








































This is an open access article under the terms of a license that permits non-commercial use, provided the original work is properly cited.  
© 2022 The Authors. Société Internationale d'Urologie Journal, published by the Société Internationale d'Urologie, Canada.

Key Words Competing Interests Article Information

Renal cell carcinoma, clear cell carcinoma, 
non-clear cell renal cell carcinoma, genetics, 
tumor microenvironment

None declared. Received on August 30, 2022 
Accepted on October 3, 2022 
This article has been peer reviewed.

Soc Int Urol J. 2022;3(6):386–396

DOI: 10.48083/BLPV3411

2022 WUOF/SIU International Consultation 
on Urological Diseases: Genetics and Tumor 
Microenvironment of Renal Cell Carcinoma

Sari Khaleel,1 Christopher Ricketts,2 W. Marston Linehan,2 Mark Ball,2 Brandon Manley,3  
Samra Turajilic,4,5 James Brugarolas,6,7 Ari Hakimi1

1 Urology Service, Department of Surgery, Memorial Sloan Kettering Cancer Center, New York, United States 2 Urologic Oncology Branch, Center for Cancer Research, 
National Cancer Institute, National Institutes of Health, Bethesda, United States 3 Department of Genitourinary Oncology, H. Lee Moffitt Cancer Center and Research 
Institute, Tampa, United States 4 The Francis Crick Institute, London, United Kingdom 5 Renal Unit, The Royal Marsden Hospital, London, United Kingdom  
6 Kidney Cancer Program, Simmons Comprehensive Cancer Center, UT Southwestern Medical Center, Dallas, United States 7Department of Internal Medicine,  
UT Southwestern Medical Center, Dallas, United States

Abstract

Renal cell carcinoma is a diverse group of diseases that can be distinguished by distinct histopathologic and genomic 
features. In this comprehensive review, we highlight recent advancements in our understanding of the genetic and 
microenvironmental hallmarks of kidney cancer. We begin with clear cell renal cell carcinoma (ccRCC), the most 
common subtype of this disease. We review the chromosomal and genetic alterations that drive initiation and 
progression of ccRCC, which has recently been shown to follow multiple highly conserved evolutionary trajectories 
that in turn impact disease progression and prognosis. We also review the diverse genetic events that define the many 
recently recognized rare subtypes within non-clear cell RCC. Finally, we discuss our evolving understanding of the 
ccRCC microenvironment, which has been revolutionized by recent bulk and single-cell transcriptomic analyses, 
suggesting potential biomarkers for guiding systemic therapy in the management of advanced ccRCC.

Introduction

Understanding the genomic landscape of clear cell renal cell carcinoma (ccRCC), which accounts for approximately 
75% of all renal cell carcinomas, has been critical to the development of targeted systemic therapies to treat this 
classically chemo- and radiotherapy-resistant disease. However, malignant cells exist in a dynamic and heterogeneous 
ecosystem of immune cells, stromal cells, cytokines, and extracellular proteins that together constitute the tumor 
microenvironment (TME)[1], which modulates tumor development and response to systemic therapies in RCC[2]. 
Better understanding of the TME of ccRCC has helped understand the heterogeneity of response to systemic therapies 
within ccRCC patients, particularly in the age of immuno-oncologic agent-based therapies. Furthermore, while 
ccRCC constitutes the majority of RCC tumors, the remaining 25% are represented by an ever-expanding group 
of tumor subtypes, each with unique histologies and genetic features. This review begins with a summary of recent 
molecular analyses of clear and non-clear cell RCC subtypes, followed by a discussion of the current understanding of 

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https://orcid.org/0000-0002-7927-1510
https://orcid.org/0000-0001-8846-136X
https://orcid.org/0000-0002-8575-499X
https://orcid.org/0000-0002-0930-8824
mailto:hakimia%40mskcc.org?subject=SIUJ
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the TME of ccRCC and its role in driving the response to 
systemic therapies in advanced ccRCC.

Genetics of Clear Cell RCC
The first step toward malignant transformation in 
ccRCC is the loss of the short arm of chromosome 
3 (3p loss), which harbors 4 tumor suppressor genes 
that constitute the most common sites of mutation in 
RCC: VHL on 3p.25, and PBRM1, BAP1, and SETD2 on 
3p.21[3,4]. Of these genes, VHL is the most commonly 
altered in both hereditary and sporadic RCC, through 
point mutations and methylation in 70%–80% and 
5%–10% of patients, respectively[3,5]. Inactivation 
of the VHL protein results in loss of regulation and 
thus constitutive activation of its ubiquitin ligase 
target, the protein HIF. Resulting de-regulation of 
HIF targets, including vascular endothelial growth 
factor (VEGF), promotes tumor cell proliferation, 
neoangiogenesis, and metastases[6,7]. PBRM1 is the 
second most commonly mutated gene (40% of cases)
[3,4], and encodes BAF180[6,8], a component of the 
switching defective/sucrose non-fermenting (SWI/
SNF) family of chromatin-remodeling complexes, 
which determine DNA accessibility to transcription 
factors and poly merases[8–10]. Similarly, BAP1, 
mutated in 10%–15% of ccRCC patients[11], encodes a 
nuclear deubiqutinase protein that interacts with host 
cell factor-1 (HCF-1), which is involved in chromatin 
remodeling[12,13]. Interestingly, BAP1 and PBRM1 
mutations are generally mutually exclusive[3,5,14]. 
Lastly, while the mechanism by which SETD2, mutated 
in 10%–15% of ccRCC, affects tumorigenesis remains 
unclear, it is suspected to involve DNA double-strand 
break repair, DNA methylation, and RNA splicing[4,15] 
(Table 1).

The mechanism of 3p loss that results in loss of hetero-
geneity (LOH) for the above genes frequently involves 
chromothripsis, a process in which some chromosomes 
undergo multiple breaks simultaneously, followed by 
random joining of chromosomal fragments, result-
ing in hundreds of genomic rearrangements[16]. This 
initial 3p loss constitutes the “first hit” event and occurs 
somatically years before the presentation of ccRCC.  

A “second hit” resulting in biallelic inactivation of VHL 
then promotes malignant transformation through 
upregulation of the hypoxia response in the presence of 
normoxia. This is usually followed by mutations involv-
ing the neighboring PBRM1, SETD2, and BAP1 genes, 
and less frequently, alterations of TP53, mTOR, TSC1, 
TSC2, PIK3CA, PTEN, KDM5C and SMARCA4[17].

Although the repertoire of mutations and somatic 
copy number alterations (SCNAs) that drive ccRCC 
is relatively narrow, molecular diversity is achieved 
through clonal evolution, i.e., selection of cell subpop-
ulations characterized by different driver mutations, 
resulting in intratumor heterogeneity (ITH)[6]. Conse-
quently, molecular profiling of tumor samples collected 
from a single spatial location may capture clonal events 
propagated in all the cancer cells of a given tumor, 
but can easily miss events in subclones, and under- or 
over-estimate the frequency of altered genes, an issue 
that is amplified by the particularly high levels of ITH 
in ccRCC[18,19]. Therefore, multi-region sampling is 
critical to capturing the clonal evolution of ccRCC, as 
demonstrated by the TRACERx Renal program[19]. 
In the interim analysis TRACERx, molecular profil-
ing of > 1200 primary tumor regions from 100 patients 
demonstrated clear evidence for highly conserved evolu-
tionary mutational patterns in ccRCC within different 
clones[20]. Broadly, 2 modes of evolution were observed: 
linear, in which only a single clonal population is 
evident, with consequently low ITH; and branched, 
which involves multiple subclonal populations with 
high ITH. These populations then evolve either through 
a linear Darwinian-like process of sequentially selected 
mutational events, or punctuated evolution, which 
is noted by short bursts of many genomic alterations 
occurring in a relatively brief period early in the tumor’s 
evolution, most likely due to SCNAs and structural 
chromosomal alterations[21].

ccRCC tumors in the TRACERx cohort that were 
characterized by linear evolution harbored only 3p 
loss and VHL mutation/methylation with low ITH, 
and were thus termed “VHL mono drivers”[20]. These 
tumors were enriched for small renal masses (SRMs, 
< 4 cm in maximal dimension), with limited progression 
and metastatic risk given the limited fitness advantage 
provided by isolated VHL mutation[22]. In contrast, 
ccRCC tumors characterized by branched evolution 
harbored high levels of ITH and parallel evolution[20], 
i.e., repeat selection of distinct driver mutations in the 
same gene or pathway, with a highly conserved order 
of genomic events across clones. Intriguingly, these 
tumors were larger and more likely to produce metas-
tases than their VHL mono driver counterparts, but 
with an intermediate metastatic efficiency resulting in 
solitary metastasis or oligometastases[20]. However, 
other studies suggest that VHL mutations alone are not 

Abbreviations 
ccRCC clear cell renal cell carcinoma
FH fumarate hydratase
RCC renal cell carcinoma
SCNAs somatic copy number alterations
SWI/SNF switching defective/sucrose non-fermenting
TAMs tumor-associated macrophages
TME tumor microenvironment

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sufficient for ccRCC development[23,24]. In contrast, 
ccRCC tumors characterized by punctuated evolution 
had low ITH and were dominated by a single clone but 
exhibited additional molecular alterations in the domi-
nant clone that distinguished them from the similarly 
monoclonal VHL mono drivers. This tumor evolu-
tion group included a VHL-wildtype subtype, a VHL- 
followed by BAP1 mutation (BAP1-driven) subtype, and 
tumors with multiple clonal driver mutations (PBRM1, 
BAP1, STED2 or PTEN). These tumors grew rapidly to 
a large size and were linked to widespread and rapid 
metastases[20]. Within this group, BAP1-deficient 
tumors were associated with higher grade and aggres-
siveness than PBRM1-deficient tumors[11,24,25], while 

PBRM1 loss was associated with metastasis tropism to 
the pancreas[26], and these tumors are characteristically 
indolent. However, PBRM1-deficient tumors can become 
more aggressive with further evolution and mutations 
in the mTOR pathway[24] or SETD2[27], with which 
PBRM1 cooperates[25].

Genetics of Non-Clear Cell Carcinoma
Papillary renal cell carcinoma

Papillary renal cell carcinoma has been classically 
subdivided into 2 subtypes on the basis of histology and 
genetic features[28]. Genetically, type 1 pRCC is asso-
ciated with frequent gains of chromosomes 7 and 17, as 
well as less frequent gains of chromosomes 2, 3, 12, 16, 

TABLE 1. 

Summary of most common genetic alterations of reviewed RCC subtypes

RCC subtype
Most common and/or 

characteristic genetic 
alterations

Most common chromosomal 
alterations

Notes

Clear cell RCC

VHL (75%-90%) 
PBRM1 (40%) 

BAP1 (10%-15%) 
SETD2 (10%-15%) 
KDM5C (6%-7%)

Loss of the short arm of 
chromosome 3 (3p), likely  

through chemothripsis

VHL is altered through point 
mutations and methylation in 
70%–80% and 5%–10% of 

patients
BAP1 and PRBM1 mutations are 

generally mutually exclusive

Papillary RCC, type 1
MET (10%-15%) in non-hereditary 

cases

Frequent gains of chromosomes 
7 (almost universal) and 17 as 
well as less frequent gains of 

chromosomes 2, 3, 12, 16, and 20

Papillary RCC, type 2 No specific pattern of mutations or SCNAs See section 2

Chromophobe RCC TP53 (~30%) and PTEN (~8%) 

Most frequently associated with 
combined loss of chromosomes 1, 

2, 6, 10, 13, and 17.
Less frequent additional individual 
losses can occur for chromosomes 

3, 5, 8, 9, 11, 18, and 21q

ChRCC tumors have a generally 
lower mutational burden than 
ccRCC or type I pRCC tumors

Renal medullary carcinoma SMARCB1 Gain of chromosome 8q 

FH-deficient RCC FH Loss of chromosome 1q 
FH gene germline mutations are 

seen with HLRCC, but FH gene can 
also be somatically mutated

SDH-deficient RCC
SDH enzyme subunit genes, 

including SDHB, SDHC, or SDHD
Deletion of chromosome 1p 

Translocation RCC
Somatic translocations of TFE3, 

followed by TFEB and MITF
Gain of chromosome 17q and 

chromosome 9p loss

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and 20[29–32]. The most frequent somatic mutational 
events in type 1 pRCC are activating mutations of the 
MET oncogene on chromosome 7, present in 10%–15% 
of type 1 pRCC cases[33]. Notably, germline activat-
ing mutations of the MET oncogene are the patho-
genic cause of hereditary papillary renal cell carcinoma 
(HPRC) syndrome, in which patients present with bilat-
eral, multifocal type 1 pRCCs[31,34] (Table 1).

In contrast, type 2 pRCC tumors are not associated 
with a specific pattern of copy number alterations, and 
are now seen to represent a heterogenous group of what 
are now distinct RCC subtypes, including transloca-
tion RCC, FH-deficient RCC, and SDH-deficient RCC. 
In light of the above heterogeneity and the absence of 
characteristic genomic features for this group, pRCC 
type 2 tumors may also be interpreted as aggressive, 
unclassified RCC that exhibit papillary features but 
require specific genomic subclassification for clinical 
outcome prediction[35]. Similarly, while type I pRCC 
is considered the “classical” morphologic entity, certain 
neoplasms that exhibit its features may also be consid-
ered variants or potential new RCC entities[36], with 
distinct molecular features, such as papillary renal 
neoplasm with reversed polarity (PRNRP)[37] and bipha-
sic hyalinizing psammomatous RCC (BHP RCC)[38], 
which have distinct driver mutations (KRAS and NF2, 
respectively).

Chromophobe renal cell carcinoma (chRCC)
Like ccRCC and pRCC type 1 tumors, most chRCC 

are characterized by a distinct pattern of chromosomal 
alterations, defined by combined loss of chromosomes 1, 
2, 6, 10, 13, and 17, seen in approximately 80% of chRCC. 
Less frequent additional individual losses can occur for 
chromosomes 3, 5, 8, 9, 11, 18, and 21q in 12%–58% of 
cases[39,40]. The histology of chRCC can include a rarer 
eosinophilic variant in which the classic pattern of chro-
mosomal losses is less common. ChRCC have a lower 
mutation burden than ccRCC or pRCC-1; only TP53 and 
PTEN are frequently mutated in ~30% and ~8% of cases, 
respectively[33,41]. Loss of CDKN2A, by either loss of 
9p21 or hypermethylation, is the next most common 
alteration, affecting 19.8%[33] (Table 1). Increased TERT 
expression has been observed in approximately 17% of 
ChRCC, resulting from either mutations or genomic 
rearrangements in the TERT gene promoter, the latter 
including intra-chromosomal rearrangements and 
translocations with chromosome 13.11[42].

Medullary Renal Carcinoma
Renal medullary carcinoma (RMC) is a rare and 

aggressive subtype of kidney cancer that accounts for 
less than 1% of all RCC and has a propensity for early 
metastases, resulting in a median overall survival of 
little more than a year[43–45]. RMC predominantly 
aff licts individuals with sickle cell trait, creating a 

preponderance of patients with African or Mediterra-
nean descent, and the young, with a median age from 19 
to 22 years[43–49]. The characteristic genetic and immu-
nohistochemical feature of RMC is the near universal 
loss of expression of the SWI/SNF-related matrix-asso-
ciated actin-dependent regulator of chromatin subfam-
ily B member 1 (SMARCB1) protein, also known as 
integrase interactor 1 (INI1), BRG1-associated factor 
47 (BAF47), or sucrose non-fermenting 5 (SNF5). The 
SMARCB1 protein is encoded by the SMARCB1 gene on 
chromosome 22q11.23, and in most tumors both copies 
of this gene are lost through a combination of mutation 
and chromosomal deletion (Table 1)[50]. SMARCB1 is 
a core subunit of the SWI/SNF chromatin remodeling 
complex; its loss results in transcriptional dysregulation 
of many pathways[9,51].

FH-deficient and SDH-deficient renal  
cell carcinoma

Hereditary leiomyomatosis and renal cell carcinoma 
(HLRCC) is a familial cancer syndrome characterized 
by the development of cutaneous and uterine leiomy-
omas and a highly aggressive form of kidney cancer 
[52–55]. HLRCC is associated with germline mutation 
of the Krebs cycle enzyme gene fumarate hydratase 
(FH); because the associated tumors demonstrate loss 
of FH enzyme activity, they are referred to as FH-defi-
cient RCC[56–58]. FH can also be mutated somatically. 
Similarly, germline mutations of several subunits of the 
Krebs cycle succinate dehydrogenase enzyme, includ-
ing SDHB, SDHC, or SDHD, have been associated with 
increased risk for paraganglioma (PGL), pheochromo-
cytoma, gastrointestinal stromal tumor (GIST), and 
RCC[59–61].

The complete loss of either FH or SDH enzyme 
activity impairs the normal function of the Krebs 
cycle, resulting in accumulation of intracellular fuma-
rate and succinate, respectively[62,63]. This accumula-
tion promotes a pseudo-hypoxic state that upregulates 
several enzymes, particularly enzymes involved in chro-
matin hypermethylation[32,64–67]. Furthermore, FH 
and SDH loss results in aberrant succination of KEAP1 
protein, which promotes constitutive upregulation of the 
NRF2-antioxidant response element (ARE) pathway and 
inactivation of the core factors responsible for replica-
tion and proofreading of mitochondrial DNA (mtDNA), 
resulting in both a significant decrease in mtDNA 
content and increased mtDNA mutation[68,69].

While SDH and FH-deficient tumors share simi-
lar genetic characteristics, a recent germline analysis 
comparing these tumors noted that while most of these 
tumors harbored germline alterations in their respec-
tive genes, SDH-deficient RCCs had a lower mutation 
burden and SCNA burden than FH-deficient RCCs[70]. 
In addition to patients with germline mutation, a small 

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number of sporadic tumors have also been shown to 
have complete somatic loss of FH, resulting in a non-he-
reditary form of FH-deficient RCC[32].

Translocation renal cell carcinoma involving 
TFE3, TFEB, or MITF gene fusions

Translocation renal cell carcinomas (T-RCCs) are 
driven by somatic chromosomal translocations that fuse 
members of the MiT transcription factor family genes, 
TFE3, TFEB, or MITF, with various partner genes that 
result in fusion proteins[31,71,72] that affect many path-
ways, such as organelle biogenesis, cell proliferation, and 
cellular fate commitment, all of which may promote 
tumorigenesis[72–74]. T-RCCs represent one of the most 
common forms of RCC in children and young adults, 
making up 20%–50% of pediatric RCC patients and 15% 
of RCC patients under the age of 45[72,75]. T-RCC in 
adults can present with a variety of histologies, includ-
ing both papillary or clear cell[32,72]. To date, fusions 
involving TFE3 are the most common, followed by 
TFEB[31,33,71,72,76].

Tumor Microenvironment of RCC
While initial profiling studies of the TME of RCC 
tumors grouped its tumor phenotypes into either 
immune-infiltrated or excluded phenotypes[77,78], more 
recent studies have found infiltrating T cell populations 
to exist in a continuum from activated antitumor to 
dysfunctional “exhausted” T cells[79,80]. Similarly, 
while the function of tumor-associated macrophages 
(TAMs) has classically been divided into either the pro-
inflammatory/antitumor M1 or the anti-inflammatory/
pro-tumor M2 phenotypes (polarizations)[81–85], recent 
evidence shows that TAM populations are highly plastic, 
existing in more of a phenotypic spectrum between the 
M1 and M2 phenotypes in vivo[81,83,84,86].

More recent studies have shifted to transcriptomic 
analyses that utilize microarray and next generation 
sequencing (RNA-seq) technologies along with compu-
tational techniques to deconvolute the TME to its cellu-
lar components and explore their role in tumor response 
to systemic therapies through an array of gene expres-
sion signatures representative of novel cell phenotypes 
and processes[77,87–89]. Such studies noted a generally 
negative correlation between enrichment of T-helper 
subtype 2 (Th2) cells and T-reg cells and survival in 
ccRCC[77], explaining previously reported negative 
association between T cell infiltration and clinical 
outcomes in ccRCC[83,84,90,91]. Similarly, worse over-
all survival and lower likelihood of response to TKI 
agents were associated with higher levels of M2-type 
macrophage infiltration in the TME[92]. This under-
standing of the TME led to investigations of prognos-
tic and theranostic transcriptomic gene signatures that 
may predict survival and response to systemic therapy 

in advanced RCC. These include angiogenesis-associ-
ated signatures to predict response to tyrosine kinase 
inhibitors[89,92,93] or immune signatures to predict 
response to immune checkpoint blockage (ICB)-based 
combinations[89,93,94]; and transcriptomic classifiers 
such as the 4 molecular subtypes (ccRCC 1-4) described 
by Beuselinck et al. for metastatic ccRCC (m-ccRCC)[95], 
which were shown to predict both survival outcomes as 
well as therapeutic response to TKI (sunitinib or pazo-
panib) monotherapy, which were attributed to inherent 
differences in the their underlying TME[95–97]. Prospec-
tive patient selection for ICB-based or TKI-monotherapy 
based on these subtypes was recently evaluated in the 
phase II BIONIKK trial, which demonstrated the feasi-
bility of biomarker-driven tailored systemic therapy in 
m-ccRCC[98], potentially maximizing therapeutic bene-
fit while reducing unnecessary toxicity from systemic 
therapy regimens in m-ccRCC.

Understanding of the TME was further revolution-
ized by single-cell based analyses such as single-cell RNA 
sequencing (scRNA-seq) and single-cell mass cytometry 
(scMC), which allow for massively parallel, high-dimen-
sional analyses of specific cell populations in the TME, 
enabling prediction of potential interactions between 
various cell populations based on their expressed surface 
molecules, promoting a much more granular under-
standing of the dynamics of the TME of RCC than 
what was offered by bulk RNA-sequencing approaches, 
which are bound to oversimplify tumor cell populations 
and their dynamic interactions[81,83,84,86,99]. In this 
regard, Chevrier et al.[86] used scMC to profile adaptive 
and innate (T cell and TAM) populations in the TME of 
73 patients with untreated advanced RCC and 5 healthy 
matched kidney samples. Using computational pheno-
type clustering, they identified 22 T cell and macrophage 
phenotypes[86], noting a “terminally exhausted” PD1+ 
cluster and a corresponding “progenitor exhausted” 
cluster of potentially ICB-responsive T cells. They also 
noted 17 different TAM clusters, arguing that the M1/
M2 polarization phenotypes are an oversimplification of 
this plastic and dynamically changing cell population. 
Finally, they noted immunosuppressed T cell compart-
ments to be associated with high levels of regulatory 
CD4 cells and a pro-tumor TAM population[86].

Following this study, Braun et al.[83] performed 
scRNA-seq and T cell receptor (TCR) sequencing of 
ccRCC tissue from 13 patients with tumors of a range 
of clinical stages to explore changes in the immune 
TME with advancing disease. They again noted signif-
icant diversity within the TAM and T cell populations, 
and found T cells to exhibit an overall trend of progres-
sive dysfunction and exhaustion with advancement in 
disease stage, which was associated with a concurrent 
shift from M1 to M2-like signatures in the TAM popu-
lation and increasing T cell and TAM interactions, again 

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confirming that TAMs play a key role in the progression 
of T cells toward exhaustion in ccRCC[83].

To examine the influence of ICB on the RCC TME, 
Krishna et al.[81] used scRNA-seq to compare the TME 
of multi-regional tumor samples from 4 ICB-treated 
to 2 ICB-naïve advanced ccRCC patients. They noted 
significant intratumoral and inter-patient heterogene-
ity, along with differences in the overall TME between 
ICB-treated versus naïve patients. Focusing on tumor 
specimens from an ICB-treated patient that exhibited 
complete response, they noted enrichment of CD8A+ 
tissue-resident populations and low TAM infiltration in 
all tumor regions. In contrast, specimens from ICB-re-
sistant patients exhibited high TAM infiltration but 
low T cell enrichment (i.e., T cell exclusion)[81]. Simi-
larly, Bi et al. compared tumors from 5 ICB-exposed 
to 3 ICB-naïve patients with advanced ccRCC, and 
noted that while ICB-exposed tumors were enriched in 
CD8+ T cells that expressed costimulatory molecules 
associated with the “progenitor exhausted” phenotype 
described by Chevrier et al.[86], they also paradoxically 
expressed inhibitory molecules associated with termi-
nally exhausted T cells, suggesting that these ICB-re-
sponsive cells were potentially undergoing a shift toward 

terminal exhaustion as well. Similarly, antitumor TAM 
populations in ICB-exposed patients were noted to 
paradoxically express molecules that correlate with a 
pro-inflammatory, antitumor phenotype, but again with 
upregulation of immune checkpoint and anti-inflam-
matory signaling genes. The authors proposed that these 
seemingly paradoxical changes in both T cell and TAM 
populations within the tumors of ICB-exposed patients 
may explain the initial response and eventual transi-
tion to resistance to ICB agents noted in ccRCC[84].  
All 3 scRNA-seq studies also identified and exter-
nally validated novel gene signatures that may allow 
for the detection of specific T cell and TAM popula-
tions[81,83,84].

Summary
The genetic determinants of RCC have become more 
clearly defined, which has led to increased understanding 
of its evolution and metastatic development, particularly 
in ccRCC. While increasing data support the role of 
the TME in determining therapeutic response, the 
molecular links to immune response are only beginning 
to be characterized. Future studies of both human tissue 
and murine models will facilitate further progress in the 
quest to understand and better manage this disease.

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 ORCID iDs

Sari Khaleel: 0000-0002-0886-0860  
Christopher Ricketts: 0000-0003-4814-7207
Mark Ball: 0000-0003-1780-2627

Brandon Manley: 0000-0002-7927-1510
Samra Turajilic: 0000-0001-8846-136X

James Brugarolas: 0000-0002-8575-499X
Ari Hakimi: 0000-0002-0930-8824

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