HPV-16 E6 and E7 protein T cell epitopes prediction analysis based ...

Vaccine 31 (2013) 2289–2294
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Vaccine
jou rn al h om epa ge: www.elsevier.com/locate/vaccine
HPV-16 E6 and E7 protein T cell epitopes prediction analysis based on
distributions of HLA-A loci across populations: An in silico approach
Yufeng Yao, Weiwei Huang, Xu Yang, Wenjia Sun, Xin Liu, Wei Cun, Yanbing Ma ∗
Institute of Medical Biology, Chinese Academy of Medical Sciences & Peking Union Medical College, Yunnan Key Laboratory of Vaccine Research & Development on Severe Infectious
Diseases, Yunnan Engineering Research Center of Vaccine Research & Development on Severe Infectious Diseases, Kunming 650118, China
a r t i c l e i n f o
Article history:
Received 15 September 2012
Received in revised form 6 February 2013
Accepted 28 February 2013
Available online 13 March 2013
Keywords:
Human papillomavirus (HPV)
Human leukocyte antigen (HLA)
T cell epitopes
Cytotoxic T lymphocytes (CTL)
Cervical cancer
In silico approach
a b s t r a c t
Human papillomavirus type 16 (HPV-16) is the most prevalent virus in human cervical cancers, as it
is present in more than half of all cases. Many studies have found continued expression of E6 and E7
proteins in the majority of cervical cancer cases, but not in normal tissues. These results indicated that
the E6 and E7 proteins could be ideal candidate therapeutic vaccines against HPV-16 infection and cervical
cancer. Using the Immune Epitope Database Analysis Resource, cytotoxic T lymphocyte (CTL) epitopes
of the HPV-16 E6 and E7 proteins were predicted according to worldwide frequency distributions of
HLA-A alleles (HLA-A*01:01, -A*02:01, -A*02:06, -A*03:01, -A*11:01, -A*24:02, -A*26:01, -A*31:01 and -
A*33:03). Our results predicted a total of 81 epitopes of HPV-16 E6 (n = 59) and E7 (n = 22). Epitope cluster
analysis showed that among the 20 clusters of HPV-16 E6, cluster 3 contained the most epitopes (10
epitopes), which was represented by HLA-A*31:01 and -A*33:03. Of the 10 clusters of HPV-16 E7, cluster
3 contained the most epitopes (5 epitopes), which was represented by HLA-A*01:01 and -A*26:01. Our
results indicated that the combination of epitopes FAFRDLCIVYR 52-62 of E6 (HLA-A*02:06, HLA-A*31:01,
and HLA-A*33:03), PYAVCDKCLKF 66-76 of E6 (HLA-A*11:01 and HLA-A*24:02), HGDTPTLHEY 2-11 of E7
(HLA-A*01:01 and HLA-A*26:01), and YMLDLQPETT 11-20 of E7 (HLA-A*02:01) could vaccinate >50% of all
individuals worldwide. Our results propose CTL epitopes or combinations of them predicted in current
study for candidate therapeutic vaccines to effectively control HPV-16 infection and development of
cervical cancer.
© 2013 Elsevier Ltd. All rights reserved.
1. Introduction
Human papillomavirus (HPV) has been identified as an etiological
factor for several anogenital diseases, especially cervical cancer
[1]. Of the >200 HPV genotypes, six ‘high-risk’ types of HPV (16, 18,
31, 45, 52 and 58) are of particular importance, because they have
been highly associated with over 80% of all cervical cancers [2–4]. Of
these six high-risk types, HPV type 16 (HPV-16) is the most prevalent,
being present in more than half of all cervical cancers [2–4]. In
addition, HPV-16 is also associated with precursors of high-grade
squamous intraepithelial lesions [3,5].
HPV-16 carries two transforming oncogenes, E6 and E7, whose
expressed proteins interact with the p53 and retinoblastoma
proteins, respectively [6–10]. Several studies have shown that continued
expression of E6 and E7 proteins is necessary for the growth
and tumorogenicity of cervical carcinoma cells [11,12]. Ideally, the
E6 and E7 proteins could be used as candidate therapeutic vaccines
∗ Corresponding author at: Institute of Medical Biology, Chinese Academy of Medical
Sciences & Peking Union Medical College, Kunming 650118, China.
E-mail address: may@imbcams.com.cn (Y. Ma).
against HPV-16 infection and cervical cancer, because of the following
two reasons [3]. First, continued expression of the E6 and
E7 proteins have been observed in the majority of cervical cancers
[3], and second, E6 and E7 are critical for the induction and maintenance
of cellular transformation in HPV-infected cells; hence, it
is unlikely that the tumor cells can escape immune attack through
antigen loss [3]. Up to now, the E6 and E7 have been experimentally
identified as target antigens by immune intervention protocols
against cervical intraepithelial lesions and cervical cancer [13–15].
CD4 and/or CD8 T cell responses play a vital role in controlling
pathogenesis of HPV and associated cervical lesions in humans [16].
Therefore, cytotoxic T lymphocytes (CTLs) are considered the major
eradicators of both HPV-infected cells and cervical cancer cells
through the adaptive immune response [17,18]. CTL peptide-based
immunotherapy for cervical cancer has been shown promising
results using a liposomal delivery system in a murine model [19].
In addition, the CTL epitopes (YMLDLQPETT) derived from the E7
protein presented by the human leukocyte antigen (HLA)-A*02:01
has been used in clinical trials of E7 peptide vaccination, in which
most patients with high-grade cervical/vulvar dysplasia and cervical
cancer had a detectable immune response in peripheral blood
cells [20–33]. Therefore, direct administration of peptides derived
0264-410X/$ – see front matter © 2013 Elsevier Ltd. All rights reserved.
http://dx.doi.org/10.1016/j.vaccine.2013.02.065

2290 Y. Yao et al. / Vaccine 31 (2013) 2289–2294
from HPV antigens provides a means of vaccination against HPV
infection and cervical cancer.
The CTL epitopes, usually 8–11 amino acids in length, bind to
the cleft of various human leukocyte antigen (HLA)-I molecules
through features embedded in the peptide sequence and, more
specifically, in anchor residues of HLA-I molecules [3]. However,
a limitation of peptide-based vaccines is HLA-specificity, as HLA
molecules are highly polymorphic. Therefore, it may be difficult to
produce a peptide-based vaccine that is effective in patients with
different HLA molecules, thus making it impractical for large-scale
vaccination programs [3].
In current study, based on the distribution characteristics of
HLA-A across all populations, we predicted putative CTL epitopes
of HPV-16 E6 and E7 proteins using immunoinformatic methods.
Our results provide likely candidate CTL epitopes or their combination
for therapeutic vaccine development to effectively control
HPV-16-induced development of cervical cancer.
2. Methods
2.1. HPV-16 E6 and E7 protein sequence retrieval and analysis
The ID of the HPV-16 E6 and E7 proteins were NC 001526 and
retrieved from the NCBI (http://www.ncbi.nlm.nih.gov/nuccore/
NC 001526). The number of amino acids, molecular weights of proteins,
isolectric points, and the percentages of strongly basic, acidic,
hydrophobic and polar amino acids in the HPV-16 E6 and E7 proteins
was calculated using Lasergene 7.1 software (DNASTAR, Inc.,
Madison, WI, USA).
2.2. Retrieval and analysis of HLA alleles
The average frequency of HLA alleles across all populations was
retrieval from a previous study [34] and the major histocompatibility
complex database (dbMHC) (http://www.ncbi.nlm.nih.gov/
projects/gv/mhc/main.fcgicmd=init).
2.3. Epitope prediction
CTL epitopes of the HPV-16 E6 and E7 proteins were predicted
based on HLA-A alleles using the Immune Epitope
Database Analysis (IEDB) Resource (http://tools.immuneepitope.
org/main/index.html). The IEDB site recommended selecting the
default prediction method. Based on the availability of predictors
and previously observed predictive performance, this selection
uses the best possible method for a given MHC molecule. Currently,
for peptide:MHC-I binding prediction for a given MHC molecule,
IEDB recommends using the Consensus Method consisting
of NetMHC (http://www.cbs.dtu.dk/services/NetMHC/), Stabilized
Matrix Method (SMM; http://70.167.3.42/smm/), and combinatorial
peptide libraries (CombLib; http://www.mimotopes.com/
peptideLibraries.asp) if any corresponding predictor is available
for the molecule. Otherwise, NetMHCpan was used. For the IEDBrecommended
method, a low percentile indicated strong binding
to HLA molecules. A 1% percentile was used in the present study.
2.4. Epitope cluster analysis
Based on sequence identity, the predicted epitopes were
grouped into clusters based on Epitope Cluster Analysis (http://
tools.immuneepitope.org/main/index.html). In the current study,
a cluster was defined as a group of sequences that have a sequence
similarity greater than an 80% minimum sequence identity threshold.
Table 1
Average frequency of HLA alleles (>3%) across all population samples.
Allele Freq Freq
Solberg et al. (2008) [34] dbMHC
A*01:01 0.048 0.059
A*02:01 0.153 0.143
A*02:06 0.035 0.029
A*03:01 0.043 0.048
A*11:01 0.117 0.105
A*24:02 0.188 0.185
A*26:01 0.034 0.029
A*31:01 0.041 0.035
A*33:03 0.041 0.035
0.699 0.668
3. Results
3.1. HPV-16 E6 and E7 protein analysis
The number of amino acids, molecular weight of proteins, isoelectric
points, and characteristics of amino acids of the HPV-16
E6 and E7 proteins are analyzed in current study. The HPV-16 E6
and E7 proteins have molecular weights of 19.187 and 11.022 kDa,
respectively. The percentage of strongly basic, acidic, hydrophobic
and polar amino acids in the HPV-16 E6 and E7 proteins were 18.4%
and 5.1%, 10.8% and 19.45%, 23.4% and 27.6%, and 35.4% and 32.7%,
respectively. There were significant differences in the percentages
of strongly basic and acidic amino acids between the HPV-16 E6
and E7 proteins (P = 0.002 and P = 0.054, respectively).
3.2. HLA-A allele analysis
The average frequency of HLA alleles (>3%) across all population
samples was obtained from previous studies [34] and
dbMHC. Nine HLA-A alleles (A*01:01, A*02:01, A*02:06, A*03:01,
A*11:01, A*24:02, A*26:01, A*31:01 and A*33:03) were analyzed
and their accumulative frequencies were 0.699 and 0.668, respectively
(Table 1).
3.3. Epitope prediction
A total of 81 epitopes of HPV-16 E6 and HPV-16 E7 were
predicted against the 9 alleles of the HLA-A loci (Tables 2 and 3).
The number of epitopes represented by the E6 protein (n = 59)
comprised 72.8% (59/81) of all predicted epitopes. Only 27.2%
of the predicted epitopes (22/81) were represented in the
E7 protein sequence. AFRDLCIVYR 53-62 , KFYSKISEYR 75-84 ,
RCMSCCRSSR 142-153 , MSCCRSSRTR 144-153 and EVYDFAFR 48-55
were the best for binding to the E6 protein in terms of percentile
(percentile = 0.1). On the other hand, TLGIVCPI 86-93 and
ETTDLYCY 18-25 were the best for binding to the E7 protein in terms
of percentile (percentile = 0.1). For the E6 protein, HLA-A*31:01
presented the most frequent epitopes (19/59), followed by A*33:03
(17/59), A*01:01 (5/59), A*11:01, A*24:02 and A*26:01 (4/59),
A*03:01 (3/59), A*02:06 (2/59), and A*02:01 (1/59). For the E7 protein,
HLA-A*11:01 presented the most frequent epitopes (16/61),
followed by A*02:01 (6/22), A*01:01 and A*26:01 (5/22), A*03:01
and A*11:01(2/22), A*02:06 and A*33:03 (1/22). Interestingly,
there were no epitopes predicted for the HPV-16 E7 protein
presented by A*24:02 and A*31:01. However, for the E6 protein,
HLA-A*31:01 presented the most frequent epitopes (19/59). For
both of the E6 and E7 proteins, HLA-A*31:01 presented the most
frequent epitopes (19/81), followed by A*33:03 (18/81), A*01:01
(10/81), A*26:01 (9/81), A*02:01 (7/81), A*11:01(6/81), A*03:01
(5/81), A*24:02 (4/81), and A*02:06 (3/81).

Y. Yao et al. / Vaccine 31 (2013) 2289–2294 2291
Table 2
Predicted epitopes of HPV16 E6 against the nine alleles of HLA-A loci.
No. Allele Start End Peptide length Sequence Method used Percentile rank
001 HLA-A*01:01 92 99 8 GTTLEQQY NetMHCpan 0.7
002 HLA-A*01:01 80 88 9 ISEYRHYCY Consensus (ANN,SMM) 0.2
003 HLA-A*01:01 77 86 10 YSKISEYRHY Consensus (ANN,SMM) 0.15
004 HLA-A*01:01 79 88 10 KISEYRHYCY Consensus (ANN,SMM) 0.75
005 HLA-A*01:01 82 91 10 EYRHYCYSLY Consensus (ANN,SMM) 0.95
006 HLA-A*02:01 18 25 8 KLPQLCTE SMM 1
007 HLA-A*02:06 52 60 9 FAFRDLCIV Consensus (ANN,SMM) 0.7
008 HLA-A*02:06 103 113 11 LCDLLIRCINC SMM 0.6
009 HLA-A*03:01 34 41 8 ILECVYCK NetMHCpan 1
010 HLA-A*03:01 106 115 10 LLIRCINCQK Consensus (ANN,SMM) 0.45
011 HLA-A*03:01 89 99 11 SLYGTTLEQQY NetMHCpan 0.9
012 HLA-A*11:01 68 75 8 AVCDKCLK NetMHCpan 0.4
013 HLA-A*11:01 33 41 9 IILECVYCK Consensus (ANN,SMM) 0.65
014 HLA-A*11:01 93 101 9 TTLEQQYNK Consensus (ANN,SMM) 0.2
015 HLA-A*11:01 92 101 10 GTTLEQQYNK Consensus (ANN,SMM) 0.5
016 HLA-A*24:02 38 45 8 VYCKQQLL NetMHCpan 1
017 HLA-A*24:02 49 59 11 VYDFAFRDLCI NetMHCpan 0.7
018 HLA-A*24:02 66 76 11 PYAVCDKCLKF NetMHCpan 1
019 HLA-A*24:02 98 108 11 QYNKPLCDLLI NetMHCpan 0.8
020 HLA-A*26:01 32 39 8 DIILECVY NetMHCpan 0.3
021 HLA-A*26:01 48 55 8 EVYDFAFR NetMHCpan 0.9
022 HLA-A*26:01 82 91 10 EYRHYCYSLY Consensus (ANN,SMM) 0.7
023 HLA-A*26:01 29 39 11 TIHDIILECVY NetMHCpan 0.6
024 HLA-A*31:01 55 62 8 RDLCIVYR NetMHCpan 0.9
025 HLA-A*31:01 77 84 8 YSKISEYR NetMHCpan 0.9
026 HLA-A*31:01 129 136 8 KQRFHNIR NetMHCpan 0.2
027 HLA-A*31:01 131 138 8 RFHNIRGR NetMHCpan 0.5
028 HLA-A*31:01 144 151 8 MSCCRSSR NetMHCpan 0.4
029 HLA-A*31:01 143 151 9 CMSCCRSSR Consensus (ANN,SMM) 0.95
030 HLA-A*31:01 8 17 10 MFQDPQERPR Consensus (ANN,SMM) 0.8
031 HLA-A*31:01 53 62 10 AFRDLCIVYR Consensus (ANN,SMM) 0.1
032 HLA-A*31:01 75 84 10 KFYSKISEYR Consensus (ANN,SMM) 0.1
033 HLA-A*31:01 129 138 10 KQRFHNIRGR Consensus (ANN,SMM) 0.35
034 HLA-A*31:01 142 151 10 RCMSCCRSSR Consensus (ANN,SMM) 0.1
035 HLA-A*31:01 144 153 10 MSCCRSSRTR Consensus (ANN,SMM) 0.1
036 HLA-A*31:01 145 154 10 SCCRSSRTRR Consensus (ANN,SMM) 1
037 HLA-A*31:01 5 15 11 RTAMFQDPQER NetMHCpan 0.7
038 HLA-A*31:01 37 47 11 CVYCKQQLLRR NetMHCpan 1
039 HLA-A*31:01 52 62 11 FAFRDLCIVYR NetMHCpan 0.7
040 HLA-A*31:01 138 148 11 RWTGRCMSCCR NetMHCpan 0.7
041 HLA-A*31:01 143 153 11 CMSCCRSSRTR NetMHCpan 0.7
042 HLA-A*31:01 144 154 11 MSCCRSSRTRR NetMHCpan 0.3
043 HLA-A*33:03 8 15 8 MFQDPQER NetMHCpan 0.5
044 HLA-A*33:03 48 55 8 EVYDFAFR NetMHCpan 0.1
045 HLA-A*33:03 77 84 8 YSKISEYR NetMHCpan 0.5
046 HLA-A*33:03 144 151 8 MSCCRSSR NetMHCpan 0.2
047 HLA-A*33:03 76 84 9 FYSKISEYR NetMHCpan 0.4
048 HLA-A*33:03 134 142 9 NIRGRWTGR NetMHCpan 0.6
049 HLA-A*33:03 143 151 9 CMSCCRSSR NetMHCpan 0.2
050 HLA-A*33:03 8 17 10 MFQDPQERPR NetMHCpan 0.5
051 HLA-A*33:03 37 46 10 CVYCKQQLLR NetMHCpan 0.3
052 HLA-A*33:03 53 62 10 AFRDLCIVYR NetMHCpan 0.6
053 HLA-A*33:03 75 84 10 KFYSKISEYR NetMHCpan 0.2
054 HLA-A*33:03 133 142 10 HNIRGRWTGR NetMHCpan 0.4
055 HLA-A*33:03 144 153 10 MSCCRSSRTR NetMHCpan 0.5
056 HLA-A*33:03 37 47 11 CVYCKQQLLRR NetMHCpan 0.9
057 HLA-A*33:03 52 62 11 FAFRDLCIVYR NetMHCpan 0.2
058 HLA-A*33:03 143 153 11 CMSCCRSSRTR NetMHCpan 0.8
059 HLA-A*33:03 144 154 11 MSCCRSSRTRR NetMHCpan 0.2
3.4. Epitopes cluster analysis
All of the predicted epitopes for HPV-16 E6 and E7 were grouped
into 20 and 10 clusters (Tables 4 and 5), respectively. Among the
20 HPV-16 E6 clusters, cluster 3 contained the most epitopes (10
epitopes), which was represented by HLA-A*31:01 and 33:03. Of
the 10 HPV-16 E7 clusters, cluster 3 contained the most epitopes (5
epitopes), which was represented by HLA-A*01:01 and -A*26:01.
4. Discussion
The E6 and E7 oncoproteins are major therapy targets for
preventing the progression of persistent HPV infection to cancer
and for treating cancers, as their expression is both necessary
and sufficient for the maintenance of the transformed phenotype,
ensuring retention of oncoprotein expression by the tumor
[3,11,12]. In the present study, based on the HPV-16 E6 and E7
protein sequences, we predicted putative HLA-A-restricted CTL epitopes
using immunoinformatic methods.
Modern immunoinformatic methodologies provide new strategies
for the design and synthesis of antigen-specific epitopic
therapeutic vaccines against viral or pathogenic infections [35,36].
The epitopes predicted in the current study could eventually
become therapeutic for HPV infection and cervical cancer, as epitopes
such as YMLDLQPETT 11-20 of E7, which were predicted in this
study, have been reportedly used in clinical trials of E7 peptide

2292 Y. Yao et al. / Vaccine 31 (2013) 2289–2294
Table 3
Predicted epitopes of HPV16 E7 against the nine alleles of HLA-A locus.
No. Allele Start End Peptide length Sequence Method used Percentile rank
1 HLA-A*01:01 4 11 8 DTPTLHEY NetMHCpan 1
2 HLA-A*01:01 18 25 8 ETTDLYCY NetMHCpan 0.4
3 HLA-A*01:01 2 11 10 HGDTPTLHEY Consensus (ANN,SMM) 0.8
4 HLA-A*01:01 19 28 10 TTDLYCYEQL Consensus (ANN,SMM) 1
5 HLA-A*01:01 19 29 11 TTDLYCYEQLN NetMHCpan 1
6 HLA-A*02:01 82 89 8 LLMGTLGI SMM 0.9
7 HLA-A*02:01 83 90 8 LMGTLGIV SMM 0.3
8 HLA-A*02:01 86 93 8 TLGIVCPI SMM 0.1
9 HLA-A*02:01 11 19 9 YMLDLQPET Consensus (ANN,SMM, CombLib Sidney2008) 0.3
10 HLA-A*02:01 11 20 10 YMLDLQPETT Consensus (ANN,SMM) 0.55
11 HLA-A*02:01 59 69 11 CKCDSTLRLCV SMM 0.4
12 HLA-A*02:06 77 86 10 RTLEDLLMGT Consensus (ANN,SMM) 1
13 HLA-A*03:01 89 97 9 IVCPICSQK Consensus (ANN,SMM) 0.6
14 HLA-A*03:01 88 97 10 GIVCPICSQK Consensus (ANN,SMM) 0.6
15 HLA-A*11:01 89 97 9 IVCPICSQK Consensus (ANN,SMM) 0.55
16 HLA-A*11:01 88 97 10 GIVCPICSQK Consensus (ANN,SMM) 1
17 HLA-A*26:01 4 11 8 DTPTLHEY NetMHCpan 0.2
18 HLA-A*26:01 18 25 8 ETTDLYCY NetMHCpan 0.1
19 HLA-A*26:01 4 12 9 DTPTLHEYM Consensus (ANN,SMM) 0.65
20 HLA-A*26:01 18 26 9 ETTDLYCYE Consensus (ANN,SMM) 0.9
21 HLA-A*26:01 18 28 11 ETTDLYCYEQL NetMHCpan 0.6
22 HLA-A*33:03 56 66 11 TFCCKCDSTLR NetMHCpan 0.8
Table 4
Cluster analysis of all epitopes of HPV16 E6 predicted.
Cluster No. Number of epitopes in the cluster Epitope No. Epitope Sequence HLA alleles
1 4 1 AFRDLCIVYR HLA-A*33:03
2 FAFRDLCIVYR HLA-A*33:03
3 RDLCIVYR HLA-A*31:01
4 FAFRDLCIV HLA-A*02:06
2 2 1 AVCDKCLK HLA-A*11:01
2 PYAVCDKCLKF HLA-A*24:02
3 7 1 CMSCCRSSR HLA-A*33:03
2 CMSCCRSSRTR HLA-A*33:03
3 MSCCRSSR HLA-A*33:03
4 RCMSCCRSSR HLA-A*31:01
5 MSCCRSSRTR HLA-A*33:03
6 MSCCRSSRTRR HLA-A*33:03
7 SCCRSSRTRR HLA-A*31:01
4 3 1 CVYCKQQLLR HLA-A*33:03
2 CVYCKQQLLRR HLA-A*33:03
3 VYCKQQLL HLA-A*24:02
5 2 1 DIILECVY HLA-A*26:01
2 TIHDIILECVY HLA-A*26:01
6 1 1 EVYDFAFR HLA-A*33:03
7 1 1 EYRHYCYSLY HLA-A*26:01
8 4 1 FYSKISEYR HLA-A*33:03
2 KFYSKISEYR HLA-A*33:03
3 YSKISEYR HLA-A*33:03
4 YSKISEYRHY HLA-A*01:01
9 4 1 GTTLEQQY HLA-A*01:01
2 GTTLEQQYNK HLA-A*11:01
3 SLYGTTLEQQY HLA-A*03:01
4 TTLEQQYNK HLA-A*11:01
10 2 1 HNIRGRWTGR HLA-A*33:03
2 NIRGRWTGR HLA-A*33:03
11 2 1 IILECVYCK HLA-A*11:01
2 ILECVYCK HLA-A*03:01
12 2 1 ISEYRHYCY HLA-A*01:01
2 KISEYRHYCY HLA-A*01:01
13 1 1 KLPQLCTE HLA-A*02:01
14 3 1 KQRFHNIR HLA-A*31:01
2 KQRFHNIRGR HLA-A*31:01
3 RFHNIRGR HLA-A*31:01
15 1 1 LCDLLIRCINC HLA-A*02:06
16 1 1 LLIRCINCQK HLA-A*03:01
17 3 1 MFQDPQER HLA-A*33:03
2 MFQDPQERPR HLA-A*33:03
3 RTAMFQDPQER HLA-A*31:01
18 1 1 QYNKPLCDLLI HLA-A*24:02
19 1 1 RWTGRCMSCCR HLA-A*31:01
20 1 1 VYDFAFRDLCI HLA-A*24:02

Y. Yao et al. / Vaccine 31 (2013) 2289–2294 2293
Table 5
Cluster analysis of all epitopes of HPV16 E7 predicted.
Cluster No. Number of epitopes in the cluster Epitope No. Epitope Sequence HLA alleles
1 1 1 CKCDSTLRLCV HLA-A*02:01
2 3 1 DTPTLHEY HLA-A*26:01
2 DTPTLHEYM HLA-A*26:01
3 HGDTPTLHEY HLA-A*01:01
3 5 1 ETTDLYCY HLA-A*26:01
2 ETTDLYCYE HLA-A*26:01
3 ETTDLYCYEQL HLA-A*26:01
4 TTDLYCYEQL HLA-A*01:01
5 TTDLYCYEQLN HLA-A*01:01
4 2 1 GIVCPICSQK HLA-A*11:01
2 IVCPICSQK HLA-A*11:01
5 1 1 LLMGTLGI HLA-A*02:01
6 1 1 LMGTLGIV HLA-A*02:01
7 1 1 RTLEDLLMGT HLA-A*02:06
8 1 1 TFCCKCDSTLR HLA-A*33:03
9 1 1 TLGIVCPI HLA-A*02:01
10 2 1 YMLDLQPET HLA-A*02:01
2 YMLDLQPETT HLA-A*02:01
vaccinations [20–33]. The majority of patients with high-grade cervical/vulvar
dysplasia and cervical cancer had a detectable immune
response after injection of the peptide E7 11-20 as therapeutic vaccine
[20–33].
The cluster analysis showed that cluster 1 of the E6 predicted
epitopes included 4 epitopes: AFRDLCIVYR 53-62 and
FAFRDLCIVYR 52-62 bound to HLA-A*33:03, RDLCIVYR 55-62 bound
to HLA-A*31:01, and FAFRDLCIV 52-60 bound to HLA-A*02:06, as
they have the common amino acid sequence, RDLCIV. We combined
these 4 epitopes and our results indicated that the combined
epitope FAFRDLCIVYR 52-62 could be presented by HLA-A*02:06,
HLA-A*31:01, and HLA-A*33:03. Several previous studies have
already proved that parts of the peptide E6 FAFRDLCIVYR 52-62 have
the antitumor effects [30,37]. These three HLA alleles were detected
in 9.9–11.7% of individuals worldwide. As another example, there
were 2 predicted epitopes for E6 in cluster 2: AVCDKCLK 68-75
and PYAVCDKCLKF 66-76 . Both were presented by HLA-A*11:01
and HLA-A*24:02, which included 29.5–30.5% of all individuals
worldwide. These results indicated that the combined epitope
PYAVCDKCLKF 66-76 could be the first choice for producing therapeutic
epitopes against HPV infection and cervical cancer, which
were HLA-A*11:01 and HLA-A*24:02 alleles. Moreover parts of the
epitope E6 PYAVCDKCLKF 66-76 has already been identified in previous
studies [38]. In addition, there were 3 predicted epitopes
for E7 in cluster 2: DTPTLHEY 4-11 and DTPTLHEYM 4-12 bound to
HLA-A*26:01, and HGDTPTLHEY 2-11 bound to HLA-A*01:01, which
contained two HLA alleles that are common to 8.2-8.8% of all
individuals worldwide. The combined epitopes HGDTPTLHEYM 2-12
indicated the it could be presented by HLA-A*01:01 and HLA-
A*26:01 and parts of the epitope E7 HGDTPTLHEYM 2-12 has
already been identified in previous studies [30]. The epitope of
E7 11-20 is presented by HLA-A*02:01, which includes 14.3–15.3%
of all individuals worldwide. As mentioned before, the antitumor
effects of this epitope have been proved in many studies
either in vitro or in vivo [20–33,13]. Therefore, the combined
epitopes FAFRDLCIVYR 52-62 of the E6 protein was presented
by HLA-A*02:06, HLA-A*31:01, and HLA-A*33:03 (9.9–11.7%),
PYAVCDKCLKF 66-76 of E6 was presented by HLA-A*11:01 and HLA-
A*24:02 (29.5–30.5%), HGDTPTLHEY 2-11 of E7 was presented by
HLA-A*01:01 and HLA-A*26:01 (8.2–8.8%), and YMLDLQPETT 11-20
of E7 was presented by HLA-A*02:01 (14.3%-15.3%), which occurs
in over half of all individuals worldwide.
Evasion of the host CTL response through mutation of key epitopes
is a major challenge to natural or therapeutic vaccine-induced
immune control of HPV-16 and treatment of cervical cancer. Therefore,
we compared several most frequent variants of E6 (Such as
L83 V) and E7 (Such as N29H) observed in some studies [39–41]
and predicted the epitope combinations. We found the combined
epitopes did not cover the major mutations in E6 (Such as L83 V)
and E7 (Such as N29H). As a result, the epitope combinations we
predicted might be feasible in therapeutic vaccines of HPV-16.
Our results indicated the likeliness of candidate CTL epitopes
to be used for production of therapeutic vaccines to effectively
control HPV-16 infections and cervical cancer development. The
HPV-16 predicted epitope vaccines based on the HLA-A distribution
could cover most individuals worldwide and overcome the
limitation of MHC-specificity. However, the method used in current
study has only predicted the binding ability between epitopes and
specific MHC molecules, but the predicted epitopes for antitumor
effects should also be proved using peptide-sensitized peripheral
blood mononuclear cells and isolated CD8 + CTL responses in vivo
or in vitro. For example, YMLDLQPETT 11-20 of E7 was presented by
HLA-A*02:01 predicted in current study. Nevertheless, Riemer et al.
found specific T cells recognized and lysed E7 11-19 -loaded but not
E7 11-20 -loaded target cells. Interestingly, both epitopes bind well to
HLA-A*02:01 [42]. Their data underscored the importance of precisely
defining CTL epitopes on tumor cells [42]. Despite several
epitopes predicted in current study were proved to be antitumor
epitopes [20–33,37,38,13,43,44], more experiments should be done
to define the CTL epitopes in the future. This will help to explore the
possibility of these epitopes for adaptive immunotherapy against
HPV-16 infections and cervical cancer.
Acknowledgements
Author contributions: Conceived and designed the experiments:
MY and YY. Performed the HLA data analysis: LX and SW. Performed
the immunoinformatic analysis: HW, YX, and CW. Wrote the paper:
MY and YY.
Funding: This work was supported by grant from PUMC talented
youth project, Yunnan Provincial Science and Technology Department
(2010ZC232), National Natural Science Foundation of China
(30900798 and 31270030) and Fundamental Research Funds for
the Central Universities (2012N08). The funders had no role in
study design, data collection and analysis, decision to publish, or
preparation of the manuscript.
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