Comparison of Papillary Renal Cell Carcinoma Type 1 and
Type 2: A Secondary Data Analysis
Melissa Paquin, PhD,1 Tracy Fasolino, PhD, FNP,2 Joe Bible, PhD,3 Mary Beth Steck, PhD,2 Joel Williams, PhD.4
1. Clemson University, Hampton, GA 30228 USA.
2. School of Nursing, Clemson University, Clemson, SC 29634.
3. Mathematical and Statistical Sciences, Clemson University, Clemson, SC 29634.
4. Department of Public Health Sciences,
Clemson University, Clemson, SC 29634
ABSTRACT
OBJECTIVE: The overall aim of this study was to determine if there
are significant differences between type 1 and type 2 papillary renal cell
carcinoma (PRCC) that can be utilized by healthcare providers.
MATERIALS AND METHODS: This study performed a secondary data
analysis using The Cancer Genome Atlas Kidney Renal Papillary Cell
Carcinoma data to determine if there are clinically significant differences
in survival, demographics (age, ethnicity, gender, and race), increased risk
factors (body mass index [BMI] smoking history, neoplasm history, and
malignancy history) and preferential genetic pathways between type 1 and
type 2 PRCC tumors.
RESULTS: Descriptive statistics were performed on a total of 156 cases to
determine demographics, increased risk factors and genetic pathways. The
hazard ratio, with type 1 as the reference group, was 2.459 (with 95% CI
0.9723, 6.217). Of the risk factor variables investigated, we found that smoking
appeared to be associated with an increased risk of type 2 (OR 3.241 95% CI
1.066, 9.853). In the pathways analysis, we observed one significant difference
between MAPK and PI3K, with the latter being significantly associated with
type 2 (OR 4.968 95% CI 1.759, 14.031 Table 6).
CONCLUSION: This study provides the framework for future more
comprehensive research on the demographic, increased risk factor and
genetic pathway differences between PRCC type 1 and type 2 tumors. Future
investigations should include a more complete dataset with additional
potential risk factors.
INTRODUCTION
Renal cell carcinoma (RCC) is
the 14th most common cancer
worldwide and was the cause
of 175,098 deaths in 20181. RCC
consists of numerous subtypes
including clear cell renal carcinoma,
papillary renal cell carcinoma and
most recently clear cell papillary
renal cell carcinoma. Currently, papillary
renal cell carcinoma (PRCC)
is the second most common type of
RCC, after clear cell renal cell carcinoma,
comprising approximately
15-20% of all RCC cases2,3.
PRCC is considered a
heterogeneous disease consisting
of two subtypes; type 1 and type
2. These subtypes are primarily
distinguished by their histology
and vary in prognosis, treatment
and patient outcomes. Type 1 is
histologically characterized by a
single layer of cells with sparse
basophilic cytoplasm and small
oval shaped nuclei that are present
in either the renal tubules or renal
papillae. This type can be associated
with both hereditary and sporadic
PRCC.4,5 Conversely, type 2 tumors
are histologically characterized by
large pseudostratified cells with
eosinophilic cytoplasm with large
spherically shaped nuclei that are
present in the renal papillae. These
tumors can be associated with
hereditary PRCC but are more often
associated with the sporadic form
of PRCC.6
Furthermore, research 6
has shown that patients with PRCC
type 2 tumors are correlated with a
higher rate of metastasis and have a
lower overall survival rate compared
with patients with type 1 tumors.7
The overall aim of this
study was to determine if there are
significant differences between type
1 and type 2 PRCC
that can be utilized
by healthcare
p r o v i d e r s .
Specifically, this
study sought to
determine if there
are clinically
s i g n i f i c a n t
d i f f e r e n c e s
in survival,
demographics (age,
ethnicity, gender,
and race), increased
risk factors (body
mass index [BMI]
smoking history,
neoplasm history,
and malignancy
history) and
preferential genetic
pathways between
type 1 and type 2
PRCC tumors.
Table 1 | Descriptive Statistics for Demographic Factors
T h e
epidemiology and
risk factors for
PRCC are largely
based on the
broader RCC. However, there are
certain conditions that may increase
an individual’s risk of developing
PRCC. For instance, individuals
with Hereditary Leiomyomatosis
and Renal Cell Cancer (HLRCC)
have a greater chance of developing
PRCC type 2. There is some evidence
that suggests individuals with renal
insufficiencies have a greater risk
of developing PRCC.8,9 Ethnicity
is also linked to increased risk
of developing RCC with African
Americans having the highest
incidence of RCCs. Sankin et al.
(2011) found that African Americans
had a four times greater incidence of
PRCC as compared to non-African
Americans.10,11
Research has demonstrated
that malignant tumors utilize a
wide variety of genetic alterations
to modify the normal cell cycle
in order to be able to divide and
grow without restrictions. These
modifications are accomplished by
altering cell signaling pathways to
promote cell growth, angiogenesis
and obstruct apoptosis.12
Considering the heterogeneous
nature of PRCC, there are numerous
genetic alterations that occur within
both type 1 and type 2 PRCC.
Approximately 20% of hereditary
type 1 tumors have been associated
with variations in the protooncogene
mesenchymal epithelial transition
(MET). However, sporadic type
1 tumors have numerous genes
associations as well as chromosomal
abnormalities. Type 2 tumors have
also been correlated with a large
number of genetic and chromosomal
alterations.4,13 Similarly, research
has shown that renal cancers in
general utilize several signaling
pathways. The alteration of MET
has been shown to activate the
MAPK and PI3K pathways as well as
other proteins involved with tumor
growth.14 Gaps in research still exist
for determining if there are pathway
preferences between type 1 and type
2 PRCC tumors.
Table 1 | Descriptive Statistics for Demographic Factors
Most research on PRCC has
either been umbrellaed under RCC
or focused on developing a basic
understanding of the disease with
minimal attention to the differences
between type 1 and type 2 PRCC
tumors. Recently, Wong et al.
(2019) investigated survival rates
associated with type 1 and type 2
PRCC. The researchers found that
type 2 PRCC was associated with
a higher all-cause mortality rate
as well as with worse reoccurrence
rates as compared
to type 1.7 As part
of our research, we
analyzed the allcause
mortality
for discrepancies
in survival rates
between type
1 and 2 PRCC.
Next, we selected
a demographic
(baseline) model
to identify a set
of demographic
variables that
are likely to be
associated with
the different types
of PRCC. Lastly, we investigated
environmental and gene pathway
associations with prevalence of the
two types of PRCC.
METHODS
Sample
This study was a secondary data
analysis using data from The
Cancer Genome Atlas Kidney Renal
Papillary Cell Carcinoma (TCGAKIRP).
A review of the literature
was conducted to determine the
appropriate inclusion criteria
which included: 1) PRCC tumors,
2) distinguishes between type 1
and type 2, 3) demographics data,
gender, race, age and ethnicity, 4)
clinical data, prognosis, treatment,
preexisting conditions, 5) increased
risk factors, smoking history,
BMI, prior neoplasms and prior
malignancies, and 6) genetic analysis
of the tumors. A further review of the
literature revealed that TCGA-KIRP
is the most current and appropriate
dataset to use for this secondary
data analysis. The cBioPortal for
cancer genomics (cBioPortal) was
used in conjunction to analyze the
TCGA-KIRP data.
TCGA-Kidney Renal
Papillary Cell Carcinoma (KIRP)
data was collected from 41
institutions from 1996 to 2013. The
database adheres to a strict inclusion
policy; TCGA tumors are untreated
samples that were snap frozen. Each
tumor sample has to have a matched
normal sample from the same
patient which generally comes in
the form of the patient’s blood. The
tumors and subsequent molecular
data are cross referenced
by Biospecimen Core
Resource (BCR) to ensure
validity. Furthermore, the
BCR analyzes each sample
for pathological quality
control. This maintains
that TCGA has a highquality
tumor samples as
well as consistent molecular
data.15 Additionally, each
sample was reviewed by
a panel of six experienced
pathologist to in order
to be classified into type
1, type 2 or unclassified
PRCC. Moreover, any
samples that were preclassified
were reassessed
by the same panel to ensure
proper classification.15
The cBioPortal is a resource
that incorporates data from TCGA
as well as actively curates data sets
from the literature into a researchfriendly
source. The cBioPortal
separates PRCC genetic variations
into categories such as copy
number variations and mutations.
Furthermore, the cBioPortal
predetermines and denotes driver
genes through specific algorithms.16
The cBioPortal allows the user to
analyze specific genes, as opposed
to TCGA, which only allows users
to view the dataset as a whole and
does not denote potential driver
genes.16 Even though the cBioPortal
contains the same data as TCGA,
the cBioPortal was used to aid in the
analysis of TCGA data.
Data extraction
Both databases showed the same
cases which totaled 292. The first
step in evaluating the dataset was
determining the demographic and
clinical data. TCGA contained a
manifest of demographic, clinical,
and environmental data. This
manifest was downloaded and
converted into an Excel file. Once
retrieved, the dataset was reviewed
and irrelevant data was removed;
such data included serum levels,
blood cell counts, IDH level, tumor
laterality, lymph node data, tumor
dimensions, treatment data, tissue
collection data, sample weights,
calcium levels, and vial numbers.
Data categories that were redundant
were also eliminated.
Next, the cBioPortal resource
was used to determine pertinent
genetic information related to PRCC.
The first step was to download
the copy number alteration (CNA)
data from this resource. A total
of 10,837 genes exhibited a copy
number variation. Genes that were
not considered to be driver genes
according to the GISTIC algorithm
were eliminated from the dataset.
This elimination left a total of 426
driver genes with CNA. The driver
genes were then put into the BCG
query to determine how many cases
included one or more of the driver
CNA genes. A total of 193 of the cases
(66%) contained one of the driver
CNA genes. In order to increase the
sample population, mutated driver
genes (as determined by Mutsig)
were added to the query bringing the
total of genes to 517 and 255 (87%)
cases. Thirty-six cases did not have
an association with one of the 517
driver genes and were eliminated.
The driver genes were divided into
categories based on their cytoband
for future reference.
The remaining 255 cases
were reviewed to determine whether
or not they were designated type 1 or
type 2 PRCC. Out of the 255 cases,
115 cases had no designation in the
type category. The pathology report
of each of the 115 cases was reviewed
to see if a pathologist had designated
the tumor as either type 1 or type 2.
Seven more cases were determined to
be a mix of type 1 and type 2 histology
and were also removed. Additionally,
eight more cases were either
mislabeled as PRCC or determined
to favor a different cancer type per
the reviewing pathologist. These
eight cases did not include a TCGA
addendum that disputed the cancer
typing and therefore were removed
from this dataset. (See Figure 1). At
the conclusion of this analysis, 88
cases were designated as type 2, 69
cases were type 1, and 83 cases were
undesignated. The 83 undesignated
cases were subsequently removed
from the dataset in order to preserve
the validity and continuity of the
data.
ANALYSIS
Descriptive Statistics and
Survival Analysis
Descriptive statistics were utilized
to determine demographics,
increased risk factors and genetic
pathways. The survival analysis
was conducted for the TCGA-KIRP
analytic file using R version 3.6.2.
, the survival(v3.2-13) and the
survminer (v0.4.9) packages.21-23 A
cox-proportional hazard model was
fitted on the overall survival times
of 156 patients (1 had a survival
time of 0 indicating that they were
diagnosed post-mortem or there
was an error in entry) to determine
if there were evidence that survival
rates differ between type 1 and 2
PRCC.
Logistic Regression
For the next three phases of
our statistical analysis, SASTM
software, Version 9.4 of the SAS
system for Windows was utilized.
The demographic model selection
included age at diagnosis, race,
ethnicity and sex, as candidate
descriptors relating to PRCC tumor
type. The demographic model
selection utilized forward selection
with a relaxed p value (<0.1) to
determine the appropriate variables
to be included in the model. The
selected demographic model
included Age at Diagnosis (OR 1.045
95% CI 1.014, 1.078, Table 5) as well
as 3 Category Race (White, Black or
African American and Other) was
used as the baseline model for the
increased risk factor variables. Each
increased risk factor variable; BMI,
smoking status, prior neoplasms
and prior malignancies, were added
univariately to the demographic
model controlling for age at diagnosis
and race to identify associations.
Figure 2 | Kaplan Meier curves for Type 1 and 2 PRCC survival
RESULTS
Descriptive Statistics
For the 69 patients designated as
type 1 tumors, 50 were male and 19
were female with a median age of 60
(range 28 to 82). In terms of race, 46
were white, 18 were black or African
American, and 5 were unspecified.
Ethnicity was reported as 62 non-
Hispanic or Latino, 2 were Hispanic
or Latino and 5 were unspecified.
For the 88 patients designated as
type 2 tumors, 61 were male and
27 were female with a median age
of 65 (range 28 to 88). In terms of
race, 66 were white, 15 were black
or African American, and 7 were
unspecified. Ethnicity was reported
as 75 were non-Hispanic or Latino, 5
were Hispanic or Latino and 8 were
unspecified (Table 1). Due to the
sparsity in the demographic factor
levels, the following variable levels
were collapsed; Asian and American
Indian.
Table 2 | Descriptive Statistics for Increased Risk Factors
Smoking categories were
defined as life-long non-smoker
(1), current smoker (2), reformed
smoker >15years (3), reformed
smoker <15 years (4) and reformed
smoker unknown length (5). Table 2
describes the smoking status of type
1 and type 2 PRCC tumors. Smoking
categories 4 and 5 were collapsed
together due to data sparsity in the
increased risk factor variables.
The existence of prior
neoplasm was defined in the
database as ‘yes’ or ‘no’. Two patients
with type 1 PRCC had known prior
neoplasm were as 9 patients with
Type 2 reported prior neoplasm.
Similarly, prior malignancies were
also defined as ‘yes’ or ‘no’. Sixteen
patients with type 1 reported prior
malignancies and 14 patients with
type 2 reported prior malignancies
(Figure 2). The most common
pathway in type 1 was the MAPK
pathway and in type 2 was the PI3K
pathway Table 3).
Table 4 | Demographics Model
Overall Survival
The hazard ratio, with type 1 as
the reference group, was 2.459
(with 95% CI 0.9723, 6.217). This
result did not provide sufficient
evidence that the two types differ
significantly in all-cause survival
(α=.05). However, given the
relatively small sample size and high
rate of censoring, it is not surprising
that our results do not provide as
striking a contrast between the two
as supported by Wong et al. (2019).
(Censoring rates were 91.3% for Type
1 and 79.5% for type 2, respectively,
which consequently prevents us
from being able to report median
survival without making parametric
assumptions). Survival rates are
illustrated via the Kaplan Meier
curve included in Figure 2.
Table 4 | Increased Risk Factor Model
Logistic Regression
Odd ratios (OR) and confidence
intervals (CI) are reported in Tables
5 and 6 for each variable in the
increased risk factor and pathway
analyses. Of the risk factor variables
investigated, we found that smoking
appeared to be associated with an
increased risk of type 2. Specifically,
being a reformed smoker of
unknown length or less than 15
years, was positively associated
with type 2 PRCC compared to lifelong
non-smokers (OR 3.241 95%
CI 1.066, 9.853 Table 5). None of
the other increased risk factors had
significant association with tumor
type. In the pathways analysis, we
observed one significant difference
between MAPK and PI3K, with the
latter being significantly associated
with type 2 (OR 4.968 95% CI
1.759, 14.031 Table 6). All pairwise
comparisons were made between
pathways and the MAPK/PI3K
comparison was the only one found
to be significant. In all analyses, type
1 was used as the reference level for
each model and the OR corresponds
to odds of type 2 Vs 1.
Table 2 | Descriptive Statistics for Increased Risk Factors
DISCUSSION
It is important to note that
current findings from the
International Society of Urological
Pathology (ISUP) suggests that
the PRCC type 1 subtype is the
most uniform morphologically,
immunohistochemically, and in
terms of molecular features. ISUP
also suggests that PRCC type 2 is
not a distinct neoplasm but rather
a combination of multiple distinct
neoplasms. As such, type 2 PRCC
is a distinctly different disease as
compared to type 1 and contains
multiple clinically and molecularly
heterogeneous subtypes.24
Additionally, the use of type 1 and
type 2 terminology is evolving as
PRCC becomes better understood.
To the best of our knowledge,
our study is the first
to collectively examine the
demographic, increased risk and
pathway associations between
type 1 and type 2 PRCC tumors.
Furthermore, while our findings
with respect to the survival analysis
were not significant, it does provide
marginal evidence to confirm the
findings of Wong et al. (2019) in that
survival rates for type 2 are shorter
than those diagnosed with type 1. 7
While our analysis was limited by
small sample size, certain variables
were linked to increased probability
of type 2 PRCC tumors. The age at
diagnosis variable was considered
significant with an older adult having
increased risk of type 2. Our result
is consistent with Wong et al. (2019)
who reported a higher age at time of
nephrectomy for patients with type
2 tumors as compared with type 1
tumors.7
Smoking was the only
increased risk factor that was
significant in determining the
probability of having the type 2
tumor type versus type 1. Individuals
who were reformed smokers of less
than 15 years (as well as reformed
smokers of unknown length) had
a greater risk of developing a type
2 tumors as compared to lifelong
non-smokers. Furthermore, type 2
PRCC tumors tend to be sporadic as
compared to type 1, meaning that
increased risk factors may have a
greater impact on the development
of type 2 tumors.6 However, further
research needs to be conducted on
the effects of smoking on the growth
of specific tumor subtypes.
Although smoking was
the only significant increased risk
factor variable, further research
should be conducted on a larger
sample size with less missingness
to better compare increased risk
factors variables between tumor
types. Specific focus should be put
on prior neoplasms since they have
been associated with a number of
renal cell cancer syndromes that
are considered to increase the risk
of PRCC. For example, the most
common renal cell cancer syndrome,
von Hippel-Lindau syndrome, is
characterized by benign tumor
growths and has a 40% chance of
developing renal cancer, including
type 2 PRCC. Additionally, hereditary
leiomyomatosis and renal cell
cancer (HLRCC), is characterized
by harmatomas with an increased
risk of developing type 2 PRCC. 8,17
Considering the number of renal
cell cancer syndromes that are
both associated with an increased
PRCC risk and are characterized by
neoplasms; further research should
be conducted to determine if prior
neoplasms is a determining factor in
PRCC subtype.
The findings in this study
have potential implications for future
treatment options. The higher rate
of MAPK pathway in type 1 supports
ongoing studies of the role of the MET
gene in clinical trials. The MET gene
codes for c-Met, a tyrosine kinase
protein that is involved with the
MAPK pathway. When c-Met binds
to its ligand, HGF, a downstream
cascade is started that leads to the
activation of the MAPK pathway
which promotes cell migration and
tumor proliferation. 18 Seeing as
20% of type 1 tumors contain a
MET mutation, it is not surprising
that MAPK is the preferred pathway
of type 1 tumors. Furthermore,
the PI3K pathway was found to
be significant in the probability
of having a type 2 tumor as well
as being the preferred pathway of
type 2. The findings in this study
support the ongoing efforts in
determine drug treatment therapies
that target the PI3K pathway. PI3K
is comprised of lipid kinases that
once activated, begin a downstream
cascade that leads to cell growth and
survival. PI3K pathway has a strong
association with the inactivation of
PTEN, which has been correlated
poor patient outcomes.19,20
CONCLUSION
Despite the imperfect database this
study found that there is a trend in
the data that is clinically significant
Furthermore, this study provides
the framework for future more
comprehensive research on the
demographic, increased risk factor
and genetic pathway differences
between PRCC type 1 and type
2 tumors. Future investigations
should include a more complete
dataset with additional potential
risk factors. Given the differences in
survival rates, such investigations
will provide clinicians a better
understanding of tumor types
allowing for quicker more accurate
diagnosis and evidence-based
treatment plans.
CONFLICT OF INTEREST
All authors listed on this study have
no conflicts of interest that may
be relevant to the contents of this
manuscript.
FUNDING
None
ACKNOWLEDGMENTS
None
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Correspondence to: Melissa Paquin, PhD.
235 Galway Lane, Hampton, GA 30228
Email: mpaquin@clemson.edu
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