- Email: [email protected]
An Update on Predictive Biomarkers in Metastatic Renal Cell Carcinoma
EUF-715; No. of Pages 3 E U RO P E A N U R O L O GY F O C U S X X X ( 2 019 ) X X X– X X X
available at www.sciencedirect.com journal homepage: www.europeanurology.com/eufocus
Mini Review – Kidney Cancer
An Update on Predictive Biomarkers in Metastatic Renal Cell Carcinoma Shaan Dudani, Marie-France Savard, Daniel Y.C. Heng * Division of Medical Oncology, Department of Oncology, Tom Baker Cancer Centre, University of Calgary, Calgary, Canada
Article info
Abstract
Article history: Accepted April 1, 2019
One of the major challenges of personalized oncology lies in identifying predictive biomarkers of response to therapy that are practical in the clinical setting. Although many new targeted and immune-based treatments have emerged in recent years as effective systemic therapy options in metastatic renal cell carcinoma (mRCC), optimizing the selection and sequencing of treatments for any individual patient with this disease remains a significant challenge. The CheckMate-214 trial demonstrated that the International mRCC Database Consortium risk model is an effective predictive biomarker in the first-line treatment of mRCC. To date this remains the only prospectively validated predictive biomarker in mRCC. A number of other promising biomarker candidates are under active investigation but require prospective validation before widespread clinical adoption. Patient summary: The International Metastatic Renal Cell Carcinoma Database Consortium risk model is currently the only validated tool that can help clinicians in determining which patients should receive sunitinib versus a combination of nivolumab and ipilimumab as a first treatment for metastatic renal cell carcinoma. Other tools are being actively investigated. © 2019 European Association of Urology. Published by Elsevier B.V. All rights reserved.
Associate Editor. Derya Tilki Keywords: Biomarkers Renal cell carcinoma Personalizedmedicine Immunotherapy Targeted therapy Checkpoint inhibitors
* Corresponding author. Division of Medical Oncology, Department of Oncology, Tom Baker Cancer Centre, 1331 29th Street NW, Calgary, Alberta, T2N 4N2, Canada. Tel.: +1 403 5213166; Fax: +1 403 2831651. E-mail address: [email protected] (Daniel Y.C. Heng).
1.
Introduction
Owing to a better understanding of the biological processes underlying the development of metastatic renal cell carcinoma (mRCC), a myriad of novel targeted and immunebased treatments have emerged over the past 14 years that have transformed the treatment landscape and outcomes for patients with this malignancy. Despite these important advances, selecting which treatment approaches to use for any given patient, and in what order, remains a significant challenge. Currently, oncologists struggle with optimizing and personalizing treatment sequencing in mRCC, as there
is a lack of validated predictive biomarkers to guide treatment selection. However, this remains an intense area of active research. In this mini-review we discuss recent advances in predictive biomarker development for mRCC, with a focus on clear-cell disease. Thus far, biomarker discovery efforts in non–clear-cell variants have been comparatively limited.
2.
Predictive biomarkers in mRCC
To date, the only predictive biomarker prospectively validated in a phase 3 randomized, controlled trial is the
https://doi.org/10.1016/j.euf.2019.04.004 2405-4569/© 2019 European Association of Urology. Published by Elsevier B.V. All rights reserved.
Please cite this article in press as: Dudani S, et al. An Update on Predictive Biomarkers in Metastatic Renal Cell Carcinoma. Eur Urol Focus (2019), https://doi.org/10.1016/j.euf.2019.04.004
EUF-715; No. of Pages 3 2
E U RO P E A N U RO L O GY F O C U S X X X ( 2 019 ) X X X– X X X
Table 1 – International Metastatic Renal Cell Carcinoma Database Consortium risk model [16]. Prognostic factor
Risk group
Median overall survival (mo)
Karnofsky performance status <80% Time from diagnosis to treatment <1 yr Hemoglobin < LLN Platelets > ULN Neutrophils > ULN Corrected calcium > ULN
Favorable risk (0 factors)
43
Intermediate risk (1–2 factors)
23
Poor risk (3 factors)
8
LLN = lower limit of normal; ULN = upper limit of normal.
International mRCC Database Consortium (IMDC) risk model. This composite marker comprises six variables and was initially developed and validated as a prognostic tool in mRCC (Table 1). It has subsequently been validated in several other mRCC subsets, including non–clear-cell and papillary RCC, in the second-, third- and fourth-line settings, and in patients receiving immune checkpoint blockade (ICB) treatments [1]. More recently, data from the CheckMate-214 trial demonstrated that the IMDC risk model can also effectively dichotomize patients with mRCC into two distinct groups that benefit from separate first-line systemic management strategies [2]. Specifically, patients with intermediate- or poor-risk disease were noted to have better clinical outcomes with combination ICB versus sunitinib, while for patients with favorable-risk disease, the opposite was true. These results suggest that favorable-risk tumors may have a distinct underlying biology, characterized by greater reliance on angiogenic drivers and/or lower responsiveness to ICB. This landmark finding not only resulted in a change in standard-of-care treatment for patients worldwide (as evidenced by recent recommendations from the European Association of Urology, European Society for Medical Oncology, and National Comprehensive Cancer Network for the use of first-line combination ICB in patients with IMDC intermediate- or poor-risk disease [3– 5]) but also represents a major milestone in the treatment of mRCC, as thus far it remains the only prospectively validated predictive biomarker in this disease. At present, all other predictive biomarker candidates remain in investigational stages. These include several promising leads involving assessment of molecular gene signatures to predict the efficacy of targeted and ICB-based therapies. In 2015, Beuselinck et al [6] identified four discrete molecular subtypes of clear-cell RCC (ccRCC) that were associated with varied outcomes and responses to sunitinib (ccrcc 1–4). Responders to sunitinib were enriched in the ccrcc2 (53%) and ccrcc3 (70%) subtypes as compared to ccrcc1 (22%) and ccrcc4 (27%), and survival outcomes followed a similar pattern. Interestingly, more recent data have demonstrated that these molecular subtypes correlate well with IMDC risk groups: ccrcc2 (characterized by a proangiogenic signature) and ccrcc3 tumors comprised 86% of all favorable-risk patients, while the ccrcc1 and ccrcc4 subtypes were almost exclusively intermediate- or poorrisk [7]. These findings may explain why favorable-risk patients seemed to have a greater benefit from sunitinib in the CheckMate-214 study. It remains to be seen whether
this classification can similarly distinguish responders from non-responders in the context of ICB-based treatments. A separate gene expression signature tool was investigated in a correlative study involving 823 patients from the IMmotion 151 trial, which compared bevacizumab plus atezolizumab versus sunitinib in mRCC [8]. In this analysis, patients with tumors characterized by angiogenesis-high signatures experienced longer progression-free survival with sunitinib when compared to those with angiogenesis-low tumors. In addition, tumors with T effector/IFNg-high or angiogenesis-low signatures exhibited better outcomes with combination therapy versus sunitinib. Notably, although perhaps unsurprisingly, angiogenesis-high subpopulations were noted to be enriched in favorable versus intermediate/poor Memorial Sloan Kettering Cancer Centre risk groups, mirroring the findings noted above suggesting that favorable-risk disease is driven by a greater dependency on angiogenic pathways and providing biologic rationale for the CheckMate-214 study results. Several other biomarkers have been associated with response to ICB in mRCC. Miao and colleagues [9] demonstrated that loss-of-function mutations in PBRM1, a gene involved in chromatin remodeling, were associated with higher clinical benefit rates in two independent cohorts of patients with metastatic ccRCC receiving nivolumab monotherapy. This interesting finding may be related to altered gene-expression profiles in PBRM1-deficient tumors. However, PBRM1-deficient tumors also appear to be more sensitive to VEGF-targeted therapy, which may limit its usefulness in selecting between the two treatment modalities [10]. It also remains to be seen whether PBRM1 loss has any correlation with the aforementioned molecular gene signature profiles. Intriguing findings have also been reported regarding the influence of the gut microbiome on response to ICB. Antibiotic use may unfavorably augment this response, with one study demonstrating higher rates of primary progressive disease and shorter survival in mRCC patients who received antibiotics within 30 days of initiating ICB [11]. In addition, several studies have shown interesting results suggesting an interaction between obesity and treatment effect. Patients with higher body mass index have been shown to have better survival outcomes when treated with VEGFtargeted agents, while the opposite association was noted for those receiving ICB [12]. These findings may be related to the adiponectin-adipoR1 axis and warrant further investigation [13].
Please cite this article in press as: Dudani S, et al. An Update on Predictive Biomarkers in Metastatic Renal Cell Carcinoma. Eur Urol Focus (2019), https://doi.org/10.1016/j.euf.2019.04.004
EUF-715; No. of Pages 3 3
E U R O P E A N U R O L O GY F O C U S X X X ( 2 019 ) X X X– X X X
It is important to note that two of the most well-studied biomarkers for ICB across many tumor types, PD-L1 and tumor mutational burden, have not been demonstrated to have predictive capability in mRCC [2,14]. Limitations to the use of PD-L1 as a predictive marker have been well described and include heterogeneity in expression between primary and metastatic sites; intratumoral heterogeneity; unclear cutoffs to define positivity; and variability in expression arising from specimen age, prior treatments, assay choice, and cells analyzed, among others. A range of other clinical, serum, molecular, genetic, pathologic, and radiologic biomarkers have been studied as predictors of response to targeted and ICB-based therapies, but as yet none have been validated for use in routine clinical practice [15].
[3] Motzer RJ, Jonasch E, Agarwal N, et al. NCCN clinical practice guidelines in oncology: kidney cancer 3.2019. J Natl Compr Canc Netw 2019. [4] Powles T, Albiges L, Staehler M, et al. Updated European Association of Urology guidelines recommendations for the treatment of firstline metastatic clear cell renal cancer. Eur Urol 2018;73:311–5. [5] Escudier B, Porta C, Schmidinger M, et al. Renal cell carcinoma: ESMO Clinical Practice Guidelines for diagnosis, treatment and follow-up. Ann Oncol 2019
https://www.ncbi.nlm.nih.gov/
pubmed/30788497 [6] Beuselinck B, Job S, Becht E, et al. Molecular subtypes of clear cell renal cell carcinoma are associated with sunitinib response in the metastatic setting. Clin Cancer Res 2015;21:1329–39. [7] Beuselinck B, Verbiest A, Couchy G, et al. Tumor molecular characteristics in patients (pts) with international metastatic renal cell carcinoma database consortium (IMDC) good (G) and intermediate/ poor (I/P) risk. Ann Oncol 2018;29:. http://dx.doi.org/10.1093/ annonc/mdy283.
3.
Conclusions
[8] Rini BI, Huseni M, Atkins MB, et al. Molecular correlates differentiate response to atezolizumab (atezo) + bevacizumab (bev) vs suni-
There has been remarkable progress in the treatment of mRCC in recent years with the introduction of a multitude of targeted and ICB-based therapies. However, the optimal selection and sequencing of treatments for any given patient remain a challenge. Fortunately, there are many promising predictive biomarker candidates under active investigation, although most require prospective validation and demonstration of clinical utility before widespread adoption. With so many candidates in the pipeline, we are hopeful that in the coming years patients and oncologists will be able to continue to shift away from a principally “one-size-fits-all” approach to treatment sequencing and move instead towards a more personalized treatment paradigm in mRCC.
tinib (sun): results from a phase III study (IMmotion151) in untreated metastatic renal cell carcinoma (mRCC). Ann Oncol 2018;29. [9] Miao D, Margolis CA, Gao W, et al. Genomic correlates of response to immune checkpoint therapies in clear cell renal cell carcinoma. Science 2018;359:801–6. [10] Carlo MI, Manley B, Patil S, et al. Genomic alterations and outcomes with VEGF-targeted therapy in patients with clear cell renal cell carcinoma. Kidney Cancer 2017;1:49–56. [11] Derosa L, Hellmann MD, Spaziano M, et al. Negative association of antibiotics on clinical activity of immune checkpoint inhibitors in patients with advanced renal cell and non-small-cell lung cancer. Ann Oncol 2018;29:1437–44. [12] Dizman N, Bergerot P, Bergerot CD, et al. Comparative effect of bodymass index on outcome with targeted therapy and immunotherapy
Conflicts of interest
in patients with metastatic renal cell carcinoma (mRCC). Ann Oncol 2018;29:. http://dx.doi.org/10.1093/annonc/mdy283. [13] Sun G, Zhang X, Zhao J, et al. Adiponectin-AdipoR1 axis in renal cell
Shaan Dudani and Marie-France Savard have nothing to disclose. Daniel
carcinoma plays a pivotal role in tumor progression and drug
Y.C. Heng has acted as a consultant and advisor for BMS, Novartis, Pfizer,
resistance. Ann Oncol 2018;29:. http://dx.doi.org/10.1093/annonc/
Ipsen, Eisai, and Merck.
mdy283. [14] Maia MC, Almeida L, Bergerot PG, et al. Relationship of tumor mutational burden (TMB) to immunotherapy response in meta-
References [1] Graham J, Dudani S, Heng DYC. Prognostication in kidney cancer: recent advances and future directions. J Clin Oncol 2018;36:3567– 73. [2] Motzer RJ, Tannir NM, McDermott DF, et al. Nivolumab plus ipili-
static renal cell carcinoma (mRCC). J Clin Oncol 2018;36:662. [15] Rodriguez-Vida A, Strijbos M, Hutson T. Predictive and prognostic biomarkers of targeted agents and modern immunotherapy in renal cell carcinoma. ESMO Open 2016;1:e000013. [16] Heng DYC, Xie W, Regan MM, et al. External validation and comparison with other models of the International Metastatic Renal-
mumab versus sunitinib in advanced renal-cell carcinoma. N Engl J
Cell Carcinoma Database Consortium prognostic model: a popula-
Med 2018;378:1277–90.
tion-based study. Lancet Oncol 2013;14:141–8.
Please cite this article in press as: Dudani S, et al. An Update on Predictive Biomarkers in Metastatic Renal Cell Carcinoma. Eur Urol Focus (2019), https://doi.org/10.1016/j.euf.2019.04.004
available at www.sciencedirect.com journal homepage: www.europeanurology.com/eufocus
Mini Review – Kidney Cancer
An Update on Predictive Biomarkers in Metastatic Renal Cell Carcinoma Shaan Dudani, Marie-France Savard, Daniel Y.C. Heng * Division of Medical Oncology, Department of Oncology, Tom Baker Cancer Centre, University of Calgary, Calgary, Canada
Article info
Abstract
Article history: Accepted April 1, 2019
One of the major challenges of personalized oncology lies in identifying predictive biomarkers of response to therapy that are practical in the clinical setting. Although many new targeted and immune-based treatments have emerged in recent years as effective systemic therapy options in metastatic renal cell carcinoma (mRCC), optimizing the selection and sequencing of treatments for any individual patient with this disease remains a significant challenge. The CheckMate-214 trial demonstrated that the International mRCC Database Consortium risk model is an effective predictive biomarker in the first-line treatment of mRCC. To date this remains the only prospectively validated predictive biomarker in mRCC. A number of other promising biomarker candidates are under active investigation but require prospective validation before widespread clinical adoption. Patient summary: The International Metastatic Renal Cell Carcinoma Database Consortium risk model is currently the only validated tool that can help clinicians in determining which patients should receive sunitinib versus a combination of nivolumab and ipilimumab as a first treatment for metastatic renal cell carcinoma. Other tools are being actively investigated. © 2019 European Association of Urology. Published by Elsevier B.V. All rights reserved.
Associate Editor. Derya Tilki Keywords: Biomarkers Renal cell carcinoma Personalizedmedicine Immunotherapy Targeted therapy Checkpoint inhibitors
* Corresponding author. Division of Medical Oncology, Department of Oncology, Tom Baker Cancer Centre, 1331 29th Street NW, Calgary, Alberta, T2N 4N2, Canada. Tel.: +1 403 5213166; Fax: +1 403 2831651. E-mail address: [email protected] (Daniel Y.C. Heng).
1.
Introduction
Owing to a better understanding of the biological processes underlying the development of metastatic renal cell carcinoma (mRCC), a myriad of novel targeted and immunebased treatments have emerged over the past 14 years that have transformed the treatment landscape and outcomes for patients with this malignancy. Despite these important advances, selecting which treatment approaches to use for any given patient, and in what order, remains a significant challenge. Currently, oncologists struggle with optimizing and personalizing treatment sequencing in mRCC, as there
is a lack of validated predictive biomarkers to guide treatment selection. However, this remains an intense area of active research. In this mini-review we discuss recent advances in predictive biomarker development for mRCC, with a focus on clear-cell disease. Thus far, biomarker discovery efforts in non–clear-cell variants have been comparatively limited.
2.
Predictive biomarkers in mRCC
To date, the only predictive biomarker prospectively validated in a phase 3 randomized, controlled trial is the
https://doi.org/10.1016/j.euf.2019.04.004 2405-4569/© 2019 European Association of Urology. Published by Elsevier B.V. All rights reserved.
Please cite this article in press as: Dudani S, et al. An Update on Predictive Biomarkers in Metastatic Renal Cell Carcinoma. Eur Urol Focus (2019), https://doi.org/10.1016/j.euf.2019.04.004
EUF-715; No. of Pages 3 2
E U RO P E A N U RO L O GY F O C U S X X X ( 2 019 ) X X X– X X X
Table 1 – International Metastatic Renal Cell Carcinoma Database Consortium risk model [16]. Prognostic factor
Risk group
Median overall survival (mo)
Karnofsky performance status <80% Time from diagnosis to treatment <1 yr Hemoglobin < LLN Platelets > ULN Neutrophils > ULN Corrected calcium > ULN
Favorable risk (0 factors)
43
Intermediate risk (1–2 factors)
23
Poor risk (3 factors)
8
LLN = lower limit of normal; ULN = upper limit of normal.
International mRCC Database Consortium (IMDC) risk model. This composite marker comprises six variables and was initially developed and validated as a prognostic tool in mRCC (Table 1). It has subsequently been validated in several other mRCC subsets, including non–clear-cell and papillary RCC, in the second-, third- and fourth-line settings, and in patients receiving immune checkpoint blockade (ICB) treatments [1]. More recently, data from the CheckMate-214 trial demonstrated that the IMDC risk model can also effectively dichotomize patients with mRCC into two distinct groups that benefit from separate first-line systemic management strategies [2]. Specifically, patients with intermediate- or poor-risk disease were noted to have better clinical outcomes with combination ICB versus sunitinib, while for patients with favorable-risk disease, the opposite was true. These results suggest that favorable-risk tumors may have a distinct underlying biology, characterized by greater reliance on angiogenic drivers and/or lower responsiveness to ICB. This landmark finding not only resulted in a change in standard-of-care treatment for patients worldwide (as evidenced by recent recommendations from the European Association of Urology, European Society for Medical Oncology, and National Comprehensive Cancer Network for the use of first-line combination ICB in patients with IMDC intermediate- or poor-risk disease [3– 5]) but also represents a major milestone in the treatment of mRCC, as thus far it remains the only prospectively validated predictive biomarker in this disease. At present, all other predictive biomarker candidates remain in investigational stages. These include several promising leads involving assessment of molecular gene signatures to predict the efficacy of targeted and ICB-based therapies. In 2015, Beuselinck et al [6] identified four discrete molecular subtypes of clear-cell RCC (ccRCC) that were associated with varied outcomes and responses to sunitinib (ccrcc 1–4). Responders to sunitinib were enriched in the ccrcc2 (53%) and ccrcc3 (70%) subtypes as compared to ccrcc1 (22%) and ccrcc4 (27%), and survival outcomes followed a similar pattern. Interestingly, more recent data have demonstrated that these molecular subtypes correlate well with IMDC risk groups: ccrcc2 (characterized by a proangiogenic signature) and ccrcc3 tumors comprised 86% of all favorable-risk patients, while the ccrcc1 and ccrcc4 subtypes were almost exclusively intermediate- or poorrisk [7]. These findings may explain why favorable-risk patients seemed to have a greater benefit from sunitinib in the CheckMate-214 study. It remains to be seen whether
this classification can similarly distinguish responders from non-responders in the context of ICB-based treatments. A separate gene expression signature tool was investigated in a correlative study involving 823 patients from the IMmotion 151 trial, which compared bevacizumab plus atezolizumab versus sunitinib in mRCC [8]. In this analysis, patients with tumors characterized by angiogenesis-high signatures experienced longer progression-free survival with sunitinib when compared to those with angiogenesis-low tumors. In addition, tumors with T effector/IFNg-high or angiogenesis-low signatures exhibited better outcomes with combination therapy versus sunitinib. Notably, although perhaps unsurprisingly, angiogenesis-high subpopulations were noted to be enriched in favorable versus intermediate/poor Memorial Sloan Kettering Cancer Centre risk groups, mirroring the findings noted above suggesting that favorable-risk disease is driven by a greater dependency on angiogenic pathways and providing biologic rationale for the CheckMate-214 study results. Several other biomarkers have been associated with response to ICB in mRCC. Miao and colleagues [9] demonstrated that loss-of-function mutations in PBRM1, a gene involved in chromatin remodeling, were associated with higher clinical benefit rates in two independent cohorts of patients with metastatic ccRCC receiving nivolumab monotherapy. This interesting finding may be related to altered gene-expression profiles in PBRM1-deficient tumors. However, PBRM1-deficient tumors also appear to be more sensitive to VEGF-targeted therapy, which may limit its usefulness in selecting between the two treatment modalities [10]. It also remains to be seen whether PBRM1 loss has any correlation with the aforementioned molecular gene signature profiles. Intriguing findings have also been reported regarding the influence of the gut microbiome on response to ICB. Antibiotic use may unfavorably augment this response, with one study demonstrating higher rates of primary progressive disease and shorter survival in mRCC patients who received antibiotics within 30 days of initiating ICB [11]. In addition, several studies have shown interesting results suggesting an interaction between obesity and treatment effect. Patients with higher body mass index have been shown to have better survival outcomes when treated with VEGFtargeted agents, while the opposite association was noted for those receiving ICB [12]. These findings may be related to the adiponectin-adipoR1 axis and warrant further investigation [13].
Please cite this article in press as: Dudani S, et al. An Update on Predictive Biomarkers in Metastatic Renal Cell Carcinoma. Eur Urol Focus (2019), https://doi.org/10.1016/j.euf.2019.04.004
EUF-715; No. of Pages 3 3
E U R O P E A N U R O L O GY F O C U S X X X ( 2 019 ) X X X– X X X
It is important to note that two of the most well-studied biomarkers for ICB across many tumor types, PD-L1 and tumor mutational burden, have not been demonstrated to have predictive capability in mRCC [2,14]. Limitations to the use of PD-L1 as a predictive marker have been well described and include heterogeneity in expression between primary and metastatic sites; intratumoral heterogeneity; unclear cutoffs to define positivity; and variability in expression arising from specimen age, prior treatments, assay choice, and cells analyzed, among others. A range of other clinical, serum, molecular, genetic, pathologic, and radiologic biomarkers have been studied as predictors of response to targeted and ICB-based therapies, but as yet none have been validated for use in routine clinical practice [15].
[3] Motzer RJ, Jonasch E, Agarwal N, et al. NCCN clinical practice guidelines in oncology: kidney cancer 3.2019. J Natl Compr Canc Netw 2019. [4] Powles T, Albiges L, Staehler M, et al. Updated European Association of Urology guidelines recommendations for the treatment of firstline metastatic clear cell renal cancer. Eur Urol 2018;73:311–5. [5] Escudier B, Porta C, Schmidinger M, et al. Renal cell carcinoma: ESMO Clinical Practice Guidelines for diagnosis, treatment and follow-up. Ann Oncol 2019
https://www.ncbi.nlm.nih.gov/
pubmed/30788497 [6] Beuselinck B, Job S, Becht E, et al. Molecular subtypes of clear cell renal cell carcinoma are associated with sunitinib response in the metastatic setting. Clin Cancer Res 2015;21:1329–39. [7] Beuselinck B, Verbiest A, Couchy G, et al. Tumor molecular characteristics in patients (pts) with international metastatic renal cell carcinoma database consortium (IMDC) good (G) and intermediate/ poor (I/P) risk. Ann Oncol 2018;29:. http://dx.doi.org/10.1093/ annonc/mdy283.
3.
Conclusions
[8] Rini BI, Huseni M, Atkins MB, et al. Molecular correlates differentiate response to atezolizumab (atezo) + bevacizumab (bev) vs suni-
There has been remarkable progress in the treatment of mRCC in recent years with the introduction of a multitude of targeted and ICB-based therapies. However, the optimal selection and sequencing of treatments for any given patient remain a challenge. Fortunately, there are many promising predictive biomarker candidates under active investigation, although most require prospective validation and demonstration of clinical utility before widespread adoption. With so many candidates in the pipeline, we are hopeful that in the coming years patients and oncologists will be able to continue to shift away from a principally “one-size-fits-all” approach to treatment sequencing and move instead towards a more personalized treatment paradigm in mRCC.
tinib (sun): results from a phase III study (IMmotion151) in untreated metastatic renal cell carcinoma (mRCC). Ann Oncol 2018;29. [9] Miao D, Margolis CA, Gao W, et al. Genomic correlates of response to immune checkpoint therapies in clear cell renal cell carcinoma. Science 2018;359:801–6. [10] Carlo MI, Manley B, Patil S, et al. Genomic alterations and outcomes with VEGF-targeted therapy in patients with clear cell renal cell carcinoma. Kidney Cancer 2017;1:49–56. [11] Derosa L, Hellmann MD, Spaziano M, et al. Negative association of antibiotics on clinical activity of immune checkpoint inhibitors in patients with advanced renal cell and non-small-cell lung cancer. Ann Oncol 2018;29:1437–44. [12] Dizman N, Bergerot P, Bergerot CD, et al. Comparative effect of bodymass index on outcome with targeted therapy and immunotherapy
Conflicts of interest
in patients with metastatic renal cell carcinoma (mRCC). Ann Oncol 2018;29:. http://dx.doi.org/10.1093/annonc/mdy283. [13] Sun G, Zhang X, Zhao J, et al. Adiponectin-AdipoR1 axis in renal cell
Shaan Dudani and Marie-France Savard have nothing to disclose. Daniel
carcinoma plays a pivotal role in tumor progression and drug
Y.C. Heng has acted as a consultant and advisor for BMS, Novartis, Pfizer,
resistance. Ann Oncol 2018;29:. http://dx.doi.org/10.1093/annonc/
Ipsen, Eisai, and Merck.
mdy283. [14] Maia MC, Almeida L, Bergerot PG, et al. Relationship of tumor mutational burden (TMB) to immunotherapy response in meta-
References [1] Graham J, Dudani S, Heng DYC. Prognostication in kidney cancer: recent advances and future directions. J Clin Oncol 2018;36:3567– 73. [2] Motzer RJ, Tannir NM, McDermott DF, et al. Nivolumab plus ipili-
static renal cell carcinoma (mRCC). J Clin Oncol 2018;36:662. [15] Rodriguez-Vida A, Strijbos M, Hutson T. Predictive and prognostic biomarkers of targeted agents and modern immunotherapy in renal cell carcinoma. ESMO Open 2016;1:e000013. [16] Heng DYC, Xie W, Regan MM, et al. External validation and comparison with other models of the International Metastatic Renal-
mumab versus sunitinib in advanced renal-cell carcinoma. N Engl J
Cell Carcinoma Database Consortium prognostic model: a popula-
Med 2018;378:1277–90.
tion-based study. Lancet Oncol 2013;14:141–8.
Please cite this article in press as: Dudani S, et al. An Update on Predictive Biomarkers in Metastatic Renal Cell Carcinoma. Eur Urol Focus (2019), https://doi.org/10.1016/j.euf.2019.04.004