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16-04-2019 | Renal cell carcinoma | Article

Towards individualized therapy for metastatic renal cell carcinoma

Journal: Nature Reviews Clinical Oncology

Authors: Ritesh R. Kotecha, Robert J. Motzer, Martin H. Voss

Publisher: Nature Publishing Group UK

Abstract

Over the past decade, the treatment landscape for patients with metastatic renal cell carcinoma (RCC) has evolved dramatically. The therapeutic options available have expanded and now include immune-checkpoint inhibitors, novel targeted agents and combination strategies, and thus optimal patient selection and treatment sequencing are increasingly pertinent for optimizing clinical outcomes. A better understanding of the underlying biology of the tumour and its microenvironment continues to drive the inception of new diagnostic and therapeutic approaches. Furthermore, many biomarkers robustly associated with treatment and disease-specific outcomes have been identified, and their integration into clinical decision-making for patients with advanced-stage disease will soon become a reality. Herein, we review relevant aspects of the molecular biology of metastatic RCC, with an emphasis on predictive and prognostic biomarkers, and suggest tailored algorithms to individualize and guide treatment approaches for specific subgroups of patients.

Introduction

Kidney cancers are the third most common genitourinary malignancy, accounting for >400,000 new cancer diagnoses and >175,000 deaths worldwide each year, with an increasing incidence13. Advances in molecular and genomic analyses have enhanced the understanding of the underlying biology for kidney cancers, and the latest update of the WHO classification designates 16 distinct disease subtypes according to molecular, genomic and syndromic features4.
The rising incidence of renal cell carcinoma (RCC), the most common form of kidney cancer, is attributable to an increased frequency of incidental radiographic diagnoses, with a shift towards lower disease stages at diagnosis5. Many chronic medical conditions, including chronic kidney disease and uncontrolled hypertension, can predispose to the development of RCC6,7. Moreover, smoking has negative prognostic relevance both at diagnosis and during treatment7,8. Another modifiable risk factor is BMI, and understanding the ‘obesity paradox’, whereby obesity is associated with an increased risk of RCC9 but also with superior overall survival (OS) outcomes in patients with this disease10, is a focus of ongoing research. This observation needs to be interpreted in the context of tumour metabolic pathways11, as well as the effects of different systemic therapies in the metastatic setting12,13.
Nephrectomy remains curative for most patients with localized disease, although one-third of patients present with metastatic disease and one-quarter of all patients ultimately experience disease relapse. The development of kidney cancer might precede a formal diagnosis by decades14; thus, recurrence risk models have been updated in the past few years to integrate genomic data. These risk models have shown robust performance in patients with high-risk disease but have yet to be implemented routinely in clinical decision-making15.
The development of innovative diagnostic and therapeutic tools has improved the prognosis of patients with advanced-stage disease. With the development of agents targeting the VEGF pathway (predominantly VEGFR-directed tyrosine kinase inhibitors (TKIs)), immune-checkpoint inhibitors (ICIs) and parallel efforts to uncover underlying the biological differences between heterogeneous disease subtypes, the application of new systemic therapies has become increasingly complex. Moreover, as frontline therapies continue to evolve, treatment selection and sequencing are increasingly critical. Therefore, in this Review, we provide an overview of the pathogenesis of RCC with a focus on molecular and genomic markers involved in disease progression and propose a framework for biomarker integration to shape clinical decision-making.

Pathogenesis and disease heterogeneity

RCC comprises a heterogeneous group of malignancies with increasingly defined genomic and clinical features, in both the sporadic and syndromic settings16 (Table 1). These different tumour types have distinct cellular origins; for example, clear cell and papillary carcinomas arise from the proximal or parietal kidney cells, whereas chromophobe carcinomas arise from the intercalated cells in the distal nephron or collecting duct17.
Table 1
Selected renal cell carcinoma genomic variants and associated syndromic manifestations
Subtype
Genomic alterations
Features and clinical manifestations
Renal cell carcinoma variants
Clear cell
• Mutations in VHL, PBRM1, SETD2, BAP1, KDM5C, TERT and/or MTOR
• Loss of chromosomes 3p, 15q, 9p and/or 14q
• Gain of chromosome 5q
Most common RCC histology
Papillary type I
• Mutations in MET, NRF2 and/or CUL3
• Gains of chromosomes 3, 7 and/or 17
Tumour biology tends to be more indolent than that of type I papillary tumours
Papillary type II
• Mutations in CDKN2A, CDKN2B, TERT, NF2, FH, MET and/or SETD2
• Translocation of TFE3
• Tumour biology tends to be less indolent than that of type I papillary tumours
• Heterogeneous class including multiple defined subtypes
Chromophobe
• Mutations in TP53 or PTEN
• Structural rearrangements of TERT promoters
• Mutations in mitochondrial DNA
• Loss of chromosomes 1, 2, 6, 10, 13, 17 and/or 21
Might have an origin in and overlap with oncocytomas
Renal medullary
• Mutations in SMARCB1 (also known as INI1 and BAF47)
• Amplification of ABL
Frequently associated with haemoglobinopathies
Translocation Xp11.2 (TFE3) or 6p21 (TFEB)
TFE3 and TFEB translocations
• Frequent copy number alterations
Account for 20–40% of childhood RCCs and 1–4% of adult RCCs
Collecting duct
Mutations in NF2, SETD2, SMARCB1, CDKN2A and/or FH
Tumours with aggressive phenotype; difficult to distinguish from renal medullary carcinoma
Mucinous tubular and spindle cell carcinoma
Multiple chromosomal losses affecting chromosomes 1, 4, 6, 8, 9, 13, 14, 15 and/or 22
• Tumour biology typically indolent
• Might be concurrent with nephrolithiasis
Tubulocystic
• Chromosomal gains (chromosomes 7 and/or 17)
• Mutations in ABL1 and/or PDFGRA
Tumour biology typically indolent
Acquired cystic disease-associated
Multiple chromosome gains affecting chromosomes 3, 7, 16, 17 and/or Y
• Often arises in cystic disease owing to end-stage renal disease
• Can be multifocal
Syndromic renal cell carcinoma with germline mutations
VHL syndrome
Germline loss of VHL
Retinal and CNS haemangioblastomas, neuroendocrine tumours, pheochromocytomas and epidydimal cystadenomas
Hereditary leiomyomatosis and RCC
Germline loss of FH
Young age at onset (~30 years of age), cutaneous leiomyomas, uterine fibroids, pheochromocytomas, paragangliomas and leiomyosarcomas
Hereditary papillary RCC
Germline alterations in HPRC (MET proto-oncogene)
• High penetrance
• Often multifocal and bilateral renal tumours with indolent growth pattern
Birt–Hogg–Dubé syndrome
Germline loss of FLCN
• Cutaneous fibrofolliculomas, pneumothorax and oncocytomas
• Autosomal dominance
Tuberous sclerosis complex
Germline loss of TSC1 or TSC2
Angiomyolipoma, angiofibromas, giant cell astrocytoma, cardiac rhabdomyomas and/or hamartomas
Cowden syndrome
Germline loss of PTEN
• Hamartoma growths and breast, thyroid and endometrial cancers and/or melanoma
• Overlap with PTEN hamartoma syndrome
Hyperparathyroidism-jaw tumour syndrome
Germline loss of CDC73
Macrocephaly, thyroid cancers, endometrial and breast cancers, prostate cancers and/or facial trichilemmomas
BAP1 hereditary cancer predisposition
Germline loss of BAP1
• Uveal and cutaneous melanoma and malignant mesothelioma and/or lung adenocarcinoma
• Usually have clear cell histologies
MITF-associated susceptibility to melanoma and RCC syndrome
Germline loss of MITF
Melanoma, pancreatic cancer and/or pheochromocytoma
Hereditary pheochromocytomas and paragangliomas
Germline loss of SDHB, SDHC or SDHD
Bilateral and extra adrenal pheochromocytomas, paraganglioma and/or gastrointestinal stromal tumours
CNS, central nervous system; RCC, renal cell carcinoma; VHL, Von Hippel–Lindau.
Large-scale genomic sequencing efforts have uncovered specific patterns that characterize RCC tumorigenesis, particularly that of clear cell RCC (ccRCC; also referred to as conventional RCC). A hallmark event present in >90% of ccRCCs is the chromothripsis of chromosome 3p, typically with a concurrent gain of 5q (>67%) and loss of 15q (45%)16. Importantly, 3p loss sets the stage for inactivation of Von Hippel–Lindau disease tumour suppressor protein (pVHL), which occurs in essentially all ccRCCs via VHL mutations or epigenetic silencing owing to promoter methylation. Mutations in genes encoding other components of the pVHL complex (such as TCEB1 (also known as ELOC)) also lead to pVHL inactivation and have distinct clinical courses1820. pVHL is responsible for the proteolytic degradation of hypoxia-inducible factor 1α (HIF1α) and HIF2α, which are transcription factors that modulate downstream pathways involved in angiogenesis, cell cycle progression and metabolism (Fig. 1). Zinc-fingers and homeoboxes protein 2 (also referred to as AFP regulator 1), a downstream target of pVHL that promotes NF-κB activation, is also integral to disease pathogenesis, and its depletion suppresses RCC growth both in vitro and in vivo21.
VHL inactivation alone is insufficient for RCC tumorigenesis; secondary mutations occur most commonly in PBRM1, SETD2, BAP1, KDM5C, PIK3CA, MTOR and PTEN. Interestingly, the overall tumour mutational burden of RCCs is low (~1–2/mB) compared with that of other tumour types that are responsive to ICIs, with higher frequencies of frameshift insertion and deletion mutations, which might ultimately contribute to the generation of neoantigens22,23. Data from the TRACERx renal study have identified secondary mutations and chromosomal changes involved in tumour evolution, providing evidence of their clinical relevance24. Tumours with low intratumoural heterogeneity and low genomic instability typically demonstrate low malignant potential. In tumours with high genomic instability and high intratumoural heterogeneity, however, secondary PBRM1 mutations are often followed by clonal activation of secondary PI3KCA and SETD2 mutations. Chromosomal losses of 9p and 14q occur at a later stage, and 9p loss drives metastasis and is a marker for poor OS25. In tumours harbouring low intratumoural heterogeneity but high genomic instability, SETD2 and BAP1 mutations are early events associated with highly aggressive and metastatic tumours at the time of diagnosis26. Considering the intratumoural and intertumoural heterogeneity between primary and metastatic sites27, novel genomic and molecular diagnostic approaches that ideally integrate data from all disease sites will be key to understanding the evolution of individual tumours.

Initial risk stratification

Several clinical nomograms have been developed to help guide treatment selection during the initial evaluations of patients with metastatic RCC and stratify patients according to their prognosis. The two most commonly used nomograms, developed by researchers from the Memorial Sloan Kettering Cancer Center (MSKCC) and the International Metastatic Renal Cell Carcinoma Database Consortium (IMDC), incorporate several clinical and laboratory parameters28,29.
The MSKCC nomogram was developed for upfront decision-making according to risk stratification in the era of cytokine therapy and has been updated for application in the current era of targeted therapies and immunotherapy3032. This tool incorporates measures of Karnofsky performance status (<80%), time from initial diagnosis to systemic therapy, low haemoglobin, hypercalcaemia and lactate dehydrogenase (LDH). A modified version of the MSKCC criteria has also been developed for decision-making in patients with previously treated RCC33.
The IMDC model was developed in the era of targeted therapies and largely overlaps with the MSKCC model, except that it includes criteria relating to thrombocytosis and neutrophilia in lieu of elevated LDH. This tool has been validated in the setting of different lines of therapy (from the first to the fourth)34 and in patients with non-clear cell histologies35.
Throughout the years, efforts have been made to introduce various additional clinical and laboratory parameters into these scoring nomograms. For example, sarcopenia is an important predictor of OS and might improve MSKCC nomogram-based risk stratification36. The presence of distant disease sites can also be used to stratify patients; adjustments to include disease sites associated with a poor prognosis (such as hepatic or osseous metastases) have been reported to improve the performance of the IMDC model37. Neutrophil-to-lymphocyte and platelet-to-lymphocyte ratios have also been added to the IMDC strata that include patients treated with frontline VEGFR TKIs38. Declines in the neutrophil-to-lymphocyte ratio on treatment can function as an early marker of clinical benefit during immune-checkpoint inhibition39, and a decline of ≥25% has been associated with superior progression-free survival (PFS) and OS in one report40. Findings of retrospective studies suggest that high eosinophil counts are associated with lower risk of progressive disease41. Thrombocytosis can enable further sub-stratification of patients with IMDC-defined intermediate-risk disease because this criterion has a stronger association with survival outcomes than the other IMDC risk variables in this specific population42. Baseline serum levels of C-reactive protein might also be predictive of a response to sunitinib: levels within the normal physiological range are associated with favourable PFS and OS43. Many of these biomarkers require further study in independently validated cohorts to confirm signal integrity; currently, none of them is integrated into standards of care in routine clinical practice.

Predictive and prognostic genomic markers

Historically, genomic data have not been integrated into clinical decision-making for patients with metastatic RCC, but emerging data have revealed associations of specific genomic alterations with treatment responses and/or survival outcomes. An annotated risk model, which includes the mutational status of PBRM1, BAP1 and TP53, has been independently validated and shown to alter the initial MSKCC model risk categorization in 39% of patients and is independently correlated with OS, PFS and improved risk model performance and patient stratification44. ClearCode-34, a multigene signature model that enables the distinction of molecular RCC subtypes, has also been shown to improve the prognostic accuracy of the IMDC model45 but remains to be prospectively validated.
Somatic mutational profiling in patients with RCC of clear cell or non-clear cell histologies provides clinicians with data that can enable enhanced prognostication. Further, in many patients with non-clear cell histologies, germline evaluation of cancer susceptibility genes (including RCC-specific and non-RCC-specific genes) can identify inherited genomic alterations with relevance to therapeutic decision-making as well as wider implications for the patient and their family.

Chromatin-modifying gene alterations

PBRM1 encodes a component of the PBAF chromatin remodelling complex of the SWI/SNF family, which is involved in the suppression of hypoxic transcriptional signatures. PBRM1 is the second most commonly altered gene in patients with ccRCC, with somatic loss-of-function mutations occurring in 30–40% of tumours. In the localized disease setting, loss of PBRM1 is associated with unfavourable clinical outcomes46,47. Interestingly, in the metastatic setting, PBRM1 loss confers favourable effects: in the analyses that led to the development of the aforementioned genomically integrated MSKCC risk model44, PBRM1 loss was independently associated with improved PFS (HR 0.67) and OS (HR 0.63) compared with the presence of wild-type PBRM1. This difference might be mediated by a pro-angiogenic effect of PBRM1 loss that results in the upregulation of targets of VEGF-directed therapies such as HIF48 and might therefore translate not only into a prognostic but also possibly into a predictive association. Similarly, retrospective analyses from the RECORD-3 randomized study comparing the VEGFR TKI sunitinib and the mTOR inhibitor everolimus suggest that PBRM1 loss is associated with improved outcomes in patients treated with either VEGFR TKIs or rapalogue mTOR inhibitors49. In addition, PBRM1 loss was associated with improved response to sunitinib alone or atezolizumab plus bevacizumab compared with atezolizumab monotherapy in the IMmotion150 trial50. Data regarding the relevance of PBRM1 mutational status and response to single-agent therapy with ICIs remain conflicting50,51.
Mutations in BAP1, detected in ~10% of patients with ccRCC, do not generally co-occur with mutations in PBRM1 and are strongly associated with unfavourable cancer-specific outcomes in both the localized and metastatic disease settings49,5255. Tumours harbouring BAP1 mutations typically have highly inflammatory tumour microenvironments (TMEs) and might be associated with promotion of aerobic glycolysis shifts in cell metabolism56,57.
In patients with RCC, secondary mutations might create vulnerabilities related to the maintenance of genomic integrity5860, and several groups have shown interest in using agents regulating chromatin function and epigenetic processes to treat patients with tumours harbouring defined secondary mutations. For example, the WEE1 inhibitor AZD1775 is being assessed in a phase II study involving patients with SETD2-deficient tumours on the basis of a synthetic lethal strategy exploiting the crucial role of WEE1 in maintaining nucleotide pools in such tumours61 (NCT03284385). Moreover, histone deacetylase inhibitors have shown clinical activity in combination strategies in metastatic RCC62,63 and conceptually might yield higher response rates in tumours harbouring specific chromatin-modifying gene alterations.
VHL inactivation and subsequent accumulation of HIF1α and HIF2α occur in the majority of RCC tumours, and, therefore, the development of HIF-targeted therapeutic approaches remains an active area of research with potentially broad applicability. X4P-001, an orally bioavailable selective inhibitor of CXC-chemokine receptor 4 (CXCR4), has been shown to downregulate HIF2α and alter immune trafficking to the TME64. This agent is being evaluated in combination with axitinib in an ongoing phase I/II trial with the rationale of overcoming and/or delaying resistance to VEGFR TKIs65. Finally, the first-in-class HIF2α antagonist PT2385 has been tested in a phase I/II dose-escalation study involving patients with ccRCC, in which no dose-limiting toxicities and preliminary efficacy have been observed66. Whether TME signatures strongly associated with angiogenesis are predictive of a response to HIF-targeted therapies needs to be explored in future studies; efforts to characterize the efficiency of another HIF inhibitor (PT2977) in patients with solid tumours screened for VHL status are underway (NCT02974738).

Pathway-specific mutations

Mutations in MTOR, TSC1 and/or TSC2 can lead to dysregulation of the PI3K–AKT–mTOR signalling pathway67. While early reports suggested an association between the presence of such mutations and benefit from rapalogue therapy68,69, subsequent cohort analyses of samples from patients treated in the RECORD-3 trial failed to confirm this association in an unselected group of patients treated with everolimus70. The loss of PTEN expression (detected by immunohistochemistry), however, was associated with superior PFS in patients treated with everolimus but showed no such association with outcome in those randomly allocated to the sunitinib arm70. Mutations affecting the TERT promoter commonly occur in non-coding regions, remain poor prognostic markers in RCC and tend to be associated with highly aggressive phenotypes, large tumour size and propensity for metastasis in non-ccRCC71.

Genomic alterations in non-clear cell renal cell carcinoma

Specific genomic alterations have been suggested to have prognostic value in patients with non-ccRCC, such as those in FH, ALK, NF2 and genes with products involved in DNA damage responses72. Type I papillary RCCs recurrently harbour MET alterations, which can enhance the efficacy of MET-targeted agents73,74. Type II papillary RCCs can be further subdivided into multiple subtypes on the basis of their molecular and genomic features, with corresponding differences in patient survival75. In addition, one subtype is associated with hypermethylation of CpG islands and loss of FH, characteristic of hereditary leiomyomatosis and RCC. Chromophobe tumours can harbour high-risk genomic features, including alterations in TP53 and PTEN and imbalanced chromosomal duplications, all of which contribute to the clinical course of this disease76. The prognostic and predictive implications of these subtypes in the metastatic setting are unclear owing to a lack of studies in adequately sized cohorts.
Various efforts are underway to study targeted agents in molecularly defined patient populations. Combinatorial therapeutic strategies involving agents targeting VEGF, mTOR or EGFR have been used to treat patients with predominant papillary histology and somatic or germline loss of FH77,78. Enrolment in SAVOIR, a phase III study to compare the specific MET inhibitor savolitinib with sunitinib in patients with advanced-stage MET-driven papillary RCC (NCT03091192), is currently on hold, underscoring the challenges associated with molecular screening criteria. The interim results of PAPMET, a randomized phase II study to assess multiple TKIs, including sunitinib, cabozantinib, savolitinib and crizotinib, in patients with papillary RCC (NCT02761057), are awaited. Of note, genomic driver identification was not required for enrolment of patients in this study.

Germline evaluation

The identification of inherited germline mutations provides insight into disease pathogenesis and highlights additional risks of secondary malignancies. Genetic counselling in patients with RCC was traditionally reserved for patients with early onset (<46 years of age)79,80, bilateral or multifocal tumours or at least one first-degree relative with RCC, but a broader population of patients, particularly those with non-clear cell histologies, could benefit from germline testing. In a non-selected, single-institution cohort of 267 patients with metastatic RCC, one-third of whom did not meet the common criteria for genetic testing, 16.1% were found to harbour germline alterations81. In those with a non-clear cell histology specifically, the prevalence of germline alterations was 17.5%. Up to 10% of patients with non-ccRCC harboured mutations with actionable therapeutic relevance. Some germline events might even have predictive value for targeted therapies: MET identification has implications for the selection of MET-specific TKIs74, and FH identification might lead to the use of treatment regimens with efficacy in patients with hereditary leiomyomatosis and RCC or tumours with a papillary component82.

Other tissue biomarkers

PD-L1 expression

The assessment of programmed cell death 1 ligand 1 (PD-L1) expression by immunohistochemistry is directly affected by the antibody assay, the threshold for positivity, the procurement of fresh versus archival tissue and prior exposure to therapy, highlighting the need for careful interpretation across studies83. Of note, in reported and ongoing studies of ICI-based regimens in patients with RCC, a variety of different programmed cell death 1 (PD-1) and PD-L1 antibody assays were used, with variations across all the above considerations — these studies have not been compared with one another. Furthermore, intratumoural and intertumoural heterogeneity between primary tumour and metastatic sites are additional challenges encountered in the routine use of these assays84. Nevertheless, a high level of PD-L1 expression is an adverse prognostic factor associated with aggressive features of RCC, such as a high nuclear grade, lymph node involvement and distant metastases8587. Moreover, high PD-L1 levels correlate with unfavourable outcomes of VEGFR TKI therapy88. While early phase development of the anti-PD-1 monoclonal antibody (mAb) nivolumab in patients with RCC suggested possible correlations between PD-L1 expression status and outcome89, this signal was not confirmed in the registration trial30. In the phase III CheckMate 025 study comparing nivolumab with everolimus in patients with previously treated metastatic RCC30, the median OS with nivolumab was 21.8 months in patients with PD-L1+ tumours versus 27.4 months in those with PD-L1 tumours. In CheckMate 214, a phase III trial investigating the combination of the anti-cytotoxic T lymphocyte antigen 4 (CTLA-4) mAb ipilimumab and nivolumab compared with sunitinib, a predictive signal of benefit from the ICI combination was observed in patients with PD-L1 expression in ≥1% of tumour cells: PFS of 22.8 months and 5.9 months in patients with PD-L1+ tumours and PD-L1 tumours, respectively, and objective response rates (ORRs) of 58% and 37%, respectively. Of note, an ORR of 37% in patients with PD-L1 tumours underscores the results of previous studies showing clinical benefit independent of PD-L1 status90.
Preliminary results from IMmotion151, a phase III randomized study comparing the combination of atezolizumab (anti-PD-L1 antibody) and bevacizumab with sunitinib, also showed a correlation between PD-L1+ status and disease response80. In this study, a PD-L1+ status was defined by expression on ≥1% of tumour-infiltrating lymphocytes. The median PFS was better in patients with PD-L1+ disease receiving combination treatment (median PFS (mPFS) 11.2 months versus 7.7 months; HR 0.74; P = 0.022), thus suggesting that patients with PD-L1+ tumours have inferior outcomes with sunitinib monotherapy91. In the phase III JAVELIN Renal-101 study, the combination of axitinib and avelumab met one of the primary end points by demonstrating superior PFS over sunitinib in patients with PD-L1+ tumours (mPFS 13.8 months versus 7.2 months; HR 0.61; P < 0.0001), as well as superior ORR (51.4% versus 25.7%; OR 3.10). The benefits observed in the investigational arm extended to the entire study population regardless of PD-L1 status92. Similarly, the phase III KEYNOTE-426 met its two primary end points and demonstrated superior OS and PFS for the combination of pembrolizumab and axitinib compared with sunitinib in all treated patients across subgroups (HR for risk of mortality 0.53; P < 0.0001; HR for mPFS 0.69; P < 0.001), a difference that was appreciated in subgroup analyses in both patients with PD-L1+ tumours and those with PD-L1 disease93. As both these phase III studies show superiority to sunitinib irrespective of PD-L1 status, other tissue-based biomarkers that help clinicians select therapies are needed.

Immune cell subsets and the tumour microenvironment

The RCC TME stands out as one with the highest degree of immune infiltration across solid tumours in pan-cancer analyses and is enriched with T cells and tumour-associated macrophages (TAMs)94,95. Immune subset delineation has enabled the identification of up to 22 distinct T cell subsets and 17 separate TAM subsets with varying immunosuppressive and pro-inflammatory phenotypes; specific TAM clusters are strongly associated with the CD8+PD-1+ T cell compartment and shorter PFS96. Moreover, TAM infiltration might be associated with outcomes of VEGFR TKI therapy97, and while CD8+ T cell immune infiltration in both primary and metastatic sites is associated with a poor prognosis98,99, infiltration might dynamically change with VEGFR TKIs100 or ICIs101.
Stromal and immune cell signatures have also been associated with treatment response. Tumours with highly angiogenic gene expression signatures are associated with IMDC favourable-risk disease102. Comprehensive molecular and genomic analyses of COMPARZ, a phase III trial comparison of first-line sunitinib versus pazopanib103, identified that highly angiogenic signatures are associated with improved response to VEGFR TKIs and, overall, improved PFS and OS compared with poorly angiogenic tumours. No difference was seen when patients were stratified by IMDC criteria102. When stratified on the basis of computational analysis of the immune infiltrate, abundant TAM infiltration was associated with poor OS, with a statistically significant association for PFS (HR 1.40; P = 0.008) and OS (HR 1.38; P = 0.019) with the presence of M2-like immunosuppressive TAMs. Immune infiltration did not differ between IMDC-defined risk groups, although the extent of TAM infiltration was associated with IMDC poor-risk disease, and the least favourable outcomes were observed in patients with tumours harbouring a poorly angiogenic signature and high levels of TAM infiltration. Interestingly, angiogenesis and TAM infiltration expression signatures were associated with statistically significant differences in outcomes only in patients treated with pazopanib, highlighting the predictive value of TME features in the context of VEGFR-directed therapies. The value of TAM infiltration in other therapeutic settings remains to be determined.
Similar findings were reported for IMmotion150, a randomized phase II trial comparing the combination of atezolizumab and bevacizumab with monotherapy with either atezolizumab and sunitinib83. Indeed, ORR and PFS in response to sunitinib were superior in patients with highly angiogenic tumours versus those with poorly angiogenic tumours: ORR 46% versus 9% (PFS values not published; HR 0.31; P < 0.001). In highly angiogenic tumours, no differences in PFS were observed across treatment arms. In poorly angiogenic tumours, PFS was better with atezolizumab and bevacizumab than with sunitinib (11.3 months versus 3.71 months; HR 0.59; P = 0.042). Tumours with mutated VHL predominantly had highly angiogenic signatures50. Analyses of both IMmotion150 and COMPARZ highlight that tumours with mutated PBRM1 tend to have upregulated tumour angiogenesis, whereas BAP1-mutant tumours more frequently harbour poorly angiogenic signatures50,102. The associations between gene expression signatures and differential survival outcomes revealed in IMmotion150 are awaiting validation in the phase III cohort104.
In IMmotion150, high expression of markers of effector T (Teff) cell recruitment and activation and myeloid cell-driven inflammation were associated with improved PFS in response to combination treatment compared with atezolizumab monotherapy (21.7 months versus 5.8 months)50. Data from the randomized CheckMate 009 biomarker-directed study of different nivolumab doses, which incorporated serial tissue biopsy sampling, highlight a dynamic immune TME with notable changes in immune cell composition and expression signatures detected shortly after initiation of treatment with nivolumab101. Such meticulously conducted trials highlight the potential insights achievable through detailed exploration of the TME in the investigational setting, and their results will undoubtedly have a major impact on future drug development. For future efforts, all the aforementioned reports highlight the crucial importance of integrating immune and stromal aspects of the TME in establishing prognostic and predictive signatures for existing treatment regimens and, most relevantly, for the rational development of novel combinations.

Circulating tumour markers

Circulating tumour DNA (ctDNA) and circulating tumour cells (CTCs) constitute peripherally detectable tumour-derived material and, therefore, provide an opportunity to assess primary and metastatic sites non-invasively. In an early report, multiple genomic alterations including those in VHL, TP53, EGFR, NF1 and ARID1A were identified peripherally in ctDNA, and genomic alterations in any gene were detected in 78.6% of patients with metastatic RCC105. Additional cohort studies have suggested the use of ctDNA load to evaluate response to therapy106,107; the presence of detectable ctDNA was associated with a higher radiographic burden of disease108. Many of these studies were limited by inclusion of a small number of samples and a lack of comparison with primary tumour tissue samples. In a more defined genomic setting, such as that of MET-deficient RCC, ctDNA analysis might prove particularly helpful for prognosticating patient outcomes and as a surrogate for tumour burden and response to targeted agents109.
Disparities have been found in the frequency of genomic alterations detected in the frontline and second-line treatment settings. Preliminary results from a large assessment of the ctDNA profile of patients receiving several first-line or second-line treatments showed an increased incidence of genomic alterations, in particular, those affecting TP53 and MTOR, after first-line treatment with VEGFR TKI therapy105. These differences have been proposed to reflect treatment-selective pressures and the effect of frontline therapy on ctDNA load but might also simply reflect the technical limitations of ctDNA assessment in this disease at advanced time points. Although the frequency of genomic alteration might be similar in primary tumour tissue and ctDNA, a substantial discordance rate can emerge with testing110, thus stressing the challenging balance between tumour heterogeneity and diagnostic resolution. ctDNA fragment size might also be associated with inferior PFS, presenting an additional feature that should perhaps be taken into account during analysis111. RCC tumours are prone to epithelial-to-mesenchymal transitions and lack epithelial markers, and thus CTC capture remains difficult and often requires single-cell analysis112. The unifying feature of VHL loss in clear cell variants provides a model opportunity to study many of these methods.
Other circulating protein and lipid markers have also been shown to have predictive and prognostic value in the metastatic setting. A small cohort of patients with metastatic disease treated with first-line sorafenib (n = 69) was grouped by either an angiogenic or an inflammatory signature on the basis of 52 circulating markers, with correlations to PFS (HR 0.2 versus 2.25; P = 0.0002)113. Additional studies have investigated the correlation between several markers in serum, such as soluble VEGF, circulating microRNAs, carbonic anhydrase 9 and inflammatory markers, such as IL-6 and IL-8, and the outcomes of treatment with several TKIs or rapalogues113117. Most of these studies were conducted in the era of targeted therapies, and hence new separate, dedicated investigations are required to address the dramatic changes in treatment paradigms brought about by the advent of ICIs.

Individualized surgical decision-making

Historically, patients with metastatic RCC have been considered for initial cytoreductive nephrectomy owing to the overall benefits of tumour burden reduction, alleviation of symptoms associated with larger masses and, most importantly, survival improvements in the era of interferon-based therapies118,119. The results of the CARMENA study, a phase III clinical trial assessing upfront cytoreductive nephrectomy followed by sunitinib versus sunitinib alone, have called this practice into question120. In this trial, patients treated only with sunitinib had non-inferior survival outcomes (HR 0.89; 95% CI 0.71–1.10), with a median OS of 18.4 months compared with 13.9 months with prior upfront cytoreductive surgery. Notable limitations of CARMENA include conduct of cytoreductive surgery in patients with poor-risk disease, who might not traditionally be considered for surgery, inequality between treatment arms and treatment crossover to patients in the sunitinib-alone group to delayed nephrectomy. Ultimately, and unintentionally, the results of CARMENA emphasize the need to adequately stratify patients according to risk when considering cytoreductive surgery. Existing risk models to predict benefit from nephrectomy in the setting of metastatic disease incorporate various laboratory and clinical parameters. One such model defines high serum levels of LDH, low serum levels of albumin, clinically invasive T3–4 tumours, retroperitoneal adenopathy, supradiaphragmatic adenopathy, liver metastasis and metastatic site symptoms as risk factors: patients meeting four or more of these criteria derive no benefit from upfront cytoreductive nephrectomy121. Similarly, a large retrospective analysis by the IMDC has demonstrated that patients with at least four IMDC prognostic markers do not clinically benefit from cytoreductive nephrectomy and in fact might have adverse outcomes with this procedure122.

Approach to systemic treatment

Favourable-risk disease

Owing to indolent disease biology, subsets of patients with metastatic ccRCC can be observed after the diagnosis of metastatic disease. In a prospective phase II trial of such active surveillance, patients with 0–1 IMDC risk factors and <2 metastatic lesions could have delayed initiation of systemic therapy with prolonged treatment-free intervals123,124. When treatment is considered, standard VEGFR TKI therapy remains the preferred frontline option. Many VEGFR TKIs have multiple targets and, thus, weighing the adverse effect profiles associated with each agent is helpful for treatment selection for individual patients. The approved first-line VEGFR TKIs most commonly used in this context include sunitinib and pazopanib (Fig. 2). In a randomized comparative study, pazopanib had a more favourable toxicity profile103, but alternative sunitinib dosing schedules (for example, 2 weeks on and 1 week off) were associated with lower toxicity and lower rates of treatment discontinuation while maintaining robust responses125.
Despite the interest in ICIs in the favourable-risk setting, the ORR and PFS of patients treated with ipilimumab plus nivolumab were lower than those achieved with sunitinib (29% versus 52% and 15.3 months versus 25.1 months (HR 2.18; P < 0.001)) despite a complete response (CR) rate of 9% with the ICI combination90. As previously mentioned, patients with favourable-risk disease tend to have highly angiogenic tumours, and results from IMmotion150 support the notion of superior clinical benefits from VEGFR TKIs in this setting50. As noted, the phase III data for axitinib in combination with pembrolizumab or avelumab are promising and demonstrated benefit over sunitinib in both patients with intermediate-risk or poor-risk disease and patients with favourable-risk disease; the future applications of these regimens across IMDC risk groups remain to be determined.

Intermediate-risk or poor-risk disease

For patients with intermediate-risk or poor-risk disease, several first-line treatments can be considered. The preferred option is the combination of ipilimumab plus nivolumab, not only because of superior OS compared with sunitinib (median not reached versus 26 months; HR 0.53) but also because of the improved ORR (42% versus 27%), with a 9% CR rate observed with the ICI combination79, distinguishing this regimen from all other approved agents. Updated follow-up results continue to show superior OS and ORR rates, with CR rates of 11% and 8% in patients with intermediate-risk or poor-risk disease and patients with favourable-risk disease, respectively126. In patients with active autoimmune disease or in those treated with >10 mg prednisone at baseline, the use of VEGFR TKIs such as pazopanib, sunitinib or cabozantinib should be considered, given the concerns of immune-mediated toxicities and inferior response rates to ICIs observed in patients with other tumour types, such as non-small-cell lung cancer127. Cabozantinib remains an alternative option in this setting, with results from the randomized phase II CABOSUN trial demonstrating improved PFS (8.2 months versus 5.6 months) and superior ORR (46% versus 18%) compared with sunitinib, with a comparable safety profile128. Secondary analyses of this trial have highlighted that cabozantinib might be the preferred agent in patients with osseous metastatic disease, given specific bone-targeting effects, with significant changes in osseous biomarkers, including bone-specific alkaline phosphatase, amino-terminal pro-peptide of type I collagen and carboxy-terminal crosslinked telopeptides of type I collagen, detected early during therapy (P < 0.0001, P = 0.009 and P < 0.0001, respectively)129,130.
Temsirolimus, previously developed for use in patients with poor-risk, untreated metastatic disease, has largely fallen out of favour owing to its limited efficacy and arduous administration schedules requiring weekly infusions. Accordingly, treatment with this agent is reserved for those patients unable to tolerate oral agents (for example, owing to nausea or malabsorption) and concurrent contraindications to ICIs131. In particular, patients with sarcomatoid de-differentiation, who most commonly present with intermediate-risk or poor-risk disease, might benefit from ICI combinations according to the results of subgroup analyses of IMmotion151 and CheckMate 214 (refs90,104).

Novel targeted and combination approaches

With the movement of ICI combination options into the frontline setting, a new therapeutic space has emerged for innovations with treatment sequencing and novel agents (Fig. 1). The characterization and understanding of the dynamic changes in the TME, in the context of primary and acquired resistance, will be of central importance to inform rational design of further therapeutic approaches. The early promises of upfront combination immunotherapy must be weighed against the notable risk of organ-specific adverse events, which require frequent use of systemic immunosuppression90, as well as the overlapping toxicity profiles that have prevented the development of certain combination therapies advancing beyond phase I testing132,133.
Several combinations of ICIs with VEGF-directed agents are poised to move into the first-line setting on the basis of results from several different ongoing clinical trials (Table 2; Supplementary Table 1). VEGF-directed blockade promotes the recruitment of suppressive immune cell populations, including regulatory T cells and myeloid-derived suppressor cells, and might also compromise antigen-presentation between TAMs, dendritic cells and Teff cells and restrict lymphocyte trafficking, supporting combination strategies and therapeutic synergy134136. As previously discussed, patients treated with pembrolizumab plus axitinib in the phase III KEYNOTE-426 trial had an improved mPFS compared with patients receiving sunitinib (15.1 months versus 11.1 months; HR 0.69) and an ORR of 59.3% versus 35.7%. In the first interim analysis, OS was also significantly higher for patients receiving the combination compared with those receiving sunitinib, with an estimated 12-month OS of 89.9% versus 78.3%, respectively (HR 0.53)93. Results from JAVELIN Renal-101 showed a superior PFS for axitinib plus avelumab compared with sunitinib in PD-L1+ tumours: 13.8 months versus 7.2 months (HR 0.61). ORRs were superior for combination therapy compared with sunitinib: 55.2% versus 25.5% in patients with PD-L1+ tumours, and 51.4% versus 25.7% in the overall population92. This benefit was irrespective of PD-L1 expression and MSKCC or IMDC risk stratification. In both clinical trials, significant additive toxicity profiles were not observed, although overlapping organ-related toxicities (such as hepatotoxic events) require further evaluation. Both phase III studies have shown superiority of these combination strategies over sunitinib irrespective of PD-L1 status and risk stratification, and thus the incorporation of pembrolizumab or avelumab plus axitinib in routine clinical practice is likely to be broader than that of ipilimumab and nivolumab, including in patients with favourable-risk disease.
Table 2
Selected ongoing phase III trials in the frontline setting of metastatic renal cell carcinoma
Investigated agents and study
Phase, disease setting and comparator
Primary end points
Efficacya and comments
• Ipilimumab + nivolumab
• CheckMate 214 (refs1,2; NCT02231749)
• III
• Untreated disease
• Sunitinib
ORR, OS and PFS in patients with intermediate-risk or poor-risk disease
Intermediate-risk or poor-risk disease:
• ORR (investigator assessed): 42% versus 29% (P = 0.0001)
• mOS: NR versus 26.6 months (HR 0.66; P < 0.0001)
• mPFS (investigator assessed): 8.2 months versus 8.3 months (HR 0.77; P = 0.001)
Favourable-risk disease:
• ORR (investigator assessed): 39% versus 50% (P = 0.14)
• mOS: NR versus NR (HR 1.22; P = 0.44)
• mPFS (investigator assessed): 13.9 months versus 19.9 months (HR 1.23; P = 0.189)
• 2 of 3 primary end points were met
• Pembrolizumab + axitinib
• KEYNOTE-426 (ref.3; NCT02853331)
• III
• Untreated disease
• Sunitinib
PFS and OS evaluation by IRC in ITT population
• 12-month OS: 89.9% versus 78.3% (HR 0.53; P < 0.0001)
• mPFS: 15.1 months versus 11.1 months (HR 0.69; P < 0.001)
• ORR: 59.3% versus 35.7%
• Avelumab + axitinib
• JAVELIN Renal-101 (ref.4; NCT02684006)
• III
• Untreated disease
• Sunitinib
PFS and OS evaluation by IRC in PD-L1+ population
• mPFS: 13.8 months versus 7.2 months (HR 0.61; P < 0.001)
• ORR in PD-L1+: 55.2% versus 25.5%
• OS not yet reported
• Atezolizumab + bevacizumab
• IMmotion151 (ref.5; NCT02420821)
• III
• Untreated disease
• Sunitinib
ORR, PFS and OS in PD-L1+ population
• ORR in PD-L1+: 43% (35–50%) versus 35% (28–42%)
• PFS in PD-L1+: 11.2 months versus 7.7 months (HR 0.74; P = 0.0217)
• OS not yet reported
IRC, independent review committee; ITT, intention-to-treat; mOS, median overall survival; mPFS, median progression-free survival; NR, not reached; ORR, objective response rate; OS, overall survival; PD-L1, programmed cell death 1 ligand 1; PFS, progression-free survival. aExperimental arm versus control arm.
Nevertheless, not all patients will require upfront combination therapy. In the phase II KEYNOTE-427 trial in patients with ccRCC, first-line single-agent pembrolizumab demonstrated efficacy across all IMDC-defined risk groups, with a reported ORR of 38%, including 3% CRs137. Such data provide support for novel staged trial designs incorporating elements of single-agent and combination therapy. Examples include TITAN (NCT02917772), an open-label phase II study of nivolumab monotherapy with an additional boost with nivolumab plus ipilimumab in the first-line or second-line setting for patients without an objective response, and the phase II OMNIVORE study (NCT03203473), with an adaptive design that incorporates nivolumab, ipilimumab plus nivolumab and active surveillance in an individualized manner according to the patient’s response to an initial course of nivolumab monotherapy. A key goal of such trials must be to investigate high-fidelity biomarkers that can enable the identification of patients who do not require combinations and thus can be spared the additional toxicity of such regimens.
With the availability of immunotherapy options in the frontline setting, interest in the development of acquired resistance has increased. Agents that antagonize the adenosine pathway are hypothesized to enhance the efficacy of ICIs; adenosine receptors might be of particular interest in the context of acquired resistance to PD-1 inhibition138. Adenosine and ATP are normally present in the TME at low concentrations that are increased in response to inflammation and/or tumour hypoxia. Extracellular ATP is dephosphorylated primarily by the ectonucleotidases CD39 and CD73, with a consequent rise in the levels of adenosine. This increase might be crucial in RCC tumorigenesis and in immune escape, as CD73 expression has been detected in metastatic tumours and increased levels of this enzyme are not only associated with poor OS139 but also with failure of ICI therapy140. Preliminary results from a phase I trial of the adenosine receptor antagonist CPI-444 demonstrate clinical activity as a single agent and in combination with atezolizumab141. An adenosine biomarker signature has preliminarily shown correlations with response and outcome to CPI-444 and underlines the potential for study population enrichment in biomarker-driven studies142.
Alternatively, other immune checkpoints are being explored for co-targeting of PD-1. Lymphocyte activation gene 3 (LAG3), which binds to MHC class II, serves as an immune checkpoint that modulates T cell function and is targeted in several trials in combination with PD-1 inhibition (such as NCT01968109 and NCT03005782). Other examples include the co-stimulatory receptors OX40 (NCT03092856 and NCT02554812) and CD27 (NCT02335918) and even the combination of PD-1-directed plus PD-L1-directed agents (NCT02118337). None of these studies is being conducted in molecularly selected patient populations. Early data suggest that isotope-labelled versions of these monoclonal antibodies might prove useful in patient selection143 and deserve further study, ideally with incorporation of such assessments into clinical trial designs.
Among other investigational strategies aimed at manipulating the immune microenvironment, some might be supported by patient selection using tissue-based markers, such as those using targeted agents that modulate tumour metabolism144,145. For example, the kynurenine-to-tryptophan ratio has shown prognostic value in ccRCC and might function as a biomarker for therapies targeting the indoleamine 2,3-dioxygenase (IDO) immune checkpoint145. Novel cytokine-based agents, such as the PEGylated IL-10 AM-0010 or the PEGylated IL-2 prodrug NKTR-214, have shown preliminary efficacy in the settings of frontline treatment and VEGFR-refractory disease146,147 and are likely to be tested more broadly. Preliminary clinical activity has also been shown with CB-839, an agent that targets glutamine degradation, and this drug is currently under further investigation148,149; changes in metabolism can be captured radiographically150, and thus incorporating novel imaging or metabolomic-type signatures might have a predictive role in this therapy approach. Common genomic alterations affecting fumarate hydratase (FH) and succinate dehydrogenase (SDH) detected in papillary RCC lead to downstream effects on DNA homologous recombination151; whether this deficiency can be exploited in synthetic lethal approaches in this rare genomically defined disease is under exploration (NCT02576444).
The rapid development of adoptive cellular therapies relies on highly selective antigen expression and target specificity and, therefore, constitutes the pinnacle of biomarker-driven immunotherapy. Chimeric antigen receptor (CAR) T cell constructs targeting carbonic anhydrase 9 have been tested in patients but were found to have on-target dose-limiting hepatotoxicity owing to the expression of carbonic anhydrase 9 in bile ducts. Pretreatment with G250, an anti-CA-IX mAb, prevented this hepatotoxicity and enabled subsequent treatment, but clinical responses to this therapy have yet to be reported152. A phase I–II study of a VEGFR-directed CAR T cell in patients with melanoma or RCC has been completed, with pending results (NCT01218867), and testing of a novel HLA-A11-restricted T cell clone formulated from a patient with RCC treated with allogeneic stem cell transplantation is underway153. The development of CAR T cells directed at the CD27 ligand, CD70, which is overexpressed in multiple solid and haematological malignancies, remains a high priority. This approach might have particular promise in RCC, given the high the levels of expression of CD70 in this disease and the regulation of this protein by pVHL–HIF-dependent pathways154, with hopes of greater success than was achieved with strategies predicated on CD70-directed antibody–drug conjugates, which have only modest clinical activity155. Finally, vaccination approaches that pair ICIs with personalized vaccines predicated on neoantigens generated in the patient’s tumour might provide the most individualized approach to treating RCC and remain under active investigation (NCT02950766 and NCT03633110).

Conclusions

With the introduction of ICIs and next-generation VEGFR TKIs, the OS of patients with advanced-stage RCC has improved remarkably. New combination strategies that achieve synergy between these two therapeutic modalities or other drug classes will likely be developed, ultimately expanding the therapeutic armamentarium of first-line therapies. Parallel advances in genomic sequencing and molecular characterizations have also broadened and enabled precise disease prognostication, but predictive biomarkers are desperately needed to guide clinical decision-making. Diagnostic tools that integrate biomarker data might help to individualize treatment plans on the basis of distinct biological features and need to be developed together with new treatments. Reassuringly, progress has enabled rapid and detailed individualization of tumours — the use of this biological data in real-time decision-making is feasible in the near future.
Supplementary information
Supplementary information is available for this paper at https://​doi.​org/​10.​1038/​s41571-019-0209-1.

Reviewer information

Nature Reviews Clinical Oncology thanks R. Figlin, E. Jonasch and W. Stadler for their contribution to the peer review of this work.

Competing interests

R.R.K. declares no competing interests. R.J.M. has received research support from Bristol-Myers Squibb, Eisai, Genentech, Novartis and Pfizer and honoraria for advisory and consulting roles from Genentech, Incyte, Merck, Novartis and Pfizer. M.H.V. has received research support from Bristol-Myers Squibb, Genentech, Novartis and Roche, travel and accommodation support from Eisai, Novartis and Takeda and has been a paid consultant for Alexion, Bayer, Calithera, Corvus, Eisai, Exelixis, GlaxoSmithKline, Natera, Novartis and Pfizer.
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Related links
US NIH ClinicalTrials.gov database: https://​www.​clinicaltrials.​gov
Literature
1.
Bray, F. et al. Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J. Clin. 68, 394–424 (2018).CrossRefPubMed
2.
Siegel, R. L., Miller, K. D. & Jemal, A. Cancer statistics, 2018. CA Cancer J. Clin. 68, 7–30 (2018).PubMedCrossRef
3.
Saad, A. M. et al. Trends in renal-cell carcinoma incidence and mortality in the United States in the last 2 decades: a SEER-based study. Clin. Genitourin. Cancer 17, 46–57 (2019).PubMedCrossRef
4.
Moch, H., Cubilla, A. L., Humphrey, P. A., Reuter, V. E. & Ulbright, T. M. The 2016 WHO classification of tumours of the urinary system and male genital organs-part A: renal, penile, and testicular tumours. Eur. Urol. 70, 93–105 (2016).PubMedCrossRef
5.
Kane, C. J., Mallin, K., Ritchey, J., Cooperberg, M. R. & Carroll, P. R. Renal cell cancer stage migration: analysis of the National Cancer Data Base. Cancer 113, 78–83 (2008).PubMedCrossRef
6.
Wong, M. C. S. et al. Incidence and mortality of kidney cancer: temporal patterns and global trends in 39 countries. Sci. Rep. 7, 15698 (2017).PubMedPubMedCentralCrossRef
7.
Gelfond, J. et al. Modifiable risk factors to reduce renal cell carcinoma incidence: insight from the PLCO trial. Urol. Oncol. 36, 340.e1–340.e6 (2018).CrossRef
8.
Tsivian, M., Moreira, D. M., Caso, J. R., Mouraviev, V. & Polascik, T. J. Cigarette smoking is associated with advanced renal cell carcinoma. J. Clin. Oncol. 29, 2027–2031 (2011).PubMedCrossRef
9.
Wang, F. & Xu, Y. Body mass index and risk of renal cell cancer: a dose-response meta-analysis of published cohort studies. Int. J. Cancer 135, 1673–1686 (2014).PubMedCrossRef
10.
Choi, Y. et al. Body mass index and survival in patients with renal cell carcinoma: a clinical-based cohort and meta-analysis. Int. J. Cancer 132, 625–634 (2013).PubMedCrossRef
11.
Hakimi, A. A. et al. An epidemiologic and genomic investigation into the obesity paradox in renal cell carcinoma. J. Natl Cancer Inst. 105, 1862–1870 (2013).PubMedPubMedCentralCrossRef
12.
Albiges, L. et al. Body mass index and metastatic renal cell carcinoma: clinical and biological correlations. J. Clin. Oncol. 34, 3655–3663 (2016).PubMedPubMedCentralCrossRef
13.
Bergerot, P. et al. Targeted therapy and immunotherapy: effect of body mass index on clinical outcomes in patients diagnosed with metastatic renal cell carcinoma. Kidney Cancer 3, 63–70 (2019).CrossRef
14.
Mitchell, T. J. et al. Timing the landmark events in the evolution of clear cell renal cell cancer: TRACERx renal. Cell 173, 611–623 (2018).PubMedPubMedCentralCrossRef
15.
Rini, B. I. et al. Validation of the 16-gene recurrence score in patients with locoregional, high-risk renal cell carcinoma from a phase 3 trial of adjuvant sunitinib. Clin. Cancer Res. 24, 4407–4415 (2018).PubMedCrossRef
16.
Ricketts, C. J. et al. The cancer genome atlas comprehensive molecular characterization of renal cell carcinoma. Cell Rep. 23, 313–326 (2018).PubMedPubMedCentralCrossRef
17.
Lindgren, D., Sjölund, J. & Axelson, H. Tracing renal cell carcinomas back to the nephron. Trends Cancer 4, 472–484 (2018).PubMedCrossRef
18.
Sato, Y. et al. Integrated molecular analysis of clear-cell renal cell carcinoma. Nat. Genet. 45, 860–867 (2013).PubMedCrossRef
19.
Hakimi, A. A., Pham, C. G. & Hsieh, J. J. A clear picture of renal cell carcinoma. Nat. Genet. 45, 849–850 (2013).PubMedCrossRef
20.
Hakimi, A. A. et al. TCEB1-mutated renal cell carcinoma: a distinct genomic and morphological subtype. Mod. Pathol. 28, 845–853 (2015).PubMedPubMedCentralCrossRef
21.
Zhang, J. et al. VHL substrate transcription factor ZHX2 as an oncogenic driver in clear cell renal cell carcinoma. Science 361, 290–295 (2018).PubMedPubMedCentralCrossRef
22.
Turajlic, S. et al. Insertion-and-deletion-derived tumour-specific neoantigens and the immunogenic phenotype: a pan-cancer analysis. Lancet Oncol. 18, 1009–1021 (2017).CrossRefPubMed
23.
Zehir, A. et al. Mutational landscape of metastatic cancer revealed from prospective clinical sequencing of 10,000 patients. Nat. Med. 23, 703–713 (2017).PubMedPubMedCentralCrossRef
24.
Turajlic, S. et al. Deterministic evolutionary trajectories influence primary tumor growth: TRACERx renal. Cell 173, 595–610 (2018).PubMedPubMedCentralCrossRef
25.
Brannon, A. R. et al. Molecular stratification of clear cell renal cell carcinoma by consensus clustering reveals distinct subtypes and survival patterns. Genes Cancer 1, 152–163 (2010).PubMedPubMedCentralCrossRef
26.
Turajlic, S. et al. Tracking cancer evolution reveals constrained routes to metastases: TRACERx renal. Cell 173, 581–594 (2018).PubMedPubMedCentralCrossRef
27.
Gerlinger, M. et al. Intratumor heterogeneity and branched evolution revealed by multiregion sequencing. N. Engl. J. Med. 366, 883–892 (2012).PubMedPubMedCentralCrossRef
28.
Heng, D. Y. et al. Prognostic factors for overall survival in patients with metastatic renal cell carcinoma treated with vascular endothelial growth factor-targeted agents: results from a large, multicenter study. J. Clin. Oncol. 27, 5794–5799 (2009).PubMedCrossRef
29.
Motzer, R. J. et al. Survival and prognostic stratification of 670 patients with advanced renal cell carcinoma. J. Clin. Oncol. 17, 2530–2540 (1999).PubMedCrossRef
30.
Motzer, R. J. et al. Nivolumab versus everolimus in advanced renal-cell carcinoma. N. Engl. J. Med. 373, 1803–1813 (2015).PubMedPubMedCentralCrossRef
31.
Choueiri, T. K. et al. Cabozantinib versus everolimus in advanced renal-cell carcinoma. N. Engl. J. Med. 373, 1814–1823 (2015).PubMedPubMedCentralCrossRef
32.
Motzer, R. J. et al. Lenvatinib, everolimus, and the combination in patients with metastatic renal cell carcinoma: a randomised, phase 2, open-label, multicentre trial. Lancet Oncol. 16, 1473–1482 (2015).CrossRefPubMed
33.
Motzer, R. J. et al. Prognostic factors for survival in previously treated patients with metastatic renal cell carcinoma. J. Clin. Oncol. 22, 454–463 (2004).PubMedCrossRef
34.
Yip, S. M. et al. Checkpoint inhibitors in patients with metastatic renal cell carcinoma: results from the international metastatic renal cell carcinoma database consortium. Cancer 124, 3677–3683 (2018).PubMedCrossRef
35.
Kroeger, N. et al. Metastatic non-clear cell renal cell carcinoma treated with targeted therapy agents: characterization of survival outcome and application of the International mRCC Database Consortium criteria. Cancer 119, 2999–3006 (2013).PubMedCrossRef
36.
Fukushima, H., Nakanishi, Y., Kataoka, M., Tobisu, K. & Koga, F. Prognostic significance of sarcopenia in patients with metastatic renal cell carcinoma. J. Urol. 195, 26–32 (2016).PubMedCrossRef
37.
McKay, R. R. et al. Impact of bone and liver metastases on patients with renal cell carcinoma treated with targeted therapy. Eur. Urol. 65, 577–584 (2014).PubMedCrossRef
38.
Chrom, P., Stec, R., Bodnar, L. & Szczylik, C. Incorporating neutrophil-to-lymphocyte ratio and platelet-to-lymphocyte ratio in place of neutrophil count and platelet count improves prognostic accuracy of the International Metastatic Renal Cell Carcinoma Database Consortium model. Cancer Res. Treat. 50, 103–110 (2018).PubMedCrossRef
39.
Templeton, A. J. et al. Change in neutrophil-to-lymphocyte ratio in response to targeted therapy for metastatic renal cell carcinoma as a prognosticator and biomarker of efficacy. Eur. Urol. 70, 358–364 (2016).PubMedCrossRef
40.
Lalani, A. A. et al. Change in neutrophil-to-lymphocyte ratio (NLR) in response to immune checkpoint blockade for metastatic renal cell carcinoma. J. Immunother. Cancer 6, 5 (2018).PubMedPubMedCentralCrossRef
41.
Zahoor, H. et al. Patterns, predictors and subsequent outcomes of disease progression in metastatic renal cell carcinoma patients treated with nivolumab. J. Immunother. Cancer 6, 107–107 (2018).PubMedPubMedCentralCrossRef
42.
Guida, A. et al. Identification of IMDC intermediate-risk subgroups in patients with metastatic clear-cell renal cell carcinoma (ccRCC) [abstract]. J. Clin. Oncol. 36 (Suppl. 15), e16577 (2018).CrossRef
43.
Pilskog, M. et al. Predictive value of C-reactive protein in patients treated with sunitinib for metastatic clear cell renal cell carcinoma. BMC Urol. 17, 74 (2017).PubMedPubMedCentralCrossRef
44.
Voss, M. H. et al. Genomically annotated risk model for advanced renal-cell carcinoma: a retrospective cohort study. Lancet Oncol. 19, 1688–1698 (2018).PubMedCrossRefPubMedCentral
45.
de Velasco, G. et al. Molecular subtypes improve prognostic value of International Metastatic Renal Cell Carcinoma Database Consortium prognostic model. Oncol. 22, 286–292 (2017).
46.
Joseph, R. W. et al. Clear cell renal cell carcinoma subtypes identified by BAP1 and PBRM1 expression. J. Urol. 195, 180–187 (2016).PubMedCrossRef
47.
Hakimi, A. A. et al. Clinical and pathologic impact of select chromatin-modulating tumor suppressors in clear cell renal cell carcinoma. Eur. Urol. 63, 848–854 (2013).PubMedCrossRef
48.
Gao, W., Li, W., Xiao, T., Liu, X. S. & Kaelin, W. G. Jr. Inactivation of the PBRM1 tumor suppressor gene amplifies the HIF-response in VHL−/− clear cell renal carcinoma. Proc. Natl Acad. Sci. USA 114, 1027–1032 (2017).PubMedCrossRefPubMedCentral
49.
Hsieh, J. J. et al. Genomic biomarkers of a randomized trial comparing first-line everolimus and sunitinib in patients with metastatic renal cell carcinoma. Eur. Urol. 71, 405–414 (2017).PubMedCrossRef
50.
McDermott, D. F. et al. Clinical activity and molecular correlates of response to atezolizumab alone or in combination with bevacizumab versus sunitinib in renal cell carcinoma. Nat. Med. 24, 749–757 (2018).PubMedPubMedCentralCrossRef
51.
Miao, D. et al. Genomic correlates of response to immune checkpoint therapies in clear cell renal cell carcinoma. Science 359, 801–806 (2018).PubMedPubMedCentralCrossRef
52.
Carlo, M. et al. Genomic alterations and outcomes with VEGF-targeted therapy in patients with clear cell renal cell carcinoma. Kidney Cancer 1, 49–56 (2017).
53.
The Cancer Genome Atlas Research Network. Comprehensive molecular characterization of clear cell renal cell carcinoma. Nature 499, 43–49 (2013).
54.
Scelo, G. et al. Variation in genomic landscape of clear cell renal cell carcinoma across Europe. Nat. Commun. 5, 5135 (2014).PubMedCrossRef
55.
Turajlic, S., Larkin, J. & Swanton, C. SnapShot: renal cell carcinoma. Cell 163, 1556–1556.e1 (2015).PubMedCrossRef
56.
Bononi, A. et al. Germline BAP1 mutations induce a Warburg effect. Cell Death Differ. 24, 1694–1704 (2017).PubMedPubMedCentralCrossRef
57.
Wang, T. et al. An empirical approach leveraging tumorgrafts to dissect the tumor microenvironment in renal cell carcinoma identifies missing link to prognostic inflammatory factors. Cancer Discov. 8, 1142–1155 (2018).PubMedPubMedCentralCrossRef
58.
Espana-Agusti, J., Warren, A., Chew, S. K., Adams, D. J. & Matakidou, A. Loss of PBRM1 rescues VHL dependent replication stress to promote renal carcinogenesis. Nat. Commun. 8, 2026 (2017).PubMedPubMedCentralCrossRef
59.
Pena-Llopis, S. et al. BAP1 loss defines a new class of renal cell carcinoma. Nat. Genet. 44, 751–759 (2012).PubMedPubMedCentralCrossRef
60.
Chiang, Y. C. et al. SETD2 haploinsufficiency for microtubule methylation is an early driver of genomic instability in renal cell carcinoma. Cancer Res. 78, 3135–3146 (2018).PubMedPubMedCentral
61.
Pfister, S. X. et al. Inhibiting WEE1 selectively kills histone H3K36me3-deficient cancers by dNTP starvation. Cancer Cell 28, 557–568 (2015).PubMedPubMedCentralCrossRef
62.
Pili, R. et al. Combination of the histone deacetylase inhibitor vorinostat with bevacizumab in patients with clear-cell renal cell carcinoma: a multicentre, single-arm phase I/II clinical trial. Br. J. Cancer 116, 874–883 (2017).PubMedPubMedCentralCrossRef
63.
Pili, R. et al. Immunomodulation by entinostat in renal cell carcinoma patients receiving high dose interleukin 2: a multicenter, single-arm, phase 1/2 trial (NCI-CTEP#7870). Clin. Cancer Res. 23, 7199–7208 (2017).PubMedPubMedCentralCrossRef
64.
McDermott, D. F. et al. A Phase (Ph) 1 dose finding study of X4P-001 (an oral CXCR4 inhibitor) and axitinib in patients with advanced renal cell carcinoma (RCC) [abstract 896P]. Ann. Oncol. 28 (Suppl. 5), mdx371.050 (2017).
65.
Atkins, M. et al. A phase 1 dose-finding study of X4P-001 (an oral CXCR4 inhibitor) and axitinib in patients with advanced renal cell carcinoma (RCC) [abstract B201]. Mol. Cancer Ther. 17 (Suppl. 1), B201 (2018).
66.
Courtney, K. D. et al. Phase I dose-escalation trial of PT2385, a first-in-class hypoxia-inducible factor-2alpha antagonist in patients with previously treated advanced clear cell renal cell carcinoma. J. Clin. Oncol. 36, 867–874 (2018).PubMedCrossRef
67.
Rausch, S. et al. mTOR and mTOR phosphorylation status in primary and metastatic renal cell carcinoma tissue: differential expression and clinical relevance. J. Cancer Res. Clin. Oncol. 145, 153–163 (2018).PubMedCrossRef
68.
Voss, M. H. et al. Tumor genetic analyses of patients with metastatic renal cell carcinoma and extended benefit from mTOR inhibitor therapy. Clin. Cancer Res. 20, 1955–1964 (2014).PubMedPubMedCentralCrossRef
69.
Kwiatkowski, D. J. et al. Mutations in TSC1, TSC2, and MTOR are associated with response to rapalogs in patients with metastatic renal cell carcinoma. Clin. Cancer Res. 22, 2445–2452 (2016).PubMedPubMedCentralCrossRef
70.
Voss, M. H. et al. PTEN expression, not mutation status in TSC1, TSC2, or mTOR, correlates with the outcome on everolimus in patients with renal cell carcinoma treated on the randomized RECORD-3 trial. Clin. Cancer Res. 25, 506–514 (2019).PubMedCrossRef
71.
Casuscelli, J. et al. Characterization and impact of TERT promoter region mutations on clinical outcome in renal cell carcinoma. Eur. Urol. Focus https://​doi.​org/​10.​1016/​j.​euf.​2017.​09.​008 (2017).CrossRef
72.
Chen, Y. B. et al. Molecular analysis of aggressive renal cell carcinoma with unclassified histology reveals distinct subsets. Nat. Commun. 7, 13131 (2016).PubMedPubMedCentralCrossRef
73.
Choueiri, T. K. et al. Biomarker-based phase II trial of savolitinib in patients with advanced papillary renal cell cancer. J. Clin. Oncol. 35, 2993–3001 (2017).PubMedCrossRef
74.
Choueiri, T. K. et al. Phase II and biomarker study of the dual MET/VEGFR2 inhibitor foretinib in patients with papillary renal cell carcinoma. J. Clin. Oncol. 31, 181–186 (2013).PubMedCrossRef
75.
The Cancer Genome Atlas Research Network. Comprehensive molecular characterization of papillary renal-cell carcinoma. N. Engl. J. Med. 374, 135–145 (2015).CrossRef
76.
Casuscelli, J. et al. Genomic landscape and evolution of metastatic chromophobe renal cell carcinoma. JCI Insight 2, 92688 (2017).PubMedCrossRef
77.
Srinivasan, R. et al. 5 Mechanism based targeted therapy for hereditary leiomyomatosis and renal cell cancer (HLRCC) and sporadic papillary renal cell carcinoma: interim results from a phase 2 study of bevacizumab and erlotinib. Eur. J. Cancer 50, 8 (2014).CrossRef
78.
Voss, M. H. et al. Phase II trial and correlative genomic analysis of everolimus plus bevacizumab in advanced non-clear cell renal cell carcinoma. J. Clin. Oncol. 34, 3846–3853 (2016).PubMedPubMedCentralCrossRef
79.
Shuch, B. et al. Defining early-onset kidney cancer: implications for germline and somatic mutation testing and clinical management. J. Clin. Oncol. 32, 431–437 (2014).PubMedCrossRef
80.
Nguyen, K. A. et al. Advances in the diagnosis of hereditary kidney cancer: initial results of a multigene panel test. Cancer 123, 4363–4371 (2017).PubMedCrossRef
81.
Carlo, M. I. et al. Prevalence of germline mutations in cancer susceptibility genes in patients with advanced renal cell carcinoma. JAMA Oncol. 4, 1228–1235 (2018).PubMedPubMedCentralCrossRef
82.
The National Comprehensive Cancer Network. NCCN Clinical Practice Guidelines in Oncology. Kidney Cancer 2, 5 (2019).
83.
Zhu, J. et al. Biomarkers of immunotherapy in urothelial and renal cell carcinoma: PD-L1, tumor mutational burden, and beyond. J. Immunother. Cancer 6, 4 (2018).PubMedPubMedCentralCrossRef
84.
Callea, M. et al. Differential expression of PD-L1 between primary and metastatic sites in clear-cell renal cell carcinoma. Cancer Immunol. Res. 3, 1158–1164 (2015).PubMedPubMedCentralCrossRef
85.
Iacovelli, R. et al. Prognostic role of PD-L1 expression in renal cell carcinoma. A systematic review and meta-analysis. Target Oncol. 11, 143–148 (2016).PubMedCrossRef
86.
Thompson, R. H., Dong, H. & Kwon, E. D. Implications of B7-H1 expression in clear cell carcinoma of the kidney for prognostication and therapy. Clin. Cancer Res. 13, 709s–715s (2007).PubMedCrossRef
87.
Thompson, R. H. et al. Costimulatory B7-H1 in renal cell carcinoma patients: indicator of tumor aggressiveness and potential therapeutic target. Proc. Natl Acad. Sci. USA 101, 17174–17179 (2004).PubMedCrossRefPubMedCentral
88.
Choueiri, T. K. et al. Correlation of PD-L1 tumor expression and treatment outcomes in patients with renal cell carcinoma receiving sunitinib or pazopanib: results from COMPARZ, a randomized controlled trial. Clin. Cancer Res. 21, 1071–1077 (2015).PubMedCrossRef
89.
Motzer, R. J. et al. Nivolumab for metastatic renal cell carcinoma: results of a randomized phase II trial. J. Clin. Oncol. 33, 1430–1437 (2015).CrossRefPubMed
90.
Motzer, R. J. et al. Nivolumab plus ipilimumab versus sunitinib in advanced renal-cell carcinoma. N. Engl. J. Med. 378, 1277–1290 (2018).PubMedPubMedCentralCrossRef
91.
Motzer, R. J. et al. IMmotion151: a randomized phase III study of atezolizumab plus bevacizumab versus sunitinib in untreated metastatic renal cell carcinoma (mRCC) [abstract]. J. Clin. Oncol. 36, 578 (2018).CrossRef
92.
Motzer, R. J. et al. Avelumab plus axitinib versus sunitinib for advanced renal-cell carcinoma. N. Engl. J. Med. 380, 1103–1115 (2019).PubMedCrossRefPubMedCentral
93.
Rini, B. I. et al. Pembrolizumab plus axitinib versus sunitinib for advanced renal-cell carcinoma. N. Engl. J. Med. 380, 1116–1127 (2019).PubMedCrossRef
94.
Senbabaoglu, Y. et al. Tumor immune microenvironment characterization in clear cell renal cell carcinoma identifies prognostic and immunotherapeutically relevant messenger RNA signatures. Genome Biol. 17, 231 (2016).PubMedPubMedCentralCrossRef
95.
Ock, C. Y. et al. Pan-cancer immunogenomic perspective on the tumor microenvironment based on PD-L1 and CD8 T-cell infiltration. Clin. Cancer Res. 22, 2261–2270 (2016).PubMedCrossRef
96.
Chevrier, S. et al. An immune atlas of clear cell renal cell carcinoma. Cell 169, 736–749 (2017).PubMedPubMedCentralCrossRef
97.
Voss, M. H. et al. Correlation of degree of tumor immune infiltration and insertion-and-deletion (indel) burden with outcome on programmed death 1 (PD1) therapy in advanced renal cell cancer (RCC) [abstract]. J. Clin. Oncol. 36, 4518 (2018).CrossRef
98.
Giraldo, N. A. et al. Orchestration and prognostic significance of immune checkpoints in the microenvironment of primary and metastatic renal cell cancer. Clin. Cancer Res. 21, 3031–3040 (2015).PubMedCrossRef
99.
Remark, R. et al. Characteristics and clinical impacts of the immune environments in colorectal and renal cell carcinoma lung metastases: influence of tumor origin. Clin. Cancer Res. 19, 4079–4091 (2013).PubMedCrossRef
100.
Zizzari, I. G. et al. TK inhibitor pazopanib primes DCs by downregulation of the beta-catenin pathway. Cancer Immunol. Res. 6, 711–722 (2018).
101.
Choueiri, T. K. et al. Immunomodulatory activity of nivolumab in metastatic renal cell carcinoma. Clin. Cancer Res. 22, 5461–5471 (2016).PubMedPubMedCentralCrossRef
102.
Hakimi, A. A. et al. Transcriptomic profiling of the tumor microenvironment reveals distinct subgroups of clear cell renal cell cancer - data from a randomized phase III trial. Cancer Discov. https://​doi.​org/​10.​1158/​2159-8290.​CD-18-0957 (2019).PubMedCrossRefPubMedCentral
103.
Motzer, R. J. et al. Pazopanib versus sunitinib in metastatic renal-cell carcinoma. N. Engl. J. Med. 369, 722–731 (2013).PubMedCrossRef
104.
Rini, B. I. et al. Molecular correlates differentiate response to atezolizumab (atezo) + bevacizumab (bev) versus sunitinib (sun): results from a phase III study (IMmotion151) in untreated metastatic renal cell carcinoma (mRCC) [abstract LBA31]. Ann. Oncol. 29 (Suppl. 8), mdy424.037 (2018).
105.
Pal, S. K. et al. Evolution of circulating tumor DNA profile from first-line to subsequent therapy in metastatic renal cell carcinoma. Eur. Urol. 72, 557–564 (2017).PubMedCrossRef
106.
Dizman, N. et al. Exceptional response to nivolumab rechallenge in metastatic renal cell carcinoma with parallel changes in genomic profile. Eur. Urol. 73, 308–310 (2018).PubMedCrossRef
107.
Feng, G. et al. Quantification of plasma cell-free DNA in predicting therapeutic efficacy of sorafenib on metastatic clear cell renal cell carcinoma. Dis. Markers 34, 105–111 (2013).PubMedPubMedCentralCrossRef
108.
Maia, M. C. et al. Association of circulating tumor DNA (ctDNA) detection in metastatic renal cell carcinoma (mRCC) with tumor burden. Kidney Cancer 1, 65–70 (2017).
109.
Ikeda, S., Schwaederle, M., Mohindra, M., Fontes Jardim, D. L. & Kurzrock, R. MET alterations detected in blood-derived circulating tumor DNA correlate with bone metastases and poor prognosis. J. Hematol. Oncol. 11, 76 (2018).PubMedPubMedCentralCrossRef
110.
Hahn, A. W. et al. Correlation of genomic alterations assessed by next-generation sequencing (NGS) of tumor tissue DNA and circulating tumor DNA (ctDNA) in metastatic renal cell carcinoma (mRCC): potential clinical implications. Oncotarget 8, 33614–33620 (2017).PubMedPubMedCentral
111.
Yamamoto, Y. et al. Increased level and fragmentation of plasma circulating cell-free DNA are diagnostic and prognostic markers for renal cell carcinoma. Oncotarget 9, 20467–20475 (2018).PubMedPubMedCentral
112.
Broncy, L. et al. Single-cell genetic analysis validates cytopathological identification of circulating cancer cells in patients with clear cell renal cell carcinoma. Oncotarget 9, 20058–20074 (2018).PubMedPubMedCentralCrossRef
113.
Zurita, A. J. et al. A cytokine and angiogenic factor (CAF) analysis in plasma for selection of sorafenib therapy in patients with metastatic renal cell carcinoma. Ann. Oncol. 23, 46–52 (2012).PubMedCrossRef
114.
Gigante, M. et al. Prognostic value of serum CA9 in patients with metastatic clear cell renal cell carcinoma under targeted therapy. Anticancer Res. 32, 5447–5451 (2012).PubMed
115.
Tran, H. T. et al. Prognostic or predictive plasma cytokines and angiogenic factors for patients treated with pazopanib for metastatic renal-cell cancer: a retrospective analysis of phase 2 and phase 3 trials. Lancet Oncol. 13, 827–837 (2012).PubMedCrossRef
116.
Deprimo, S. E. et al. Circulating protein biomarkers of pharmacodynamic activity of sunitinib in patients with metastatic renal cell carcinoma: modulation of VEGF and VEGF-related proteins. J. Transl Med. 5, 32 (2007).PubMedPubMedCentralCrossRef
117.
Voss, M. H. et al. Circulating biomarkers and outcome from a randomised phase II trial of sunitinib versus everolimus for patients with metastatic renal cell carcinoma. Br. J. Cancer 114, 642–649 (2016).PubMedPubMedCentralCrossRef
118.
Flanigan, R. C. et al. Nephrectomy followed by interferon Alfa-2b compared with interferon Alfa-2b alone for metastatic renal-cell cancer. N. Engl. J. Med. 345, 1655–1659 (2001).PubMedCrossRef
119.
Mickisch, G. H. J., Garin, A., van Poppel, H., de Prijck, L. & Sylvester, R. Radical nephrectomy plus interferon-alfa-based immunotherapy compared with interferon alfa alone in metastatic renal-cell carcinoma: a randomised trial. Lancet 358, 966–970 (2001).PubMedCrossRef
120.
Mejean, A. et al. Sunitinib alone or after nephrectomy in metastatic renal-cell carcinoma. N. Engl. J. Med. 379, 417–427 (2018).PubMedCrossRef
121.
Culp, S. H. et al. Can we better select patients with metastatic renal cell carcinoma for cytoreductive nephrectomy? Cancer 116, 3378–3388 (2010).PubMedCrossRef
122.
Heng, D. Y. C. et al. Cytoreductive nephrectomy in patients with synchronous metastases from renal cell carcinoma: results from the International Metastatic Renal Cell Carcinoma Database Consortium. Eur. Urol. 66, 704–710 (2014).PubMedCrossRef
123.
Park, I. et al. Active surveillance for metastatic or recurrent renal cell carcinoma. J. Cancer Res. Clin. Oncol. 140, 1421–1428 (2014).PubMedCrossRef
124.
Rini, B. I. et al. Active surveillance in metastatic renal-cell carcinoma: a prospective, phase 2 trial. Lancet Oncol. 17, 1317–1324 (2016).PubMedCrossRef
125.
Jonasch, E. et al. Phase II study of two weeks on, one week off sunitinib scheduling in patients with metastatic renal cell carcinoma. J. Clin. Oncol. 36, 1588–1593 (2018).PubMedCrossRefPubMedCentral
126.
Tannir, N. M. et al. Thirty-month follow-up of the phase III CheckMate 214 trial of first-line nivolumab + ipilimumab (N+I) or sunitinib (S) in patients (pts) with advanced renal cell carcinoma (aRCC) [abstract]. J. Clin. Oncol. 37, 547 (2019).CrossRef
127.
Arbour, K. C. et al. Impact of baseline steroids on efficacy of programmed cell death-1 and programmed death-ligand 1 blockade in patients with non–small-cell lung cancer. J. Clin. Oncol. 36, 2872–2878 (2018).PubMedCrossRef
128.
Choueiri, T. K. et al. Cabozantinib versus sunitinib as initial targeted therapy for patients with metastatic renal cell carcinoma of poor or intermediate risk: the alliance A031203 CABOSUN trial. J. Clin. Oncol. 35, 591–597 (2017).PubMedCrossRef
129.
Escudier, B. et al. Cabozantinib, a new standard of care for patients with advanced renal cell carcinoma and bone metastases? Subgroup analysis of the METEOR trial. J. Clin. Oncol. 36, 765–772 (2018).PubMedCrossRefPubMedCentral
130.
Choueiri, T. K. et al. Cabozantinib versus sunitinib as initial therapy for metastatic renal cell carcinoma of intermediate or poor risk (alliance A031203 CABOSUN randomised trial): progression-free survival by independent review and overall survival update. Eur. J. Cancer 94, 115–125 (2018).PubMedPubMedCentralCrossRef
131.
Hudes, G. et al. Temsirolimus, interferon alfa, or both for advanced renal-cell carcinoma. N. Engl. J. Med. 356, 2271–2281 (2007).PubMedCrossRef
132.
Amin, A. et al. Safety and efficacy of nivolumab in combination with sunitinib or pazopanib in advanced or metastatic renal cell carcinoma: the CheckMate 016 study. J. Immunother. Cancer 6, 109 (2018).PubMedPubMedCentralCrossRef
133.
Chowdhury, S. et al. A phase I/II study to assess the safety and efficacy of pazopanib (PAZ) and pembrolizumab (PEM) in patients (pts) with advanced renal cell carcinoma (aRCC). J. Clin. Oncol. 35, 4506–4506 (2017).CrossRef
134.
Terme, M. et al. VEGFA-VEGFR pathway blockade inhibits tumor-induced regulatory T cell proliferation in colorectal cancer. Cancer Res. 73, 539–549 (2013).PubMedCrossRef
135.
Bouzin, C., Brouet, A., De Vriese, J., DeWever, J. & Feron, O. Effects of vascular endothelial growth factor on the lymphocyte-endothelium interactions: identification of caveolin-1 and nitric oxide as control points of endothelial cell anergy. J. Immunol. 178, 1505–1511 (2007).PubMedCrossRef
136.
Gavalas, N. G. et al. VEGF directly suppresses activation of T cells from ascites secondary to ovarian cancer via VEGF receptor type 2. Br. J. Cancer 107, 1869–1875 (2012).PubMedPubMedCentralCrossRef
137.
McDermott, D. F. et al. Pembrolizumab monotherapy as first-line therapy in advanced clear cell renal cell carcinoma (accRCC): results from cohort A of KEYNOTE-427 [abstract]. J. Clin. Oncol. 36, 4500 (2018).CrossRef
138.
Leone, R. D. & Emens, L. A. Targeting adenosine for cancer immunotherapy. J. Immunother. Cancer 6, 57 (2018).PubMedPubMedCentralCrossRef
139.
Yu, Y. I. et al. Ecto-5′-nucleotidase expression is associated with the progression of renal cell carcinoma. Oncol. Lett. 9, 2485–2494 (2015).PubMedPubMedCentralCrossRef
140.
Hotson, A. et al.Clinical activity of adenosine 2A receptor (A2AR) inhibitor CPI-444 is associated with tumor expression of adenosine pathway genes and tumor immune modulation [abstract O4]. J. Immunother. Cancer 5 (Suppl. 2), 86 (2017).
141.
Fong, L. et al. Safety and clinical activity of adenosine A2a receptor (A2aR) antagonist, CPI-444, in anti-PD1/PDL1 treatment-refractory renal cell (RCC) and non-small cell lung cancer (NSCLC) patients [abstract]. J. Clin. Oncol. 35, 3004 (2017).CrossRef
142.
Hotson, A. et al. Adenosine signature genes associate with tumor regression in renal cell carcinoma (RCC) patients treated with the adenosine A2A receptor (A2AR) antagonist, CPI-444 [abstract P54]. J. Immunother. Cancer 6 (Suppl. 1), 114 (2018).
143.
Bensch, F. et al. 89Zr-atezolizumab imaging as a non-invasive approach to assess clinical response to PD-L1 blockade in cancer. Nat. Med. 24, 1852–1858 (2018).PubMedCrossRef
144.
Meric-Bernstam, F. G. et al. A phase 1/2 study of CB-839, a first-in-class glutaminase inhibitor, combined with nivolumab in patients with advanced melanoma (MEL), renal cell carcinoma (RCC), or non-small cell lung cancer (NSCLC) [abstract O16]. J. Immunother. Cancer 5 (Suppl. 2), 86 (2017).
145.
Lucarelli, G. et al. Activation of the kynurenine pathway predicts poor outcome in patients with clear cell renal cell carcinoma. Urol. Oncol. 35, 461.e15–461.e27 (2017).CrossRef
146.
Diab, A. et al. NKTR-214 (CD122-biased agonist) plus nivolumab in patients with advanced solid tumors: preliminary phase 1/2 results of PIVOT [abstract]. J. Clin. Oncol. 36, 3006 (2018).CrossRef
147.
Tannir, N. M. et al. Pegilodecakin with nivolumab (nivo) or pembrolizumab (pembro) in patients (pts) with metastatic renal cell carcinoma (RCC) [abstract]. J. Clin. Oncol. 36, 4509 (2018).CrossRef
148.
Tannir, N. M. et al. Phase 1 study of glutaminase (GLS) inhibitor CB-839 combined with either everolimus (E) or cabozantinib (Cabo) in patients (pts) with clear cell (cc) and papillary (pap) metastatic renal cell cancer (mRCC) [abstract]. J. Clin. Oncol. 36, 603 (2018).CrossRef
149.
Tannir, N. M. et al. CANTATA: a randomized phase 2 study of CB-839 in combination with cabozantinib versus placebo with cabozantinib in patients with advanced/metastatic renal cell carcinoma [abstract]. J. Clin. Oncol. 36 (Suppl. 15), TPS4601 (2018).CrossRef
150.
Courtney, K. D. et al. Isotope tracing of human clear cell renal cell carcinomas demonstrates suppressed glucose oxidation in vivo. Cell Metab. 28, 793–800 (2018).PubMedCrossRefPubMedCentral
151.
Sulkowski, P. L. et al. Krebs-cycle-deficient hereditary cancer syndromes are defined by defects in homologous-recombination DNA repair. Nat. Genet. 50, 1086–1092 (2018).PubMedPubMedCentralCrossRef
152.
Lamers, C. H., Klaver, Y., Gratama, J. W., Sleijfer, S. & Debets, R. Treatment of metastatic renal cell carcinoma (mRCC) with CAIX CAR-engineered T cells-a completed study overview. Biochem. Soc. Trans. 44, 951–959 (2016).PubMedCrossRef
153.
Cherkasova, E. et al. Detection of an immunogenic HERV-E envelope with selective expression in clear cell kidney cancer. Cancer Res. 76, 2177–2185 (2016).PubMedPubMedCentralCrossRef
154.
Ruf, M. et al. pVHL/HIF-regulated CD70 expression is associated with infiltration of CD27+lymphocytes and increased serum levels of soluble CD27 in clear cell renal cell carcinoma. Clin. Cancer Res. 21, 889–898 (2015).PubMedCrossRef
155.
Pal, S. K. et al. A phase 1 trial of SGN-CD70A in patients with CD70-positive, metastatic renal cell carcinoma. Cancer 125, 1124–1132 (2019).PubMedCrossRef