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Novel Biomarkers in Metastatic Renal Cell Carcinoma
Patrick Penttilä, M.D
Doctoral Programme in Clinical Research
Department of Oncology
University of Helsinki and Helsinki University Hospital
Helsinki, Finland
To be publicly discussed with the permission of Faculty of Medicine, University of Helsinki, in the Auditorium of the Department of Oncology, on Friday October 5th,2018, at 12 o’clock noon
Helsinki 2018
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Supervised by
Adjunct Professor and Chief Medical Officer Petri Bono, M.D., Ph.D.
Helsinki University and Helsinki University Hospital
Helsinki, Finland
Reviewed by
Docent Peeter Karihtala, M.D., Ph.D.
Department of Oncology and Radiotherapy
University of Oulu and Oulu University Hospital
Oulu, Finland
and
Docent Peter Boström, M.D., Ph.D.
Department of Urology
University of Turku and Turku University Hospital
Turku, Finland
Discussed with
Professor Axel Bex, M.D., Ph.D.
Department of Urology
Netherlands Cancer Institute
Amsterdam, Netherlands
The Faculty of Medicine uses the Urkund system (plagiarism recognition) to examine all doctoral dissertations.
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“Let us now pause for a moment of science”
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Table of Contents
Table of Contents ............................................................................... 4 Original Publications ......................................................................... 7 Abbreviations ..................................................................................... 8 Abstract ............................................................................................ 11 Review of the Literature .................................................................. 13 1. Renal Cell Carcinoma ........................................................................................ 13
1.1 Epidemiology ................................................................................................. 13
1.2 Pathology ........................................................................................................ 14 1.2.1 Clear-Cell Renal Cell Carcinoma ......................................................................14
1.2.2 Papillary Renal Cell Carcinoma .......................................................................15
1.2.3 Chromophobe Renal Cell Carcinoma ...............................................................16
1.2.4 Other Defined Subtypes ....................................................................................16
1.2.5 Sarcomatoid Differentiation in Renal Cell Carcinoma .....................................17
1.3 Genetic Environment ...................................................................................... 17 1.3.1 VHL Gene Alteration .......................................................................................18
1.3.2 Met Proto-oncogene and HPRCC .....................................................................18
1.3.3 Other Hereditary Syndromes ............................................................................19
2. Tumorigenesis and the Tumor Microenvironment ........................................ 19
2.1 Tumorigenesis ................................................................................................ 19 2.1.1 Hypoxia-induced Pathway ................................................................................20
2.1.1.1 HIF- - ...............................................21
2.1.2 mTOR Pathway ................................................................................................23
2.1.3 c-Met Signaling Pathway ..................................................................................24
2.1.4 Angiogenesis ....................................................................................................25
2.2 Tumor Microenvironment ............................................................................. 27 2.2.1 Tumor Escape ...................................................................................................29
2.2.2 Tumor Heterogeneity ........................................................................................31
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3. Diagnosis and Management of RCC ............................................................... 32
3.1 Diagnosis ....................................................................................................... 32 3.1.1 Imaging ............................................................................................................33
3.1.2 Staging and Pathology Assessment .................................................................. 33
3.1.3 Small Renal Masses........................................................................................... 36
3.2 Prognostication ............................................................................................... 37
3.3 Treatment ........................................................................................................ 39 3.3.1 Cytoreductive Nephrectomy ............................................................................. 39
3.3.2 Metastasectomy and Other Local Therapeutic Options for Metastases ............40
3.3.3 Systemic Therapy .............................................................................................41
3.3.3.1 Immunotherapy ...........................................................................................41
3.3.3.2 Tyrosine Kinase Inhibitors .........................................................................42
3.3.3.3 mTOR Inhibitors ........................................................................................43
3.3.3.4 Novel Approaches ......................................................................................45
3.3.3.5 Optimal Sequencing ...................................................................................47
3.3.3.5 Adjuvant Therapy .......................................................................................50
4. Biomarkers in Renal Cell Carcinoma ............................................................. 53
4.1 Prognostic and Predictive Biomarkers .......................................................... 54 4.1.1 Clinical-related Biomarkers .............................................................................. 55
4.1.2 Vascular Endothelial-derived Growth Factor ................................................... 58
4.1.3 Carbonic Anhydrase IX ....................................................................................59
4.1.4 Cytokine and Angiogenic Factors ..................................................................... 60
4.1.5 Single Nucleotide Polymorphisms .................................................................... 61
4.1.6 Immune Markers .............................................................................................. 62
4.1.7 Mechanism-based Adverse Events .................................................................... 64
4.2 Incorporation of Biomarkers into Clinical Practice and Future Directions .... 66
Aims of the Study ................................................................................................... 67
Patients and Methods............................................................................................. 68
1. Patients and Treatment ..................................................................................... 68 1.1 Assessment of Tumor Response and Adverse Events ..........................................69
1.2 Assessment of Pneumonitis .................................................................................69
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1.3 Immunohistochemistry ........................................................................................ 70
2. Statistical Analysis ........................................................................................... 71
3. Ethical Considerations ...................................................................................... 72
Results and Discussion ........................................................................................... 73
1. The use of angiotensin system inhibitors (ASIs) in the treatment of TKI-induced hypertension (I) ....................................................................................... 73
2. c-Met expression is associated with the outcome among mRCC patients treated with sunitinib (II) ...................................................................................... 77
3. Everolimus-induced pneumonitis predicts the treatment outcome among mRCC patients treated with everolimus (III) ....................................................... 84
4. Hyponatremia associates with a poor outcome among mRCC patients treated with everolimus (IV) ............................................................................................ 89
Limitations of the Study Materials and Methods ............................................... 95
Conclusions ............................................................................................................. 97
Acknowledgements................................................................................................. 99
References ............................................................................................................. 101
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Original Publications
This thesis is based on the following publications, which are referred to in the text by their Roman numerals:
I Penttilä P, Rautiola J, Poussa T, Peltola K, Bono P. Angiotensin Inhibitors as Treatment of Sunitinib/Pazopanib-induced Hypertension in Metastatic Renal Cell Carcinoma. Clin Genitourin Cancer. 2017 Jun;15(3):384-390.
II Peltola KJ, Penttilä P, Rautiola J, Joensuu H, Hänninen E, Ristimäki A, Bono P. Correlation of c-Met Expression and Outcome in Patients With Renal Cell Carcinoma Treated With Sunitinib. Clin Genitourin Cancer. 2017 Aug;15(4):487-494.
III Penttilä P, Donskov F, Rautiola J, Peltola K, Laukka M, Bono P. Everolimus-induced pneumonitis associates with favourable outcome in patients with metastatic renal cell carcinoma. Eur J Cancer 2017 Aug;81:9-16.
IV Penttilä P, Bono P, Peltola K, Donskov F. Hyponatremia associates with poor outcome in metastatic renal cell carcinoma patients treated with everolimus: Prognostic impact. Acta Oncol 2018, 2018 Jun;4:1-6
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Abbreviations
AE Adverse event
Ang-1 & -2 Angiopoietin 1 and -2
AS Active surveillance
ASI Angiotensin system inhibitor
BHD Birt-Hogg-Dubé
c-Met Tyrosine protein kinase Met
CAFs Cytokine and angiogenic factors
CAIX Carbonic anhydrase IX
ccRCC Clear-cell renal cell carcinoma
chRCC Chromophobe renal cell carcinoma
ColIV Collagen IV
CR Complete response
ctDNA Circulating tumor DNA
DFS Disease-free survival
EC Endothelial cells
ECOG Eastern Cooperative Oncology Group
ESMO European Society for Medical Oncology
FDA Food and Drug Administration
FH Fumarate hydratase
FLCN Folliculin
HGF Hepatocyte growth factor
HIF Hypoxia-induced factor
HLRCC Hereditary leiomyomatosis and renal cell cancer
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HPRC Hereditary papillary renal cancer
HTN Hypertension
IFN Interferon
IL Interleukin
IMDC International Metastatic Renal Cell Carcinoma Database Consortium
LDH Lactate dehydrogenase
MDSC Myeloid suppressor cells
MiRNA MicroRNA
MMP Matrix metalloproteinase
mRCC Metastatic renal-cell carcinoma
MSKCC Memorial Sloan Kettering Cancer Center
mTOR Mammalian target of rapamycin
NK Natural killer
ORR Objective response rate
OS Overall survival
PD Progressive disease
PD-1 Programmed death 1
PD-L1 Programmed death ligand-1
PDGF Platelet-derived growth factor
PLGF Placenta growth factor
PFS Progression-free survival
PR Partial response
pRCC Papillary renal cell carcinoma
PTP1B Protein tyrosine phosphatase 1B
PTEN Phosphatase and tensin homologue
RCC Renal cell carcinoma
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SD Stable disease
SDHB Succinate dehydrogenase B
SDHD Succinate dehydrogenase D
SMGs Significantly mutated genes
SNP Single nucleotide polymorphism
SRM Small renal mass
TGF- transforming growth factor
TIMP Tissue inhibitor of metalloproteinases
TKI Tyrosine kinase inhibitor
TNM Tumor-node-metastasis
TRAIL Tumor necrosis factor-related apoptosis-inducing ligand
TSP-1 Thrombospondin 1
T-regs Regulatory T-cells
VEGF Vascular endothelial growth factor
VEGFR Vascular endothelial growth factor receptor
VHL von Hippel-Lindau
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ABSTRACT
During the past decade, basic and translational cancer research has provided new
information regarding the mechanisms underlying tumor growth, proliferation, and
metastasis in renal cell carcinoma (RCC). Understanding of the molecular
pathogenesis of RCC has identified new targets for therapeutic intervention.
Angiogenesis, involving complex signaling pathways such as vascular endothelial
growth factor (VEGF), platelet-derived growth factor, and angiopoietins, plays a
pivotal role in tumor growth and progression, and therapies targeting angiogenic
factors from the VEGF family have become an effective strategy in the treatment of
metastatic renal-cell carcinoma (mRCC) since 2006.
Sunitinib and pazopanib are tyrosine kinase inhibitors (TKI) and they are globally
used as first-line treatments for mRCC. Although they primarily act through
inhibiting angiogenesis, they might also have an effect on the function of immune
cells such as T-regs. Despite the initial enthusiasm for these modern-era targeted
therapies, however, some patients only receive moderate benefit, and resistance to
these therapies eventually develops in all patients. At present, no biomarkers
predicting treatment efficacy are known, despite intensive translational research
during the last decade.
While the main component of the angiogenic process in RCC is VEGF, targeting of
the mammalian target of the rapamycin (mTOR) pathway is important, because
activation of the upstream PI3K/Akt/mTOR signaling pathways is one method by
which constitutive HIF- imus is an orally
available mTOR inhibitor and is commonly used after TKI treatment
failure/intolerance. As with TKI inhibitors, however, there are wide differences in
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responses to everolimus treatment, and it remains unclear which patients most
benefit from the treatment.
Our results confirm TKI-induced hypertension (HTN) as a strong predictor of a good
prognosis and response among patients treated with sunitinib/pazopanib. We
additionally demonstrated that the use angiotensin system inhibitors (ASIs) in the
treatment of TKI-induced HTN may have synergistic beneficial effects when used in
conjunction with sunitinib or pazopanib.
In our investigation of tumor tyrosine protein kinase Met (c-Met) expression and the
outcome, we demonstrated that high levels of c-Met expression associate with a
worse prognosis among mRCC patients treated with sunitinib. We also demonstrated
that high c-Met expression is associated with a poor outcome among patients without
bone metastases at baseline, suggesting that the prognostic role may vary depending
on the location of the metastases.
In our two studies conducted on everolimus-treated patients, we showed that
everolimus-induced pneumonitis associates with an improved outcome, suggesting
a role as a surrogate marker of the efficacy of everolimus treatment. We additionally
confirmed the prognostic value of hyponatremia in a large cohort of everolimus-
treated patients.
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Review of the Literature
1. Renal Cell Carcinoma
1.1 Epidemiology
Kidney cancer accounts for 5% and 3% of all adult malignancies in men and women,
respectively, making it the sixth most common cancer in men and tenth most
common in women. The reported figures, however, also include urothelial cancer of
the renal pelvis; renal cell carcinoma (RCC) accounts fo .
(1)
In Finland, the reported incidence of RCC in 2003–2007 was 9.1 per 100 000 men
and 5.8 per 100 000 women (2). Worldwide, as well as in Finland, RCC incidence
has shown signs of plateauing, with a simultaneous decrease in mortality rates (3, 4).
This phenomenon is at least partly explained by earlier detection, allowing earlier
intervention when the disease in potentially curable; currently, more than 50% of
RCCs are detected incidentally (5, 6)
Well-known risk factors for RCC include cigarette smoking, hypertension, and
obesity (7-14). Evidence also suggests that patients with end-stage renal failure,
acquired renal cystic disease, tuberous sclerosis syndrome, and patients who undergo
kidney transplantation are at increased risk of developing RCC (15-18).
Additionally, epidemiologic studies have demonstrated that a family history of RCC
is a risk factor for the disease (19, 20), and indeed, several hereditary syndromes
predisposing patients to RCC have been identified.
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In the United States, the overall 5-year cancer-specific survival (CSS) for patients
with histologically confirmed kidney cancer of all stages between 1975 and 2009
was 60.4%. The stage-specific 5-year CSS rates were 87.0% for the localized stage,
62.9% for the regional stage, and 9.3% for the distant stage. (21)
1.2. Pathology
RCC is an extremely heterogeneous disease and comprises several different
subclasses, the most common being clear-cell (75%), papillary (7–15%), and
chromophobe (3–5%) carcinoma (22, 23). In addition to these common histological
variants, several relatively uncommon subclasses such as collecting duct carcinoma,
renal medullary carcinoma, tubulocystic renal cell carcinoma, and acquired cystic
disease-associated renal cell carcinoma have been identified (23). Recent important
changes from existing tumor types also include classification according to molecular
alterations and familial predisposition syndromes. These subclasses, however, only
account for less than one percent of all RCCs (23).
1.2.1 Clear-cell Renal Cell Carcinoma
Clear-cell renal cell carcinoma (ccRCC) is the most common variant of all RCCs and
it originates from the epithelium of the proximal convoluted tubules (23).
Histologically, ccRCCs are characterized by cells with a lipid- and glycogen-rich
cytoplasmic content and frequently also present with an eosinophilic granular
cytoplasm. Microscopically, an alveolar, acinar, or solid architectural pattern is
commonly detected (24). Most cases of ccRCC are sporadic, with only 5% being
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associated with hereditary syndromes such as von Hippel-Lindau disease and
tuberous sclerosis. Both sporadic and hereditary forms of the disease are strongly
associated with deletion in chromosome 3p, the site of the von Hippel-Lindau gene,
with deletions at this site found in up to 96% of ccRCCs (25, 26).
1.2.2 Papillary Renal Cell Carcinoma
Papillary renal cell carcinoma (pRCC) is histologically characterized by a papillary
or tubulopapillary architecture. The proportion of papillae varies and they often
present with hemorrhage, necrosis, and cystic degeneration (27). Like ccRCC, pRCC
may occur sporadically or in association with hereditary syndromes, and it is
typically associated with trisomies of chromosomes 7, 16, and 17, renal cortical
adenomas, and multifocal tumors (22).
Based on histologic appearance and biological behavior, two subtypes of pRCC have
been identified: type 1 and type 2. It has been demonstrated that type 1 pRCC is
associated with prolonged survival as compared to type 2 pRCC, most likely due to
the fact that type 1 pRCC is typically detected at an earlier stage (28, 29). Clinical features differentiating pRCCs from ccRCCs and chromophobe renal cell
carcinomas (chRCCs) include spontaneous hemorrhaging as a presenting feature in
8% of cases, as well as multifocal presentation and calcification on imaging in 30%
of cases (30).
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1.2.3 Chromophobe Renal Cell Carcinoma
Less aggressive than other malignant renal tumors, chRCC accounts for 3–5% of all
RCCs (22). It is characterized by huge pale cells with a reticulated cytoplasm,
prominent cell membrane, and perinuclear halos (23, 31). A very close relationship
between chRCCs and oncocytomas has been suggested, with both arising from
intercalated cells of the collecting duct and showing similar alterations in
mitochondria and mitochondrial DNA (32). Although this relationship is still under
discussion, it has been hypothesized that chRCCs might represent a more aggressive
counterpart for oncocytomas (33). ChRCC is associated with the loss of several
chromosomes, as well a hereditary syndrome, Birt-Hogg-Dubé syndrome (34, 35).
1.2.4 Other Defined Subtypes
In addition to the above-mentioned subtypes, the WHO 2016 classification
recognizes several other less common renal cell tumor subtypes. These include
collecting duct carcinoma, renal medullary carcinoma, tubulocystic RCC, acquired
cystic disease-associated RCC, MiT family translocation RCC, succinate
dehydrogenase-deficient renal carcinoma, mucinous tubular and spindle cell
carcinoma, clear cell papillary RCC, hereditary leiomyomatosis and renal cell
carcinoma-associated RCC, and papillary adenoma (23). As these subclasses only
account for a small proportion of RCCs, they will not be discussed in further detail.
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1.2.5 Sarcomatoid Differentiation in RCC
Sarcomatoid RCC was first described as a tumor with pronounced cytologic atypia
containing enlarged pleomorphic or malignant spindle cells (36). It used to be
considered a histologic entity of its own, but it has since been demonstrated that
sarcomatoid differentiation instead represents a transformation to a higher-grade
malignancy and has been documented in numerous recognized histologic subtypes
of RCC (23, 37). It occurs in approximately 8% of RCCs, but it remains unknown
whether any specific histologic subtype has a predilection for sarcomatoid change
(38). Furthermore, research has demonstrated that sarcomatoid RCC is associated
with a worse prognosis, regardless of the underlying RCC subtype (38, 39).
1.3 Genetic Environment
The genetic environment and genetic changes associated with RCC are
heterogeneous. Several hereditary syndromes predisposing patients to RCC, among
other malignancies, have been described; von Hippel-Lindau disease (VHL),
hereditary leiomyomatosis and renal cell cancer (HLRCC), hereditary papillary renal
cancer (HPRC), and Birt-Hogg-Dubé syndrome (BHD) are the four most common
autosomal dominantly inherited syndromes with distinct histologic features and
genetic alterations (40). Hereditary RCC may account for 4–10% of all RCCs, but
many of the chromosomal changes seen in the hereditary forms of the disease are
also frequent in the sporadic forms (41, 42).
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1.3.1 VHL Gene Alteration
VHL gene alteration is a broad concept of genetic abnormality that includes VHL
gene mutation, promoter hypermethylation, and loss of heterozygosity. The VHL
gene is a tumor suppressor gene and its alteration is present in 50–70% of clear cell
RCCs (25, 43). Through its important role in the regulation of the hypoxia pathway,
VHL gene alteration and subsequent functional loss of VHL protein is thought to
play a significant part in tumorigenesis and tumor angiogenesis (44). In addition to
RCC, the VHL gene has also been implicated in the development of other tumors,
such as hemangioblastomas, pancreatic cysts, and pheochromocytomas (45).
Although understanding of the underlying role of VHL gene alteration in the
pathogenesis of RCC has led to the development of several novel therapeutic
approaches, most importantly vascular endothelial growth factor (VEGF)-targeted
agents, the clinical significance of VHL gene alteration in RCC is yet to be
established.
1.3.2 Met Proto-oncogene and HPRCC
Contrary to the VHL gene, the MET gene, located on chromosome 7, is a proto-
oncogene rather than a tumor suppressor gene (46). Activating mutations of the Met
proto-oncogene have been associated with hereditary papillary renal cell carcinoma
(HPRCC) (46-49) and to a lesser extent with sporadic forms of RCC. HPRCC is
characterized by multifocal, bilateral type I papillary renal cell carcinomas, but in
comparison to VHL disease, it is quite rare (50, 51). The Met proto-oncogene and its
product, c-Met, a tyrosine kinase receptor, have been implicated as promoters of
malignant transformation and tumorigenesis, and will be discussed in detail later in
this thesis.
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1.3.3 Other Hereditary Syndromes
Other hereditary syndromes associated with RCC are Birt-Hogg-Dubé (BHD)
syndrome and hereditary leiomyomatosis and renal cell cancer (HLRCC). BHD
syndrome is an autosomal, dominantly inherited condition characterized by the
development of benign skin tumors, pulmonary cysts, and pneumothorax (52). The
BHD-associated gene, folliculin (FLCN), is located on chromosome 17 and it acts
as a tumor suppressor gene. In BHD syndrome, however, this gene is inactivated
(35). Patients with BHD syndrome have a predisposition for RCCs, and unlike other
hereditary syndromes with this predisposition, RCCs associated with BHD are
histologically diverse. Over two-thirds of the RCCs associated with BHD are of
chromophobe or hybrid chromophobe/oncocytoma histology, however (53)
Fumarate hydratase, the gene product of the fumarate hydratase (FH) gene, is an
enzyme involved in the Krebs cycle. Mutations in the FH gene cause HLRCC. It acts
as a tumor suppressor gene, and the impaired function of the FH gene may lead to
chronic hypoxia, encouraging increased expression of VEGF and thus promoting
tumor formation. HLRCC is associated with type 2 papillary RCCs. (54, 55)
2. Tumorigenesis and the Tumor Microenvironment
2.1 Tumorigenesis
Most of our understanding of tumorigenesis in RCC is based on the work of Dr
Linehan et al. through the study of familial kidney cancer and VHL disease at the
20
National Institutes of Health, Bethesda, US (56-58). At least twelve different genes
are known to promote the development of RCC: VHL, MET, folliculin (FLCN),
tuberous sclerosis complexes-1 and -2, (TSC-1 and -2), TFE3, TFEB, MITF, FH,
succinate dehydrogenase B (SDHB), succinate dehydrogenase D (SDHD), and
phosphatase and tensin homologue (PTEN) genes (58). All these genes are
associated with the regulation of essential mechanisms involved in tumor
development. However, to emphasize the genetic heterogeneity of RCC, an analysis
of more than 500 primary ccRCC tumor samples identified 19 significantly mutated
genes (SMGs), including VHL, PBRM1, SETD2, KDM5C, PTEN, BAP1, MTOR,
and TP53. Furthermore, in approximately 20% of cases, none of these SMGs were
present (59). Probably the most thoroughly investigated and well-described
pathways in RCC are the hypoxia-induced pathway, the mammalian target of
rapamycin (mTOR) pathway, and the c-Met signaling pathway, all promoting
angiogenesis and cell survival, which are essential events in the tumorigenesis of
RCC. These three pathways are described in more detail below.
2.1.1 Hypoxia-induced Pathway
As described earlier, the VHL gene is a tumor suppressor gene. It encodes the VHL
protein, which under normal circumstances targets hypoxia-inducible transcription
factors (HIF-1 - -mediated proteolysis (60). However, under
hypoxic conditions, as well as when the VHL complex is defective due to genetic
alterations, this cascade is interrupted, leading to the accumulation of HIFs. Guo et
al. demonstrated that alterations in genes involved in the ubiquitin-mediated
proteolysis pathway (VHL, BAP1, CUL7, BTRC) are frequent in ccRCC and are
significantly associated with overexpression of HIF- - (61). The
overexpression of the transcription factors, in turn, leads to the upregulation of
several important downstream promoters of angiogenesis such as vascular
21
endothelial growth factor (VEGF), epidermal growth factor, transforming growth
- -derived growth factor (PDGF), erythropoietin, and glucose
transporter 1 (62). Important pathways involved in RCC biology are presented in
Figure 1.
2.1.1.1 HIF- -
Both HIF- - function as transcription factors mediating the
activation of target genes in response to hypoxic conditions. With the inactivation of
the VHL protein, VHL-associated degradation of HIF- -
leading to the maintained activity of these transcription factors irrespective of the
oxygen levels (63, 64). The mTOR pathway also leads to the accumulation of HIF-
the stimulation of ribosomal translation of mRNAs, including the
translation of HIF- , as demonstrated in Figure 1 (65).
HIFs promote tumorigenesis in a variety of ways, including angiogenesis,
metabolism, proliferation, metastasis, and differentiation. The overexpression of
HIF- s a critical factor in renal carcinogenesis in several
studies (66-70). The tumorigenic potential of HIF-
controversial, with contradicting evidence making its role in the development of
RCC less clear (71, 72). C teins as factors
clearly influencing tumor progression by direct regulation of both unique and shared
target genes, as well as by exerting distinct effects on critical oncoproteins and tumor
suppressors.
22
Figure 1. Important pathways involved in renal cell carcinoma biology and
tumorigenesis (Adapted from Klatte et al. Molecular biology of renal cortical
tumors. Urol Clin North Am 2008) (65).
23
2.1.2 mTOR Pathway
The mTOR pathway regulates cell growth, proliferation, survival, and metabolism
(73). Research has shown that in addition to its role in RCC, it is involved in the
development and carcinogenesis of several different malignancies (74, 75).
mTOR is a serine/threonine kinase that in response to growth factors, hormones, and
nutrients activates protein synthesis and contributes to many different cell functions,
including protein degradation and angiogenesis (76). This response is controlled and
activated by the phosphatidylinositol-3-kinase/Akt (PI3K/Akt) pathway and opposed
by the activity of PTEN and TSC-1 and -2 (77, 78). Mutations of PTEN and TSC-1
and -2 leading to the loss of their activity and abnormal activation of the PI3K/AKT
pathway have been demonstrated to play a role in RCC progression (78-80).
The mTOR protein forms two structurally and functionally distinct complexes,
mTOR complex 1 (mTORC1) and complex 2 (mTORC2). mTORC1 is involved in
the regulation of several oncogenic proteins, such as HIF- , and c-
Myc, and mTORC2 is important for cell survival (73). Genetic alterations upstream
of or at mTOR leading to abnormal activation of the mTOR pathway promote the
upregulation of these oncogenic proteins through key downstream effectors, such as
the ribosomal S6 kinase and the eukaryotic translation initiation factor 4E binding
protein (74, 81, 82). In an analysis of over 500 primary ccRCC tumor samples, the
authors demonstrated that alterations targeting multiple components of the
PI3K/AKT/mTOR pathway were present in 28% of the tumor samples (59).
Furthermore, these alterations were associated with a worse or better outcome,
depending on their respective action on the PI3K/AKT pathway, suggesting a role as
a therapeutic target in ccRCC (59).
24
2.1.3 c-Met Signaling Pathway
The c-Met receptor tyrosine kinase is a product of the Met proto-oncogene located
on chromosome 7q21-31. The ligand for c-Met, hepatocyte growth factor (HGF),
promotes cell proliferation, survival, motility, differentiation, and morphogenesis.
Under normal physiologic conditions, the c-Met signaling pathway regulates tissue
homeostasis, but it has been found to play a role in the tumorigenesis of various
human malignancies (83-85).
c-Met signaling is a complex entity involving several molecular events. The major
downstream responses of c-Met activation include activation of the
RAS/RAF/MAPK cascade and the PI3K/Akt signaling axis (86). The mitogen
activated protein kinase (MAPK) activates a large number of genes, which in turn
results in increased cell proliferation and cell motility (87). The PI3K/Akt signaling
axis, on the other hand, is responsible for cell survival in response to c-Met activation
(88).
In rodent models, activating mutations of Met have been shown to cause a variety of
tumors, including sarcomas, lymphomas, and carcinomas, suggesting that aberrant
Met signaling can also cause human cancer (89). In RCC, activating mutations in the
c-Met kinase domain were first discovered in both sporadic and inherited forms of
papillary RCC (48, 90).
c-Met/HGF signaling has been shown to promote tumor angiogenesis by directly
acting on endothelial cells (ECs), inducing proliferation and migration (91, 92). In a
more indirect manner, c-Met/HGF plays a part in activating the angiogenic switch
by inducing VEGF-A expression and by suppressing thrombospondin 1 (TSP-1), a
negative regulator of angiogenesis (93). Indeed, evidence suggests that there is
considerable crosstalk between c-Met/HGF signaling and various other signaling
pathways involved in cancer progression, as well as resistance to therapy.
25
Although c-Met/HGF and VEGF/VEGFR do not directly associate with each other,
they share synergistic angiogenesis-activating intermediates such as the PI3K/Akt
and MAPK cascades, described earlier. Furthermore, HIF-dependent expression of
c-Met has been documented in several types of carcinoma cells (94-96). These
studies suggest that anti-angiogenic therapy, which leads to hypoxic conditions,
induces the accumulation of HIFs, further inducing c-Met expression. This, in turn,
might promote the MET-dependent spread of cancer cells, making it an interesting
target for therapeutic intervention.
2.1.4 Angiogenesis
The supply of oxygen and nutrients is crucial for the survival of tumor cells, and
indeed, neovascularization is considered a key event for tumor progression in RCC.
Through a complex process called the ‘angiogenic switch’, tumor cells induce
angiogenesis and ensure the further supply of oxygen and nutrients, as well as the
disposal of metabolic waste products and carbon dioxide by new sprouting vessels
(97). The regulation of tumor angiogenesis is depicted in Figure 2.
HIFs activate the expression of several proangiogenic factors such as VEGF, VEGF
receptors, angiopoietins (Ang-1 and -2), fibroblast growth factor 2 (FGF-2), platelet-
derived growth factor B (PDGF-B), the Tie-2 receptor, and matrix
metalloproteinases (MMP-2 and -9) (98). The role of VEGF, particularly VEGF-A,
and its receptor, VEGFR-2, is well established regarding angiogenesis in RCC.
VEGF-A and VEGFR-2 regulate the proliferation of ECs and the development of
vasculature by their concentration and gradient, respectively (99). While VEGF-A
and VEGFR-2 represent a key signaling system in the early stages of blood vessel
growth (100), secondary stages of the process require a high level of activity of
several other growth factors and their receptors.
26
Angiopoietins, Ang-1 and Ang-2, are endothelial cell-derived growth factors that act
by binding to their receptors, Tie-1 and Tie-2. While the role of Tie-1 in angiopoietin
signaling remains unclear, studies in Tie-2 knockout mice have demonstrated that
the initial phases of angiogenesis are able to proceed normally, but the remodeling
and hierarchical reorganization of the sprouting vessels are disturbed (101, 102).
Current understanding depicts the Ang-Tie pathway as a mediator of vessel growth,
remodeling, and maturation through the regulation of EC permeability, EC–
extracellular matrix interaction, and inflammation (103). Ang-1 stabilizes vessels
and decreases vascular permeability (104), whereas Ang-2 is involved in vessel
regression and destabilization (105). Since tumor vasculature often appears as
primitive, hemorrhagic, and disorganized, it is not surprising that elevated levels of
Ang-2 have been found in several cancers and appear to be associated with a worse
prognosis (106-108). Thus, blocking of Ang-2 and targeting of the Ang–Tie system
has been of great interest in anti-angiogenic drug development.
PDGF-B and FGF-2 are angiogenic factors frequently expressed in tumors, and their
expression has been linked to cancer progression and metastasis (109-111). PDGF-
B is involved in the recruitment of pericytes and vascular smooth muscle cells, while
FGF-2 displays a broad spectrum of biological functions. Several studies have
reported FGF-2 and PDGF-B to have angiogenic synergism (112-114). Interestingly,
murine models have shown that whereas FGF-2 and PDGF-B work in concert to
promote vessel maturation and stabilization under physiological conditions, tumor
vasculature induced by these factors is primitive and disorganized (114). It has been
suggested that this might represent cross-communication with VEGF, counteracting
the recruitment activity of PDGF-B (115), but the specific molecular mechanisms by
which this happens remain to be elucidated.
As depicted in Figure 2, tumor neovascularization is a process induced by multiple
individual growth factors, cytokines, and their complex interactions. While not all of
27
these interactions are thoroughly understood, increasing understanding of the
underlying mechanisms of neovascularization has led to the development of several
novel anti-angiogenic therapies.
Figure 2. Regulation of tumor angiogenesis (Adapted from Cao et al. Regulation of
tumor angiogenesis and metastasis by FGF and PDGF signaling pathways, Journal
of Molecular Medicine, 2008) (115).
2.2 Tumor Microenvironment
The tumor microenvironment is a complex mixture of proliferating tumor cells,
tumor stroma, blood vessels, tumor infiltrating immune cells, and a variety of
associated tissue cells. The tumor controls this unique environment through
molecular and cellular events taking place in the surrounding tissues, thus promoting
28
tumor survival, progression, and metastasis. As a tumor progresses, the changes seen
in the tumor microenvironment resemble those seen in the process of chronic
inflammation. This ‘host reaction to the tumor’ is thought to be strongly involved in
shaping the tumor microenvironment. Although the initial goal of this response is to
wipe out the tumor, like any invader, evidence suggests that the tumor actively
downregulates these anti-tumor responses through a variety of strategies. (116, 117)
Major components of the tumor microenvironment are tumor-infiltrating
lymphocytes (TILs). TILs include B lymphocytes and T lymphocytes, which can be
further categorized into CD4+ T-cells, CD8+ cytotoxic T cells, and natural killer
(NK) cells. CD4+ T-cells differentiate into CD4+ helper and regulatory T-cells (T-
reg). While CD4+ helper T-cells coordinate immune responses through the secretion
of various cytokines, regulatory T-cells serve to limit adaptive immune responses by
cytokine- and cell-contact-dependent mechanisms. CD8+ T-cells normally function
to promote the lysis of infected, neoplastic, or otherwise targeted host cells. For T-
cells to work effectively, the antigen/antigens must be presented to T-lymphocytes
by major histocompatibility complex (MHC) molecules on the surface of an antigen-
presenting cell. NK cells, on the other hand, do not require prior antigen exposure
and they display lytic activity against cells that do not express MHC molecules. Thus,
they form an important safeguard against neoplastic cells lacking the means to
present self-antigens to T-lymphocytes. (118-120)
Other immune cells present in tumors include dendritic cells (DCs), macrophages,
and myeloid suppressor cells (MDSCs). The main function of DCs is to process and
present antigens to T-lymphocytes. Macrophages present in tumors are known as
tumor-associated macrophages (TAMs). Under physiologic conditions,
macrophages play a part in both the innate and the adaptive immune responses
through phagocytosis, antigen presentation, and the excretion of cytokines. TAMs,
however, are re-programmed to inhibit lymphocyte functions and may also possess
29
tumor growth promoting angiogenic potential (121, 122). Similarly, MDSCs are
normal host cells of bone marrow origin with immunosuppressive and angiogenic
properties. Under pathologic conditions, systemic signals result in MDSCs
prematurely leaving the bone marrow and engaging in T-cell suppression at
extramedullary locations (123, 124). Murine models have additionally demonstrated
that MDSCs may promote tumor angiogenesis (125).
2.2.1 Tumor Escape
The tumor microenvironment forms an effective barrier against immune cell
functions. The process by which a tumor escapes immune recognition,
“immunoediting”, was first described by Dunn et al. (116). Although during the early
phases, lymphocytes responsible for immune surveillance are able to recognize and
eliminate malignant cells, tumor cells begin to undergo progressive selection
favoring the proliferation of malignant cells more likely to evade immune
recognition. The cancer cells additionally directly inhibit immune cells through
overexpression of immune-checkpoint molecules (116). Several mechanisms
leading to the dysfunction of immune cells and downregulation of the immune
response by tumors have been identified. The four main pathways by which this
happens are interference with the induction of anti-tumor responses, inadequate
effector cell function, insufficient recognition signals, and the development of
immunoresistance by the tumor (118). Given the pivotal role of immunoediting and
chronic inflammation in tumorigenesis, it is not surprising that research suggests that
the amount and presence of different immune cell populations in the tumor
microenvironment is associated with the disease course.
As described earlier, effector T-cells are responsible for the lysis of neoplastic cells.
The developing microenvironment of a growing tumor, however, prevents these
30
immunological reactions by secreting suppressive factors expressing inhibitory
molecules and by attracting T-regs, TAMs, and MDSCs (126-128). T-regs
downregulate the proliferation and anti-tumor functions of both CD4+ and CD8+
effector T-lymphocytes (129, 130). This is achieved by the expression of various
molecules on T-regs, such as CTLA-4, preventing T-cell activation and DC functions
(131, 132). Activated T-regs also induce cell death in effector T-cells by a granzyme-
dependent pathway (133). The proliferation of T-regs is induced by interleukin-10
(IL-10), - , and prostaglandin E2, all of which
are abundant in the tumor microenvironment (134). Indeed, T-regs appear to be
present in higher numbers in more aggressive and advanced cancers (135). In RCC,
higher levels of T-regs are correlated with a poorer prognosis (136-139). Similarly,
TAMs inhibit cytotoxic T-cell responses through several mechanisms. For example,
they produce IL-10, which in turn induces the expression of programmed death
ligand 1 (PD-L1). PD-L1 is a ligand for an inhibitory cell-surface receptor,
programmed death 1 (PD-1), which negatively regulates T-cell activation (140).
TAMs additionally promote tumor-associated angiogenesis and tumor cell invasion
(141, 142). In ccRCC, higher numbers of TAMs are associated with a poor prognosis
(143-145). In pRCC, however, an opposite relationship was noted (146).
MDSCs are another immunosuppressive immune cell population that, like T-regs
and TAMs, promote tumor evasion by suppressing T-cell immunity. Among other
effects, MDSCs deplete nutrients necessary for appropriate T-cell function, block
MHC-bound peptides from being presented to T-cells, and induce the conversion of
naive T-cells to a T-reg phenotype (147-149). Research also implicates MDSCs as a
part of STAT3-VEGF-dependent angiogenesis (125). In RCC, elevated levels of
MDSCs are associated with a poor prognosis (150).
31
Growing knowledge and understanding of immune responses and immune cells as a
part of tumorigenesis and tumor progression have sparked interest in the
development of several promising immunotherapeutic agents, such as vaccines, T-
cell modulators, and immune checkpoint inhibitors.
2.2.2 Tumor Heterogeneity
Previous work on solid cancers has demonstrated that extensive genetic
heterogeneity exists among individual tumors (151-153). This intratumor
heterogeneity may contribute to treatment failure and drug resistance (154-156), and
can manifest as spatial heterogeneity, with an uneven distribution of genetically
diverse tumor subpopulations, and temporal heterogeneity, with dynamic variations
in the genetic diversity of an individual tumor over time.
Gerlinger et al. investigated gene-expression signatures in RCC from spatially
separated tumor samples obtained from primary renal carcinomas and associated
metastatic sites. Up to 69% of all somatic mutations were not detectable across every
tumor region. Additionally, gene-expression signatures of both a good and poor
prognosis were detected in different regions of the same tumor. (157) As
personalized medicine approaches traditionally rely on single tumor biopsy samples,
intratumor heterogeneity may have significant consequences regarding
antineoplastic treatment strategies. It is thought that tumor heterogeneity underlies
resistance to therapy, thus complicating the selection of globally effective therapies
(158). These observations emphasize the importance of multidimensional
therapeutic approaches, as well as the need to develop ways to dynamically monitor
patients during treatment.
32
3 Diagnosis and Management of Renal Cell Carcinoma
Since renal cell carcinoma often develops without any early warning signs, a rather
high proportion of patients present with metastases at diagnosis. The most common
clinical presentations of the disease are hematuria, abdominal pain, and a palpable
mass in the flank or abdomen. This “classic triad”, however, is only present in less
than 10% of patients (159). Research has shown that in 25–30% of patients, the
disease has metastasized at the time of diagnosis (160, 161)
While surgical resection remains the golden standard of care for localized RCCs,
relapse occurs in almost a third of the patients (162).
3.1 Diagnosis
The widespread application of computed tomography (CT) and ultrasonography for
other indications has led to the increased detection of RCCs. More than 50% of RCCs
are detected incidentally (163). RCC is sometimes referred to as the “internist’s
cancer”, since in addition to the “classic triad” of symptoms, the disease is sometimes
accompanied by hypercalcemia, unexplained fever, erythrocytosis, and Stauffer’s
syndrome (signs of cholestasis unrelated to tumor infiltration of the liver or intrinsic
liver disease, which typically resolves after kidney tumor resection). Laboratory
examinations of serum creatinine, hemoglobin, leukocyte and platelet counts, lactate
dehydrogenase (LDH), C-reactive protein (CRP), and albumin-corrected calcium are
recommended if suspicion of RCC arises. (164)
33
3.1.1 Imaging
RCC is usually suspected based on ultrasonography findings, prompting further
imaging with computed tomography. According to the ESMO 2016 guidelines,
contrast-enhanced chest, abdominal, and pelvic CT are mandatory for accurate
staging. The use of bone scanning and CT or magnetic resonance imaging (MRI) of
the brain is recommended only when suggestive clinical symptoms or laboratory
findings are present and not as a part of routine clinical practice. (164)
In most cases, a single examination using CT provides sufficient information for
accurate staging and surgical planning with no need for additional imaging (165).
MRI is mainly an alternative in patients requiring further imaging and in cases of
allergies, pregnancy, or surveillance. MRI is also an excellent alternative in patients
with renal insufficiency. Positron emission tomography/computed tomography
(PET/CT) scanning is not a standard investigation in the diagnosis and staging of
RCC and its use is not recommended. (164)
3.1.2 Staging and Pathology Assessment
For staging, the tumor-node-metastasis (TNM) staging system should be used. The
TNM staging system is presented in Table 1.
A core needle biopsy is recommended before treatment with ablative therapies, as
well as in patients with an unresectable solid renal mass and metastatic disease before
starting systemic therapy. It is additionally practical in patients with significant
comorbidities where the risk of biopsy is outweighed by the risk of surgery. (166,
167). The role of core needle biopsy is less clear regarding solitary renal masses
<4 cm in diameter, as the probability of malignancy within a small renal mass has
been found to be inversely related to tumor size (168). In a study by Halverson et al.
34
in which patients underwent both percutaneous renal mass biopsy and subsequent
partial or radical nephrectomy, there was complete concordance between the
histology rendered from core needle biopsy and that rendered by surgery (169).
Given that core needle biopsy has a high sensitivity and specificity in confirming the
histopathological nature of a tumor, it probably has a role when deciding on the
active surveillance and operative management of a small renal mass (167). A
specimen provided by nephrectomy, however, provides the final histopathological
diagnosis, classification, and grading. Regarding the assessment of pathology, the
ESMO 2016 guidelines recommend reporting the tumor histological subtype, the
International Society of Urological Pathology (ISUP) nucleolar grading, sarcomatoid
and/or rhabdoid differentiation, the presence of necrosis and presence of microscopic
vascular invasion, in addition to TNM staging in routine clinical practice. (164)
35
Table 1. TNM classification System
Primary Tumor (T)
TX Primary tumor cannot be assessedT0 No evidence of primary tumorT1 limited to the kidney
T1aT1b
T2 Tumor >7.0 cm in greatest dimension, limited to the kidney
T2aT2b
T3 Tumor extends into major veins or perinephric tissues but not into the ipsilateral adrenal gland and not beyond Gerota’s fascia
T3a
T3b
T3c
T4 Tumor invades beyond Gerota’s fascia (including contiguous extension into the ipsilateral adrenal gland)
Tumor
Tumor >10 cm, limited to the kidney
Tumor grossly extends into the renal vein or its segmental (muscle containing) branches, or tumor invades perirenal and/or renal sinus fat (peripelvic) but not beyond Gerota’s fascia
Tumor grossly extends into the vena cava below the diaphragm
Tumor grossly extends into the vena cava above the diaphragm or invades the wall of the vena cava
Regional Lymph Nodes (N)
NXN0 N1 N2
Regional lymph nodes cannot be assessed No regional lymph node metastasisMetastasis in regional lymph node(s)More than one lymph node involved
Distant Metastases (M)
cM0 cM1 pM1
Clinically no distant metastasisClinically distant metastasisPathologically proven distant metastasis, e.g. needle biopsy
Anatomic stage/prognostic groups
Stage IStage IIStage III
Stage IV
T1T2T1-2 T3T4Any
N0N0N1AnyAnyAny
M0M0M0M0M0M1
Adapted from the AJCC Cancer Staging Handbook, 7th edition (2010)
36
3.1.3 Small Renal Masses
An increasingly common and problematic clinical entity encountered by clinicians
is small renal masses (SRMs) less than 4 cm in size. Based on epidemiological
studies, SRMs account for nearly one-half of all newly diagnosed renal masses (170).
Therapeutic options regarding SRMs include extirpative surgery (radical or partial
nephrectomy), ablative therapies, and active surveillance (AS). Kutikov et al.
demonstrated that 20–30% of solitary renal masses presumed to be renal cell
carcinoma on preoperative imaging had benign pathologic findings on resection
(171). Furthermore, in those lesions that are RCC, the majority of tumors are of low
grade and unlikely to develop metastases (172, 173).
There are no definitive clinical guidelines for the management of SRMs. General
recommendations for patient selection favoring AS include increased age, decreased
life expectancy, suitability for surgery, and a decreased risk of metastatic disease.
Several studies have shown that the risk of metastatic progression while on AS is
<2%, making it a viable alternative to surgery (174-178). The main trigger for
operative intervention, on the other hand, is believed to be the tumor growth rate
(GR). Evidence regarding tumor GR is somewhat controversial, as some studies have
demonstrated a high GR among AS patients developing metastases (174, 179), while
a number of studies have demonstrated a low or zero GR for tumors of malignant
pathology (176). Although the malignant potential of an SRM is likely to be defined
by several important factors, progression to metastatic disease is exceptionally low
in tumors that demonstrate a low or zero GR and remain <3 cm in diameter (177,
180).
In a multi-institutional prospective clinical trial investigating AS for the management
of SMRs, the authors demonstrated non-inferiority of AS versus primary
intervention. At five years, cancer-specific survival was 99% and 100% for primary
intervention and AS, respectively, suggesting that AS should become part of the
37
standard discussion for the management of SRMs (180). An informed decision by
the patient and physician, including a discussion of the small but real risk of cancer
progression, loss of a window of opportunity for nephron-sparing surgery, and the
lack of curative treatments for mRCC should, however, precede any treatment
decisions.
3.2 Prognostication
For localized RCC, several multivariable models such as the University of California
Los Angeles Integrated Staging System (UISS) and SSIGN score have been
developed to predict cancer-specific survival after nephrectomy. In these models,
prognostication is mainly based on clinical and histological parameters of the tumor,
as presented in Table 2. Although these models can predict the natural history of
RCC, they are not designed to account for the effect of targeted therapies in patients
with mRCC. (181, 182)
Table 2. The UISS and SSIGN prognostic models assessing postoperative cancer-
specific mortality
Model Sample Size Target population
Predictors C-index
UISS 661 RCC of all stages
- AJCC- Fuhrman grade - ECOG-PS
82–86%
SSIGN 1801 Localized clear cell RCC
- TNM (1997)- Tumor size - Nuclear grade - Tumor necrosis
81–82%
AJCC = American Joint Committee on Cancer; ECOG-PS = Eastern Cooperative Oncology Group
Performance Status
38
Prognostication of metastatic RCC relies heavily on traditional markers of risk. In
addition to pathological and histological staging and grading, several clinical and
biological prognostic markers have been identified.
While it seems that age, gender, and race are not associated with the disease course
or outcome, the association between the performance status and prognosis is well
established. Two different scales are used to evaluate the patient’s performance
status, namely the Eastern Cooperative Oncology Group (ECOG) scale and
Karnofsky performance status (KPS) score; both have demonstrated good reliability
and validity.
Other validated prognostic factors for mRCC include serum hemoglobin lower than
the lower limit of normal, platelets greater than the upper limit of normal, serum
LDH greater than the upper limit of normal, corrected serum calcium greater than
the upper limit of normal, neutrophils greater than the upper limit of normal, and a
time from diagnosis to treatment of <1 year.
The two most widely used prognostic models combining these markers into risk
classifications are the Memorial Sloan Kettering Cancer Center (MSKCC) risk
classification (183) and the International Metastatic Renal Cell Carcinoma Database
Consortium (IMDC) risk classification (184). These models apply exclusively to
patients with mRCC.
In addition to the more traditional markers of risk, gene signatures are known to
detect risk groups in RCC (185, 186). However, in current clinical practice, no
specific molecular markers can be recommended for routine use, as is discussed later.
39
3.3 Treatment
Surgical resection of the tumor is the golden standard of care in localized RCC.
While radical nephrectomy remains the most frequently used alternative, several
novel minimally invasive techniques have emerged, mostly for the treatment of T1
tumors. Current evidence suggests that localized RCCs are best managed by partial
nephrectomy (or nephron-sparing surgery) when feasible. The role of techniques
such as radio-frequency ablation and cryoablation remains to be further elucidated.
Disease recurrence occurs in 20–30% of patients after partial or radical nephrectomy.
(187). The role of adjuvant therapies in localized RCC is discussed separately in
conjunction with systemic therapies.
The treatment of mRCC mainly relies on systemic therapy, with surgery having a
less well-defined role than in localized disease.
3.3.1 Cytoreductive Nephrectomy
A combined analysis by Flanigan et al. investigating the role of cytoreductive
nephrectomy in the era of immunotherapy demonstrated that nephrectomy plus
interferon was associated with longer median overall survival than interferon alone
(13.6 vs. 7.8 months), representing a 31% decrease in the risk of death (P = 0.002)
(188). Since then, cytoreductive nephrectomy has been a part of routine clinical
practice in patients with a good performance status and large primary tumors with
limited volumes of metastatic disease. It is not recommended, however, in patients
with a poor performance status (164).
A recent phase III multicenter trial, CARMENA, investigated the role of
nephrectomy among 450 mRCC patients with an intermediate or poor MSKCC risk
classification. In this study, patients were randomized to undergo nephrectomy and
then receive sunitinib or receive sunitinib alone. The median OS was 18.4 months in
40
the sunitinib-alone group and 13.9 months in the nephrectomy–sunitinib group,
demonstrating the non-inferiority of sunitinib alone versus nephrectomy followed by
sunitinib. (189) However, as Motzer and Russo pointed out in their editorial, the
interpretation of the results from the CARMENA trial is complicated by several
factors, including slow enrollment, a lack of information regarding the selection
factors for performing nephrectomy, and the high percentage of poor-risk patients
(43%). As such, these data should emphasize the importance of patient selection
rather than lead to the abandonment of nephrectomy. (190)
3.3.2 Metastasectomy and Other Local Therapeutic Options for Metastases
Common metastatic sites in RCC include the lung, bone, liver, and brain, but
metastases can be found at any anatomical site (191, 192). In addition to traditional
surgery, options for local therapy of metastases include whole brain radiotherapy,
conventional radiotherapy, stereotactic radiosurgery, stereotactic body
radiotherapy, cyberknife radiotherapy, and hypofractionated radiotherapy.
The benefits of metastasectomy as well as other local therapeutic options have been
under fervent discussion, with no general guidelines existing as to whether a patient
should be referred for local treatment of metastases. Dabestani et al. systematically
reviewed the potential benefits of these treatments, suggesting a survival benefit
with complete metastasectomy vs. either incomplete or no metastasectomy (193).
Complete metastasectomy was additionally associated with improved symptom
control. The role of other local therapies is less clear, with consensus mainly
existing on the palliative benefits in selected patients.
41
The ESMO 2016 guidelines do not recommend metastasectomy or other local
therapies in routine practice, but rather after multidisciplinary review for selected
patients (164).
3.3.3 Systemic Therapy
The low response rates of mRCC to classical cytotoxic agents such as
fluoropyrimidines or vinblastine, as well as hormonal treatment and radiotherapy,
has inevitably led to the development of alternative therapies. As ccRCC is
considered an immunogenic tumor, immunotherapy comprised the first wave of
systemic therapies. As our knowledge of the underlying mechanisms and the
pathogenesis of RCC has evolved, an array of new therapies has emerged from anti-
VEGF and tyrosine kinase inhibition to modern approaches of dual inhibition and
immune checkpoint inhibition.
3.3.3.1 Immunotherapy
Before the advent of targeted therapies, systemic therapy of mRCC comprised
interferon- -2 (IL-2).
IL-2 is a cytokine that enhances the proliferation and function of T-cell lymphocytes.
Although high-dose IL-2 therapy only produces modest response rates of 15–16% in
the treatment of mRCC, the responses that do occur seem durable (194).
Additionally, research has shown that approximately 5–7% of patients achieve
complete remission (195, 196). High-dose IL-2 treatment, however, is associated
with severe adverse events, such as capillary leak syndrome, resembling the clinical
manifestations of septic shock and producing major morbidity in patients receiving
42
IL-2 (197). The significant toxicity profile has limited the use of high-dose IL-2 in
the treatment of mRCC.
, and antiproliferative
activities. the treatment of
mRCC patients (198, 199). Negrier et al. demonstrated that the
and low-dose IL-2 resulted in prolonged progression-free survival and improved
response rates as compared to either agent alone (200). No benefit was seen
regarding overall survival, however.
Current guidelines suggest high-dose IL-
bevacizumab as alternatives for the standard options among patients with a good or
intermediate prognosis (164). In the era of targeted therapies, however, the role and
use of these agents has become limited.
3.3.3.2 Tyrosine Kinase Inhibitors (TKIs)
The introduction of molecularly targeted therapies revolutionized the treatment of
advanced RCC. Inhibition of the VEGF pathway has emerged as the primary
therapeutic intervention for most patients with advanced disease. TKIs are multi-
targeted kinase inhibitors that inhibit signaling in a variety of key receptors such as
VEGFR-1, -2, and -3, PDGFR- - -RET, macrophage colony-stimulating
factor 1 (CSF-1R), FMS-like tyrosine kinase 3 receptor (FLT3), and c-KIT (201).
Among the first TKIs approved for the treatment of mRCC were sunitinib and
sorafenib. Sorafenib was approved after showing prolonged PFS and increased ORR
when compared to placebo among mRCC patients resistant to standard therapy
(202). It is currently recommended as one of several options in the second line
setting. Sunitinib is the current standard of care for treatment-naïve mRCC patients
43
after having demonstrated significantly longer median PFS and a higher objective
(201).
Pazopanib, a second-generation TKI, has since been approved after demonstrating
non-inferiority when compared to sunitinib, providing yet another option for first-
line therapy (203).
Other current second-line options include axitinib, a highly potent and selective
inhibitor of the various kinase domains of VEGFRs, distinguishing it from previous
multi-targeted TKIs. In a phase III randomized clinical trial (AXIS), axitinib
demonstrated an improved PFS when compared to sorafenib in the second-line
treatment setting (204). Axitinib has also demonstrated clinical activity and safety in
the first-line setting, but no significant PFS benefit was noted in a phase III
randomized comparison between axitinib and sorafenib (205).
3.3.3.3 mTOR Inhibitors
The mammalian target of rapamycin (mTOR) is a downstream effector of the PI3-
K/Akt pathway that exerts its biological functions as two distinct complexes,
mTORC1 and mTORC2, as depicted earlier (206, 207). Everolimus and
temsirolimus are allosteric inhibitors of mTOR that have shown clinical activity in
mRCC. A phase III trial enrolling 626 mRCC patients with poor prognostic features
comparing temsirolimus, IFN , and the combination of both showed that
temsirolimus alone resulted in longer OS and PFS in this patient population (208).
Based on these results, temsirolimus is still recommended as a first-line therapy for
poor-risk mRCC patients (164). However, the use of temsirolimus in first-line
treatment for patients with a poor risk classification has been called into question by
44
the results from the RECORD-3 trial, in which sunitinib showed superiority over
everolimus, even among patients with a poor prognosis (209).
Everolimus is the standard of care for mRCC patients whose disease progresses after
initial anti-VEGFR therapy. It was approved based on the results from Renal Cell
Cancer Treatment With Oral RAD001 Given Daily (RECORD-1), a phase III trial,
in which everolimus exhibited superior efficacy to placebo (210).
Although mTOR inhibitors initially showed promising clinical activity in mRCC,
their effects have proven to be far from durable and only a subset of patients
experience significant clinical benefit from these agents.
The cellular action mechanisms of TKIs and mTOR inhibitors are depicted in Figure
3.
45
Figure 3. Cellular action mechanisms of TKIs and mTOR inhibitors (Adapted from
Rini et al. Resistance to targeted therapy in renal-cell carcinoma, Lancet 2009)
(211).
3.3.3.4 Novel Approaches
Most patients treated with either VEGF- or mTOR-targeted therapy ultimately
develop resistance to these drugs. Whether, and more importantly how, this
phenomenon is embedded in the intricate network of complex regulation and
feedback loops these different pathways possess remains to be elucidated. With first-
line sunitinib or pazopanib, the median PFS ranges from 8 to 11 months for all
46
patients (201, 203). Additionally, only a few treatments have shown a survival
benefit in previously treated mRCC patients.
The upregulation of prometastatic MET and AXL as a result of VHL protein
dysfunction in ccRCC has been implicated as a potential mediator of resistance to
anti-VEGFR therapy (212). Cabozantinib is an inhibitor of tyrosine kinases,
including MET, VEGFR, and AXL (213). In the fairly recent phase III METEOR
trial comparing cabozantinib and everolimus among patients who progressed after
initial VEGFR tyrosine-kinase treatment, cabozantinib became the first agent to
show a survival benefit in all efficacy endpoints (PFS, OS, and ORR) in previously
treated mRCC patients (214). Given the dim estimations of a PFS of only 5.6 months
for first-line VEGFR-targeted therapy among patients with an intermediate–poor risk
classification (215), cabozantinib has also been investigated in the first-line setting
in the CABOSUN trial. In this setting, cabozantinib significantly increased median
PFS (8.2 vs. 5.6 months) and was additionally associated with an increased reduction
in the rate of progression when compared to sunitinib (216). As these results
demonstrate a possible role for this dual inhibitor in the first-line setting,
cabozantinib is currently recommended as an alternative after prior to anti-VEGFR
therapy (164).
As Dunn et al. described, one of the mechanisms by which tumors evade host
immune reactions is by directly inhibiting immune cells through overexpression of
immune checkpoint molecules (116). Recently, immune checkpoint blockade has
become a new avenue of immunotherapy in several tumor types.
PD-L1 is a ligand for an inhibitory cell-surface receptor, programmed death 1 (PD-
1), which negatively regulates T-cell activation (140). In RCC, studies have shown
that PD-L1 expression is associated with a poor prognosis (217-219). Nivolumab is
a PD-1 immune checkpoint inhibitor that selectively blocks the interaction between
PD-1 and PD-1L. Nivolumab has shown consistent efficacy in the treatment of
47
advanced melanoma, both alone and in combination with a CTLA-4 inhibitor,
ipilimumab (220). In addition to melanoma, nivolumab has been approved for the
treatment of squamous and nonsquamous non-small-cell lung cancer and Hodgkin’s
disease (221, 222). In advanced RCC, nivolumab has been granted approval based
on the results from a phase III study comparing nivolumab with everolimus in
previously treated mRCC patients. In this setting, nivolumab was associated with
improved OS and ORR, as well as fewer grade 3 or 4 adverse events (223).
According to the ESMO 2016 guidelines, nivolumab is currently recommended in
second- and third-line settings, depending on the patient’s risk classification (164).
Several ongoing studies are evaluating nivolumab as well as other anti-PD-1/PD-L1
agents in combination with VEGF-targeted therapies.
3.3.3.5 Optimal Sequencing
With several new agents available for the treatment of advanced RCC, sequencing
of these therapies is of great relevance. The treatment strategies according to the
ESMO 2016 guidelines portrayed in Figure 4 are on the verge of a major change.
When choosing the first-line treatment, the options have traditionally been relatively
limited. Current data support the use of VEGF tyrosine kinase therapies, sunitinib
and pazopanib, in this setting. In some institutions, high-dose interleukin-2 therapy
is used for young and healthy patients with a good performance status, as it is the
only therapy associated with durable long-term responses (194-196). Recent data
from the CheckMate-214 and CABOSUN studies are, however, likely to change the
first-line treatment paradigm of mRCC. Results from the phase II CABOSUN trial
showed a median PFS of 8.6 months compared with 5.3 months for previously
untreated mRCC patients taking cabozantinib or sunitinib, respectively (P = 0.0008)
48
(224). Similarly, results from the CheckMate-214 study demonstrated significantly
improved OS with combination immunotherapy (nivolumab+ipililumab) in
comparison to standard of care sunitinib among 1082 treatment-naïve mRCC
patients (225).
Optimal sequencing of drug classes beyond first-line treatment remains unknown.
Options include TKIs, cabozantinib, nivolumab, and the combination of lenvatinib,
a receptor tyrosine kinase inhibitor, and everolimus. In the AXIS trial, which led to
the approval of axitinib, patients with first-line immunotherapy demonstrated a
median PFS of 12 months on axitinib as compared to 6.5 months on sorafenib,
supporting its use among patients with prior immunotherapy (226). As approval for
the combination of lenvatinib and everolimus was based on a phase II study with
only 100 patients (227) as compared to the large randomized phase III trials
investigating cabozantinib and nivolumab (214, 228), the level of evidence favors
the use of cabozantinib and nivolumab among post-TKI patients. Ongoing trials
investigating these agents alone and/or in combinations in first- and later-line
settings may change current clinical guidelines regarding the optimal sequencing of
varied therapies.
49
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3.3.3.6 Adjuvant Therapy in RCC
The only curative treatment for patients with stage I–III RCC is surgery. However,
the 5-year relapse rates after surgical treatment in patients with stage II–III disease
are 30% to 40% (162). With an increasing variety of novel agents now available for
systemic therapy, the search for effective adjuvant therapy, i.e. systemic therapy
following surgery, in underway. The first positive phase III adjuvant therapy trial (S-
TRAC) investigated the use of sunitinib in an adjuvant setting. With disease-free
survival (DFS) as the primary endpoint, the study demonstrated a median DFS
duration of 6.8 years versus 5.6 years in the placebo arm. However, the largest trial
to date, the ASSURE trial, in which nearly 2000 patients with completely resected
RCC were randomly assigned to sunitinib, sorafenib, or placebo, showed no
significant differences between treatment arms in DFS or OS (229). Important
differences between S-TRAC and ASSURE are that whereas S-TRAC recruited
patients with pT3-4 disease, ASSURE also enrolled patients with PT1b and pT2
disease. Additionally, S-TRAC only included patients with ccRCC histology, and
the final dropout rates due to treatment toxicity were significantly different in the
two studies. A recent phase III trial, PROTECT, evaluated the efficacy and safety of
pazopanib versus placebo among 1538 post-nephrectomy patients with localized or
locally advanced RCC. Results from the primary analyses of DFS showed no benefit
of pazopanib over placebo (230). However, a relationship between dose exposure
and an improved clinical outcome was noted, suggesting that patients achieving
higher levels of pazopanib exposure derived more clinical benefit from the treatment
(231).
As promising as the results from S-TRAC are, there are several questions
surrounding these findings. It has been hypothesized that adjuvant sunitinib may
simply delay the time to recurrence with no effect on the cure rate. In addition,
51
concern over possible resistance to therapies for metastatic RCC after adjuvant
sunitinib has been raised. (232)
Specific reasons as to why sunitinib improved DFS in S-TRAC and not in ASSURE
remain unanswered. Similarly, it has been hypothesized that the lack of significant
benefit in the PROTECT trial could be due to the lack of efficacy with 600 mg
pazopanib. In addition to the three completed adjuvant trials, there are several
ongoing phase III trials (depicted in Table 3), which may shed light on these
questions. A closer examination of whether the differences between trials reflect
differences in patient selection, dosing, and/or study design is of critical importance.
Several trials investigating immunotherapy agents in an adjuvant setting are
additionally recruiting patients. These trials are targeting high-risk populations, and
they include PROSPER investigating nivolumab, IMMotion investigating
atezolizumab, KEYNOTE investigating pembrolizumab, and CheckMate
investigating the combination of nivolumab and ipilimumab. As these agents have
shown impressive clinical activity in patients with mRCC, the results are eagerly
anticipated.
52
Table 3. Completed and ongoing adjuvant trials
Trial Randomization TreatmentDetails
N Inclusion Criteria(Stage/Grade)
Inclusion Criteria (Histology)
Results
ASSURE Sorafenib
Sunitinib
400 mg daily, 54 wks
37.5 mg daily (4 wks on/2 wks off), 54 wks
1943 pT1b N0 M0 (grade 3-4)pT2-pT4 N0 M0pT (any) N1 M0
Any histology
DFS:HR 0.97 (CI 0.80-1.17) vs. placebo
DFS:HR 1.02 (CI 0.85-1.23) vs. placebo
S-TRAC Sunitinib 50 mg daily (4 wks on/2 wks off), 9 cycles
615 pT3 N0 M0 (grades 2-4)pT4 N0 M0pT (any) N1 M0
ccRCC only
DFS:HR 0.76 (CI 0.59-0.98; P = 0.03)
PROTECT Pazopanib 600 mg daily, 1 year
1500 pT2 N0 M0 (grades 3-4)pT3-4 N0 M0pT (any) N1 M0
ccRCC only
DFS:HR 0.86 (CI 0.70-1.06)
ATLAS Axitinib 5 mg twice per day, 3 years
724 pT2 N0 M0pT (any) N1 M0
ccRCC only
Ongoing
SORCE Sorafenib 400 mg daily, 1 year or 400 mg daily, 3 years
1420 pT1a N0 M0 (grade 4)pT1b N0 M0 (grades 3-4) pT2-4 N0 M0pT1b-4 N1 M0
Any histology
Ongoing
EVEREST Everolimus 10 mg daily
1545 pT1b N0 M0 (grades 3-4)pT2-4 N0 M0pT (any) N1 M0
Any histology
Ongoing
DFS = disease-free survival
53
4 Biomarkers in mRCC
The term ‘biomarker’ refers to a broad subcategory of medical signs, which can be
measured accurately and reproducibly. They stand in contrast to medical symptoms
perceived by patients themselves and are by definition objective, quantifiable
characteristics of biological processes and are commonly used in basic and clinical
research. Examples of biomarkers include everything from blood pressure to more
complex tests of blood and other tissues. (233)
The ever-growing understanding of the biology and the molecular mechanisms
underlying the pathogenesis of RCC have offered novel means to predict tumor
behavior. Indeed, since the discovery of the inactivation of the VHL gene in the
majority of ccRCCs, several new and promising tissue- and blood-based biomarkers
have been identified. The complexity of the molecular pathways and the
heterogeneous nature and wide diversity of RCCs at the molecular level have,
however, made the incorporation of these biomarkers into clinical practice
challenging. Presently, the prognostication of RCC relies heavily on clinical factors
and, even more importantly, despite molecularly-targeted therapies comprising the
foundation of treatment strategies in mRCC, treatment decisions are solely based on
clinical factors. Current biomarkers mainly provide information regarding the
outcome independent of treatment, and no validated predictive biomarkers are
available. The search for predictive biomarkers identifying patients for more
personalized treatment of advanced RCC is rigorous and ongoing.
54
4.1 Prognostic and Predictive Biomarkers
Prognostic biomarkers are biomarkers used to identify the likelihood of a clinical
event such as disease progression or recurrence, whereas predictive biomarkers
identify individuals more likely to experience a favorable or unfavorable effect from
exposure to a medical product or agent than similar individuals without the
biomarker. As discussed earlier, the current prediction of a patient’s clinical outcome
mainly relies on clinical and pathological variables in both localized and metastatic
RCC; these variables, however, poorly reflect the individual tumor biology seen in
RCC. Biomarkers improving prognostication are urgently needed to allow for more
accurate prognostic models.
Most of the identified biomarkers in RCC are directly associated with the VHL
defect. The VHL protein (pVHL) regulates hypoxia inducible factor- -
which is a transcription factor inducing the transcription of several hypoxia-regulated
genes (234). Under normal oxygen tension, pVHL binds to HIF-
subsequently destroyed by the cell through ubiquitinization. With genetic alterations
rendering the VHL gene inactive, however, the loss of pVHL leads to dysregulation
of this cascade, resulting in overexpression of various angiogenic proteins. (235)
Enhanced understanding of this molecular pathway played a key role in identifying
new approaches in drug development for mRCC and has since also led to the
discovery of several potential biomarkers. Several molecular markers have indeed
been investigated, but so far none have proven reliable in predicting the treatment
outcome. Their use is therefore not recommended in routine clinical practice.
However, the success of targeted therapies relies on appropriate patient selection to
identify patients likely to respond to treatment and to avoid unnecessary toxicity.
55
4.1.1 Clinical-related Biomarkers
Motzer et al. were the first to investigate pretreatment clinical features and survival.
In a study among 670 mRCC patients, a low Karnofsky performance status, high
serum lactate dehydrogenase, low hemoglobin, high corrected calcium, and a time
from diagnosis to treatment of <1 year were identified to associate with shorter
survival. Based on the presence or absence of these risk factors, a risk classification,
the Memorial Sloan Kettering Cancer Center (MSKCC) prognostic model, was
constructed, categorizing patients into favorable, intermediate, and poor risk groups
for which the median survival times were separated by 6 months or more. In the
favorable risk group with zero risk factors, the median time to death was 20 months.
In the intermediate and poor risk groups, median survival was 10 and 4 months,
respectively. (236)
Later, in 2009, Heng et al. validated the use of components of the MSKCC prognostic
model in a landmark retrospective study among 645 mRCC patients treated with
VEGF pathway inhibitors. In addition to the previously described risk factors, Heng
et al. demonstrated that high pretreatment platelet and neutrophil counts were
associated with shorter survival. Based on these findings, a prognostic model, the
International Metastatic Renal Cell Carcinoma Database Consortium (IMDC), was
constructed, demonstrating a median OS of 27 and 8.8 months in intermediate and
poor-risk patients, respectively. Among patients with a favorable risk profile, the
median OS was not reached, and the 2-year OS was 75%. (237) These results were
later externally validated among 1028 mRCC patients treated with sunitinib,
sorafenib, pazopanib, bevacizumab, or axitinib (184).
The MSKCC and IMDC prognostic models are still used for the prognostication of
mRCC patients, as well as the stratification of patients for clinical trials. As these
models have mainly been used in the era of cytokines and anti-VEGF therapies, their
56
role with modern immunotherapy agents remains to be elucidated. The MSKCC and
IMDC prognostic models are depicted in Table 4.
The further search for new predictive and prognostic markers has additionally
provided preliminary evidence of hyponatremia as a prognostic factor for mRCC
patients. Jeppesen et al. were the first to demonstrate that baseline sodium levels
below the normal range were associated with shorter survival among mRCC patients
treated with IL-2 and IFN- (238). Since then, several studies have shown a similar
association between hyponatremia and a poor outcome among mRCC patients
treated with tyrosine kinase inhibitors (239-241). Sodium values are not, however,
routinely used in clinical practice to improve the prognostication of mRCC patients.
57
Table 4. Comparison between the MSKCC and the IMDC Prognostic Risk
Criteria for RCC
MSKCC Criteria IMDC CriteriaRisk factorsKarnofsky performance status < 80%
x x
Time from diagnosis to treatment < 1 year
x x
xHemoglobin level below LLN
x x
Corrected serum calcium level above ULN
x x
Platelet count above ULN xNeutrophil count above ULN
x
Entry Population CriteriaPatients with metastatic RCC
Treated with interferon alfa as initial systemic therapy
Treated with first-line TKI therapy
Distribution of Risk Groups0 criteria (favorable) 168 pts (25%) 133 pts (21%)1–2 criteria (intermediate) 355 pts (53%) 301 pts (47%)
ia (poor) 147 pts (22%) 152 pts (32%)Median OS, by Risk Group0 criteria (favorable) 20.0 mo Not reached1–2 criteria (intermediate) 10.0 mo 27.0 mo
4.0 mo 8.8 mo LDH = lactate dehydrogenase; LLN = lower limit of normal; ULN = upper limit of normal; pts =
patients; mo = months
58
4.1.2 Vascular Endothelial-derived Growth Factor
The VEGF family comprises five different mammalian ligands: VEGF-A, VEGF-B,
VEGF-C, VEGF-D, and placenta growth factor (PLGF). These growth factors
function as regulators of angiogenesis and lymphangiogenesis, and they bind to three
different but structurally related receptor tyrosine kinases: VEGFR-1, VEGFR-2,
and VEGFR-3 (242).
In RCC, where angiogenesis is an essential event of tumorigenesis, upregulation of
VEGF due to the loss of pVHL is well documented (243, 244). VEGF-A, which is
the most widely studied member of the family, has been shown to promote
tumorigenesis through a variety of ways. It acts on tumor endothelial cells to increase
their proliferation, migration, and permeability and it inhibits vessel maturation
(245). An immunomodulatory role has also been suggested (246). Since many tumor
cells express VEGFRs, VEGF-A may also possess more direct effects in supporting
tumor growth and invasion (247).
Previous research has demonstrated that a low baseline concentration of VEGF-A is
prognostic for overall survival in mRCC in univariate analyses (248, 249). High
serum VEGF-A levels have additionally been associated with the tumor stage and
grade and a poor prognosis (250). Rini et al. reported that low VEGF-C and soluble
VEGFR-3 concentrations were associated with longer PFS and higher response rates
in bevacizumab-refractory patients treated with sunitinib (251). These results are
supported by a phase III multicenter trial demonstrating an association with low
baseline levels of VEGF-C and prolonged PFS among a total of 750 mRCC patients
(248). Additionally, soluble VEGFR-3 concentrations correlated with the outcome
among patients treated with sunitinib, but not among patients on the
59
arm, suggesting a possible role for soluble VEGFR-3 in predicting sunitinib
treatment efficacy (248).
Evidence regarding soluble VEGFR-2 as a biomarker remains controversial.
Gruenwald et al. noticed decreased soluble VEGFR-2 concentrations during
sunitinib treatment, but this kinetic modulation was insufficient to predict the tumor
response (252). On the other hand, Terakawa et al. showed that increased VEGFR-2
expression in a specimen obtained from radical nephrectomy correlated significantly
with longer PFS (253).
4.1.3 Carbonic Anhydrase IX
Carbonic anhydrase IX (CAIX) is a HIF-1a-regulated, transmembrane protein that
regulates intracellular pH in response to hypoxia. In ccRCC, CAIX is overexpressed
due to inactivation of the VHL gene product, resulting in overexpression of HIF-1a,
a transcription factor for CAIX (25).
CAIX is present in more than 80% of primary and metastatic RCCs, whereas it is
detected in only 9% of normal kidneys (254). Several studies have reported increased
CAIX expression as an independent predictor of longer disease-specific survival in
mRCC (255-257), but recent studies have been unable to confirm this finding (186,
258). Considering that 94–100% of ccRCCs stain positively for CAIX (254, 257), it
may be helpful in establishing a diagnosis, but its value as a biomarker improving
prognostication or predicting the treatment response is unclear.
60
4.1.4 Cytokine and Angiogenic Factors
Several cytokines as well as other proteins of the angiogenic cascade have been
evaluated as biomarkers for the response in RCC. A recent retrospective analysis of
phase II and phase III trials evaluated 17 different cytokine and angiogenic factors
(CAFs) among patients with mRCC treated with pazopanib, identifying seven
promising biomarkers: interleukin 6 (IL-6), interleukin 8 (IL-8), VEGF, osteopontin,
E-selectin, hepatocyte growth factor (HGF), and tissue inhibitor of
metalloproteinases (TIMP)-1. In the study, low concentrations of these factors
correlated with increased tumor shrinkage, PFS, or both. IL-6, IL-8, and osteopontin
were additionally shown to be stronger prognostic markers than any single clinical
classification when stratified by the ECOG performance status, MSKCC risk group,
or HENG risk group. Based on the results, a CAF signature was built demonstrating
a significantly shorter PFS and OS for pazopanib-treated patients in the high CAF
group (259).
Another study investigating CAFs in sorafenib-treated mRCC patients identified six
baseline markers that significantly correlated with PFS. Higher concentrations of
osteopontin, CAIX, VEGF, collagen IV (ColIV), and soluble VEGFR-2 were
associated with shorter PFS, while the opposite relationship was seen for tumor
necrosis factor-related apoptosis-inducing ligand (TRAIL). In a dichotomized CAF
signature for these six biomarkers, ‘signature-negative’ patients demonstrated
improved PFS with a hazard ratio of 0.2 (P = 0.00002). Furthermore, a significant
interaction was noted between the CAF signature and treatment arm (sorafenib alone
or sorafenib + , suggesting a possible predictive role for this biomarker panel
(260).
61
While several studies have yielded promising results for CAFs as prognostic, and
even predictive, biomarkers in mRCC, some issues have limited their clinical
usefulness. First, CAFs have mainly been investigated among mRCC patients treated
with TKIs. Little or no data exist regarding CAFs in mRCC patients treated with
other therapies such as mTOR inhibitors. Given the significant number of therapies
available for mRCC, prospective studies comparing CAFs between different
treatment arms are needed. Second, methodological problems regarding
measurement and cut-off values for these biomarkers exist, limiting their more
widespread application in clinical settings.
4.1.5 Single Nucleotide Polymorphisms
Single nucleotide polymorphisms (SNPs) are variations occurring in a single
nucleotide at a specific position in the genome. They are common mutations that can
alter the function of genes in any chromosome. Several studies have investigated the
possible association of various SNPs and the outcome in mRCC. The main focus has
been on identifying prognostic and predictive SNPs for VEGF-targeted therapy.
Two studies investigating SNPs involved in the pharmacokinetic and
pharmacodynamic pathways of sunitinib suggested that polymorphisms in VEGFR1,
VEGFR3, CYP3A5, NR1/3, and ABCB1 might be able to define a subset of patients
with a decreased sunitinib response and tolerance (261, 262). Additionally, three
separate studies investigated two SNPs of VEGFR1 in sunitinib-treated patients,
demonstrating a favorable association between these mutations and the outcome
(263-265). However, a recent meta-analysis by Liu et al. was unable to verify this
association, thus questioning their clinical use as biomarkers of the sunitinib
response (266).
62
Escudier et al. investigated 15 different SNPs among patients in the phase III AXIS
trial, a study comparing axitinib and sorafenib in a second-line setting. Although the
authors were able to identify a polymorphism in VEGFR2 predicting improved OS
and PFS for sorafenib-treated patients, the authors concluded that
sensitivity/specificity limitations preclude its use for selecting patients for sorafenib
treatment (267).
For pazopanib-treated patients, three polymorphisms in IL-8 and HIF-1a and five
polymorphisms in HIF-1a, NR1/2, and VEGF-A were shown to be significantly
associated with PFS and the response rate in a study by Xu et al. (268). The same
authors pursued these suggestive associations among patients from the phase III trial
COMPARZ comparing pazopanib and sunitinib, and from an observational study of
sunitinib-treated patients. In a combined analysis, IL-8 polymorphisms were
associated with shorter OS in both pazopanib- and sunitinib-treated mRCC patients,
raising the possibility that IL-8 polymorphisms may be associated with the outcome,
irrespective of the treatment (269).
4.1.6 Immune Markers
Several clinical and laboratory factors have been investigated and identified as being
prognostic and predictive for mRCC patients treated -2. These
include the performance status, clear cell histology, MSKCC risk classification, C-
reactive protein (CRP), neutrophil levels, and the number of metastatic sites. For
modern immunotherapy agents, the search for predictive and prognostic biomarkers
is ongoing and some preliminary results have already shown promise.
63
PD-1 is an immune checkpoint molecule expressed in activated T and B cells. It
binds to two ligands, PD-L1 and PD-L2. The interaction of PD-1 and PD-L1 leads
to immune suppression through negative regulation of activated T cell effector
functions. In RCC, PD-L1 is overexpressed in 30% of tumors and correlates with a
more advanced tumor stage, higher Fuhrman grade, sarcomatoid differentiation, and
poor survival (270, 271). With the emerging role of anti-PD-1/PD-L1 agents in the
treatment of mRCC, PD-L1 expression is currently being investigated as a potential
predictive biomarker for patients treated with these agents. A phase I trial using a
5% cut-off value for PD-L1 expression among previously treated mRCC patients
reported higher response rates for nivolumab among PD-L1-positive patients as
compared to PD-L1-negative patients (22% vs. 8%) (219). Research has revealed
similar associations among patients with metastatic melanoma and non-small-cell
lung cancer (272). In the recent phase III CheckMate 214 study comparing the
combination of nivolumab and ipilimumab with sunitinib, the ORR significantly
favored the combination over sunitinib in intermediate/poor-risk patients with
baseline PD-L1 expression of (58% vs. 25%; P = 0.0002) (225). Additionally,
in a phase I study of atezolizumab, an anti-PD-L1 agent, exploratory subanalyses
demonstrated that upregulation of PD-L1 in on-treatment biopsies as compared to
baseline expression was associated with an improved response rate, suggesting a
possible role as an on-treatment predictive biomarker (273).
Several issues, however, limit the use of PD-L1 expression as a predictive biomarker.
First and foremost, it fails to identify all responders and not all PD-L1-positive
tumors respond to treatment. Furthermore, there are currently no standardized cut-
off points to define the positivity of a sample.
Another promising immune-related biomarker is tumor T-cell infiltration. In a phase
I study investigating nivolumab, baseline tumor CD3+ and CD8+ T-cell infiltrates
correlated with a decrease in the tumor burden (272). A prospectively designed
64
exploratory analysis from the S-TRAC trial also identified tumor CD8+ T-cell
density as predicting longer DFS for sunitinib but not placebo (274).
Other biomarkers under investigation include PD-L2 expression, the presence of
CD20+ B-cells, gene expression profiling, and the T-cell receptor repertoire, but
none of them have so far been validated.
4.1.7 Mechanism-based Adverse Events
Current understanding depicts treatment-related toxicity events as on-target effects
resulting from the inhibition of a given pathway. It therefore stands to reason that
these on-target effects are associated with treatment efficacy and could serve as
surrogate markers of the pharmacodynamic effect. Research has indeed shown that
a class-specific adverse event of VEGFR inhibitors, hypertension (HTN), associates
with an improved outcome. Following the preliminary results of Bono et al.,
demonstrating a favorable association of bevacizumab-induced HTN and the
outcome (275), a phase III trial investigating the combination of bevacizumab and
interferon- vs. interferon- later confirmed these findings. In the study,
patients on combination therapy who developed grade 2 HTN had significantly
longer PFS (13.2 vs. 8.0 months; P = 0.001) and OS (41.6 vs. 16.2 months;
P = 0.001) as compared to patients who did not develop HTN (276). A retrospective
analysis among 544 mRCC patients by Rini et al. revealed a similar association of
sunitinib-induced HTN and the outcome (277). Supporting evidence exists for
axitinib (278), sorafenib (226), and tivozanib (279).
65
In addition to hypertension, several other class-specific adverse events have been
examined as potential biomarkers of anti-VEGFR treatment efficacy. Donskov et al.
examined hand-foot syndrome, fatigue, neutropenia, and thrombocytopenia among
sunitinib-treated mRCC patients. In the analyses, neutropenia was significantly
associated with longer PFS and OS, and hand-foot syndrome with longer OS (280).
Similarly, Rautiola et al. demonstrated that in addition to HTN, neutropenia and
thrombocytopenia were predictors of improved treatment efficacy. A biomarker
profile categorizing patients into favorable, intermediate, and poor risk profiles
according to the presence of these AEs was constructed, demonstrating a clear
difference in OS to the benefit of patients with all three AEs vs. patients with none
of the AEs (OS; not reached vs. 5.3 months; P < 0.001) (281). Evidence regarding
treatment-induced hypothyroidism is less clear. While many smaller trials have
suggested a possible association of hypothyroidism and the treatment outcome, a
large meta-analysis comprising several retrospective and prospective trials found no
significant association of acquired hypothyroidism and the outcome for mRCC
patients treated with sunitinib (282).
For mTOR inhibitors, previous research has shown that non-infectious pneumonitis,
a class effect of mTOR inhibitors, may associate with an improved outcome. In the
largest study to date, Atkinson et al. demonstrated that pneumonitis independently
predicted improved OS (HR 0.32; P < 0.001) for mRCC patients treated with
everolimus. Other class-specific AEs, including serum cholesterol, triglyceride, and
glucose levels, have also been investigated. In a phase III trial among intermediate
and poor-risk temsirolimus-treated mRCC patients, increases in on-treatment serum
cholesterol levels above baseline values were associated with longer PFS (HR 0.81,
P < 0.0001) and OS (HR 0.77, P < 0.0001). However, no such association was noted
for hypertriglyceridemia or hyperglycemia (283).
66
4.2 Incorporation of Biomarkers into Clinical Practice and Future Directions
Despite the widespread use of targeted therapies in mRCC, reliable predictive
biomarkers are still lacking, and the accepted biomarkers are mainly prognostic and
clinical-related. Even though mechanism-based toxicities, such as hypertension,
seem to serve as surrogate markers of treatment efficacy, they do not help in patient
selection. As discussed earlier, several promising results exist regarding modern
immunotherapy agents. However, most of the published evidence remains at an
immature stage with inconsistencies for many of the investigated biomarkers. In
addition to problems arising from the lack of cut-off points and standardization,
tumor heterogeneity and the use of archival tissue samples remain unresolved issues.
Promising avenues that have recently emerged are the sampling of circulating tumor
DNA (ctDNA) and microRNA (miRNA) profiling. ctDNA is obtained from
peripheral blood and is therefore non-invasive and allows for dynamic evaluation of
tumor genomics and the response to treatment. A few studies have already shown
that ctDNA may be a useful tool in monitoring the tumor burden and response to
treatment (284, 285). Similarly, miRNA from blood plasma is easily obtained and
recent studies have demonstrated that specific miRNA signatures may predict the
disease outcome and recurrence in ccRCC (286-288).
With the renaissance of immunotherapy agents in the treatment of mRCC, the
integration and optimization of these therapies into new treatment algorithms is of
vital importance. While decades of research have demonstrated the immunological
and molecular heterogeneity of RCC, novel technologies such as real-time PCR,
microarrays, and next-generation sequencing are bringing us closer to identifying
robust and reliable prognostic and predictive biomarkers, allowing for a molecularly
stratified biomarker-guided treatment approach.
67
AIMS OF THE STUDY
The aim of the study was to discover markers predicting treatment efficacy for
targeted therapies used in the treatment of mRCC and to gain knowledge of those
deeper factors through which we could enhance treatment efficacy.
Specific aims of the study were:
1) To identify clinical prognostic and predictive biomarkers for mRCC patients
treated with targeted therapy;
2) To identify molecular prognostic and predictive biomarkers for mRCC
patients treated with targeted therapy;
3) To investigate the mechanism-based treatment-related adverse events of
targeted therapies and their correlation with clinical efficacy.
68
PATIENTS AND METHODS
1. Patients and Treatment
The patient cohorts are described in detail in the original publications I–IV. The first
study included 303 consecutive mRCC patients treated with a first-line anti-VEGF
agent, either sunitinib or pazopanib, at the Comprehensive Cancer Center, Helsinki
University Hospital, between October 18, 2006 and December 31, 2014. None of the
patients had received prior TKI therapy. Eighteen patients (5.9%) had received prior
interferon alfa. Both sunitinib and pazopanib were administered according to
standard care until disease progression or unacceptable toxicity. Altogether, 23%
(N = 59) of the patients started sunitinib with intermittent dosing (4 weeks on-
treatment, 2 weeks off-treatment) and 77% (N = 208) with continuous dosing.
Pazopanib was administered continuously.
The second study comprised mRCC patients treated with first-line sunitinib at the
Comprehensive Cancer Center, Helsinki University Hospital, between October 18,
2006 and May 31, 2012. We were able to identify 137 patients for whom sufficient
clinical data and sufficient histologic specimens were available.
Studies III and IV were performed in collaboration with Aarhus University Hospital,
Denmark. We identified a total of 85 patients who were treated with everolimus after
prior anti-VEGFR therapy failure at the Comprehensive Cancer Center, Helsinki
University Hospital, between October 18, 2006 and December 31, 2014. A similar
cohort comprising 148 patients treated between January 25, 2010 and June 6, 2016
at Aarhus University Hospital, Denmark, was collected. In both cohorts, everolimus
was administered according to standard care until disease progression or
unacceptable toxicity.
69
1.1 Assessment of the Tumor Response and Adverse Events
In all studies, the response to treatment was assessed by computed tomography at 8-
to 12-week intervals. Treatment efficacy was reported according to the Response
Evaluation Criteria in Solid Tumors (RECIST) versions 1.0 and 1.1. Adverse events
were captured every 4–6 weeks and were graded according to the Common
Terminology Criteria for Adverse Events (CTCAE) versions 3.0 and 4.0.
1.2 Assessment of Pneumonitis
To correctly assess whether a patient developed pneumonitis during everolimus
treatment, medical files, including CT scan reports, were retrospectively reviewed
for pneumonitis. The radiographic studies were subjected to a blinded review by an
experienced radiologist for findings indicative of pneumonitis. A more detailed
description is provided in the original publication (III). Figure 5 depicts typical
radiographic findings indicative of pneumonitis.
Figure 5. CT scans at the onset of pneumonitis showing peripheral, bilateral ground
glass opacities.
70
1.3 Immunohistochemistry
In the second study, the expression of c-Met was analyzed from formalin-fixed
paraffin-embedded samples by using an anti-c-Met rabbit monoclonal ready-to-use
antibody by Roche, and BenchMark XT immunostainer by Ventana Medical
Systems. The staining was carried out according to the manufacturer’s protocol. c-
Met staining was scored by two independent evaluators with good concordance
(kappa value 0.7) according to intensity. A 4-tier system was used with 0 as no
staining, 1+ weak, 2+ strong, and 3+ as very strong staining intensity. The c-Met
immunohistochemistry score was also determined by assessing the proportion of
stained cells as the intensity of the staining. Negative (0): fewer than 50% of tumor
cells with membrane and/or cytoplasmic
tumor cells with weak or higher membrane and/or cytoplasmic staining but <50% of
tumor cells with a
cells with moderate or strong membrane and/or cytoplasmic staining but <50% of
tumor cells with a
with a strong membrane and/or cytoplasmic staining intensity. Figure 6 displays
examples of each category.
Figure 6. Examples of negative, moderate, strong, and very strong staining (A–D)
of anti-c-Met antibody with 400x magnification. (Reprinted from Peltola KJ et al.
Correlation of c-Met Expression and Outcome in Patients With Renal Cell
Carcinoma Treated With Sunitinib. Clin Genitourin Cancer. 2017 Aug;15(4):487-
494 with the permission of Elsevier)
71
2. Statistical Analysis
All the statistical analyses were performed using IBM SPSS Statistics for Windows
(versions 22.0–24.0, Armonk, NY, USA, IBM Corp.). Tests used to assess the
association of various clinicopathological factors were the Mann-Whitney U-test for
continuous data and the chi-squared test for categorical data. In all original
publications, OS was defined as the time from treatment initiation to death from any
cause, and (ii) PFS as the time from treatment initiation to the first event (tumor
progression or death from any cause). The Kaplan-Meyer method was used to
estimate the median survival times with 95% confidence intervals for both OS and
PFS, censoring the patients who were alive or had no disease progression at the last
follow-up visit.
Uni- and multivariate Cox proportional hazard models were used to assess the
association of OS and PFS with various clinicopathological factors. Interactions
between the factors of interest were tested with inclusion of the interaction terms in
the models. The proportional hazards assumptions were assessed graphically,
obtaining plots of (log(-logS(t)) versus time and Schoenfeld residuals versus time.
Uni- and multivariate logistic regression models were used to investigate the effects
of various clinicopathological factors and categorical end-point data. The results are
expressed as odds ratios (ORs) with 95% CI.
To control for time bias when investigating potentially time-biased parameters, time-
dependent Cox regression models and landmark survival analyses were applied.
According to the landmark method, PFS and OS were defined as the time from the
landmark to progression or death from any cause.
All statistical tests were two-sided and P-values of <0.05 were considered as
statistically significant.
72
3. Ethical Considerations
The Hospital District of Helsinki and Uusimaa (HUS) accepted the research plan
§36/28.4.2014 and the Ethics Committee of the Department of Surgery of HUS
accepted the research plan §190/23.10.2013. The National Authority for Welfare and
Health licensed the use of pathological archive material.
73
RESULTS AND DISCUSSION
Ever since the introduction of targeted therapies in the treatment of metastatic RCC
in the mid-2000s, the search for reliable biomarkers has been of great interest. Even
though there are presently no such biomarkers that could identify patients for specific
treatments, the ever-growing knowledge of the intricate molecular and immunogenic
mechanisms of tumor biology in RCC has brought us closer to the moment of finding
clinically applicable predictive biomarkers. This is even more important now that the
first-line options to treat advanced RCC are on the verge of a major change.
1. The use of angiotensin system inhibitors (ASIs) in the treatment
of TKI-induced hypertension (I)
Hypertension (HTN) is a class effect of VEGF receptor tyrosine kinases and it has
been reported as an adverse event of all agents in this class (289-292). This class
effect also seems to function as a surrogate marker of treatment efficacy in patients
with mRCC (277, 278, 281, 293). As antihypertensive medication, angiotensin
system inhibitors have been of interest in the treatment of TKI-induced HTN, as
xenograft models have demonstrated that ASIs may possess antiangiogenic potential
(294, 295)
We investigated treatment-induced hypertension and the use of ASIs among 303
consecutive mRCC patients treated with a first-line anti-VEGFR agent. Of these 303
patients, 197 (65%) patients had baseline hypertension and 126 (64%) of these
74
patients had ASI as the baseline antihypertensive medication, either as monotherapy
or in combination with other antihypertensive medication. Treatment-induced HTN,
defined as a recurrent, persistent, or symptomatic rise in diastolic BP of >20 mmHg
or a systolic BP rise to >150 mmHg, if previously within the normal range (CTCAE
, developed among 110 patients, of whom the majority were ASI
users (82.7%).
We found no difference in the outcome between patients with and without baseline
HTN (OS: 20.3 months (95%CI 16.4–24.2 months) vs. 20.1 months (95%CI 15.5–
24.7 months); PFS: 8.2 months (95%CI 6.6–9.7 months) vs. 8.2 months (95%CI 5.4–
11.0 months); P = 0.54 and P = 0.72, respectively). Similarly, we found no
significant differences in outcomes between patients with and without baseline ASI
use. On the other hand, patients who developed HTN during treatment had
significantly longer OS (unadjusted HR 0.41, 95%CI 0.30–0.55, P < 0.001) and PFS
(HR 0.43, 95%CI 0.33–0.56, P < 0.001). The same was true for patients receiving
ASI as a new drug or dose escalation (N = 91) when compared to the remaining
patients (OS: 37.7 months (95%CI 26.0–49.5) vs. 16.5 months (95%CI 12.9–20.2),
HR 0.38, 95%CI 0.26–0.54, P < 0.001; PFS: 16.7 months (95%CI 6.1–27.2) vs. 6.6
months (95%CI 5.8–7.5), HR 0.41, 95% CI 0.30–0.57, P < 0.001).
Due to the strong multicollinearity of HTN and ASI use, they could not be fitted in
the same multivariate analyses. We therefore conducted subgroup analyses among
patients with TKI-induced HTN. In these analyses, ASI users (N = 91) had longer
OS (37.5 vs. 18.1 months; P = 0.001) and PFS (17.1 vs. 7.2 months; P = 0.004) than
ASI nonusers. These results were supported by multivariate analyses conducted in
the same subset of patients, demonstrating superior OS (adjusted HR, 0.35; 95%CI
75
0.18–0.66; P = 0.001) and PFS (adjusted HR, 0.42; 95%CI 0.23–0.77; P = 0.005)
for ASI users when adjusted for the baseline HENG risk classification.
Regarding TKI-induced HTN, our results are well in line with previously published
findings. In our patient cohort, treatment-induced hypertension was detected in
36.3% of patients. The previously reported incidence of TKI-induced all-grade HTN
has varied between 17% and 49.6% (296). Several studies have depicted TKI-
induced HTN as a predictive biomarker of treatment efficacy (278, 280, 281, 293,
297, 298). We found a similar association in our data, with TKI-induced HTN having
a strong association with prolonged OS and PFS, even when corrected for the
possible time bias from longer treatment. Due to disproportionate group sizes,
patients treated with sunitinib (88%) and pazopanib (22%) were not separately
analyzed.
Several retrospective studies have investigated the use of ASIs in cancer patients. In
a meta-analysis comparing ASIs in a variety of cancer patients, their use was
associated with a significant improvement in DFS and OS. Benefits were noted
among patients with urinary tract cancer, colorectal cancer, pancreatic cancer, and
prostate cancer (299). In RCC, a few studies have reported a significant association
between ASI use and an improved clinical outcome among mRCC patients receiving
VEGF-targeted therapy. All of the previous studies have investigated ASI use at
baseline or within 30 days of therapy initiation (300-302). The largest study to date,
investigating this association among 4736 patients, demonstrated a significant
improvement in OS (HR 0.84, P = 0.0105) for ASI users vs. users of other
antihypertensive medication. Additionally, this association was more prominent
among patients on VEGF-targeted therapy (302). Contradicting evidence also exists,
however, as Sorich et al. recently noted no difference in the outcome for baseline
ASI users vs. nonusers among 1545 mRCC patients treated with sunitinib and
76
pazopanib. In this study, however, for patients treated with sunitinib there was also
a trend towards improved survival and baseline ASI use. (303) There are no obvious
differences between these studies explaining the contrasting results, but classifying
baseline ASI use within the first 30 days of VEGF-targeted therapy may contribute
to the disparity, as TKI-induced HTN, a validated mechanism-based adverse event,
often manifests within the first weeks of therapy. Furthermore, the concomitant use
of other antihypertensive medication was in most studies unaccounted for.
To the best of our knowledge, our study is the first to investigate ASI use in the
treatment of TKI-induced HTN. Within this subset of patients, we demonstrated a
significant association between the use of ASIs and an improved outcome. There are
currently no guidelines for cancer patients suffering from HTN as a result VEGF-
targeted therapies. The pathogenesis of TKI-induced hypertension is also not
completely understood. Key hypotheses include the inhibition of endothelial nitric
oxide (NO) synthase, increased vascular stiffness, inhibition of the renin-angiotensin
system, and a decrease in the density of microvessels leading to an increase in
systemic vascular resistance (304-307). Regarding the role of the renin-angiotensin
system in cancer development, preclinical data have shown that angiotensin II may
promote cancer development, as it is involved in the regulation of apoptotic
mechanisms and angiogenesis through the up-regulation of VEGF expression and
neovascularization (308, 309). It additionally facilitates cellular growth and
proliferation through several growth factors (310). In RCC, angiotensin II has been
demonstrated to correlate with tumor aggressiveness and decreased survival (311).
In murine models, ASIs have been shown to prevent the development of metastatic
lung nodules, as well as potentiate the antiangiogenic effects of sunitinib (294, 295).
77
McKay et al. hypothesized that the improved outcome noted among ASI users could
represent a synergistic interaction of TKIs and ASIs (302). The mechanisms by
which this happens are yet to be elucidated, but it has been suggested that ASIs may
reduce sarcopenia (312). Earlier research has shown that sarcopenia combined with
a body mass index of <25 kg m 2 predicts sunitinib-induced early dose-limiting
toxicities and sorafenib-induced dose-limiting toxicities in mRCC patients (313,
314). The use of ASIs could thus improve the therapeutic index of VEGF-targeted
therapies, resulting in improved efficacy.
Our results support the hypothesis of possible synergistic interactions between TKIs
and ASIs. Based on our study, conclusions on whether the possible underlying
interactions are the result of combinatory effects on angiogenesis, tumor cell
proliferation, reduced blood flow to the tumor, or improvement in the therapeutic
index cannot be drawn. In the absence of published guidelines for the treatment of
HTN in this setting, ASIs do, however, present an interesting option as
antihypertensive medication, as they might offer a survival benefit as well as feasible
management of TKI-induced HTN in patients with mRCC. We do recognize that for
these results to be properly validated, further studies, preferably in a randomized
prospective setting, are warranted.
2. c-Met expression is associated with the outcome among mRCC
patients treated with sunitinib (II)
c-Met, a receptor tyrosine kinase, is involved in cell growth/differentiation,
neovascularization, and tissue repair in normal tissues. Interfering and directly
78
activating mutations leading to dysregulation of c-Met and its ligand, hepatocyte
growth factor (HGF), have been implicated in tumor development, invasion, and
angiogenesis in several malignancies (83-85). In RCC, both clear cell and papillary
subtypes have demonstrated excessive activation of the c-Met/HGF pathway (315,
316). Furthermore, overexpression of c-Met is associated with poor pathologic
features and prognosis in RCC (317). In our study, we sought to analyze the possible
association of c-Met expression and the outcome among mRCC patients treated with
first-line sunitinib.
c-Met expression was analyzed from 137 formalin-fixed paraffin-embedded tumor
samples collected at diagnosis. The samples consisted of resected primary RCC
tumors (N = 134) and core needle biopsies (N = 3) originally retrieved for diagnostic
purposes. Clinical data were collected, including patient characteristics, treatments,
adverse events, hospitalizations, and outcomes. c-Met expression was divided into
two groups, with low expression consisting of expression levels 0 to 1 and high
expression consisting of levels 2 and 3.
Among the 137 patients, 78 (56.9%) patients had low c-Met expression and 59
(43.1%) high c-Met expression. Patients with low c-Met were significantly older
than patients with high c-Met (P = 0.007). Additionally, patients with low c-Met
were significantly more likely to belong to the favorable HENG criteria risk group
(P < 0.001) and have a clear cell histology (P = 0.024).
In survival analyses, patients with low c-Met expression had significantly longer PFS
(14.3 months vs. 6.5 months; P <0.001) and OS (32.1 months vs. 20.1; P = 0.049)
than patients with high c-Met expression. In multivariate analyses, low c-MET
79
expression remained significantly associated with improved PFS when adjusted for
the HENG risk classification (adjusted HR 0.61; P = 0.016). For OS, we found no
statistically significant association (adjusted HR 0.75; P = 0.34). In a logistic
regression model investigating c-Met expression and the response to treatment,
patients with high c-Met expression were more than four times more likely to have
PD as their best response to sunitinib therapy (unadjusted OR 4.19; P = 0.004).
Based on previous hypotheses regarding an association between c-Met and the
presence of bone metastases, we conducted further subgroup analyses among
patients with (N = 31) and without (N = 106) bone metastases present at the onset of
treatment. Even though the level of c-Met expression and presence of bone
metastases were not significantly associated with each other (P = 0.47), in survival
analyses, low c-Met expression was associated with improved OS (unadjusted HR
0.63; P = 0.034) and PFS (unadjusted HR 0.47; P < 0.001) in patients without bone
metastases, but not among patients with bone metastases.
To the best of our knowledge, this is the first study to establish an association
between c-Met expression and the outcome among patients treated with first-line
anti-VEGFR therapy. Our results are similar to previously published data in the sense
that higher c-Met expression was associated with a poor prognosis and response rate.
We hypothesize that c-Met expression could thus serve as a biomarker defining
patients who are more likely to benefit from alternative therapy.
Regarding the reasons why c-Met expression was associated with PFS but not OS in
multivariate analyses, we hypothesize that this difference may be associated with the
availability of active therapies beyond first-line sunitinib. Additionally, the number
80
of registered events was higher when using PFS as the endpoint (129 events for PFS
vs. 113 events for OS), possibly leading to a lack of statistical power in OS analysis.
In the oncology literature, there is a general trend for the HR for OS to be closer to
1.00 than the HR for PFS. Due to the longer median duration of OS, a greater number
of events is required to reach statistical significance. (318)
Preclinical evidence suggests that inhibition of VEGF activity leads to the induction
of compensatory mechanisms upregulating growth factors and cytokines. The
developing resistance is thought to be largely indirect, with angiogenic tumors
adapting to the presence of angiogenesis inhibitors. While the specific therapeutic
target remains inhibited, several adaptive mechanisms, including the activation of
alternative pro-angiogenic pathways, recruitment of bone marrow-derived
proangiogenic-cells, and increased pericyte coverage of the tumor vasculature, lead
to tumor evasion and ultimately resistance to antiangiogenic therapy (319). In a
xenograft study investigating sunitinib efficacy, the investigators demonstrated
increased levels of HGF in the stroma of sunitinib-resistant tumors, suggesting that
HGF-stimulated c-Met signaling may enable tumor cells to bypass the desired effects
of anti-VEGFR therapy (320). Regulation of c-Met activity during anti-VEGF
therapy is illustrated in Figure 7. Given the possible role of the c-Met/HGF pathway
in the development of resistance to anti-VEGFR therapy, our results stand to reason,
as patients with high c-Met expression achieved less benefit from sunitinib treatment.
Previous research in breast cancer has depicted c-Met as a mediator of the
interactions between tumor cells and the bone microenvironment, suggesting that it
is involved in the bone metastatic process (321). D’Amico et al. demonstrated that
c-Met expression on RCC stem cells drives renal cancer progression to bone, since
RCC stem cells expressing c-Met directly formed bone lesions. Furthermore, in the
same study, treatment with a selective c-Met inhibitor hindered the development of
81
bone metastases, indicating that HGF/c-Met signaling is relevant in the metastatic
process induced by RCC stem cells (322). These findings are supported by clinical
data from the METEOR phase III trial, in which patients with bone metastases
treated with the dual inhibitor cabozantinib had a 46% risk reduction for OS and 67%
risk reduction for PFS when compared to patients treated with everolimus.
Additionally, the objective response rate in bone scans was 17% vs 0%, for
cabozantinib and everolimus, respectively (323). In our analyses, we found that in
the subgroup of patients without bone metastases, low c-Met expression predicted
an improved outcome. The implications of this finding are presently unclear, but it
could be hypothesized that low c-Met expression among patients without bone
metastases represents a subset of patients in whom resistance to antiangiogenic
therapy has not yet developed or is less likely to develop.
In the METEOR phase III trial comparing cabozantinib and everolimus among
mRCC patients after previous VEGFR tyrosine-kinase treatment, the authors
additionally investigated MET expression levels and the treatment outcome. While
the study did demonstrate relevant clinical activity for cabozantinib vs. everolimus,
it did not provide evidence for a significant relationship between the treatment
modality, c-Met expression, and the outcome. However, in as many as 41.5% of
patients, c-Met expression could not be evaluated. Additionally, archival tumor
tissue rather than fresh biopsies was used in the analyses (324). A recent phase II
trial investigating crizotinib, a small-molecule TKI inhibiting MET, anaplastic
lymphoma kinase (ALK), and ROS proto-oncogene 1 receptor tyrosine kinase
(ROS1), demonstrated that crizotinib induced long-lasting disease control in
metastatic papillary renal cell carcinoma type 1 with MET mutation and long-term
stable disease in a case with MET amplification (325). These results are of special
interest now that the CABOSUN trial has demonstrated that cabozantinib may have
82
a role as first-line therapy and has indeed already been approved by the Food and
Drug Administration (FDA) to be used as such in the United States (326). Our
results, along with the growing evidence implicating the c-Met/HGF pathway as one
of the key regulators of tumorigenesis and angiogenic escape, warrant further studies
investigating c-Met as biomarker among patients treated with dual c-Met/VEGFR
inhibitors.
83 Fi
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84
3. Everolimus-induced pneumonitis predicts the treatment outcome
among mRCC patients treated with everolimus (III)
Several studies have depicted the treatment-related adverse events associated with
targeted therapies as possible predictive markers of treatment efficacy. Treatment-
induced hypertension was one of the first biomarkers shown to be favorably
associated with the efficacy of sunitinib treatment (277). Since then, multiple
different adverse events have been associated with the outcome. In addition to
hypertension, Donskov et al. demonstrated that on-treatment neutropenia was
significantly associated with longer OS and PFS and hand-foot syndrome with OS
(280). Similar results were recorded by Rautiola et al., who demonstrated that the
development of hypertension, neutropenia, and thrombocytopenia was significantly
associated with OS and PFS (281). For mTOR inhibitors, such on-treatment
biomarkers predicting treatment efficacy do not presently exist. We sought to
investigate the possible association between everolimus-induced pneumonitis and
the treatment outcome.
Our study population comprised two patient cohorts: cohort A (N = 85) and a
validation cohort B (N = 148), which were analyzed separately. In cohort A, 29
(34.1%) patients had CT-verified pneumonitis during everolimus treatment. Eight
cases were grade 1 (27.6%), 18 cases grade 2 (62.1%), and three cases were grade 3
(10.3%). No grade 4 (life-threatening) pneumonitis was recorded. In cohort B, 29
(19.6%) patients had CT-verified pneumonitis. Among patients with pneumonitis,
the median time to onset was 2.4 months (range 0.4–7.5 months) in cohort A and 2.8
months (range 0.1–14.1 months) in cohort B.
85
In survival analyses, everolimus-induced pneumonitis was significantly associated
with OS (cohort A: 24.7 vs. 8.5 months; P < 0.001; cohort B: 12.9 vs. 6.0 months;
P = 0.02) and PFS (cohort A: 5.5 vs. 3.2 months; P = 0.002; cohort B: 6.0 vs. 2.8
months; P = 0.02) in both cohorts. Additionally, pneumonitis was associated with
an improved clinical benefit rate (CBR): 57.1% vs. 24.1% and 79.3% vs. 24.1% for
cohorts A and B, respectively. In multivariate analyses, pneumonitis remained
significantly associated with both OS (cohort A: HR, 0.22; 95%CI 0.12–0.44;
P < 0.001; cohort B: HR 0.58; 95%CI 0.36–0.94; P = 0.03) and PFS (cohort A: HR
0.37; 95%CI 0.21–0.66; P = 0.001; cohort B: HR 0.61; 95%CI 0.39–0.95; P = 0.03)
when adjusted for age, the number of prior treatment lines, and the MSKCC
classification for previously treated patients.
Pneumonitis, like all treatment-related adverse events, is dependent on the time spent
on the study treatment. To account for this possible time bias resulting from longer
treatment, we performed a multivariate analysis with pneumonitis as a time-
dependent covariate. To provide sufficient statistical power, the two patients cohorts
were combined for this analysis after it was verified that the association between
pneumonitis and OS was not significantly different in the two cohorts. In a Cox
regression model adjusted for age, gender, cohort, the number of previous treatment
lines, and the MSKCC risk classification for previously treated patients, CT-verified
pneumonitis was independently associated with longer OS (HR, 0.67; 95%CI 0.46–
0.97; P = 0.03). A landmark analysis demonstrated similar results with a significant
association between pneumonitis and improved OS (17.4 vs. 7.8 months; P = 0.01).
86
In cohort B, data had also been collected on patients who had clinical symptoms of
pneumonitis/pneumonia but no radiological evidence of either. We therefore divided
patients in cohort B into three groups: patients with pneumonitis verified by CT
(N = 29), patients with clinical symptoms of pneumonitis/pneumonia (N = 25), and
patients with no clinical or radiological signs of pneumonitis/pneumonia (N = 94).
In a subgroup analysis comparing OS, PFS, and the CBR between these groups, there
was no statistically significant difference in any of the endpoints between patients
with CT-verified pneumonitis and patients with clinical symptoms of
pneumonitis/pneumonia.
In this study, we demonstrated that everolimus-induced pneumonitis significantly
associated with all efficacy endpoints. Furthermore, we validated these findings in
an independent patient cohort. Previous work on this subject has revealed that
mTOR-inhibitor-induced pneumonitis associates with OS among mRCC patients
treated with everolimus/temsirolimus (328). Research has additionally shown that
the mean tumor shrinkage and SD according to the RECIST were significantly higher
in patients with pneumonitis (329). However, neither study controlled for bias
resulting from longer treatment, an important aspect when investigating adverse
events caused by a given treatment. We accounted for the possible time bias by
performing a landmark analysis, as well as multivariate analyses with pneumonitis
as a time-dependent covariable. Both methods demonstrated a significant association
between pneumonitis and longer OS, suggesting that our results are unlikely to be
impacted by time bias.
In our study, the incidence of CT-verified pneumonitis was 34.1% and 19.6% in
cohorts A and B, respectively. In the previous literature, the incidence of pneumonitis
has varied widely, with reported figures between 13.5% and 48.7% among mRCC
87
patients treated with mTOR inhibitors (210, 329-332). In a large meta-analysis
comprising 2233 patients with breast cancer, neuroendocrine tumors, or mRCC, the
reported pulmonary toxicity was only 10.4% (333). Prior work has, however,
demonstrated that mTOR-inhibitor-related adverse events are more common among
patients with compromised renal function (334, 335), a phenomenon more prevalent
among patients with mRCC. Another possible explanation for the discrepancy in the
reported incidence of mTOR-inhibitor-induced pneumonitis may be due to its non-
specific clinical and radiological nature, leading to inaccurate reporting of this
adverse event. Clinically, pneumonitis manifests heterogeneously and symptoms
may include fever, fatigue, coughing, and dyspnea, nonspecific signs and symptoms
that do not facilitate diagnosis. Moreover, the traditional chest X-ray may not show
any abnormalities (335). As the diagnosis of pneumonitis is difficult and is often
made by excluding other causes such as infections, it is of relevance that clinicians
recognize pneumonitis as a possible, and a rather common, adverse event of mTOR-
inhibitor treatment.
The precise pathogenesis of mTOR-inhibitor-induced pneumonitis remains unclear.
Hypothesized mechanisms include both direct toxic effects and immunological
toxicity, and the combination of both. In a study by Morelon et al. investigating
sirolimus-associated interstitial pneumonitis, patients displayed signs of
lymphocytic alveolitis in bronco-alveolar lavage fluid analyses (336). The authors
suggested that this might represent an autoimmune response to exposed cryptic
antigens, leading to lymphocytic alveolitis and interstitial pneumonitis. The rapid
response of pneumonitis to treatment withdrawal, however, is in support of more
direct toxic effects (337, 338). A dose-dependent effect has also been suggested.
Weiner et al. investigated the relationship between plasma concentration levels of
sirolimus and pneumonitis. While the risk of pneumonitis was indeed higher among
88
patients with increased levels of sirolimus, pneumonitis also occurred at relatively
low concentration levels (335). With regard to our finding of pneumonitis being
associated with a favorable outcome, no clear evidence exists on whether this
represents an on-target dose-dependent effect or whether there are more intricate
immunological pathways at work.
The management of mTOR-inhibitor-induced pneumonitis comprises treatment
withdrawal or dose reduction and corticosteroid administration, as shown by the
RECORD-1 trial, in which among the 18 patients with grade 2 pneumonitis,
complete resolution was obtained in 11 patients and 1 patient improved to grade 1 as
a result of corticosteroid therapy (10/18), dose adjustment (12/18), and/or
discontinuation (3/18) (332). Similarly, in an overview among organ transplant
patients receiving mTOR inhibitors, treatment withdrawal resulted in complete
resolution in 94.3% of pneumonitis cases (339). Even though most cases of
pneumonitis are relatively unaggressive and respond well to the therapeutic measures
described above, life-threatening lung toxicity may occasionally occur (337, 340).
Current guidelines suggest that everolimus treatment can be continued, or
temporarily interrupted, among patients with mild symptoms (grades 1 to 2). In more
severe cases of pneumonitis (grade 3), reintroduction of everolimus may also be
attempted (341, 342). While we did not investigate how patients with pneumonitis
were managed after the diagnosis of this adverse event, our results do prompt careful
consideration regarding permanent treatment withdrawal, as these patients seem to
draw clear benefit from everolimus treatment.
Our results add to the growing field of on-treatment biomarker research in mRCC.
They demonstrate pneumonitis to be a strong biomarker of everolimus efficacy. As
pneumonitis occurs relatively early after treatment initiation, with a median onset
89
after 2.4 and 2.8 months in cohorts A and B, respectively, it may also be clinically
applicable in reassuring the patient and the treating physician of clinical benefit
during therapy.
4. Hyponatremia associates with a poor outcome among mRCC
patients treated with everolimus (IV)
Hyponatremia is commonly encountered among cancer patients. Most frequently, it
occurs with small cell lung cancer (SCLC) as a result of the syndrome of
inappropriate secretion of antidiuretic hormone (SIADH) (343). It is also relatively
common among other tumor types, although the distribution of causes is different.
In mRCC, the reported incidence of hyponatremia varies between 14–25% (238-
241).
Previous research has shown that hyponatremia is a predictor of a poor outcome in
several different medical conditions, such as liver cirrhosis (344), congestive heart
failure (345), and infectious diseases such as pneumonia (346), childhood meningitis
(347), and necrotizing soft-tissue infection (348). Furthermore, several studies have
reported a similar association between hyponatremia and a poor outcome in cancer
patients (349-351).
Vasudev et al. were the first to show that hyponatremia is associated with shorter OS
and DFS among pre-nephrectomy patients in localized RCC (352). Since then,
evidence has accumulated on the possible prognostic role of hyponatremia in mRCC.
Indeed, hyponatremia has been shown to associate with a poor outcome among
mRCC patients treated with cytokines (238) and TKIs (239-241). Evidence
90
regarding the prognostic value of hyponatremias among everolimus-treated mRCC
patients is lacking, however.
The mechanisms underlying hyponatremia are not entirely understood. Of the
several mechanisms that have been suggested, an intriguing hypothesis is the
possible association of chronic inflammation and hyponatremia through elevated
interleukin-6 levels (240).
Our aim was to evaluate baseline and on-treatment sodium as prognostic biomarkers
in mRCC patients treated with everolimus. We additionally evaluated the association
between baseline sodium and baseline neutrophils or thrombocytes, markers that are
often elevated in chronic inflammation.
Our study population comprised 233 mRCC patients treated with everolimus at
Helsinki University Central Hospital, Finland (N = 85) and Aarhus University
Hospital, Denmark (N = 148). Among these 233 patients, 65 (27.9%) had sodium
below the lower limit of normal (LLN) at baseline. Within 12 weeks of treatment
initiation, 41 (18.4%) patients had sodium <LLN. Of the 65 patients with baseline
Baseline
sodium <LLN was significantly associated with elevated baseline neutrophils above
the upper limit of normal (ULN) (P = 0.002), and a similar tendency was noted for
baseline sodium <LLN and thrombocytes >ULN (P = 0.08). Additionally, baseline
sodium correlated inversely with baseline neutrophils (Spearman’s r = -0.23;
P = 0.001) and baseline thrombocytes (Spearman’s r = -0.25; P < 0.001) when
evaluated as continuous variables.
In univariate survival analyses, baseline sodium <LLN was significantly associated
with shorter OS (6.1 months vs. 10.3 months; P < 0.001) and PFS (2.8 months vs.
3.5 months; P = 0.04). Patients with on-treatment hyponatremia had significantly
91
shorter OS (5.4 months vs. 9.9 months; P < 0.001) and PFS (2.8 months vs. 4.0
months; P < 0.001), as well as a significantly worse CBR (17.5% vs. 50.0%;
P < 0.001). In multivariate analyses adjusted for the IMDC risk classification and
factors not included in the IMDC classification that were significantly associated
with the outcome in univariate analysis (at P < 0.1), baseline sodium <LLN
remained significantly associated with OS (adjusted HR 1.46; P = 0.02) and on-
treatment sodium <LLN with OS (adjusted HR 1.80; P = 0.002) and PFS (adjusted
HR 1.71; P = 0.004).
We additionally conducted subgroup analyses among patients with and without
baseline sodium <LLN and on-treatment sodium <LLN, demonstrating that shifts
from baseline sodium values <LLN to , and vice versa, were associated with
the outcome. Results from the subgroup analyses are depicted in Table 5.
Since baseline neutrophil and sodium values were inversely correlated, we
additionally analyzed the association between baseline hyponatremia and the
outcome among patients with (N = 40) and without baseline neutrophilia (N = 193).
Among patients with normal baseline neutrophil values, baseline sodium <LLN was
associated with shorter OS (6.9 vs. 11.4 months; unadjusted HR 1.96; P < 0.001).
Among patients with baseline neutrophils >ULN, baseline sodium <LLN was not
predictive of shorter OS, but survival was equally modest in both groups (5.2 vs. 4.2
months; unadjusted HR 0.83; P = 0.60). Similarly, among patients with normal
baseline thrombocytes (N = 189), baseline sodium <LLN was associated with
shorter OS (5.9 vs. 11.0 months; unadjusted HR 2.29; P < 0.001), whereas in patients
with baseline thrombocytes >ULN (N = 44), baseline hyponatremia did not
significantly affect OS (7.3 vs 5.8 months; unadjusted HR 1.04; P = 0.91).
92
Table 5. Shifts in baseline/on-treatment sodium values and OS
A. Baseline sodium - a reference group
Baseline
Sodium
Sodium
during
treatment
N Median OS HR P
147 11.1 Ref.
<LLN 11 5.6 2.00 0.037
<LLN 35 8.3 1.68 0.012
<LLN <LLN 30 5.4 2.6 <0.001
B. Two different reference groups demonstrating the dynamic association of sodium
values and OS
Baseline
Sodium
Sodium
during
treatment
N Median OS HR P
147 11.1 Ref.
<LLN 11 5.6 2.00 0.037
<LLN 35 8.3 Ref.
<LLN <LLN 30 5.4 1.60 0.08
93
Our results demonstrate that both baseline and on-treatment hyponatremia are
associated with shorter OS and on-treatment hyponatremia additionally with shorter
PFS and a worse CBR. To the best of our knowledge, this is the largest study to
confirm the prognostic role of hyponatremia among everolimus-treated patients. Our
subgroup analyses additionally suggest that sodium is probably a dynamic
biomarker, as shifts from baseline , and vice
versa, were associated with OS among this patient cohort. This dynamic nature of
the prognostic value of hyponatremia has not previously been investigated.
As the negative prognostic impact of hyponatremia has been demonstrated in mRCC
patients treated with cytokines (238) and TKIs (239-241), possible future
applications of these results in clinical practice may include the incorporation of
sodium in prognostic models. The assessment of hyponatremia among mRCC
patients treated with novel immunotherapy agents is therefore warranted.
Additionally, the normalization of sodium values during treatment may reassure the
treating physician of clinical benefit and thus encourage the continuation of
everolimus therapy.
The underlying mechanisms of hyponatremia are unclear. In addition to SIADH,
other possibilities include renal dysfunction, poor adrenal gland function, existing
comorbidities, concomitant medications (such as diuretics and steroids), cancer
therapy, and/or its adverse effects, such as diarrhea and vomiting (353-357). One
intriguing hypothesis is the association of chronic inflammation and hyponatremia.
Chronic inflammation has a well-established role in tumorigenesis (358).
Experimental and clinical studies suggest that chronic inflammation may lead to the
overproduction of interleukin-6 (IL-6), which induces neutrophilia and the secretion
of ADH, resulting in hyponatremia (359). In our analyses, there was a significant
association between baseline sodium and baseline neutrophils/thrombocytes.
Furthermore, our subgroup analyses demonstrated that baseline hyponatremia was
94
associated with worse OS and PFS among patients with baseline
neutrophils/th
neutrophils/thrombocytes >ULN, where OS and PFS were modest. We find these
results in support of the notion that hyponatremia may be associated with chronic
inflammation in mRCC. More research is needed in this area, however.
Another interesting question is whether the treatment of hyponatremia associates
with the treatment outcome. Several recommendations and treatment algorithms for
hyponatremia exist, but none specifically address patients with cancer (360). In a
retrospective analysis among 57 hyponatremic patients with cancer of various
origins, the median OS for the 32 patients among whom sodium levels returned to
normal was significantly longer (13.6 vs. 5.1 months; P < 0.001) (361). It is,
however, difficult to establish whether the improvement in OS reflects the treatment
of hyponatremia, the continuation of anticancer therapy, an improvement in the
patients’ clinical condition, or bias resulting from the longer follow-up. An opposite
view depicts the negative prognostic impact of hyponatremia to result from the
underlying pathology rather than hyponatremia itself (362). Considering our results,
further studies examining hyponatremia among cancer patients are warranted.
In conclusion, we demonstrated that sodium independently associated with the
outcome among mRCC patients treated with everolimus. As it is a readily available,
inexpensive, and reproducible laboratory parameter, it has great potential as a
clinically applicable prognostic biomarker. Additionally, monitoring on-treatment
sodium values may in the future aid in clinical decision making. The association of
baseline neutrophilia/thrombocytosis and hyponatremia is an intriguing finding,
supporting previous evidence of chronic inflammation as a driver of disease
progression and metastasis and warranting further investigations among mRCC
patients and cancer patients in general.
95
Limitations of the Study Materials and Methods
In study I, the methods used to assess treatment-induced hypertension as well as the
use of antihypertensive medication were based on data obtained from patient case
records. This may have led to underestimation of the incidence of hypertension if it
was not properly documented. Similarly, this may have led to overestimation of the
true use of antihypertensive medication, as patient case records rather than
prescription records were used. Furthermore, data regarding why some patients were
treated with ASIs and some not were lacking, possibly representing confounding
factors, e.g. renal dysfunction.
In study II, archival tumor samples rather than fresh tumor biopsies were used. This
may have affected our results in a few ways. Firstly, from a technical standpoint, the
detected c-Met expression may vary based on the age of the sample. Secondly, c-
Met expression seen in an older sample may not represent the true c-Met expression
of the tumor at the onset of systemic therapy due to tumor biology and behavior.
However, there was no significant difference in the median age of tumor samples for
low and high c-Met expression (5.1 vs. 5.0 years; P = 0.62). In further analyses, we
did note a statistically significant association between the frequency of low and high
c-Met expression and the age of the tissue samples, with older samples having a
higher percentage of low c-Met expression as compared to the less aged samples
(65.2% vs. 48.5%, P = 0.049). As this might call into question the fidelity of the c-
Met assays for the older samples, we hypothesize that the difference might rather be
explained by the more aggressive tumor behavior in patients with high c-Met
expression. As described earlier, all patients in this study were identified from
hospital case records based on the initiation of sunitinib treatment. Patients with
older tissue samples therefore had a longer time between the collection of the sample
(nephrectomy) and initiation of sunitinib therapy, most likely due to less aggressive
96
tumor behavior. Although this might explain the association between the tissue
sample age and c-Met expression, no definitive conclusions on the fidelity of the c-
Met assays can be drawn, and we recognize this as a potential limitation of this study.
Additionally, the number of patients with bone metastases was relatively low
(N = 31) and this could be the reason why c-Met expression did not reach statistical
significance among this patient subgroup.
In study III, a potential limitation in addition to its retrospective nature arises from
the assessment of pneumonitis. Due to overlapping symptoms and similar radiologic
findings, pneumonitis can be difficult to differentiate from other diseases of the lung
parenchyma such as pneumonia, and we recognize this as a potential limitation. The
study did, however, comprise two independent patient cohorts and the radiographs
were subjected to a blinded radiologic review. Additionally, when comparing the
difference in the outcome between patients with CT-
patients with clinical symptoms of pneumonitis/pneumonia.
In study IV, we assessed on-treatment hyponatremia based on the highest sodium
value within 12 weeks of treatment initiation. This may have led to underestimation
of the true incidence of hyponatremia, as a patient would be categorized as
normonatremic with only one on-treatment normonatremic sodium value. This
stricter assessment of hyponatremia may therefore limit the clinical applicability of
the results concerning on-treatment hyponatremia.
97
Conclusions
As the past decade has seen marked advances in the treatment of metastatic RCC in
the form of targeted agents, including sorafenib, sunitinib, bevacizumab, pazopanib,
axitinib, everolimus, and temsirolimus, a number of additional targets aside from
VEGF and mTOR have been identified. Recent phase III trials have demonstrated
that cabozantinib and the combination of nivolumab and ipilimumab have relevant
clinical activity in first-line therapy when compared with sunitinib.
Recommendations regarding first-line therapy are likely to be updated in the near
future based on the results from CABOSUN and Checkmate-214. (224, 225)
As the number of treatment options for mRCC has increased, it has become
increasingly important to identify predictive biomarkers that would allow the
clinician to identify patients who are more likely to benefit from the differing
treatments. At present, there are no biomarkers in clinical use. A few have shown
promise in this regard, including the tumor PD-L1 expression level for nivolumab-
treated patients.
In our study, we demonstrated that high levels of c-Met expression associate with a
worse outcome among mRCC patients treated with sunitinib. We also found that
high c-Met expression is associated with a poor outcome among patients without
bone metastases at baseline, suggesting that the prognostic role may vary based on
the location of the metastases. These results are of special interest given the results
of the CABOSUN trial and should be further investigated among patients treated
with cabozantinib. In the future, the evaluation of tumor c-Met expression may be
incorporated into clinical practice to improve the prognostication of mRCC. The role
of c-Met expression as a possible predictive biomarker, however, remains to be
determined.
98
We additionally demonstrated that the use of angiotensin system inhibitors may have
beneficial effects in conjunction with sunitinib and pazopanib in the treatment of
TKI-induced hypertension. In the future, these results may guide the choice of
antihypertensive medication for patients being treated with angiogenesis inhibitors.
However, for these results to be properly validated, the use of ASIs in the treatment
of TKI-induced HTN needs to be investigated in a randomized prospective setting.
Finally, we identified on-treatment biomarkers for everolimus-treated patients. To
the best of our knowledge, we showed for the first time that treatment-related
pneumonitis is significantly associated with longer overall survival and progression-
free survival, as well as a higher clinical benefit rate, in two independent patient
cohorts. These results verified pneumonitis as a strong marker of everolimus
efficacy. Furthermore, our results demonstrated that both baseline and on-treatment
hyponatremia independently associate with shorter overall survival and that on-
treatment shifts in sodium values from baseline , and vice versa, are
associated with the outcome. These findings add to the growing field of on-treatment
biomarker research in metastatic renal cell carcinoma.
The routine use and incorporation of biomarkers into clinical practice, whether to aid
in treatment selection or to reassure clinicians of the clinical benefit during treatment,
is slowly developing. Future directions in mRCC biomarker research should include
further investigation of promising leads such as tumor PD-L1 expression and c-Met
expression.
99
Acknowledgements
This study was carried out in the Laboratory of Molecular Oncology, University of
Helsinki and in the Department of Oncology, Helsinki University Hospital. It was
financed by grants from the Oncologists Association, the Finnish-Norwegian
Medicine Foundation and the Ida Montin Foundation.
I express my sincere gratitude to my supervisor Adjunct Professor and Chief Medical
Officer Petri Bono for his active guidance and advice regarding research, academic
writing and medicine in general. Petri, You always found the time to answer my late
calls and emails despite Your busy schedule. Your enthusiasm, passion and
determination, as well as Your ever-positive attitude, made this journey not only
possible, but also a pleasant one.
I am also grateful to Associate Professor Frede Donskov for great collaboration and
especially his invaluable guidance in our two last studies. Frede, I sincerely enjoyed
our conversations via email – Your ideas and rigorous approach to both statistical
analyses and the questions behind them truly drove me forward.
I thank the members of my thesis committee, Docent Pipsa Saharinen, Docent Juha
Klefström and Medical Doctor Harry Nisen for constructive comments and for
encouraging me to think “outside the box”.
I want to thank the reviewers of this thesis, Docent Petter Boström and Docent Peeter
Karihtala, for their efficient work around a tight schedule and their observant
feedback and critique helping me to improve my thesis.
Dr. Roy Siddall is warmly acknowledged for the fast and high-quality editing of the
language.
100
I want to thank all the collaborators: Katriina Peltola for all the help in writing and
editing, Tuija Poussa for her assistance with statistical analyses, Marjut Laukka for
her valuable help with the interpretation of radiographs and Juhana Rautiola for not
only his input and expertise, but also for his friendship. I would also like to thank all
other coauthors: Heikki Joensuu, Erkki Hänninen and Ari Ristimäki.
A special thanks goes to my friend and colleague Jaakko Allonen, with whom I
shared a work space for a better part of a year. We had great conversations, not only
about research and work, but life in general. I also want to thank other friends and
colleagues who have helped and supported me along the way at work, in personal
life or both: Antti Kerppola, Tuomas Pyrhönen, Tuomas Kaprio, Timo Laihinen,
Nico Aksentjeff, Olavi Parkkonen and Lauri Jouhi.
My parents and my brothers deserve my deepest thanks for love and support. And
Johanna – thank you for acting as my “practice audience” for so many times. Your
patience with me extends far beyond one can imagine. Your love, as well as Your
practical and sensible attitude towards life, reminded me to keep my feet on the
ground.
Kauniainen
September 2018
Patrick Penttilä
101
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