Breast cancer vaccination comes to age: impacts of bioinformatics

Introduction

Cancer is known as one of the leading causes of morbidity and mortality worldwide. The global occurrence of cancer is expected to increase up to 15 million with around 12 million deaths in the year 2020.¹ Cancers, as large family of formidable diseases, are involved with the anomalous growth of the cells, which can potentially invade or spread to other tissues. The majority of cancer incidence (90%-95%) is due to the environmental factors while inherited genetics are considered as a minor factor (5%-10%).² Risk of the cancer incidence is significantly increased with age and changes in lifestyle in the currently developing world.

Among various cancers, breast cancer is one of the most common malignancies and the leading cause of death among women worldwide. The incidence of this kind of cancer is increased over the last decade in countries like India and China around 30%, while it is doubled or even tripled in Korea, Japan and Singapore.³ According to the National Cancer Institute (NCI) of the United States, in 2013, there were 232 340 and 2240 new cases of breast cancer respectively in female and male, while the estimated death was about 39 620 in the female and 410 in the male.⁴ The number of breast cancer incidence is expected to grow up to 30%-40% by 2020.⁵ Such an increase requires more attention in terms of developing effective therapeutic and preventive methods for the breast cancer.

A vast number of efforts have been made to prevent, diagnose and treat cancer while several methods have been developed during the last decades regarding stage and type of cancer as well as the medical condition of patients. Chemotherapy, radiotherapy, surgery and biological therapies are the most common treatments that can be planned by physicians depending on different key factors such as patient's age, health, lifestyle, and the type and stage of cancer. The proposed methods have substantially improved cancer-associated morbidity and mortality in the western societies. However, there is a limited access to these advanced cancer therapy tools in the developing countries. Additionally, further improvements in the clinical outcome of the proposed treatment methods seem to be unlikely, in large part because of limitations such as the drug resistance. Altogether, despite substantial advancements in the early diagnosis and treatment of cancer, it remains as a major public health burden around the globe, and hence, necessitates the development of much more effective strategies for the prevention, diagnosis and therapy of cancer.

The use of vaccination for cancer treatment and prevention is highly attractive and innovative and has remained controversial in preclinical and clinical investigations. Despite experimental evidence showing a clinical benefit of the anticancer potentials of vaccines through induction of immune responses, their clinical applications are yet to be approved.⁶

Cancer vaccine can defiantly treat the malignancy through a dynamic activation of the individual's immune system, by which no/little side effects may occur, unlike the commonly used chemotherapies. This strategy can be developed for both cancer prevention and therapy in contrary to the classic concept of the vaccination against infectious diseases. However, designing a vaccine for cancer has confronted with various challenges, in part due to the existence of a large number of potential antigens (Ags) as the target for the immune system. Additionally, many of these Ags may arise during or after the tumorigenesis process. Despite encouraging successes in cancer immunotherapy, the field of vaccine design for the cancer therapy has been a challenging arena for many researchers in the recent decades.

Though remarkable advances achieved in vaccine design, there is no generally accepted universal strategy/tool for rationally designing of vaccines. The procedure for vaccine design is still a time-consuming and costly empirical task. However, the computational methods can significantly be used to design vaccines with strongly reduced time and cost through in silico mapping of thousands of biological components. Recently, the impact of these tools on vaccine design has been highlighted from various aspects such as reverse vaccinology, structural vaccinology, system vaccinology and epitope prediction.⁷

Because of large incidence, the breast cancer is one of the important candidates for vaccine design.⁸ Furthermore, in the last 2 decades, there have been essential signs of progress in analyzing the molecular mechanism of breast tumorigenesis and developing immunologic therapies to fight the tumor-related Ags by means of antibodies (Abs). The growing attention to the immunotherapy of breast cancer has led researchers to work on vaccines to activate the immune system in order to prevent the host cells from the initiation of cancer and/or to destroy the developed cancer cells. The main objective of this review was to investigate the recent developments on the breast cancer vaccine and discuss the role of bioinformatics tools for the in-silico computational design of breast cancer vaccine.

Breast cancer

During initiation of the tumor, a normal cell is altered into a cancerous one through an evolutionary complex process, including several genetic and epigenetic variations. These variations are the basis of the initiation and progression for a tumor in the presence of enough growth signals with an insensitivity to the antigrowth signals, evasion from the programmed cell death, unlimited replicative potential, sustained angiogenesis, and finally the ability to invade and metastasize.^3,9,10 Despite considerable attempts and impressive advances to understand the molecular mechanisms of cancerous cells, it remains a major challenge for biomedical scientists.

Breast cancer is generally acknowledged to be a molecularly heterogeneous disease. It consists of an extensive spectrum of molecular, pathogenic and clinical features with different prognostic and therapeutic consequences ^11,12 Despite continuous study and analysis of these features and considerable achievements, physicians still trust on the traditional clinicopathologic features for tumor diagnosis, and hence, administration of treatment modalities. Recent researches show that a complete characterization of breast cancer can be accomplished by molecular classification as the gold standard.^13,14 Within different schemes for molecular classification of the breast cancer, three special receptors on the outside of the cancer cells play a key role as tumor markers, including estrogen receptor (ER), progesterone receptor (PR), and human epidermal growth factor receptor 2 (HER2). Based on these special receptors, three important subtypes are considered for the breast cancer, including (i) hormone receptor-positive, (ii) HER2-positive, and (iii) triple-negative cancers. The breast cancer is called hormone receptor-positive if it expresses both the ER and PR receptors. The growth of tumor is depended on both receptors. About 60% to 75% of all breast cancer cases have ER and/or PR receptors. Furthermore, the breast cancer expressing HER2 is called HER2-positive and their growth is depended on this gene. About 20-25% of breast cancers encompass this gene. Finally, the tumors without ER, PR, and/or HER2 is called triple-negative and constitute about 15% of invasive breast cancers. These tumors may grow faster than the hormone receptor-positive tumors and more sensitive to the chemotherapy modalities. The above three receptors are routinely found in the breast cancers, which can serve as the reliable markers for making a decision on the treatment modalities.

A number of factors have been shown to increase the risk of breast cancer development, including genetic and non-genetic factors. Among them, around 20% of the breast cancer cases were caused by genetic risk factors.¹⁵ The inherited mutations in a set of 40 genes are significantly associated with the developing breast cancer. Among the genes involved with the breast cancer incidence, mutations in BRCA1, BRCA2, TP53, STK11, CDH1 and PTEN play critical roles in the development and progression of cancer.¹⁰ Mutations in BRIP1, CHEK2, PALB2 and ATM seem to have a moderate role, while mutations in the rest genes show a weak association. BRCA1, BRCA2 and TP53 are the most potent genes to predispose hereditary breast cancer development. Mutations in BRCA1 and BRCA2 appear to extremely affect the lifetime risk of mutation carriers by the age of 70 years with the incident rates of 46-87% and 26-84%, respectively. Mutations in TP53 also associate in the development of breast cancer approximately in 30% of the cases.¹⁶ Additionally, other susceptibility genes have been identified that associate with an increased risk of breast cancer.

An exciting opportunity has been provided through the widespread study of mutations in the breast cancer susceptibility genes to recognize people with a high risk of breast cancer development. However, investigating the impacts of mutations in these genes and translation of their clinical roles appears to be a striking issue for a large number of researchers. A number of online databases are available for breast cancer, including several scientific details related to breast cancer such as Breast cancer Information Core (BIC) database and Breast Cancer Linkage Consortium (BCLC).

Vaccine design for cancer prevention and treatment

The preventive and therapeutic roles of the immune system in the cancer development had remained controversial until the recent observation that confirmed the ability of the immune system to destroy many nascent cancers before they are clinically diagnosed.¹³ However, during the development of cancers, the cancerous cells can often evade the defensive mechanism of the immune system. Accordingly, much attention has been paid to the field of cancer immunotherapy by planning several research projects to design and develop cancer vaccines. For instance, Sipuleucel-T is the first cancer vaccine that has been approved in 2010 by the United States Food and Drug Administration (FDA) for the treatment of prostate cancer.¹⁴ However, the widespread acceptability and usage of vaccine for the prevention and treatment of cancer demand rationalized translational research approaches.

The human immune system defends the body against external invading agents through mounting a humoral or cellular response. During a humoral response, an interaction between B cells and an external antigen leads to additional separation of the B cells into plasma cells or memory cells in the presence of helper T cells. Particular Abs are secreted by the plasma cells against Ags. Furthermore, the memory cells are long-term cells with the ability to show a quick response to the same Ag. On the other hand, through the cellular response, T cells interact with non-native part of the external protein exposed to the surface of other cells by major histocompatibility (MHC) proteins.

A critical role of the immune system is the identification and elimination of tumors – a function so-called immunosurveillance. The tumor cells express Ags that are either under-expressed or are not expressed by the normal cells. The CD4⁺ and CD8⁺ T-cell responses are initiated by the activated professional Ag-presenting cells (APC) such as dendritic cells (DCs), to which tumor cells, in turn, respond by activating different signaling paths and expressing various aberrant Ags.¹⁷ These Ags are processed into the MHC class II-binding peptides by the endosome, or into the MHC class I-binding peptides by the proteasome.¹⁸ The MHC class I peptide epitopes are transferred to the endoplasmic reticulum by the transporter of antigen processing (TAP), and then, translocated to the surface of the cell through interaction with the MHC class I molecules. Concurrently, tumor antigens are presented to both CD4⁺ and CD8⁺ T cells by respectively the professional APC in the presence of MHC class II and MHC class I, with an efficient cross-priming the Ag-specific immune response.¹⁹Fig. 1 shows the Ag-processing pathways within a cell. The CD8⁺ T-cell response is initiated and intensified by the activated CD4⁺ T cells explicitly by making co-stimulatory cytokines, and implicitly by regulating a set of co-stimulatory molecules on the APC providing accessory signals for the activation of T cells.¹⁷ In consequence, as shown in Fig. 2, the CD8⁺ T cells migrate towards the tumor sites and lyse the cancerous cells.²⁰ The tumor cells may also accompany the tumor Ags towards CD8⁺ and/or CD4⁺ T cells specific for the tumor immunity. Accordingly, the primed and activated immune system can detect the tumor Ags, which are harmful to the nature of the ensuing response. Altogether, the activation of the immune system mechanism(s) against the cancer cells can be an effective strategy for the prevention and treatment of cancer.

Fig. 1

Antigen processing pathways in a dendritic cell. The dendritic cells process the Ags through the MHC class II-binding peptides by the endosomes, or the MHC class I-binding peptides by the proteasomes. The MHC class I peptide epitopes are transferred to the endoplasmic reticulum by transporter of antigen processing (TAP). Then, they are translocated to the surface of cell through interaction with the MHC class I molecules. Tumor Ags are presented to both CD4⁺ and CD8⁺ T cells in the presence of MHC II and I.

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Fig. 1. Antigen processing pathways in a dendritic cell. The dendritic cells process the Ags through the MHC class II-binding peptides by the endosomes, or the MHC class I-binding peptides by the proteasomes. The MHC class I peptide epitopes are transferred to the endoplasmic reticulum by transporter of antigen processing (TAP). Then, they are translocated to the surface of cell through interaction with the MHC class I molecules. Tumor Ags are presented to both CD4⁺ and CD8⁺ T cells in the presence of MHC II and I.

Fig. 2

The migration of activated CD⁸ T cells to tumor sites and lyse tumor cells. Once dendritic cells processed the tumor antigens and presented them to the lymphocytes, the CD8⁺ T cells migrate towards the tumor sites. Accordingly, the primed and activated immune system cells, CD8⁺ and/or CD4⁺ T cells, can detect the tumor Ags.

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Fig. 2. The migration of activated CD⁸ T cells to tumor sites and lyse tumor cells. Once dendritic cells processed the tumor antigens and presented them to the lymphocytes, the CD8⁺ T cells migrate towards the tumor sites. Accordingly, the primed and activated immune system cells, CD8⁺ and/or CD4⁺ T cells, can detect the tumor Ags.

Tumor immunity can be activated through the interaction of vaccines with the tumor Ags in various platforms. The vaccine platform is generally designed based on the functions of B cells, T cells, or professional APC.²¹ These platforms may target Ags through peptide, protein or engineered plasmid DNA, or target cells such as DCs, autologous tumor cells or even tumor cell lysates derived from a patient. The active platforms under clinical development are peptide plus adjuvant,²¹ plasmid DNA,²² recombinant virus and bacteria,¹⁷ dendritic cell vaccines,²³ tumor cell vaccines,²⁴ heat-shock protein,²⁵ and exosome-based vaccines.²⁶ A detailed review of these platforms and their advantages and disadvantages have previously been described.¹⁷ In general, a vaccine can directly induce the T-cell immunity in 2 different ways, including genetical modification of tumor cells to express co-stimulatory molecules for the direct presentation of Ags, or modification of professional APC to express tumor Ags by gene transfer or direct loading of Ags.²⁷

Vaccine design for breast cancer

Although the design and development of a vaccine for particular tumor cell Ags appear to be a straightforward approach, there are several potential challenges that make some limitations.²⁸ For example, as a major issue, only low levels of an Ag may be expressed by the tumor cells even if the Ag associates with a specific kind of tumor cell. The Ags may be localized in a cryptic position or secured form. The antigenic profile of tumors may also be modified by growing the tumor or expression of the Ags by a fraction of the tumor cells. Furthermore, the MHC proteins may be expressed in a very low level by the tumor cells, and therefore, a more robust immune response is required to be generated.²⁹ Another issue seems to be associated with the inaccessibility of cells deep within the solid tumors. Despite the existence of the above potential challenges in the immunotherapy of solid tumors, many attempts have been done to design and construct efficient vaccines for the treatment of breast cancer. Table 1 shows a summary of the constructed vaccines for the breast cancer therapy using different Ags expressed in the normal tissues and overexpressed or mutated in tumor cells. Table 2 shows the list of breast cancer vaccines that have been patented.

The aforementioned problems make the development of efficient vaccines very difficult. The essential problem is the self-antigenic action of tumor Ags, at which they may act moderately immunogenic. Several attempts have been done to overcome these difficulties and increase the response of the immune system by choosing proper Ags and effectively presenting them to the immune system. In fact, these Ags may be found in the regular cells, while they are mutated or overexpressed in the cancerous cells. The explored vaccine platforms used in the vaccination of breast cancer include (i) whole tumor cell vaccines (allogeneic, autologous), (ii) dendritic cell vaccines, (iii) recombinant protein vaccines, (iv) peptide vaccines, (v) DNA vaccines, and (vi) recombinant viral vectors. In the following part, we briefly review these platforms.

The impact of bioinformatics on vaccine design

The use of high-throughput methods in different experimental applications of biological studies such as genomics and proteomics have produced a variety of data that could not be handled and analyzed by the traditional methods. During the last years, techniques and applications of bioinformatics have played critical roles in the mining and understanding of large volumes of biological data. Furthermore, rapidly growing of computational methods along with the extensive volume of experimental data on the immunology has created a novel field of research entitled immunoinformatics. The field of immunoinformatics is a subdivision of bioinformatics focused on design and development of computational and mathematical models for the analysis of the immune systems by means of immunological data.⁶⁵ The efficiency of developed vaccines against cancer can excitedly be increased through in-silico modeling and analysis of the cancer cells mechanisms by the immunoinformatics tools. We provide some key insights into the immunoinformatics applications in the following contexts.

Epitope databases

Several publicly available databases have been established to record computationally and experimentally extracted data on T-cell and B-cell epitopes and also binders to the major MHC molecules. Currently, the largest databases in use are SYFPEITHI,⁶⁹ MHCBN,⁷² and IEDB.⁷³ Of these, IEDB is a well annotated and frequently maintained repository that provides wide-ranging tumor-related information and their experimental details. The SYFPEITHI is a continuously maintained database that is used in epitope prediction and binding motifs analysis and consists of constitutive MHC binders and T-cell epitopes collected from different resources. A number of databases have been established for the discovery of cancer vaccine target, including DFRMLI,⁷⁴ CIG-DB,⁷⁵ TANTIGEN,⁷⁶ and CTDatabase.⁷⁷ The DFRMLI is a database of immunological datasets from important databanks that is used for the machine learning applications for training and testing purposes. The CIG-DB classifies T-cell receptors and immunoglobulins for the cancer treatment and hematological tumors groups by mining, training, and clustering of literatures (the server is currently not available). The Peptide Database includes a list of manually organized T-cell defined tumor antigens as well as an integrated unique categorized library of overexpressed tumor antigens. The TANTIGEN provides a scheme for the classification of Ags and their annotation. It also provides detailed information on the T-cell epitopes and experimentally confirmed HLA ligands. The CTDatabase is also called "Cancer-Testis" and consists of only Ags from the last category. Furthermore, Bcipep⁷⁸ and CED⁷⁹ are databases for the B-cell epitopes, providing a great possibility for an expressive measurement of epitope immunoproperty. Except for the MHCBN and EPIMHC, all the aforementioned databases can be integrated with experimentally derived information collected from the literature. Fig. 3 shows schematic representation for the publicly available databases for the epitope prediction that are grouped into three categories, including B-cell, T-cell and HLA databases.

Fig. 3

Databases for B-cell and T-cell epitope selection.

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Fig. 3. Databases for B-cell and T-cell epitope selection.

Conclusion

Computational and mathematical techniques have been widely used for modeling, designing, developing and analysis of the cancer vaccines. They are considered as important research tools in drug discovery for cancer immunotherapy. Today, genome databases increasingly accumulate a large volume of data, including critical data on the human immunologic mechanism. This has coined the field of immunoinformatics that deal with data collected from computational approaches and experimental immunology. The output of immunoinformatics studies provides foundations for the design and development of drugs and vaccines using in silico approaches.

The role of the immune system in controlling cancer cells has recently been annotated for the cancer treatment and prevention based on immunotherapy approaches. The developed computational models for simulation of the immune system processes give pivotal insights to understand the kinetics of cancerous cells, their biological pathways, and underlying mechanisms. Furthermore, the complex interactions between evolving pathogens and the host cellular immune system is characterized and investigated through the immunoinformatics tools. As a result, design and development of laboratory experiments is made highly easier without the uncertainty of systems.

Ethical approval

None to be declared.

Conflict of interest

Authors declare no conflict of interest.

Acknowledgment

The authors like to acknowledge the Research Center for Pharmaceutical Nanotechnology at Tabriz University of Medical Sciences and also the Department of Computer Science, Faculty of Mathematical Sciences, the University of Tabriz for their technical support. Also, the authors would like to thank the Iranian Elite National Foundation “Bonyad Melli Nokhbegan” and the Iranian National Science Foundation (INSF) for the financial support (Grant No: 95005404).

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