Submitted - October 18, 2022 | Revised December 4, 2022 Accepted - December 7, 2022 | | ePublished - December 31, 2022
https://doi.org/10.52733/KCJ21n4-or1
ABSTRACT
Fumarate hydratase-deficient renal cell carcinomas are an aggressive form
of kidney cancer that often results in poor prognosis and high fatality rates.
The implications of somatic mutations are not well described, and standard
treatment has not been established for this renal cell carcinoma subtype.
Further molecular characterization of fumarate hydratase-deficient renal
cell carcinomas could potentially help to identify biomarkers that can be
exploited with future targeted therapies. 2199 renal cell carcinomas were
analyzed by DNA sequencing (592-gene panel) and whole- transcriptome
sequencing and 40 tumors were identified with pathogenic FH mutations.
Co-occurrence of mutation with other cancer-related genes were assessed
along with immune profiles and immunotherapy biomarkers. Fumarate
hydratase-deficient renal cell carcinomas had a lower prevalence of comutation
with common renal cell carcinoma driver mutations such as
VHL and chromatin remodeling genes when compared to wild type renal
cell carcinoma. Conversely, prevalence of several cancer-related genes
(MAX, BRCA1, PMS2, BRAF, NF2, and AKT1) was higher in fumarate
hydratase-deficient renal cell carcinomas. Immunotherapy biomarkers
(mismatch repair deficiency and tumor mutational burden) were detected
at low frequency in mutant and wild type renal cell carcinomas, while PDL1
expression occurred at higher frequency in fumarate hydratase-deficient
renal cell carcinomas. Fumarate hydratase-mutated kidney tumors may have
a different mutational and immune landscape than wild type tumors. The
absence of VHL mutations in a significant number of fumarate hydratasedeficient
renal cell carcinomas suggest that FH mutations may drive
tumorigenesis using distinct angiogenic pathways. Our study highlights
potential therapeutic implications that will require further study.
INTRODUCTION
Fumarate hydratase (FH) is a
key component of the Krebs
cycle, and loss of FH function
leads to multiple disorders, including
aggressive forms of cancer.
Heterozygous germline mutations
leading to FH deficiency are associated
with hereditary predisposition to
multiple tumors.1,2 Fumarate can act
as an oncometabolite by inhibiting
multiple α-ketoglutarate (α-KG)-dependent
dioxygenases, which in turn
stabilizes hypoxia inducible factor 1
subunit alpha (HIF1α), creating a
state of pseudohypoxia that leads to
angiogenesis and tumor growth.3-5
Thus, functional FH is often referred
to as a tumor suppressor. FH-deficient
renal cell carcinoma (RCC) is
an aggressive form of renal cancer
that was first described as part of hereditary
leiomyomatosis and renal
cell cancer (HLRCC) syndrome.1,6
Although it was initially regarded
as papillary type II RCC, the term
FH-deficient RCC is now preferred
as it can present in other histological
subtypes of RCC. Germline mutations
in the FH gene impart a high
risk for developing tumors at an early
age,6-8 and HLRCC patients who
develop RCC have significantly shorter survival when diagnosed with
advanced stage compared to early
stage.9 To improve early detection
rates and survival, regular screening
for RCC in HLRCC individuals
is recommended. 10,11 FH-deficient
RCC can also develop by sporadic
loss-of-function mutation in the FH
gene; however, there is no consensus
on the implications of FH alterations
in RCC outside of the HLRCC
syndrome.12,13
F H-deficient t umors o ften
result in poor prognosis and high
fatality rates, while standard
treatment in the advanced
disease stage setting has not been
established for this aggressive RCC
subtype.9 While immunotherapy
combination strategies have
improved outcomes for patients
with clear cell RCC (ccRCC), 14 they
have not been heavily tested in
variant histologies. It is also unclear
how the mutational landscape in
FH-deficient patients augments
sensitivity to immunotherapy or
other targeted therapies. Therefore,
further characterization of FHdeficient
RCC with comprehensive
molecular profiling could potentially
help to identify biomarkers that can
be exploited with future targeted
therapies. The goal of this study is
to enhance our knowledge of the
molecular landscape of FH-deficient
renal tumors (hereafter referred to
as FH-mut tumors) in relation to
wild type tumors (WT) lacking FH
alterations. From DNA and RNA
analysis, co-occurrence of mutation
with other cancer-related genes were
assessed along with immune profiles and immunotherapy biomarkers.
MATERIALS AND METHODS
Sample collection from participants
A total of 2199 RCCs underwent
comprehensive tumor profiling at
Caris Life Sciences (Phoenix, AZ,
USA). This study was conducted in
accordance with guidelines of the
Declaration of Helsinki, Belmont
Report, and U.S. Common Rule.
In keeping with 45 CFR 46.101 (b),
this study was performed utilizing
retrospective, deidentified clinical
data from patients with renal cancer.
Therefore, this study was considered
Institutional Review Board exempt
and no patient consent was necessary
from the subjects.
Next-Generation Sequencing
(NGS)
NGS was performed on genomic
DNA isolated from formalin-fixed
paraffin-embedded (FFPE) tumor
samples using the NextSeq or
NovaSeq platform (Illumina, Inc.,
San Diego, CA, USA). For NextSeq,
a custom-designed SureSelect XT
assay was used to enrich 592 wholegene
targets (Agilent Technologies,
Santa Clara, CA). All variants were
detected with >99% confidence
based on allele frequency and
amplicon coverage, with an average
sequencing depth of coverage of
>500x and an analytic sensitivity of
5%. For NovaSeq, a hybrid pull-down
panel of baits designed to enrich for
more than 700 clinically relevant
genes at high coverage (>500x) and
high read-depth was used, along
with another panel designed to
enrich for an additional >20,000
genes at lower depth (>250x). Genetic
variants identified were interpreted
by board-certified molecular
geneticists and categorized as
pathogenic, likely pathogenic, or
variant of unknown significance,
according to ACMG standards. All
variants were detected with greater
than 99% confidence based on allele
frequency and amplicon coverage,
with an average sequencing depth of
coverage of greater than 500 and an
analytic sensitivity of 5%.
For RNA sequencing (RNASeq),
biotinylated RNA baits were
hybridized to the synthesized and
purified cDNA targets and the baittarget
complexes were amplified in
a post capture PCR reaction. The
resultant libraries were quantified,
normalized, and the pooled
libraries were denatured, diluted,
and sequenced; the reference
genome used was GRCh37/hg19
and analytical validation of this
test demonstrated ≥97% positive
percent agreement (PPA), ≥99%
negative percent agreement (NPA)
and ≥99% overall percent agreement
(OPA) with a validated comparator
method. Transcripts per million
(TPM) values were generated using
the Salmon expression pipeline for
transcription counting.
Multiple test platforms were
used to determine the microsatellite
instability (MSI) or mismatch
repair (MMR) status of the tumors,
including fragment analysis (FA),
IHC, and NGS. For IHC the following
antibodies were used: M1 antibody
for MLH1 (Roche Diagnostics,
Belmont, CA, USA), G219-1129
antibody for MSH2 (Roche
Diagnostics, Belmont, CA, USA), 44
antibody for MSH6 (Thermo Fisher
Scientific, Carlsbad, CA, USA),
and EPR3947 antibody for PMS2
(Abcam, Waltham, MA, USA). For
NGS, 7,000 target microsatellite loci
were examined and compared to the
reference genome hg19). The tumor
was determined MSI-high (MSI-H)
by FA if two or more mononucleotide
out of the five markers included in
the assay were abnormal; the tumor
was considered mismatch repair
deficient (dMMR) by IHC if complete
absence of protein expression of any
of the four proteins was observed;
the tumor was considered MSI-H
by NGS by a threshold of 46 or more
altered loci per tumor. MSI or MMR
status of the tumor was determined
in the order of IHC, FA, and NGS.
TMB was measured by
counting all non-synonymous
mutations found per tumor that had
not been previously described as
germline alterations in dbSNP151,
Genome Aggregation Database
(gnomAD) databases or benign
variants identified by Caris
geneticists. A cutoff point of >=10
mutations per MB was used based on
the KEYNOTE-158 pembrolizumab
trial.15,16
Immune cell fractions were
calculated from deconvolution
of bulk RNA-Seq data using the
QuantiSeq computational pipeline.17
Interferon-gamma score (IFN score)
was calculated based on weighted
sum of TPM values of 18 genes as
previously described.18
Immunohistochemistry (IHC)
IHC was performed on full FFPE
sections of glass slides using
automated staining techniques, per
the manufacturer’s instructions, and
were optimized and validated per
CLIA/CAO and ISO requirements.
The staining was scored for intensity
(0 = no staining; 1+ = weak staining;
2+ = moderate staining; 3+ = strong
staining) and staining percentage
(0–100). Results were categorized
as positive or negative by defined
thresholds specific to each marker
based on published clinical
literature that associates biomarker
status with patient responses to
therapeutic agents. A boardcertified
pathologist evaluated all
IHC results independently. The
primary antibody used against PDL1
was SP142 (Spring Biosciences,
San Francisco, CA, USA). The
staining was regarded as positive
if its intensity on the membrane
of the tumor cells was 2+ (on a
semiquantitative scale of 0–3: 0 for
no staining, 1+ for weak staining,
2+ for moderate staining, or 3+ for
strong staining) and the percentage
of positively stained cells was >5%.
Statistical Analysis
The molecular features of tumors
carrying pathogenic or likely
pathogenic (P/LP) and WT FH
tumors were compared. Categorical
data was assessed using a chisquare
or Fisher Exact test, where
appropriate. Immune cell abundance
in the tumor micro-environment
were estimated using the method
described above17 and significance
was tested using a nonparametric
Wilcoxon rank-sum test. Gene
expression for immune checkpoint
genes was normalized to the median
gene expression in the control group
and fold change was calculated;
significance was tested using
nonparametric Wilcoxon ranksum
test. P-values were adjusted
for multiple hypothesis testing by
Bonferroni or Benjamini-Hochberg.
All statistical analyses were two
sided at a significance level set to
0.05.
Availability of data and
materials
The deidentified sequencing data
are owned by Caris Life Sciences.
The datasets generated during and
analyzed during the current study
are available from the authors
upon reasonable request and with
permission of Caris Life Sciences.
RESULTS
Basic Cohort Description
A total of 2199 RCCs were included in
this analysis, with ages ranging from
two to 90 years old, and a median
age of 63. In this cohort, 29.4% were
females and 70.6% were males. Nine
hundred forty-four (42.9%) samples
were from a primary tumor, and
1240 (56.4%) samples were from a
metastatic or distant site (Table
1). Among all tumors, 2143 (97.4%)
of the tumors did not harbor a FH
mutation (wildtype, WT), 40 (1.82%)
tumors had a pathogenic mutation or
likely pathogenic mutation (P+LP-mt),
and 16 (0.73%) tumors had a variant
of unknown significance (VUS-mt)
(Table 1). There was no significant
difference in gender distribution
among the various FH-mutant groups
(P+LP-mt vs VUS-mt vs WT: 30% vs
37.5% vs 29.4% for female and 70% vs
62.5% vs 70.6% for male). However,
more P+LP-mt tumors were from
the renal primary site compared to
VUS-mt or WT tumors (52.5% vs
12.5% vs 43%) (Table 1). We sorted
the data to differentiate two main
histological subtypes of renal cancer:
tumors clearly noted as clear cell
histology (ccRCC) and those with
unclear or other histological subtypes
(non-ccRCC). Among the 704 ccRCC
tumors included in this analysis, 11
(1.56%) were P+LP-mt. Among 1495
non-ccRCC tumors, 29 (1.94%) were
P+LP-mt tumors.
Mutational landscape of FHmutated
Renal Cell Carcinoma
When examining the co-occurrence
of cancer-related mutations in our
cohort (Figure 1), a lower prevalence
of FH co-mutation was observed with
VHL in P+LP-mt tumors compared
to WT tumors overall (27.5% vs
55.8%, p-value=3 .63E - 04).
The lower frequency of VHL comutation
was observed in both
ccRCC to non-ccRCC, but the
difference between P+LP-mt and
WT tumors was more significant
in the latter (p-value=1.15 E-03).
A similar pattern was observed
with chromatin remodeling genes,
with lower prevalence of comutation
with PBRM1 and SETD2
in P+LP-mt tumors (10% vs 30.2%,
p-value=0.006; and 2.5% vs 20.7%,
p-value=0.005, respectively). Again,
this difference was observed in
both ccRCC and non-CCRCC,
but more significantly in the
latter. (p-value=0.016 for PBRM1;
p-value=0.008 for SETD2).
Conversely, co-mutation
prevalence in P+LP-mt tumors was
higher in several cancer-related
genes. With the cell cycle gene,
MAX, co-mutations were detected
at a higher rate in P+LP-mt tumors
compared to WT tumors (2.63% vs
0.14%, p-value=0.07) and observed
only in the ccRCC histology subtype
(p-value=0.048). BRCA1 comutations
were higher in P+LP-mt
tumors (5% vs 0.38, p-value=0.014)
only in the non-ccRCC histology
subtype. Alternately, PMS2 comutations
were higher in P+LPmt
tumors (6.45% vs 0.41%,
p-value=0.11) only in the ccRCC
histology subtype. Among genes
in the RAS pathway, higher BRAF
co-mutations in P+LP-mt tumors
(5% vs 0.42%, p-value=0.016)
were observed in both histology
subtypes, yet more significantly in
ccRCC (p-value=0.031). NF2 comutations
were higher in P+LP-mt
tumors compared to WT (25% vs
5.4%, p-value=5.41E-05) only in
non-ccRCC. Other prevalent comutations
that increased in P+LP-mt
tumors were detected in AKT1 (2.56%
vs 0.14%, p-value=0.07) and WT1
(2.56% vs 0.05%, p-value=0.036),
both of which were only observed
in ccRCC (p-value=0.032 for AKT1;
p-value=0.016 for WT1) (Figure 2).
Distribution of mutations among
ccRCC and non-ccRCC, and primary
vs metastatic tumors is further
illustrated in Supplemental Figure 1.
Mismatch repair deficiency,
or MSI-high status, was found at low
frequency (1.27%) in WT tumors,
and not observed in P+LP-mt
tumors (Figure 2). TMB-high status
was also very low in both P+LP-mt
tumors and WT tumors with no
significant difference between the
groups (2.56% vs 2.34%, respectively.
p-value=0.608). On the other hand,
PD-L1 expression was detected at a
higher frequency of P+LP-mt tumors
compared to WT tumors (37.8% vs
19.1%, p-value=0.004). This pattern
was observed in both ccRCC and nonccRCC,
although only significant in
the latter (p-value=0.005).
Immune landscape of FHmutated
Renal Cancer
Immune infiltration analysis
indicated P+LP-mt tumors had
less NK cell and M2 macrophage
infiltration compared to WT
tumors (Figure 3). This pattern was
observed in both ccRCC and nonccRCC,
though significant only in
non-ccRCC. Most other immune cell
types demonstrated slightly lower
infiltration in P+LP-mt tumors,
with the exception of dendritic
cells which were higher in ccRCC
and lower in non-ccRCC. Most
differences in expression levels of
immune checkpoint genes were not
statistically significant, except for
lower expression of LAG3 and PDCD1
in P+LP-mt tumors relative to WT
tumors. P+LP-mt tumors exhibited
higher expression of CD274 in both
histology subtypes. Although not
statistically significant, expression
of CTLA4 and IFNG were highest in
ccRCC tumors (Figure 3A, 3D). The
IFN signature was higher in ccRCC,
but significantly lower only in nonccRCC
(Figure 3C, 3D).
DISCUSSION
Numerous reports have described
distinct prognostic indicators and
therapeutic options for specific
RCC subtypes,19-22 yet most of the
available literature and clinical
trials have not been conducted in
the context of genomic mutations,
including FH status. Although the
relatively small numbers of FHmut
tumors within each histology
subgroup limits the conclusions
that can be drawn, some of the
results point to possible trends that
may warrant further investigation.
Examination of co-mutated genes
revealed that some mutations which
are commonly found in RCC (VHL,
PBRM1, SETD2) often do not occur
concurrently in FH-mut tumors.
This suggests that loss of FH may be
an important driver of tumorigenesis
in RCC independently of classic RCC
drivers. The presence or absence of
such co-mutations may also affect
the efficacy of targeted therapies
that have recently been developed
for RCC naively of FH status. For
example, PBRM1 deficiency has
been associated with clinical benefit
from ICI therapy,23 and recently
transcriptome-based molecular
profiles integrated with PBRM1
status and angiogenesis signatures
have been developed to further
facilitate clinical guidance.23-25 With
advances in the use of molecular
subtypes for predictive value, FH
status and associated mutations
may therefore be considered in the
cases where common RCC driver
mutations are absent.
We observed VHL mutations
occurring most frequently in ccRCC,
which agrees with reports citing
the prevalence of VHL mutations in
ccRCC. In our study, VHL mutations
were decreased in all FH-mut
tumors with the most significant
decrease in non-ccRCC (Figure 1).
Neoangiogenesis linked to the von-
Hippel Lindau (VHL) gene has been
commonly implicated as the main
pathway of tumorigenesis in renal
cancers. 6 As VHL inactivation
leads to HIF activation, subsequent
tumorigenesis may occur in
a similar fashion to what has
subsequently been attributed to FH
deficiency. Reduced FH expression
in ccRCC was demonstrated via
HIF stabilization to increase VEGF
production.27 Thus, HIF inhibitors
and other drugs targeting metabolic
pathways have become the basis of
emerging targeted therapies against
RCC and may also be applicable to
FH-deficient tumors.28,29
Antiangiogenic tyrosine kinase
inhibitors (TKI) of the VEGF
pathway led to improved outcomes in
several studies involving metastatic
RCCs.30-33 Further improvements
have also been demonstrated
when TKIs were combined with
immunotherapies.34 The similar
angiogenic effects that FH mutations
and VHL mutations have indicates
that FH-mut tumors may also
respond well to TKIs; however,
the distinctions between these
RCCs may warrant independent
clinical trials to validate this as a
therapeutic option. Clinical trials
with FH-deficient tumors in HLRCC
(AVATAR trial, NCT01130519) have
shown promising results inhibiting
angiogenesis by targeting the VEGF
and EGFR pathways, which led
to NCCN recommendation of this
regimen in the treatment of HLRCC
patients.35,36 One smaller study
compared erlotinib/bevacizumab
combinations to immunotherapy/
TKI combinations in FH-deficient
RCC, reporting more favorable
clinical outcomes in the latter. 37
Further molecular comparisons
between responders and nonresponders
may help to identify
biomarkers that influence response
to this treatment.
The most common co-mutation
between FH-mut and WT tumors
was in the NF2 gene (Figure 1), which
prevents tumor growth through
inhibition of different pathways
including the RAS, PI3K/AKT, and
HIPPO pathways.38,39 Concurrent
mutations between FH and NF2
have also been previously reported
in RCC.12,37,40 Previous studies have
demonstrated that targeting YAP1
resulted in reduced tumor growth in
NF2-mutated RCCs, suggesting that
a significant subset of the FH-mut
population may also benefit from
targeting the Hippo pathway.41-43
Concurrent mutations were
also observed between FH and
DNA damage repair gene, BRCA1
(Figure 1). BRCA1 mutations
are an indication for the use of
Poly (ADP-ribose) polymerase
(PARP) inhibitors in some tumor
types. PARP inhibitors alone or in
combination with other agents are
also undergoing evaluation in VHLdeficient
RCCs based on the DNA
repair detect status and/or potential
sensitivity of this genomic status to
immunotherapies. 44-46 However,
one phase II study determined the
combination of PARP inhibitors
and PD-L1 inhibitors to be
ineffective in VHL-mutated RCC. 47
Accumulation of fumarate has
also been reported to suppress the
homologous recombination (HR)
repair pathway, 48 which would not
be detected by DNA sequencing,
but suggests the sensitivity of FHmut
tumors to DNA damaging
agents. Co-mutations between FH
and AKT1 were higher specifically in
ccRCC. AKT signaling and the mTOR
signaling pathway have also been
linked to DNA damage response,49
suggesting that targeting these
pathways may be useful for a subset
of the population. Combinations of
mTOR inhibitors and angiogenesis
inhibitors have demonstrated robust
activity in RCC patients.50 Other
studies have used gene expression
signatures to reveal prognostic
value of angiogenesis signatures and
T-cell-inflamed GEP signatures for
use of VEGF and mTOR inhibitors
in RCCs.51
RCCs have often been
described as highly immunogenic,
and numerous studies have
demonstrated the efficacy of
immunotherapies. In our study the
analysis of immune checkpoint gene
expression did not reveal significant
differences between FH-mut and
WT tumors. LAG3 exhibited the
most significant difference in
immune gene expression, which was
observed specifically in non-ccRCC
(Figure 3). Recent studies suggested
that targeting LAG-3 might provide a
promising partner in combinatorial
immunotherapies in RCC,52 although
our data may suggest differences
between histology subtypes in this
context. The analysis of immune
cell infiltration also did not reveal
significant changes between FHmut
and WT tumors for most
immune cells, with the exception
of lower infiltration of NK cells and
M2 macrophages in the non-RCC
population (Figure 3).
The IFN-γ response-related
signature (IFN score) has been
developed as a predictive biomarker
for immunotherapy.53,54 When a
similar IFN-γ response-related
signature was described as a
prognostic indicator in ccRCCs,
a high-risk group exhibited low
sensitivity to several drugs, not
including immunotherapies.55 In
our study, the IFN-γ responserelated
signature was significantly
reduced in FH-mut tumors in the
non-ccRCC group, whereas the IFN
score was higher in ccRCC (Figure
3). Although the development
of biomarkers for stratification
of patients in immunotherapy
selection is ongoing in RCC, these
results suggest that variability in
histological subtypes might be
considered in future studies.
In our study, there were no
significant differences observed
between FH-mut and WT tumors
for prevalence of TMB and MSI-H
status (Figure 2). A previous
study also reported low TMB and
stable MSI status in both somatic
and germline FH-mut RCCs.12
However, RCCs have been reported
to have positive responses to
immunotherapies and combination
therapies with TKIs. A study of 336
ccRCC patients reported that higher
TMB was correlated with lower
immune cell infiltration and poor
survival outcomes.56 Thus, contrary
to what has been observed in some
other cancer types, findings from
numerous studies suggest that higher
mutation rates in RCC are associated
with immunologically cold tumor
microenvironments. In contrast
to TMB and MSI biomarkers,
measurement of PD-L1 expression
by IHC revealed higher expression
of PD-L1 in FH-mut tumors, which
was more pronounced in non-ccRCC
(Figure 2). Several studies have
described PD-L1 as a prognostic
indicator of poor survival rates in
immunotherapy-naive RCCs.57-59
Other studies have demonstrated the
efficacy of ICI therapy for RCC, and
the CheckMate 214 trial led to FDA
approval of ICI combination therapy
in 2018.60,61 However, most of these
studies do not describe the genomic
context to identify biomarkers that
could differentiate responders from
non-responders. In other tumor
types, biomarkers developed to
stratify patients for immunotherapy
selection have included PD-L1
expression, TMB, MSI, and TME.
However, the characteristics of
RCCs have thus far precluded them
from effective use of predictive
ICI biomarkers. Follow-up studies
from the CheckMate 214 trial
reported that biomarkers previously
associated with ICI benefit were not
predictive in RCC.62
A few studies with small cohorts
have analyzed drug response from
FH-deficient RCCs with varying
results. One study with 18 patients
reported that patients with FHdeficient
RCC who received
immunotherapy had better clinical
outcomes than patients who
received antiangiogenic therapy
alone.40 Conversely, another study
with 24 patients reported that FHdeficient
RCCs responded better to
various antiangiogenics compared
to immunotherapies or mTOR
inhibitors.63 Therefore, further
studies are needed to investigate
the benefits of immunotherapies in
FH-deficient renal cancers with the
use of other potentially predictive
biomarkers.
Although a significant
number of RCC patients respond
to immunotherapy, a significant
portion of this population also
exhibits resistance, or develops
resistance within several months.
There is increasing interest in
utilizing TKIs in combination
with ICIs as a first- or second-line
treatment, as well as understanding
mechanisms of resistance.37,64,65 In
addition to promoting angiogenesis
and tumor migration, activation
of HIF and increased VEGF also
influence changes in the tumor
microenvironment, including the
release of immunosuppressive factors
such as PD-L1.34 Alternately, reports
have described that antiangiogenic
molecules can overcome
immunosuppressive networks
and thereby influence sensitivity
of tumors to immunotherapies.66
Analyzing the molecular features of
FH-deficient RCCs in the context of
drug response will be beneficial for
better understanding the clinical
and therapeutic implications of such
a dynamic system.
Study Limitations
The primary limitation of this
study is the small cohort size.
Although it is the largest cohort of
FH-deficient RCC tumors that has
been described by comprehensive
molecular profiling, a total sample
size of 40 FH-mut samples limits
the comparative analyses that could
be performed. Although there is
considerable heterogeneity among
RCCs, small cohort size also limits
conclusions about prevalence in
histological subtypes, molecular
subtypes, or comparisons between
primary and metastatic tumors.
Furthermore, this study does not
include other variables which may
impact the molecular landscape of
the sequenced samples, including
demographic information, or
clinical information about staging
or treatment regimens. The lack of
clinical outcomes data associated
with the cohort also limits the
clinical implications that can be
drawn from the observed molecular
characteristics. RNA-Seq data from
FFPE samples also has inherent
limitations which should be
considered with immune profiling
methods used in this study. Despite
the rare incidence of FH-mut RCCs,
the accurate diagnosis of FH status
is important due to their aggressive
nature. The accumulation of
succinate dehydrogenase in FHdeficient
tumors provides a highly
immunoreactive target in recently
improved IHC-based screening
methods that could provide clinical
utility for many renal cancers.67,68
In conclusion, comprehensive
genomic profiling of FH-deficient
RCCs yielded results in this study
that were generally consistent
with prior reports of genetic
this difference wacharacteristics of FH-deficient
RCCs, while analysis of a large
sample size enabled observation
of some interesting trends. FHdeficient
RCCs exhibit molecular
characteristics that may distinguish
them from WT-RCCs. Common
RCC driver mutations of renal
cell carcinoma (RCC) are reduced
in FH-mutated RCC, while some
other co-mutations are increased
that may point to new therapeutic
options. Overall, these findings
point to several possible trends in
FH-deficient RCC that could be
validated in a larger clinical study,
while comprehensive molecular
analysis may provide the potential
for identifying novel therapeutic
approaches in FH-deficient RCC.
SUPPLEMENTAL INFORMATION
Access supplemental information
online. https://kidney-cancerjournal.
com/KCJ21n4-or1.php
AUTHORS DISCLOSURES:
SW, AH, AG, AH, and CN are employees of
Caris Life Sciences. BN reports a consulting
or advisory role for Exelixis. PCB reports
a consulting or advisory role for Bayer,
BMS, Caris Life Sciences, Clovis Oncology,
Dendreon, Eisai, EMD Serono, and Pfizer;
PCB reports research funding from Blue
Earth Diagnostics. CJR reports a consulting
or advisory role for Advanced Accelerator
Applications, Bayer, Clovis Oncology,
Dendreon, Myovant Sciences, and Roivant;
CJR reports receiving honoraria from
Bayer and Janssen Oncology; CJR reports
research funding from Clovis Oncology
and Genzyme. RRM reports a consulting
or advisory role for Astellas Medivation,
AstraZeneca, Bayer, Bristol-Myers
Squibb, Calithera Biosciences, Caris Life
Sciences, Dendreon, Exelixis, Janssen,
Merck, Myovant Sciences, Novartis, Pfizer,
Sanofi, Sorrento Therapeutics, Tempus,
and Vividion Therapeutics; RRM reports
research funding from Bayer, Pfizer, and
Tempus. EIH reports a consulting or
advisory role for Agensys, AstraZeneca,
Bayer, Dendreon, and Sanofi; EIH reports
receiving honoraria from AstraZeneca,
Bayer, Dendreon, Sanofi, and Seattle
Genetics. EIH reports funding for
travel and/or the speaker’s bureau from
Sanofi, Agensys, and Bayer; EIH reports
research funding from Agensys, AIQ
Solutions, Astellas Pharma, AstraZeneca,
AstraZeneca, Bayer, Boehringer Ingelheim,
Bristol-Myers Squibb, Calibr, Caris Life
Sciences, Celgene, Celldex, Champions
Oncology, Corcept Therapeutics,
Curemeta, Daiichi Sankyo Inc., Dendreon,
eFFECTOR Therapeutics, Eisai, Esanik,
Five Prime Therapeutics, Fortis,
Genentech/Roche, GlaxoSmithKline,
Ignyta, Infinity Pharmaceuticals, Inovio
Pharmaceuticals, Janssen Research &
Development, Medivation, Merck, Merck
Sharp & Dohme, Millennium, Mirati
Therapeutics, Modra Pharmaceuticals,
Novartis, Oncolys BioPharma, Pellficure,
Peloton Therapeutics, Pharmacyclics,
Plexxikon, Seattle Genetics, Synta, Tokai
Pharmaceuticals, Zenith Epigenetics, and
Zenith Epigenetics. All other authors have
declared no conflicts of interest.
REFERENCE
1. Tomlinson IP, Alam NA, Rowan AJ,
et al. Germline mutations in FH predispose
to dominantly inherited uterine fibroids,
skin leiomyomata and papillary renal cell
cancer. Nat Genet 2002;30:406-10.
2. Wei MH, Toure O, Glenn GM, et
al. Novel mutations in FH and expansion
of the spectrum of phenotypes expressed in
families with hereditary leiomyomatosis and
renal cell cancer. J Med Genet 2006;43:18-
27.
3. Isaacs JS, Jung YJ, Mole DR, et al.
HIF overexpression correlates with biallelic
loss of fumarate hydratase in renal cancer:
novel role of fumarate in regulation of HIF
stability. Cancer Cell 2005;8:143-53.
4. Pollard PJ, Brière JJ, Alam
NA, et al. Accumulation of Krebs cycle
intermediates and over-expression of
HIF1alpha in tumours which result from
germline FH and SDH mutations. Hum Mol
Genet 2005;14:2231-9.
5. Xiao M, Yang H, Xu W, et al.
Inhibition of α-KG-dependent histone
and DNA demethylases by fumarate and
succinate that are accumulated in mutations
of FH and SDH tumor suppressors. Genes
Dev 2012;26:1326-38.
6. Grubb RL, 3rd, Franks ME, Toro J,
et al. Hereditary leiomyomatosis and renal
cell cancer: a syndrome associated with an
aggressive form of inherited renal cancer. J
Urol 2007;177:2074-9; discussion 9-80.
7. Launonen V, Vierimaa O, Kiuru
M, et al. Inherited susceptibility to uterine
leiomyomas and renal cell cancer. Proc Natl
Acad Sci U S A 2001;98:3387-92.
8. Toro JR, Nickerson ML, Wei MH, et
al. Mutations in the fumarate hydratase gene
cause hereditary leiomyomatosis and renal
cell cancer in families in North America. Am
J Hum Genet 2003;73:95-106.
9. Forde C, Lim DHK, Alwan Y, et al.
Hereditary Leiomyomatosis and Renal Cell
Cancer: Clinical, Molecular, and Screening
Features in a Cohort of 185 Affected
Individuals. Eur Urol Oncol 2020;3:764-72.
10. Chayed Z, Kristensen LK, Ousager
LB, Rønlund K, Bygum A. Hereditary
leiomyomatosis and renal cell carcinoma: a
case series and literature review. Orphanet J
Rare Dis 2021;16:34.
11. Menko FH, Maher ER, Schmidt
LS, et al. Hereditary leiomyomatosis and
renal cell cancer (HLRCC): renal cancer risk,
surveillance and treatment. Fam Cancer
2014;13:637-44.
12. Gleeson JP, Nikolovski I, Dinatale
R, et al. Comprehensive Molecular
Characterization and Response to Therapy
in Fumarate Hydratase-Deficient Renal Cell
Carcinoma. Clin Cancer Res 2021;27:2910-
9.
13. Kiuru M, Lehtonen R, Arola J, et al.
Few FH mutations in sporadic counterparts
of tumor types observed in hereditary
leiomyomatosis and renal cell cancer
families. Cancer Res 2002;62:4554-7.
14. Thouvenin J, Masson C, Boudier P,
et al. Complete Response in Metastatic Clear
Cell Renal Cell Carcinoma Patients Treated
with Immune-Checkpoint Inhibitors:
Remission or Healing? How to Improve
Patients' Outcomes? Cancers (Basel)
2023;15.
15. Marabelle A, Fakih M, Lopez J,
et al. Association of tumour mutational
burden with outcomes in patients with
advanced solid tumours treated with
pembrolizumab: prospective biomarker
analysis of the multicohort, open-label,
phase 2 KEYNOTE-158 study. Lancet Oncol
2020;21:1353-65.
16. Merino DM, McShane LM,
Fabrizio D, et al. Establishing guidelines to
harmonize tumor mutational burden (TMB):
in silico assessment of variation in TMB
quantification across diagnostic platforms:
phase I of the Friends of Cancer Research
TMB Harmonization Project. J Immunother
Cancer 2020;8.
17. Finotello F, Mayer C, Plattner
C, et al. Molecular and pharmacological
modulators of the tumor immune contexture
revealed by deconvolution of RNA-seq data.
Genome Med 2019;11:34.
18. Cristescu R, Mogg R, Ayers M, et
al. Pan-tumor genomic biomarkers for PD-1
checkpoint blockade-based immunotherapy.
Science 2018;362.
19. Dall'Oglio MF, Antunes AA,
Pompeo AC, Mosconi A, Leite KR, Srougi
M. Prognostic relevance of the histological
subtype of renal cell carcinoma. Int Braz J
Urol 2008;34:3-8.
20. Keegan KA, Schupp CW, Chamie
K, Hellenthal NJ, Evans CP, Koppie TM.
Histopathology of surgically treated renal
cell carcinoma: survival differences by
subtype and stage. J Urol 2012;188:391-7.
21. Leibovich BC, Lohse CM,
Crispen PL, et al. Histological subtype is
an independent predictor of outcome for
patients with renal cell carcinoma. J Urol
2010;183:1309-15.
22. Muglia VF, Prando A. Renal cell
carcinoma: histological classification and
correlation with imaging findings. Radiol
Bras 2015;48:166-74.
23. Miao D, Margolis CA, Gao W, et al.
Genomic correlates of response to immune
checkpoint therapies in clear cell renal cell
carcinoma. Science 2018;359:801-6.
24. Motzer RJ, Banchereau R, Hamidi
H, et al. Molecular Subsets in Renal
Cancer Determine Outcome to Checkpoint
and Angiogenesis Blockade. Cancer Cell
2020;38:803-17.e4.
25. Jee B, Seo E, Park K, et al.
124 Kidney Cancer Journal | 21 (4) DEC 2023 Kidney-Cancer-Journal.com
Molecular Subtypes Based on Genomic and
Transcriptomic Features Correlate with the
Responsiveness to Immune Checkpoint
Inhibitors in Metastatic Clear Cell Renal Cell
Carcinoma. Cancers (Basel) 2022;14.
26. Gnarra JR, Tory K, Weng Y, et al.
Mutations of the VHL tumour suppressor
gene in renal carcinoma. Nat Genet
1994;7:85-90.
27. Sudarshan S, Shanmugasundaram
K, Naylor SL, et al. Reduced expression
of fumarate hydratase in clear cell renal
cancer mediates HIF-2α accumulation and
promotes migration and invasion. PLoS One
2011;6:e21037.
28. Chakraborty S, Balan M,
Sabarwal A, Choueiri TK, Pal S. Metabolic
reprogramming in renal cancer: Events of
a metabolic disease. Biochim Biophys Acta
Rev Cancer 2021;1876:188559.
29. Stransky LA, Vigeant SM, Huang
B, et al. Sensitivity of VHL mutant kidney
cancers to HIF2 inhibitors does not require
an intact p53 pathway. Proc Natl Acad Sci U
S A 2022;119:e2120403119.
30. Choueiri TK, Halabi S, Sanford
BL, 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 2017;35:591-
7.
31. Motzer RJ, Hutson TE, Cella D, et
al. Pazopanib versus sunitinib in metastatic
renal-cell carcinoma. N Engl J Med
2013;369:722-31.
32. Motzer RJ, Hutson TE, Tomczak
P, et al. Overall survival and updated results
for sunitinib compared with interferon
alfa in patients with metastatic renal cell
carcinoma. J Clin Oncol 2009;27:3584-90.
33. Sternberg CN, Davis ID, Mardiak
J, et al. Pazopanib in locally advanced or
metastatic renal cell carcinoma: results of
a randomized phase III trial. J Clin Oncol
2010;28:1061-8.
34. Mennitto A, Huber V, Ratta R, et
al. Angiogenesis and Immunity in Renal
Carcinoma: Can We Turn an Unhappy
Relationship into a Happy Marriage? J Clin
Med 2020;9.
35. NCCN Guidelines-Kidney Cancer.
NCCN, 2022. (Accessed 2022/11/10,
at https://www.nccn.org/guidelines/
guidelines-detail?category=1&id=1440.)
36. Srinivasan R, Gurram S, Harthy
MA, et al. Results from a phase II study
of bevacizumab and erlotinib in subjects
with advanced hereditary leiomyomatosis
and renal cell cancer (HLRCC) or sporadic
papillary renal cell cancer. Journal of Clinical
Oncology 2020;38:5004-.
37. Xu Y, Kong W, Cao M, et al.
Genomic Profiling and Response to Immune
Checkpoint Inhibition plus Tyrosine Kinase
Inhibition in FH-Deficient Renal Cell
Carcinoma. Eur Urol 2022.
38. Dey A, Varelas X, Guan KL.
Targeting the Hippo pathway in cancer,
fibrosis, wound healing and regenerative
medicine. Nat Rev Drug Discov 2020;19:480-
94.
39. Petrilli AM, Fernández-Valle C.
Role of Merlin/NF2 inactivation in tumor
biology. Oncogene 2016;35:537-48.
40. Sun G, Zhang X, Liang J, et al.
Integrated Molecular Characterization of
Fumarate Hydratase-deficient Renal Cell
Carcinoma. Clin Cancer Res 2021;27:1734-
43.
41. Calses PC, Crawford JJ, Lill JR,
Dey A. Hippo Pathway in Cancer: Aberrant
Regulation and Therapeutic Opportunities.
Trends Cancer 2019;5:297-307.
42. Sourbier C, Liao PJ, Ricketts CJ,
et al. Targeting loss of the Hippo signaling
pathway in NF2-deficient papillary kidney
cancers. Oncotarget 2018;9:10723-33.
43. White SM, Avantaggiati ML,
Nemazanyy I, et al. YAP/TAZ Inhibition
Induces Metabolic and Signaling Rewiring
Resulting in Targetable Vulnerabilities
in NF2-Deficient Tumor Cells. Dev Cell
2019;49:425-43.e9.
44. Ged Y, Chaim JL, DiNatale RG, et
al. DNA damage repair pathway alterations
in metastatic clear cell renal cell carcinoma
and implications on systemic therapy. J
Immunother Cancer 2020;8.
45. Pletcher JP, Bhattacharjee S, Doan
JP, et al. The Emerging Role of Poly (ADPRibose)
Polymerase Inhibitors as Effective
Therapeutic Agents in Renal Cell Carcinoma.
Front Oncol 2021;11:681441.
46. Scanlon SE, Hegan DC, Sulkowski
PL, Glazer PM. Suppression of homologydependent
DNA double-strand break repair
induces PARP inhibitor sensitivity in VHLdeficient
human renal cell carcinoma.
Oncotarget 2018;9:4647-60.
47. Kotecha R, Lee C-H, McHugh DJ,
et al. A phase II study of talazoparib and
avelumab in VHL deficient clear cell renal
cell carcinoma. Journal of Clinical Oncology
2022;40:347-.
48. Sulkowski PL, Sundaram RK, Oeck
S, et al. Krebs-cycle-deficient hereditary
cancer syndromes are defined by defects in
homologous-recombination DNA repair.
Nat Genet 2018;50:1086-92.
49. Ma Y, Vassetzky Y, Dokudovskaya
S. mTORC1 pathway in DNA damage
response. Biochim Biophys Acta Mol Cell
Res 2018;1865:1293-311.
50. Feldman DR, Ged Y, Lee CH,
et al. Everolimus plus bevacizumab is an
effective first-line treatment for patients
with advanced papillary variant renal cell
carcinoma: Final results from a phase II
trial. Cancer 2020;126:5247-55.
51. Donskov F, Pinto CA, Predoiu
R, et al. Molecular analysis and favorable
clinical outcomes in real-world patients with
metastatic renal cell carcinoma. Acta Oncol
2022;61:1268-77.
52. Zelba H, Bedke J, Hennenlotter J,
et al. PD-1 and LAG-3 Dominate Checkpoint
Receptor-Mediated T-cell Inhibition in
Renal Cell Carcinoma. Cancer Immunol Res
2019;7:1891-9.
53. Ayers M, Lunceford J, Nebozhyn
M, et al. IFN-γ-related mRNA profile
predicts clinical response to PD-1 blockade.
J Clin Invest 2017;127:2930-40.
54. Cui C, Xu C, Yang W, et al.
Ratio of the interferon-γ signature to the
immunosuppression signature predicts anti-
PD-1 therapy response in melanoma. NPJ
Genom Med 2021;6:7.
55. Liu L, Du X, Fang J, et al.
Development of an Interferon Gamma
Response-Related Signature for Prediction of
Survival in Clear Cell Renal Cell Carcinoma.
J Inflamm Res 2021;14:4969-85.
56. Zhang C, Li Z, Qi F, Hu X, Luo J.
Exploration of the relationships between
tumor mutation burden with immune
infiltrates in clear cell renal cell carcinoma.
Ann Transl Med 2019;7:648.
57. Lu Y, Song Y, Xu Y, et al.
The prevalence and prognostic and
clinicopathological value of PD-L1 and
PD-L2 in renal cell carcinoma patients:
a systematic review and meta-analysis
involving 3,389 patients. Transl Androl Urol
2020;9:367-81.
58. Möller K, Fraune C, Blessin NC,
et al. Tumor cell PD-L1 expression is a
strong predictor of unfavorable prognosis
in immune checkpoint therapy-naive clear
cell renal cell cancer. Int Urol Nephrol
2021;53:2493-503.
59. Ueda K, Suekane S, Kurose H,
et al. Prognostic value of PD-1 and PDL1
expression in patients with metastatic
clear cell renal cell carcinoma. Urol Oncol
2018;36:499.e9-.e16.
60. Motzer RJ, Escudier B, McDermott
DF, et al. Nivolumab versus Everolimus in
Advanced Renal-Cell Carcinoma. N Engl J
Med 2015;373:1803-13.
61. Motzer RJ, Tannir NM, McDermott
DF, et al. Nivolumab plus Ipilimumab
versus Sunitinib in Advanced Renal-Cell
Carcinoma. N Engl J Med 2018;378:1277-
90.
62. Motzer RJ, Choueiri TK,
McDermott DF, et al. Biomarker analysis
from CheckMate 214: nivolumab plus
ipilimumab versus sunitinib in renal cell
carcinoma. J Immunother Cancer 2022;10.
63. Carril-Ajuria L, Colomba E, Cerbone
L, et al. Response to systemic therapy in
fumarate hydratase-deficient renal cell
carcinoma. Eur J Cancer 2021;151:106-14.
64. Sharma R, Kadife E, Myers M,
Kannourakis G, Prithviraj P, Ahmed N.
Determinants of resistance to VEGF-TKI and
immune checkpoint inhibitors in metastatic
renal cell carcinoma. J Exp Clin Cancer Res
2021;40:186.
65. Vano YA, Ladoire S, Elaidi R, et
al. First-Line Treatment of Metastatic Clear
Cell Renal Cell Carcinoma: What Are the
Most Appropriate Combination Therapies?
Cancers (Basel) 2021;13.
66. Kwilas AR, Donahue RN, Tsang
KY, Hodge JW. Immune consequences of
tyrosine kinase inhibitors that synergize
with cancer immunotherapy. Cancer Cell
Microenviron 2015;2.
67. Trpkov K, Siadat F.
Immunohistochemical screening for the
diagnosis of succinate dehydrogenase deficient
renal cell carcinoma and fumarate
hydratase-deficient renal cell carcinoma.
Ann Transl Med 2019;7:S324.
68. Gupta S, Swanson AA, Chen YB,
et al. Incidence of succinate dehydrogenase
and fumarate hydratase-deficient renal cell
carcinoma based on immunohistochemical
screening with SDHA/SDHB and FH/2SC.
Hum Pathol 2019;91:114-22
* # Correspondence: Elisabeth I. Heath, MD, FACP
Karmanos Cancer Institute, 4100 John R St, Detroit, MI 48201, USA
Download PDF
