7 Increased alkaline phosphatase

Motzer RJ et al J Clin Oncol 2002 20289 (460 patients treated with IFN-alpha alone as initial therapy)

Motzer Criteria

bull Low risk ndash 0 risk factors median survival 30 months

bull Intermediate risk ndash 1-2 risk factors median survival 14 months

bull Poor risk ndash gt 3 risk factors median survival 5 months

bull Criteria developed during cytokine era

Motzer RJ et al J Clin Oncol 2002 20289

What do These Models Have in Common

bull Readily available patient characteristics bull Readily available tumor characteristics bull No tumor specific markers bull Is current practice a ldquopersonalized approachrdquo bull Still seems quite rudimentary in 2015 bull Can we do better

An Ongoing Struggle With Cancers hellipincluding kidney cancers

Traditional treatment model for many cancers (including RCCA) Like trying to fit a square peg into a round hole Tumors might appear the same but tumor biology response to therapy etc is very heterogeneous

hellipfrom the movie ldquoApollo 13rdquo

Renal Tumor Histology

Malignant Histology Frequency Origin Genetic Alteration

Clear cell 70-80 Proximal convoluted tubule 3p25 (VHL)

Papillary type I 5 Distal convoluted tubule 7q-31 (c-Met)

Papillary type II 10 Distal convoluted tubule 1q42 (FH)

Chromophobe 5 Distal convoluted tubule multiple (incl 17p)

Collecting duct lt1 Collecting ductMonosomy (16141522)

Medullary lt1 Collecting duct sickle cell

An ldquoexplosionrdquo of new information and strategies

The Cancer Genome Atlas (TCGA) bull TCGA project began in 2005 bull Primary aim

ndash Catalogue genetic mutations responsible for the development of cancer using high throughput genome sequencing techniques to improve our ability to diagnose treat and prevent cancer

bull Supervising bodies ndash National Cancer Institute ndash National Human Genome Research Institute

bull Funded by the US government

The Cancer Genome Atlas (TCGA) bull Initial focus towards three malignancies

ndash GBM Lung and Ovarian CA bull Goal characterize 20-25 tumors bull Up to 500 different patient tumor samples

ndash Obtained prior to adjuvant therapies bull Whole-genome and exome sequencing reveals

ndash Every tumor has different mutations ndash Mutations drive tumor biology

Comprehensive Molecular Characterization of Clear Cell RCCA

bull 346 Collaborators from numerous institutions bull Tumors from 446 patient assayed - at least one molecular

platform ndash RNA sequencing ndash DNA methylation arrays ndash miRNA sequencing ndash SNP arrays ndash Exome sequencing ndash Reverse phase protein arrays

bull Genetic changes underlying clear cell RCCA ndash alterations in genes (ie VHL) controlling cellular oxygen sensing ndash maintenance of chromatin states (ie PBRM1)

Nature 2013 Jul 4499(7456)43-9

Motzer Criteria

1 Low Karnoksky PS (le 70) 2 High LDH gt 15x normal 3 Low Hgb lt lower limit of normal 4 High corrected serum calcium gt 100 5 Absence of prior nephrectomy 6 Presence of liver mets 7 Increased alkaline phosphatase

Motzer RJ et al J Clin Oncol 2002 20289 (460 patients treated with IFN-alpha alone as initial therapy)

Motzer Criteria

bull Low risk ndash 0 risk factors median survival 30 months

bull Intermediate risk ndash 1-2 risk factors median survival 14 months

bull Poor risk ndash gt 3 risk factors median survival 5 months

bull Criteria developed during cytokine era

Motzer RJ et al J Clin Oncol 2002 20289

What do These Models Have in Common

bull Readily available patient characteristics bull Readily available tumor characteristics bull No tumor specific markers bull Is current practice a ldquopersonalized approachrdquo bull Still seems quite rudimentary in 2015 bull Can we do better

An Ongoing Struggle With Cancers hellipincluding kidney cancers

Traditional treatment model for many cancers (including RCCA) Like trying to fit a square peg into a round hole Tumors might appear the same but tumor biology response to therapy etc is very heterogeneous

hellipfrom the movie ldquoApollo 13rdquo

Renal Tumor Histology

Malignant Histology Frequency Origin Genetic Alteration

Clear cell 70-80 Proximal convoluted tubule 3p25 (VHL)

Papillary type I 5 Distal convoluted tubule 7q-31 (c-Met)

Papillary type II 10 Distal convoluted tubule 1q42 (FH)

Chromophobe 5 Distal convoluted tubule multiple (incl 17p)

Collecting duct lt1 Collecting ductMonosomy (16141522)

Medullary lt1 Collecting duct sickle cell

An ldquoexplosionrdquo of new information and strategies

The Cancer Genome Atlas (TCGA) bull TCGA project began in 2005 bull Primary aim

ndash Catalogue genetic mutations responsible for the development of cancer using high throughput genome sequencing techniques to improve our ability to diagnose treat and prevent cancer

bull Supervising bodies ndash National Cancer Institute ndash National Human Genome Research Institute

bull Funded by the US government

The Cancer Genome Atlas (TCGA) bull Initial focus towards three malignancies

ndash GBM Lung and Ovarian CA bull Goal characterize 20-25 tumors bull Up to 500 different patient tumor samples

ndash Obtained prior to adjuvant therapies bull Whole-genome and exome sequencing reveals

ndash Every tumor has different mutations ndash Mutations drive tumor biology

Comprehensive Molecular Characterization of Clear Cell RCCA

bull 346 Collaborators from numerous institutions bull Tumors from 446 patient assayed - at least one molecular

platform ndash RNA sequencing ndash DNA methylation arrays ndash miRNA sequencing ndash SNP arrays ndash Exome sequencing ndash Reverse phase protein arrays

bull Genetic changes underlying clear cell RCCA ndash alterations in genes (ie VHL) controlling cellular oxygen sensing ndash maintenance of chromatin states (ie PBRM1)

Nature 2013 Jul 4499(7456)43-9

Motzer Criteria

bull Low risk ndash 0 risk factors median survival 30 months

bull Intermediate risk ndash 1-2 risk factors median survival 14 months

bull Poor risk ndash gt 3 risk factors median survival 5 months

bull Criteria developed during cytokine era

Motzer RJ et al J Clin Oncol 2002 20289

What do These Models Have in Common

bull Readily available patient characteristics bull Readily available tumor characteristics bull No tumor specific markers bull Is current practice a ldquopersonalized approachrdquo bull Still seems quite rudimentary in 2015 bull Can we do better

An Ongoing Struggle With Cancers hellipincluding kidney cancers

Traditional treatment model for many cancers (including RCCA) Like trying to fit a square peg into a round hole Tumors might appear the same but tumor biology response to therapy etc is very heterogeneous

hellipfrom the movie ldquoApollo 13rdquo

Renal Tumor Histology

Malignant Histology Frequency Origin Genetic Alteration

Clear cell 70-80 Proximal convoluted tubule 3p25 (VHL)

Papillary type I 5 Distal convoluted tubule 7q-31 (c-Met)

Papillary type II 10 Distal convoluted tubule 1q42 (FH)

Chromophobe 5 Distal convoluted tubule multiple (incl 17p)

Collecting duct lt1 Collecting ductMonosomy (16141522)

Medullary lt1 Collecting duct sickle cell

An ldquoexplosionrdquo of new information and strategies

The Cancer Genome Atlas (TCGA) bull TCGA project began in 2005 bull Primary aim

ndash Catalogue genetic mutations responsible for the development of cancer using high throughput genome sequencing techniques to improve our ability to diagnose treat and prevent cancer

bull Supervising bodies ndash National Cancer Institute ndash National Human Genome Research Institute

bull Funded by the US government

The Cancer Genome Atlas (TCGA) bull Initial focus towards three malignancies

ndash GBM Lung and Ovarian CA bull Goal characterize 20-25 tumors bull Up to 500 different patient tumor samples

ndash Obtained prior to adjuvant therapies bull Whole-genome and exome sequencing reveals

ndash Every tumor has different mutations ndash Mutations drive tumor biology

Comprehensive Molecular Characterization of Clear Cell RCCA

bull 346 Collaborators from numerous institutions bull Tumors from 446 patient assayed - at least one molecular

platform ndash RNA sequencing ndash DNA methylation arrays ndash miRNA sequencing ndash SNP arrays ndash Exome sequencing ndash Reverse phase protein arrays

bull Genetic changes underlying clear cell RCCA ndash alterations in genes (ie VHL) controlling cellular oxygen sensing ndash maintenance of chromatin states (ie PBRM1)

Nature 2013 Jul 4499(7456)43-9

What do These Models Have in Common

bull Readily available patient characteristics bull Readily available tumor characteristics bull No tumor specific markers bull Is current practice a ldquopersonalized approachrdquo bull Still seems quite rudimentary in 2015 bull Can we do better

An Ongoing Struggle With Cancers hellipincluding kidney cancers

Traditional treatment model for many cancers (including RCCA) Like trying to fit a square peg into a round hole Tumors might appear the same but tumor biology response to therapy etc is very heterogeneous

hellipfrom the movie ldquoApollo 13rdquo

Renal Tumor Histology

Malignant Histology Frequency Origin Genetic Alteration

Clear cell 70-80 Proximal convoluted tubule 3p25 (VHL)

Papillary type I 5 Distal convoluted tubule 7q-31 (c-Met)

Papillary type II 10 Distal convoluted tubule 1q42 (FH)

Chromophobe 5 Distal convoluted tubule multiple (incl 17p)

Collecting duct lt1 Collecting ductMonosomy (16141522)

Medullary lt1 Collecting duct sickle cell

An ldquoexplosionrdquo of new information and strategies

The Cancer Genome Atlas (TCGA) bull TCGA project began in 2005 bull Primary aim

ndash Catalogue genetic mutations responsible for the development of cancer using high throughput genome sequencing techniques to improve our ability to diagnose treat and prevent cancer

bull Supervising bodies ndash National Cancer Institute ndash National Human Genome Research Institute

bull Funded by the US government

The Cancer Genome Atlas (TCGA) bull Initial focus towards three malignancies

ndash GBM Lung and Ovarian CA bull Goal characterize 20-25 tumors bull Up to 500 different patient tumor samples

ndash Obtained prior to adjuvant therapies bull Whole-genome and exome sequencing reveals

ndash Every tumor has different mutations ndash Mutations drive tumor biology

Comprehensive Molecular Characterization of Clear Cell RCCA

bull 346 Collaborators from numerous institutions bull Tumors from 446 patient assayed - at least one molecular

platform ndash RNA sequencing ndash DNA methylation arrays ndash miRNA sequencing ndash SNP arrays ndash Exome sequencing ndash Reverse phase protein arrays

bull Genetic changes underlying clear cell RCCA ndash alterations in genes (ie VHL) controlling cellular oxygen sensing ndash maintenance of chromatin states (ie PBRM1)

Nature 2013 Jul 4499(7456)43-9

An Ongoing Struggle With Cancers hellipincluding kidney cancers

Traditional treatment model for many cancers (including RCCA) Like trying to fit a square peg into a round hole Tumors might appear the same but tumor biology response to therapy etc is very heterogeneous

hellipfrom the movie ldquoApollo 13rdquo

Renal Tumor Histology

Malignant Histology Frequency Origin Genetic Alteration

Clear cell 70-80 Proximal convoluted tubule 3p25 (VHL)

Papillary type I 5 Distal convoluted tubule 7q-31 (c-Met)

Papillary type II 10 Distal convoluted tubule 1q42 (FH)

Chromophobe 5 Distal convoluted tubule multiple (incl 17p)

Collecting duct lt1 Collecting ductMonosomy (16141522)

Medullary lt1 Collecting duct sickle cell

An ldquoexplosionrdquo of new information and strategies

The Cancer Genome Atlas (TCGA) bull TCGA project began in 2005 bull Primary aim

ndash Catalogue genetic mutations responsible for the development of cancer using high throughput genome sequencing techniques to improve our ability to diagnose treat and prevent cancer

bull Supervising bodies ndash National Cancer Institute ndash National Human Genome Research Institute

bull Funded by the US government

The Cancer Genome Atlas (TCGA) bull Initial focus towards three malignancies

ndash GBM Lung and Ovarian CA bull Goal characterize 20-25 tumors bull Up to 500 different patient tumor samples

ndash Obtained prior to adjuvant therapies bull Whole-genome and exome sequencing reveals

ndash Every tumor has different mutations ndash Mutations drive tumor biology

Comprehensive Molecular Characterization of Clear Cell RCCA

bull 346 Collaborators from numerous institutions bull Tumors from 446 patient assayed - at least one molecular

platform ndash RNA sequencing ndash DNA methylation arrays ndash miRNA sequencing ndash SNP arrays ndash Exome sequencing ndash Reverse phase protein arrays

bull Genetic changes underlying clear cell RCCA ndash alterations in genes (ie VHL) controlling cellular oxygen sensing ndash maintenance of chromatin states (ie PBRM1)

Nature 2013 Jul 4499(7456)43-9

Traditional treatment model for many cancers (including RCCA) Like trying to fit a square peg into a round hole Tumors might appear the same but tumor biology response to therapy etc is very heterogeneous

hellipfrom the movie ldquoApollo 13rdquo

Renal Tumor Histology

Malignant Histology Frequency Origin Genetic Alteration

Clear cell 70-80 Proximal convoluted tubule 3p25 (VHL)

Papillary type I 5 Distal convoluted tubule 7q-31 (c-Met)

Papillary type II 10 Distal convoluted tubule 1q42 (FH)

Chromophobe 5 Distal convoluted tubule multiple (incl 17p)

Collecting duct lt1 Collecting ductMonosomy (16141522)

Medullary lt1 Collecting duct sickle cell

An ldquoexplosionrdquo of new information and strategies

The Cancer Genome Atlas (TCGA) bull TCGA project began in 2005 bull Primary aim

ndash Catalogue genetic mutations responsible for the development of cancer using high throughput genome sequencing techniques to improve our ability to diagnose treat and prevent cancer

bull Supervising bodies ndash National Cancer Institute ndash National Human Genome Research Institute

bull Funded by the US government

The Cancer Genome Atlas (TCGA) bull Initial focus towards three malignancies

ndash GBM Lung and Ovarian CA bull Goal characterize 20-25 tumors bull Up to 500 different patient tumor samples

ndash Obtained prior to adjuvant therapies bull Whole-genome and exome sequencing reveals

ndash Every tumor has different mutations ndash Mutations drive tumor biology

Comprehensive Molecular Characterization of Clear Cell RCCA

bull 346 Collaborators from numerous institutions bull Tumors from 446 patient assayed - at least one molecular

platform ndash RNA sequencing ndash DNA methylation arrays ndash miRNA sequencing ndash SNP arrays ndash Exome sequencing ndash Reverse phase protein arrays

bull Genetic changes underlying clear cell RCCA ndash alterations in genes (ie VHL) controlling cellular oxygen sensing ndash maintenance of chromatin states (ie PBRM1)

Nature 2013 Jul 4499(7456)43-9

Renal Tumor Histology

Malignant Histology Frequency Origin Genetic Alteration

Clear cell 70-80 Proximal convoluted tubule 3p25 (VHL)

Papillary type I 5 Distal convoluted tubule 7q-31 (c-Met)

Papillary type II 10 Distal convoluted tubule 1q42 (FH)

Chromophobe 5 Distal convoluted tubule multiple (incl 17p)

Collecting duct lt1 Collecting ductMonosomy (16141522)

Medullary lt1 Collecting duct sickle cell

An ldquoexplosionrdquo of new information and strategies

The Cancer Genome Atlas (TCGA) bull TCGA project began in 2005 bull Primary aim

ndash Catalogue genetic mutations responsible for the development of cancer using high throughput genome sequencing techniques to improve our ability to diagnose treat and prevent cancer

bull Supervising bodies ndash National Cancer Institute ndash National Human Genome Research Institute

bull Funded by the US government

The Cancer Genome Atlas (TCGA) bull Initial focus towards three malignancies

ndash GBM Lung and Ovarian CA bull Goal characterize 20-25 tumors bull Up to 500 different patient tumor samples

ndash Obtained prior to adjuvant therapies bull Whole-genome and exome sequencing reveals

ndash Every tumor has different mutations ndash Mutations drive tumor biology

Comprehensive Molecular Characterization of Clear Cell RCCA

bull 346 Collaborators from numerous institutions bull Tumors from 446 patient assayed - at least one molecular

platform ndash RNA sequencing ndash DNA methylation arrays ndash miRNA sequencing ndash SNP arrays ndash Exome sequencing ndash Reverse phase protein arrays

bull Genetic changes underlying clear cell RCCA ndash alterations in genes (ie VHL) controlling cellular oxygen sensing ndash maintenance of chromatin states (ie PBRM1)

Nature 2013 Jul 4499(7456)43-9

An ldquoexplosionrdquo of new information and strategies

The Cancer Genome Atlas (TCGA) bull TCGA project began in 2005 bull Primary aim

ndash Catalogue genetic mutations responsible for the development of cancer using high throughput genome sequencing techniques to improve our ability to diagnose treat and prevent cancer

bull Supervising bodies ndash National Cancer Institute ndash National Human Genome Research Institute

bull Funded by the US government

The Cancer Genome Atlas (TCGA) bull Initial focus towards three malignancies

ndash GBM Lung and Ovarian CA bull Goal characterize 20-25 tumors bull Up to 500 different patient tumor samples

ndash Obtained prior to adjuvant therapies bull Whole-genome and exome sequencing reveals

ndash Every tumor has different mutations ndash Mutations drive tumor biology

Comprehensive Molecular Characterization of Clear Cell RCCA

bull 346 Collaborators from numerous institutions bull Tumors from 446 patient assayed - at least one molecular

platform ndash RNA sequencing ndash DNA methylation arrays ndash miRNA sequencing ndash SNP arrays ndash Exome sequencing ndash Reverse phase protein arrays

bull Genetic changes underlying clear cell RCCA ndash alterations in genes (ie VHL) controlling cellular oxygen sensing ndash maintenance of chromatin states (ie PBRM1)

Nature 2013 Jul 4499(7456)43-9

The Cancer Genome Atlas (TCGA) bull TCGA project began in 2005 bull Primary aim

ndash Catalogue genetic mutations responsible for the development of cancer using high throughput genome sequencing techniques to improve our ability to diagnose treat and prevent cancer

bull Supervising bodies ndash National Cancer Institute ndash National Human Genome Research Institute

bull Funded by the US government

The Cancer Genome Atlas (TCGA) bull Initial focus towards three malignancies

ndash GBM Lung and Ovarian CA bull Goal characterize 20-25 tumors bull Up to 500 different patient tumor samples

ndash Obtained prior to adjuvant therapies bull Whole-genome and exome sequencing reveals

ndash Every tumor has different mutations ndash Mutations drive tumor biology

Comprehensive Molecular Characterization of Clear Cell RCCA

bull 346 Collaborators from numerous institutions bull Tumors from 446 patient assayed - at least one molecular

platform ndash RNA sequencing ndash DNA methylation arrays ndash miRNA sequencing ndash SNP arrays ndash Exome sequencing ndash Reverse phase protein arrays

bull Genetic changes underlying clear cell RCCA ndash alterations in genes (ie VHL) controlling cellular oxygen sensing ndash maintenance of chromatin states (ie PBRM1)

Nature 2013 Jul 4499(7456)43-9

The Cancer Genome Atlas (TCGA) bull Initial focus towards three malignancies

ndash GBM Lung and Ovarian CA bull Goal characterize 20-25 tumors bull Up to 500 different patient tumor samples

ndash Obtained prior to adjuvant therapies bull Whole-genome and exome sequencing reveals

ndash Every tumor has different mutations ndash Mutations drive tumor biology

Comprehensive Molecular Characterization of Clear Cell RCCA

bull 346 Collaborators from numerous institutions bull Tumors from 446 patient assayed - at least one molecular

platform ndash RNA sequencing ndash DNA methylation arrays ndash miRNA sequencing ndash SNP arrays ndash Exome sequencing ndash Reverse phase protein arrays

bull Genetic changes underlying clear cell RCCA ndash alterations in genes (ie VHL) controlling cellular oxygen sensing ndash maintenance of chromatin states (ie PBRM1)

Nature 2013 Jul 4499(7456)43-9

Comprehensive Molecular Characterization of Clear Cell RCCA

bull 346 Collaborators from numerous institutions bull Tumors from 446 patient assayed - at least one molecular

platform ndash RNA sequencing ndash DNA methylation arrays ndash miRNA sequencing ndash SNP arrays ndash Exome sequencing ndash Reverse phase protein arrays

bull Genetic changes underlying clear cell RCCA ndash alterations in genes (ie VHL) controlling cellular oxygen sensing ndash maintenance of chromatin states (ie PBRM1)

Nature 2013 Jul 4499(7456)43-9

Comprehensive Molecular Characterization of Clear Cell RCCA

bull Identified 19 significantly mutated genes bull Potential therapeutic targets

ndash PI3KAktmTOR pathway recurrently mutated (altered in ~28 of tumors)

bull Widespread DNA Hypohypermethylation ndash HYPO - associated with mutation of methyltransferase SETD2 ndash HYPER ndash associated with tumors of higher stage and grade

bull Crosstalk between pathways ndash Mutations of chromatin remodeling complex (ie PBRM1) affect

other pathways

Nature 2013 Jul 4499(7456)43-9

Comprehensive Molecular Characterization of Clear Cell RCCA

bull Aggressive cancers associated with metabolic shift ndash Down-regulation of genes involved in the TCA cycle ndash Decreased AMPK and PTEN protein levels ndash Up-regulation of the pentose phosphate pathway and

the glutamine transporter genes ndash Increased acetyl-CoA carboxylase protein ndash Altered promoter methylation of miR-21 and GRB10 ndash Potential opportunities for disease treatment

Somatic Alterations in Clear Cell RCCA Nature 2013

mRNA amp miRNA Patterns Reflect Molecular Subtypes of Clear Cell RCCA Nature 2013

BAP1 amp PBRM1 Mutations bull BAP1 and PBRM1

ndash Two-hit tumor suppressor genes ndash Regulate seemingly different gene expression programs ndash Mutations are mutually exclusive

bull BAP1 Mutations ndash Present in 15 of clear-cell renal cell carcinomas ndash Associated with high nuclear grade stage and tumor aggression

when compared with tumors exclusively mutated for PBRM1

bull PBRM1 Mutations ndash Present in 50 of clear-cell renal cell carcinomas

bull Combined loss of BAP1 and PBRM1 genes ndash Present in small percent (lt5) of tumors ndash Some reports reveal association with rhabdoid features

Pentildea-Llopis et al Nat Genet 2012 Jun 10 44(7) 751ndash759 Gossage et al Genes Chromosomes Cancer 2014 Jan53(1)38-51

Molecular Characterization of RCCA with BAP1 amp PBRM1 Mutations

Joseph RW et al J Urol 2015 Aug 20 [Epub ahead of print]

BAP1 amp PBRM1 Mutations in Non-Clear Cell RCCA

bull 458 patients treated surgically for ccRCC pRCC chRCC bull IHC to evaluate PBRM1 and BAP1 protein expression bull Loss of PBRM1 and BAP1 staining

ndash Clear cell = 43 (80187) and 10 (18187) ndash Papillary = 3 (259) and 0 (061) ndash Chromophobe = 6 (117) and 0 (017)

bull Loss of PBRM1 or BAP1 are key events in ccRCC whereas other pathways may support tumorigenesis in non-ccRCC subtypes

Ho TH et al Urol Oncol 2015 Jan33(1)23e9-14

Take Home Messages bull ldquoTip of the Icebergrdquo as it relates to the Molecular

Characterization of Renal Cell Carcinomas

bull Some mutations are ubiquitous in renal tumors (ie VHL PBRM1) whereas some are only present in a subset of cancer cells within the same tumor (ie BAP1 SETD2) ndash Implications for core biopsy results and interpretations

bull Early results of genomendashwide sequencing establish a foundation for an integrated pathological and molecular genetic classification of RCC ndash Paves the way for subtype-specific treatments

exploiting genetic vulnerabilities

References 1 Karam JA et al Phase 2 trial of neoadjuvant axitinib in patients with locally advanced

nonmetastatic clear cell renal cell carcinoma Eur Urol 2014 Nov66(5)874-80 2 Karakiewicz P et al Neoadjuvant sutent induction therapy may effectively down-stage renal

cell carcinoma atrial thrombi Eur Urol 2008 53(4)845ndash848 3 Frank et al An outcome prediction model for patients with clear cell renal cell carcinoma

treated with radical nephrectomy based on tumor stage size grade and necrosis the SSIGN score J Urol 20021682395-2400

4 Zisman et al Risk group assessment and clinical outcome algorithm to predict the natural history of patients with surgically resected renal cell carcinoma J Clin Oncol 2002 Dec 120(23)4559-66

5 Motzer RJ et al Interferon-alfa as a comparative treatment for clinical trials of new therapies against advanced renal cell carcinoma J Clin Oncol 2002 Jan 120(1)289-96

6 Gossage L et al VHL The story of a tumor suppressor gene Nat Rev Cancer 2015 Jan15(1)55-64

7 Cancer Genome Atlas Research Network Comprehensive Molecular Characterization of Clear Cell Renal Cell Carcinoma Nature 2013 Jul 4499(7456)43-9

8 Pentildea-Llopis et al BAP1 loss defines a new class of renal cell carcinoma Nat Genet 2012 Jun 10 44(7) 751ndash759

9 Gossage L et al Clinical and pathological impact of VHL PBRM1 BAP1 SETD2 KDM6A and JARID1c in clear cell renal cell carcinoma Genes Chromosomes Cancer 2014 Jan53(1)38-51

10 Joseph RW et al Clear cell renal cell carcinoma subtypes identified by BAP1 and PBRM1 expression J Urol 2015 Aug 20 [Epub ahead of print]

11 Ho TH et al Loss of PBRM1 and BAP1 expression is less common in non-clear cell renal cell carcinoma than in clear cell renal cell carcinoma Urol Oncol 2015 Jan33(1)23e9-14

• Implications for the Human Genome Project on the Management of Renal Cell Carcinoma
• Is This an Indolent Tumor
• Could we have predicted this event
• Can We Predict Treatment ResponseNeoadjuvant TKI
• Slide Number 5
• Slide Number 6
• Can We Optimize Therapeutic StrategiesIVC Tumor Thrombus
• Pre-operative TKIIVC Tumor Thrombus
• Slide Number 9
• Slide Number 10
• Slide Number 11
• Slide Number 12
• Can We Optimize Systemic Therapies
• Can We Optimize Systemic Therapies
• Optimizing Systemic Therapies
• There are Success Stories Using Current Decision Tools
• Current Tools for Determining PrognosisTreatment for Kidney Cancers
• Slide Number 18
• Overall Survival for RCCAUCLA Integrated Staging System (UISS)
• Overall Survival for RCCAUCLA Integrated Staging System (UISS)
• Overall Survival for RCCAUCLA Integrated Staging System (UISS)
• Karnofsky Performance Status
• Motzer Criteria
• Motzer Criteria
• What do These Models Have in Common
• An Ongoing Struggle With Cancershellipincluding kidney cancers
• Slide Number 27
• Slide Number 28
• Renal Tumor Histology
• Slide Number 30
• The Cancer Genome Atlas (TCGA)
• The Cancer Genome Atlas (TCGA)
• Comprehensive Molecular Characterization of Clear Cell RCCA
• Comprehensive Molecular Characterization of Clear Cell RCCA
• Comprehensive Molecular Characterization of Clear Cell RCCA
• Slide Number 36
• Slide Number 37
• BAP1 amp PBRM1 Mutations
• Molecular Characterization of RCCA with BAP1 amp PBRM1 Mutations
• BAP1 amp PBRM1 Mutations in Non-Clear Cell RCCA
• Take Home Messages
• References

Somatic Alterations in Clear Cell RCCA Nature 2013

mRNA amp miRNA Patterns Reflect Molecular Subtypes of Clear Cell RCCA Nature 2013

BAP1 amp PBRM1 Mutations bull BAP1 and PBRM1

ndash Two-hit tumor suppressor genes ndash Regulate seemingly different gene expression programs ndash Mutations are mutually exclusive

bull BAP1 Mutations ndash Present in 15 of clear-cell renal cell carcinomas ndash Associated with high nuclear grade stage and tumor aggression

when compared with tumors exclusively mutated for PBRM1

bull PBRM1 Mutations ndash Present in 50 of clear-cell renal cell carcinomas

bull Combined loss of BAP1 and PBRM1 genes ndash Present in small percent (lt5) of tumors ndash Some reports reveal association with rhabdoid features

Pentildea-Llopis et al Nat Genet 2012 Jun 10 44(7) 751ndash759 Gossage et al Genes Chromosomes Cancer 2014 Jan53(1)38-51

Molecular Characterization of RCCA with BAP1 amp PBRM1 Mutations

Joseph RW et al J Urol 2015 Aug 20 [Epub ahead of print]

BAP1 amp PBRM1 Mutations in Non-Clear Cell RCCA

bull 458 patients treated surgically for ccRCC pRCC chRCC bull IHC to evaluate PBRM1 and BAP1 protein expression bull Loss of PBRM1 and BAP1 staining

ndash Clear cell = 43 (80187) and 10 (18187) ndash Papillary = 3 (259) and 0 (061) ndash Chromophobe = 6 (117) and 0 (017)

bull Loss of PBRM1 or BAP1 are key events in ccRCC whereas other pathways may support tumorigenesis in non-ccRCC subtypes

Ho TH et al Urol Oncol 2015 Jan33(1)23e9-14

Take Home Messages bull ldquoTip of the Icebergrdquo as it relates to the Molecular

Characterization of Renal Cell Carcinomas

bull Some mutations are ubiquitous in renal tumors (ie VHL PBRM1) whereas some are only present in a subset of cancer cells within the same tumor (ie BAP1 SETD2) ndash Implications for core biopsy results and interpretations

bull Early results of genomendashwide sequencing establish a foundation for an integrated pathological and molecular genetic classification of RCC ndash Paves the way for subtype-specific treatments

exploiting genetic vulnerabilities

References 1 Karam JA et al Phase 2 trial of neoadjuvant axitinib in patients with locally advanced

nonmetastatic clear cell renal cell carcinoma Eur Urol 2014 Nov66(5)874-80 2 Karakiewicz P et al Neoadjuvant sutent induction therapy may effectively down-stage renal

cell carcinoma atrial thrombi Eur Urol 2008 53(4)845ndash848 3 Frank et al An outcome prediction model for patients with clear cell renal cell carcinoma

treated with radical nephrectomy based on tumor stage size grade and necrosis the SSIGN score J Urol 20021682395-2400

4 Zisman et al Risk group assessment and clinical outcome algorithm to predict the natural history of patients with surgically resected renal cell carcinoma J Clin Oncol 2002 Dec 120(23)4559-66

5 Motzer RJ et al Interferon-alfa as a comparative treatment for clinical trials of new therapies against advanced renal cell carcinoma J Clin Oncol 2002 Jan 120(1)289-96

6 Gossage L et al VHL The story of a tumor suppressor gene Nat Rev Cancer 2015 Jan15(1)55-64

7 Cancer Genome Atlas Research Network Comprehensive Molecular Characterization of Clear Cell Renal Cell Carcinoma Nature 2013 Jul 4499(7456)43-9

8 Pentildea-Llopis et al BAP1 loss defines a new class of renal cell carcinoma Nat Genet 2012 Jun 10 44(7) 751ndash759

9 Gossage L et al Clinical and pathological impact of VHL PBRM1 BAP1 SETD2 KDM6A and JARID1c in clear cell renal cell carcinoma Genes Chromosomes Cancer 2014 Jan53(1)38-51

10 Joseph RW et al Clear cell renal cell carcinoma subtypes identified by BAP1 and PBRM1 expression J Urol 2015 Aug 20 [Epub ahead of print]

11 Ho TH et al Loss of PBRM1 and BAP1 expression is less common in non-clear cell renal cell carcinoma than in clear cell renal cell carcinoma Urol Oncol 2015 Jan33(1)23e9-14

• Implications for the Human Genome Project on the Management of Renal Cell Carcinoma
• Is This an Indolent Tumor
• Could we have predicted this event
• Can We Predict Treatment ResponseNeoadjuvant TKI
• Slide Number 5
• Slide Number 6
• Can We Optimize Therapeutic StrategiesIVC Tumor Thrombus
• Pre-operative TKIIVC Tumor Thrombus
• Slide Number 9
• Slide Number 10
• Slide Number 11
• Slide Number 12
• Can We Optimize Systemic Therapies
• Can We Optimize Systemic Therapies
• Optimizing Systemic Therapies
• There are Success Stories Using Current Decision Tools
• Current Tools for Determining PrognosisTreatment for Kidney Cancers
• Slide Number 18
• Overall Survival for RCCAUCLA Integrated Staging System (UISS)
• Overall Survival for RCCAUCLA Integrated Staging System (UISS)
• Overall Survival for RCCAUCLA Integrated Staging System (UISS)
• Karnofsky Performance Status
• Motzer Criteria
• Motzer Criteria
• What do These Models Have in Common
• An Ongoing Struggle With Cancershellipincluding kidney cancers
• Slide Number 27
• Slide Number 28
• Renal Tumor Histology
• Slide Number 30
• The Cancer Genome Atlas (TCGA)
• The Cancer Genome Atlas (TCGA)
• Comprehensive Molecular Characterization of Clear Cell RCCA
• Comprehensive Molecular Characterization of Clear Cell RCCA
• Comprehensive Molecular Characterization of Clear Cell RCCA
• Slide Number 36
• Slide Number 37
• BAP1 amp PBRM1 Mutations
• Molecular Characterization of RCCA with BAP1 amp PBRM1 Mutations
• BAP1 amp PBRM1 Mutations in Non-Clear Cell RCCA
• Take Home Messages
• References

mRNA amp miRNA Patterns Reflect Molecular Subtypes of Clear Cell RCCA Nature 2013

BAP1 amp PBRM1 Mutations bull BAP1 and PBRM1

ndash Two-hit tumor suppressor genes ndash Regulate seemingly different gene expression programs ndash Mutations are mutually exclusive

bull BAP1 Mutations ndash Present in 15 of clear-cell renal cell carcinomas ndash Associated with high nuclear grade stage and tumor aggression

when compared with tumors exclusively mutated for PBRM1

bull PBRM1 Mutations ndash Present in 50 of clear-cell renal cell carcinomas

bull Combined loss of BAP1 and PBRM1 genes ndash Present in small percent (lt5) of tumors ndash Some reports reveal association with rhabdoid features

Pentildea-Llopis et al Nat Genet 2012 Jun 10 44(7) 751ndash759 Gossage et al Genes Chromosomes Cancer 2014 Jan53(1)38-51

Molecular Characterization of RCCA with BAP1 amp PBRM1 Mutations

Joseph RW et al J Urol 2015 Aug 20 [Epub ahead of print]

BAP1 amp PBRM1 Mutations in Non-Clear Cell RCCA

bull 458 patients treated surgically for ccRCC pRCC chRCC bull IHC to evaluate PBRM1 and BAP1 protein expression bull Loss of PBRM1 and BAP1 staining

ndash Clear cell = 43 (80187) and 10 (18187) ndash Papillary = 3 (259) and 0 (061) ndash Chromophobe = 6 (117) and 0 (017)

bull Loss of PBRM1 or BAP1 are key events in ccRCC whereas other pathways may support tumorigenesis in non-ccRCC subtypes

Ho TH et al Urol Oncol 2015 Jan33(1)23e9-14

Take Home Messages bull ldquoTip of the Icebergrdquo as it relates to the Molecular

Characterization of Renal Cell Carcinomas

bull Some mutations are ubiquitous in renal tumors (ie VHL PBRM1) whereas some are only present in a subset of cancer cells within the same tumor (ie BAP1 SETD2) ndash Implications for core biopsy results and interpretations

bull Early results of genomendashwide sequencing establish a foundation for an integrated pathological and molecular genetic classification of RCC ndash Paves the way for subtype-specific treatments

exploiting genetic vulnerabilities

References 1 Karam JA et al Phase 2 trial of neoadjuvant axitinib in patients with locally advanced

nonmetastatic clear cell renal cell carcinoma Eur Urol 2014 Nov66(5)874-80 2 Karakiewicz P et al Neoadjuvant sutent induction therapy may effectively down-stage renal

cell carcinoma atrial thrombi Eur Urol 2008 53(4)845ndash848 3 Frank et al An outcome prediction model for patients with clear cell renal cell carcinoma

treated with radical nephrectomy based on tumor stage size grade and necrosis the SSIGN score J Urol 20021682395-2400

4 Zisman et al Risk group assessment and clinical outcome algorithm to predict the natural history of patients with surgically resected renal cell carcinoma J Clin Oncol 2002 Dec 120(23)4559-66

5 Motzer RJ et al Interferon-alfa as a comparative treatment for clinical trials of new therapies against advanced renal cell carcinoma J Clin Oncol 2002 Jan 120(1)289-96

6 Gossage L et al VHL The story of a tumor suppressor gene Nat Rev Cancer 2015 Jan15(1)55-64

7 Cancer Genome Atlas Research Network Comprehensive Molecular Characterization of Clear Cell Renal Cell Carcinoma Nature 2013 Jul 4499(7456)43-9

8 Pentildea-Llopis et al BAP1 loss defines a new class of renal cell carcinoma Nat Genet 2012 Jun 10 44(7) 751ndash759

9 Gossage L et al Clinical and pathological impact of VHL PBRM1 BAP1 SETD2 KDM6A and JARID1c in clear cell renal cell carcinoma Genes Chromosomes Cancer 2014 Jan53(1)38-51

10 Joseph RW et al Clear cell renal cell carcinoma subtypes identified by BAP1 and PBRM1 expression J Urol 2015 Aug 20 [Epub ahead of print]

11 Ho TH et al Loss of PBRM1 and BAP1 expression is less common in non-clear cell renal cell carcinoma than in clear cell renal cell carcinoma Urol Oncol 2015 Jan33(1)23e9-14

• Implications for the Human Genome Project on the Management of Renal Cell Carcinoma
• Is This an Indolent Tumor
• Could we have predicted this event
• Can We Predict Treatment ResponseNeoadjuvant TKI
• Slide Number 5
• Slide Number 6
• Can We Optimize Therapeutic StrategiesIVC Tumor Thrombus
• Pre-operative TKIIVC Tumor Thrombus
• Slide Number 9
• Slide Number 10
• Slide Number 11
• Slide Number 12
• Can We Optimize Systemic Therapies
• Can We Optimize Systemic Therapies
• Optimizing Systemic Therapies
• There are Success Stories Using Current Decision Tools
• Current Tools for Determining PrognosisTreatment for Kidney Cancers
• Slide Number 18
• Overall Survival for RCCAUCLA Integrated Staging System (UISS)
• Overall Survival for RCCAUCLA Integrated Staging System (UISS)
• Overall Survival for RCCAUCLA Integrated Staging System (UISS)
• Karnofsky Performance Status
• Motzer Criteria
• Motzer Criteria
• What do These Models Have in Common
• An Ongoing Struggle With Cancershellipincluding kidney cancers
• Slide Number 27
• Slide Number 28
• Renal Tumor Histology
• Slide Number 30
• The Cancer Genome Atlas (TCGA)
• The Cancer Genome Atlas (TCGA)
• Comprehensive Molecular Characterization of Clear Cell RCCA
• Comprehensive Molecular Characterization of Clear Cell RCCA
• Comprehensive Molecular Characterization of Clear Cell RCCA
• Slide Number 36
• Slide Number 37
• BAP1 amp PBRM1 Mutations
• Molecular Characterization of RCCA with BAP1 amp PBRM1 Mutations
• BAP1 amp PBRM1 Mutations in Non-Clear Cell RCCA
• Take Home Messages
• References

BAP1 amp PBRM1 Mutations bull BAP1 and PBRM1

ndash Two-hit tumor suppressor genes ndash Regulate seemingly different gene expression programs ndash Mutations are mutually exclusive

bull BAP1 Mutations ndash Present in 15 of clear-cell renal cell carcinomas ndash Associated with high nuclear grade stage and tumor aggression

when compared with tumors exclusively mutated for PBRM1

bull PBRM1 Mutations ndash Present in 50 of clear-cell renal cell carcinomas

bull Combined loss of BAP1 and PBRM1 genes ndash Present in small percent (lt5) of tumors ndash Some reports reveal association with rhabdoid features

Pentildea-Llopis et al Nat Genet 2012 Jun 10 44(7) 751ndash759 Gossage et al Genes Chromosomes Cancer 2014 Jan53(1)38-51

Molecular Characterization of RCCA with BAP1 amp PBRM1 Mutations

Joseph RW et al J Urol 2015 Aug 20 [Epub ahead of print]

BAP1 amp PBRM1 Mutations in Non-Clear Cell RCCA

bull 458 patients treated surgically for ccRCC pRCC chRCC bull IHC to evaluate PBRM1 and BAP1 protein expression bull Loss of PBRM1 and BAP1 staining

ndash Clear cell = 43 (80187) and 10 (18187) ndash Papillary = 3 (259) and 0 (061) ndash Chromophobe = 6 (117) and 0 (017)

bull Loss of PBRM1 or BAP1 are key events in ccRCC whereas other pathways may support tumorigenesis in non-ccRCC subtypes

Ho TH et al Urol Oncol 2015 Jan33(1)23e9-14

Take Home Messages bull ldquoTip of the Icebergrdquo as it relates to the Molecular

Characterization of Renal Cell Carcinomas

bull Some mutations are ubiquitous in renal tumors (ie VHL PBRM1) whereas some are only present in a subset of cancer cells within the same tumor (ie BAP1 SETD2) ndash Implications for core biopsy results and interpretations

bull Early results of genomendashwide sequencing establish a foundation for an integrated pathological and molecular genetic classification of RCC ndash Paves the way for subtype-specific treatments

exploiting genetic vulnerabilities

References 1 Karam JA et al Phase 2 trial of neoadjuvant axitinib in patients with locally advanced

nonmetastatic clear cell renal cell carcinoma Eur Urol 2014 Nov66(5)874-80 2 Karakiewicz P et al Neoadjuvant sutent induction therapy may effectively down-stage renal

cell carcinoma atrial thrombi Eur Urol 2008 53(4)845ndash848 3 Frank et al An outcome prediction model for patients with clear cell renal cell carcinoma

treated with radical nephrectomy based on tumor stage size grade and necrosis the SSIGN score J Urol 20021682395-2400

4 Zisman et al Risk group assessment and clinical outcome algorithm to predict the natural history of patients with surgically resected renal cell carcinoma J Clin Oncol 2002 Dec 120(23)4559-66

5 Motzer RJ et al Interferon-alfa as a comparative treatment for clinical trials of new therapies against advanced renal cell carcinoma J Clin Oncol 2002 Jan 120(1)289-96

6 Gossage L et al VHL The story of a tumor suppressor gene Nat Rev Cancer 2015 Jan15(1)55-64

7 Cancer Genome Atlas Research Network Comprehensive Molecular Characterization of Clear Cell Renal Cell Carcinoma Nature 2013 Jul 4499(7456)43-9

8 Pentildea-Llopis et al BAP1 loss defines a new class of renal cell carcinoma Nat Genet 2012 Jun 10 44(7) 751ndash759

9 Gossage L et al Clinical and pathological impact of VHL PBRM1 BAP1 SETD2 KDM6A and JARID1c in clear cell renal cell carcinoma Genes Chromosomes Cancer 2014 Jan53(1)38-51

10 Joseph RW et al Clear cell renal cell carcinoma subtypes identified by BAP1 and PBRM1 expression J Urol 2015 Aug 20 [Epub ahead of print]

11 Ho TH et al Loss of PBRM1 and BAP1 expression is less common in non-clear cell renal cell carcinoma than in clear cell renal cell carcinoma Urol Oncol 2015 Jan33(1)23e9-14

• Implications for the Human Genome Project on the Management of Renal Cell Carcinoma
• Is This an Indolent Tumor
• Could we have predicted this event
• Can We Predict Treatment ResponseNeoadjuvant TKI
• Slide Number 5
• Slide Number 6
• Can We Optimize Therapeutic StrategiesIVC Tumor Thrombus
• Pre-operative TKIIVC Tumor Thrombus
• Slide Number 9
• Slide Number 10
• Slide Number 11
• Slide Number 12
• Can We Optimize Systemic Therapies
• Can We Optimize Systemic Therapies
• Optimizing Systemic Therapies
• There are Success Stories Using Current Decision Tools
• Current Tools for Determining PrognosisTreatment for Kidney Cancers
• Slide Number 18
• Overall Survival for RCCAUCLA Integrated Staging System (UISS)
• Overall Survival for RCCAUCLA Integrated Staging System (UISS)
• Overall Survival for RCCAUCLA Integrated Staging System (UISS)
• Karnofsky Performance Status
• Motzer Criteria
• Motzer Criteria
• What do These Models Have in Common
• An Ongoing Struggle With Cancershellipincluding kidney cancers
• Slide Number 27
• Slide Number 28
• Renal Tumor Histology
• Slide Number 30
• The Cancer Genome Atlas (TCGA)
• The Cancer Genome Atlas (TCGA)
• Comprehensive Molecular Characterization of Clear Cell RCCA
• Comprehensive Molecular Characterization of Clear Cell RCCA
• Comprehensive Molecular Characterization of Clear Cell RCCA
• Slide Number 36
• Slide Number 37
• BAP1 amp PBRM1 Mutations
• Molecular Characterization of RCCA with BAP1 amp PBRM1 Mutations
• BAP1 amp PBRM1 Mutations in Non-Clear Cell RCCA
• Take Home Messages
• References

Molecular Characterization of RCCA with BAP1 amp PBRM1 Mutations

Joseph RW et al J Urol 2015 Aug 20 [Epub ahead of print]

BAP1 amp PBRM1 Mutations in Non-Clear Cell RCCA

bull 458 patients treated surgically for ccRCC pRCC chRCC bull IHC to evaluate PBRM1 and BAP1 protein expression bull Loss of PBRM1 and BAP1 staining

ndash Clear cell = 43 (80187) and 10 (18187) ndash Papillary = 3 (259) and 0 (061) ndash Chromophobe = 6 (117) and 0 (017)

bull Loss of PBRM1 or BAP1 are key events in ccRCC whereas other pathways may support tumorigenesis in non-ccRCC subtypes

Ho TH et al Urol Oncol 2015 Jan33(1)23e9-14

Take Home Messages bull ldquoTip of the Icebergrdquo as it relates to the Molecular

Characterization of Renal Cell Carcinomas

bull Some mutations are ubiquitous in renal tumors (ie VHL PBRM1) whereas some are only present in a subset of cancer cells within the same tumor (ie BAP1 SETD2) ndash Implications for core biopsy results and interpretations

bull Early results of genomendashwide sequencing establish a foundation for an integrated pathological and molecular genetic classification of RCC ndash Paves the way for subtype-specific treatments

exploiting genetic vulnerabilities

References 1 Karam JA et al Phase 2 trial of neoadjuvant axitinib in patients with locally advanced

nonmetastatic clear cell renal cell carcinoma Eur Urol 2014 Nov66(5)874-80 2 Karakiewicz P et al Neoadjuvant sutent induction therapy may effectively down-stage renal

cell carcinoma atrial thrombi Eur Urol 2008 53(4)845ndash848 3 Frank et al An outcome prediction model for patients with clear cell renal cell carcinoma

treated with radical nephrectomy based on tumor stage size grade and necrosis the SSIGN score J Urol 20021682395-2400

4 Zisman et al Risk group assessment and clinical outcome algorithm to predict the natural history of patients with surgically resected renal cell carcinoma J Clin Oncol 2002 Dec 120(23)4559-66

5 Motzer RJ et al Interferon-alfa as a comparative treatment for clinical trials of new therapies against advanced renal cell carcinoma J Clin Oncol 2002 Jan 120(1)289-96

6 Gossage L et al VHL The story of a tumor suppressor gene Nat Rev Cancer 2015 Jan15(1)55-64

7 Cancer Genome Atlas Research Network Comprehensive Molecular Characterization of Clear Cell Renal Cell Carcinoma Nature 2013 Jul 4499(7456)43-9

8 Pentildea-Llopis et al BAP1 loss defines a new class of renal cell carcinoma Nat Genet 2012 Jun 10 44(7) 751ndash759

9 Gossage L et al Clinical and pathological impact of VHL PBRM1 BAP1 SETD2 KDM6A and JARID1c in clear cell renal cell carcinoma Genes Chromosomes Cancer 2014 Jan53(1)38-51

10 Joseph RW et al Clear cell renal cell carcinoma subtypes identified by BAP1 and PBRM1 expression J Urol 2015 Aug 20 [Epub ahead of print]

11 Ho TH et al Loss of PBRM1 and BAP1 expression is less common in non-clear cell renal cell carcinoma than in clear cell renal cell carcinoma Urol Oncol 2015 Jan33(1)23e9-14

• Implications for the Human Genome Project on the Management of Renal Cell Carcinoma
• Is This an Indolent Tumor
• Could we have predicted this event
• Can We Predict Treatment ResponseNeoadjuvant TKI
• Slide Number 5
• Slide Number 6
• Can We Optimize Therapeutic StrategiesIVC Tumor Thrombus
• Pre-operative TKIIVC Tumor Thrombus
• Slide Number 9
• Slide Number 10
• Slide Number 11
• Slide Number 12
• Can We Optimize Systemic Therapies
• Can We Optimize Systemic Therapies
• Optimizing Systemic Therapies
• There are Success Stories Using Current Decision Tools
• Current Tools for Determining PrognosisTreatment for Kidney Cancers
• Slide Number 18
• Overall Survival for RCCAUCLA Integrated Staging System (UISS)
• Overall Survival for RCCAUCLA Integrated Staging System (UISS)
• Overall Survival for RCCAUCLA Integrated Staging System (UISS)
• Karnofsky Performance Status
• Motzer Criteria
• Motzer Criteria
• What do These Models Have in Common
• An Ongoing Struggle With Cancershellipincluding kidney cancers
• Slide Number 27
• Slide Number 28
• Renal Tumor Histology
• Slide Number 30
• The Cancer Genome Atlas (TCGA)
• The Cancer Genome Atlas (TCGA)
• Comprehensive Molecular Characterization of Clear Cell RCCA
• Comprehensive Molecular Characterization of Clear Cell RCCA
• Comprehensive Molecular Characterization of Clear Cell RCCA
• Slide Number 36
• Slide Number 37
• BAP1 amp PBRM1 Mutations
• Molecular Characterization of RCCA with BAP1 amp PBRM1 Mutations
• BAP1 amp PBRM1 Mutations in Non-Clear Cell RCCA
• Take Home Messages
• References

BAP1 amp PBRM1 Mutations in Non-Clear Cell RCCA

bull 458 patients treated surgically for ccRCC pRCC chRCC bull IHC to evaluate PBRM1 and BAP1 protein expression bull Loss of PBRM1 and BAP1 staining

ndash Clear cell = 43 (80187) and 10 (18187) ndash Papillary = 3 (259) and 0 (061) ndash Chromophobe = 6 (117) and 0 (017)

bull Loss of PBRM1 or BAP1 are key events in ccRCC whereas other pathways may support tumorigenesis in non-ccRCC subtypes

Ho TH et al Urol Oncol 2015 Jan33(1)23e9-14

Take Home Messages bull ldquoTip of the Icebergrdquo as it relates to the Molecular

Characterization of Renal Cell Carcinomas

bull Some mutations are ubiquitous in renal tumors (ie VHL PBRM1) whereas some are only present in a subset of cancer cells within the same tumor (ie BAP1 SETD2) ndash Implications for core biopsy results and interpretations

bull Early results of genomendashwide sequencing establish a foundation for an integrated pathological and molecular genetic classification of RCC ndash Paves the way for subtype-specific treatments

exploiting genetic vulnerabilities

References 1 Karam JA et al Phase 2 trial of neoadjuvant axitinib in patients with locally advanced

nonmetastatic clear cell renal cell carcinoma Eur Urol 2014 Nov66(5)874-80 2 Karakiewicz P et al Neoadjuvant sutent induction therapy may effectively down-stage renal

cell carcinoma atrial thrombi Eur Urol 2008 53(4)845ndash848 3 Frank et al An outcome prediction model for patients with clear cell renal cell carcinoma

treated with radical nephrectomy based on tumor stage size grade and necrosis the SSIGN score J Urol 20021682395-2400

4 Zisman et al Risk group assessment and clinical outcome algorithm to predict the natural history of patients with surgically resected renal cell carcinoma J Clin Oncol 2002 Dec 120(23)4559-66

5 Motzer RJ et al Interferon-alfa as a comparative treatment for clinical trials of new therapies against advanced renal cell carcinoma J Clin Oncol 2002 Jan 120(1)289-96

6 Gossage L et al VHL The story of a tumor suppressor gene Nat Rev Cancer 2015 Jan15(1)55-64

7 Cancer Genome Atlas Research Network Comprehensive Molecular Characterization of Clear Cell Renal Cell Carcinoma Nature 2013 Jul 4499(7456)43-9

8 Pentildea-Llopis et al BAP1 loss defines a new class of renal cell carcinoma Nat Genet 2012 Jun 10 44(7) 751ndash759

9 Gossage L et al Clinical and pathological impact of VHL PBRM1 BAP1 SETD2 KDM6A and JARID1c in clear cell renal cell carcinoma Genes Chromosomes Cancer 2014 Jan53(1)38-51

10 Joseph RW et al Clear cell renal cell carcinoma subtypes identified by BAP1 and PBRM1 expression J Urol 2015 Aug 20 [Epub ahead of print]

11 Ho TH et al Loss of PBRM1 and BAP1 expression is less common in non-clear cell renal cell carcinoma than in clear cell renal cell carcinoma Urol Oncol 2015 Jan33(1)23e9-14

• Implications for the Human Genome Project on the Management of Renal Cell Carcinoma
• Is This an Indolent Tumor
• Could we have predicted this event
• Can We Predict Treatment ResponseNeoadjuvant TKI
• Slide Number 5
• Slide Number 6
• Can We Optimize Therapeutic StrategiesIVC Tumor Thrombus
• Pre-operative TKIIVC Tumor Thrombus
• Slide Number 9
• Slide Number 10
• Slide Number 11
• Slide Number 12
• Can We Optimize Systemic Therapies
• Can We Optimize Systemic Therapies
• Optimizing Systemic Therapies
• There are Success Stories Using Current Decision Tools
• Current Tools for Determining PrognosisTreatment for Kidney Cancers
• Slide Number 18
• Overall Survival for RCCAUCLA Integrated Staging System (UISS)
• Overall Survival for RCCAUCLA Integrated Staging System (UISS)
• Overall Survival for RCCAUCLA Integrated Staging System (UISS)
• Karnofsky Performance Status
• Motzer Criteria
• Motzer Criteria
• What do These Models Have in Common
• An Ongoing Struggle With Cancershellipincluding kidney cancers
• Slide Number 27
• Slide Number 28
• Renal Tumor Histology
• Slide Number 30
• The Cancer Genome Atlas (TCGA)
• The Cancer Genome Atlas (TCGA)
• Comprehensive Molecular Characterization of Clear Cell RCCA
• Comprehensive Molecular Characterization of Clear Cell RCCA
• Comprehensive Molecular Characterization of Clear Cell RCCA
• Slide Number 36
• Slide Number 37
• BAP1 amp PBRM1 Mutations
• Molecular Characterization of RCCA with BAP1 amp PBRM1 Mutations
• BAP1 amp PBRM1 Mutations in Non-Clear Cell RCCA
• Take Home Messages
• References

Take Home Messages bull ldquoTip of the Icebergrdquo as it relates to the Molecular

Characterization of Renal Cell Carcinomas

bull Some mutations are ubiquitous in renal tumors (ie VHL PBRM1) whereas some are only present in a subset of cancer cells within the same tumor (ie BAP1 SETD2) ndash Implications for core biopsy results and interpretations

bull Early results of genomendashwide sequencing establish a foundation for an integrated pathological and molecular genetic classification of RCC ndash Paves the way for subtype-specific treatments

exploiting genetic vulnerabilities

References 1 Karam JA et al Phase 2 trial of neoadjuvant axitinib in patients with locally advanced

nonmetastatic clear cell renal cell carcinoma Eur Urol 2014 Nov66(5)874-80 2 Karakiewicz P et al Neoadjuvant sutent induction therapy may effectively down-stage renal

cell carcinoma atrial thrombi Eur Urol 2008 53(4)845ndash848 3 Frank et al An outcome prediction model for patients with clear cell renal cell carcinoma

treated with radical nephrectomy based on tumor stage size grade and necrosis the SSIGN score J Urol 20021682395-2400

4 Zisman et al Risk group assessment and clinical outcome algorithm to predict the natural history of patients with surgically resected renal cell carcinoma J Clin Oncol 2002 Dec 120(23)4559-66

5 Motzer RJ et al Interferon-alfa as a comparative treatment for clinical trials of new therapies against advanced renal cell carcinoma J Clin Oncol 2002 Jan 120(1)289-96

6 Gossage L et al VHL The story of a tumor suppressor gene Nat Rev Cancer 2015 Jan15(1)55-64

7 Cancer Genome Atlas Research Network Comprehensive Molecular Characterization of Clear Cell Renal Cell Carcinoma Nature 2013 Jul 4499(7456)43-9

8 Pentildea-Llopis et al BAP1 loss defines a new class of renal cell carcinoma Nat Genet 2012 Jun 10 44(7) 751ndash759

9 Gossage L et al Clinical and pathological impact of VHL PBRM1 BAP1 SETD2 KDM6A and JARID1c in clear cell renal cell carcinoma Genes Chromosomes Cancer 2014 Jan53(1)38-51

10 Joseph RW et al Clear cell renal cell carcinoma subtypes identified by BAP1 and PBRM1 expression J Urol 2015 Aug 20 [Epub ahead of print]

11 Ho TH et al Loss of PBRM1 and BAP1 expression is less common in non-clear cell renal cell carcinoma than in clear cell renal cell carcinoma Urol Oncol 2015 Jan33(1)23e9-14

• Implications for the Human Genome Project on the Management of Renal Cell Carcinoma
• Is This an Indolent Tumor
• Could we have predicted this event
• Can We Predict Treatment ResponseNeoadjuvant TKI
• Slide Number 5
• Slide Number 6
• Can We Optimize Therapeutic StrategiesIVC Tumor Thrombus
• Pre-operative TKIIVC Tumor Thrombus
• Slide Number 9
• Slide Number 10
• Slide Number 11
• Slide Number 12
• Can We Optimize Systemic Therapies
• Can We Optimize Systemic Therapies
• Optimizing Systemic Therapies
• There are Success Stories Using Current Decision Tools
• Current Tools for Determining PrognosisTreatment for Kidney Cancers
• Slide Number 18
• Overall Survival for RCCAUCLA Integrated Staging System (UISS)
• Overall Survival for RCCAUCLA Integrated Staging System (UISS)
• Overall Survival for RCCAUCLA Integrated Staging System (UISS)
• Karnofsky Performance Status
• Motzer Criteria
• Motzer Criteria
• What do These Models Have in Common
• An Ongoing Struggle With Cancershellipincluding kidney cancers
• Slide Number 27
• Slide Number 28
• Renal Tumor Histology
• Slide Number 30
• The Cancer Genome Atlas (TCGA)
• The Cancer Genome Atlas (TCGA)
• Comprehensive Molecular Characterization of Clear Cell RCCA
• Comprehensive Molecular Characterization of Clear Cell RCCA
• Comprehensive Molecular Characterization of Clear Cell RCCA
• Slide Number 36
• Slide Number 37
• BAP1 amp PBRM1 Mutations
• Molecular Characterization of RCCA with BAP1 amp PBRM1 Mutations
• BAP1 amp PBRM1 Mutations in Non-Clear Cell RCCA
• Take Home Messages
• References

References 1 Karam JA et al Phase 2 trial of neoadjuvant axitinib in patients with locally advanced

nonmetastatic clear cell renal cell carcinoma Eur Urol 2014 Nov66(5)874-80 2 Karakiewicz P et al Neoadjuvant sutent induction therapy may effectively down-stage renal

cell carcinoma atrial thrombi Eur Urol 2008 53(4)845ndash848 3 Frank et al An outcome prediction model for patients with clear cell renal cell carcinoma

treated with radical nephrectomy based on tumor stage size grade and necrosis the SSIGN score J Urol 20021682395-2400

4 Zisman et al Risk group assessment and clinical outcome algorithm to predict the natural history of patients with surgically resected renal cell carcinoma J Clin Oncol 2002 Dec 120(23)4559-66

5 Motzer RJ et al Interferon-alfa as a comparative treatment for clinical trials of new therapies against advanced renal cell carcinoma J Clin Oncol 2002 Jan 120(1)289-96

6 Gossage L et al VHL The story of a tumor suppressor gene Nat Rev Cancer 2015 Jan15(1)55-64

7 Cancer Genome Atlas Research Network Comprehensive Molecular Characterization of Clear Cell Renal Cell Carcinoma Nature 2013 Jul 4499(7456)43-9

8 Pentildea-Llopis et al BAP1 loss defines a new class of renal cell carcinoma Nat Genet 2012 Jun 10 44(7) 751ndash759

9 Gossage L et al Clinical and pathological impact of VHL PBRM1 BAP1 SETD2 KDM6A and JARID1c in clear cell renal cell carcinoma Genes Chromosomes Cancer 2014 Jan53(1)38-51

10 Joseph RW et al Clear cell renal cell carcinoma subtypes identified by BAP1 and PBRM1 expression J Urol 2015 Aug 20 [Epub ahead of print]

11 Ho TH et al Loss of PBRM1 and BAP1 expression is less common in non-clear cell renal cell carcinoma than in clear cell renal cell carcinoma Urol Oncol 2015 Jan33(1)23e9-14

• Implications for the Human Genome Project on the Management of Renal Cell Carcinoma
• Is This an Indolent Tumor
• Could we have predicted this event
• Can We Predict Treatment ResponseNeoadjuvant TKI
• Slide Number 5
• Slide Number 6
• Can We Optimize Therapeutic StrategiesIVC Tumor Thrombus
• Pre-operative TKIIVC Tumor Thrombus
• Slide Number 9
• Slide Number 10
• Slide Number 11
• Slide Number 12
• Can We Optimize Systemic Therapies
• Can We Optimize Systemic Therapies
• Optimizing Systemic Therapies
• There are Success Stories Using Current Decision Tools
• Current Tools for Determining PrognosisTreatment for Kidney Cancers
• Slide Number 18
• Overall Survival for RCCAUCLA Integrated Staging System (UISS)
• Overall Survival for RCCAUCLA Integrated Staging System (UISS)
• Overall Survival for RCCAUCLA Integrated Staging System (UISS)
• Karnofsky Performance Status
• Motzer Criteria
• Motzer Criteria
• What do These Models Have in Common
• An Ongoing Struggle With Cancershellipincluding kidney cancers
• Slide Number 27
• Slide Number 28
• Renal Tumor Histology
• Slide Number 30
• The Cancer Genome Atlas (TCGA)
• The Cancer Genome Atlas (TCGA)
• Comprehensive Molecular Characterization of Clear Cell RCCA
• Comprehensive Molecular Characterization of Clear Cell RCCA
• Comprehensive Molecular Characterization of Clear Cell RCCA
• Slide Number 36
• Slide Number 37
• BAP1 amp PBRM1 Mutations
• Molecular Characterization of RCCA with BAP1 amp PBRM1 Mutations
• BAP1 amp PBRM1 Mutations in Non-Clear Cell RCCA
• Take Home Messages
• References