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Breast cancer vaccination comes to age: impacts of bioinformatics

Introduction: Breast cancer, as one of the major causes of cancer death among women, is the central focus of this study. The recent advances in the development and application of computational tools and bioinformatics in the field of immunotherapy of malignancies such as breast cancer have emerged the new dominion of immunoinformatics, and therefore, next generation of immunomedicines . Methods: Having reviewed the most recent works on the applications of computational tools, we provide comprehensive insights into the breast cancer incidence and its leading causes as well as immunotherapy approaches and the future trends. Furthermore, we discuss the impacts of bioinformatics on different stages of vaccine design for the breast cancer, which can be used to produce much more efficient vaccines through a rationalized time- and cost-effective in silico approaches prior to conducting costly experiments. Results: The tools can be significantly used for designing the immune system-modulating drugs and vaccines based on in silico approaches prior to in vitro and in vivo experimental evaluations. Application of immunoinformatics in the cancer immunotherapy has shown its success in the pre-clinical models. This success returns back to the impacts of several powerful computational approaches developed during the last decade. Conclusion: Despite the invention of a number of vaccines for the cancer immunotherapy, more computational and clinical trials are required to design much more efficient vaccines against various malignancies, including breast cancer.

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 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 crosspriming the Ag-specific immune response. 19 Fig. 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.
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 exosomebased 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 costimulatory 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.

Autologous tumor cell-based vaccines
Autologous tumor cell-based vaccines (ATCVs) are based on the tumor cells lysate obtained from patients. Then, the vaccine is used to train the immune system to detect and kill the cancerous cells. The ATCVs consist of several known and unknown potential Ags, and therefore, they have a variety of epitopes giving the capability of replying to a highly different set of tumor cells. 56 In addition, all the existing Ags within the ATCVs are extracted from the antigen repertoire of a patient. In summary, the major advantages of this kind of vaccines are their safety, multivalency, and patient specificity, at which it can be called as personalized vaccination. However, these vaccines have poor immunogenicity and production inconsistency. 57

Allogeneic tumor cell-based vaccines
Allogeneic tumor cell-based vaccines are another type of vaccines that use the collected cell lines from a similar class of cells. This kind of vaccine is a cost-effective, reproducible and easily designed type of vaccines in comparison with ATCVs, in large part because of simplicity in mass production and storage. Furthermore, the allogeneic cell lines have the capability to contain one/ two tumor-associated Ags for a specific tumor with a low complexity of tumor's Ag, inducing unfavorable selective pressure and promote tumor escape. 57

Peptide-based vaccines
Peptide-based vaccines are another approach of the cancer immunotherapy, in which peptides are directly derived from the tumor-associated Ags (TAAs). These vaccines are specifically designed to associate with T cells in the presence of the MHC class I or II molecules. The most common TAA epitopes of the breast cancer are extracted from HER2/neu, MUC1, and CEA proteins. The TAA extracted peptides can be effectively utilized to stimulate the responses of CD4 + and CD8 + T cells. Hence, it is important to find appropriate peptides adapting to natural variations of human leukocyte antigens (HLAs).
Comparing with other therapies and vaccines, the most attractive advantage of this approach is the specificity of the target response and its low expected toxicity. The peptide-based vaccines are easily manu factured, and can induce a fairly high-level of immunological response. The efficiency of peptide-based vaccines as neo-adjuvant immunotherapy has experimentally been proven in some experiments on NeuVax-E75 (epitope for HER2/neu and GM-CSF) and DPX-0907 (HLA-A2-TAAs) expressed in the breast, ovarian and prostate cancers. 58 Peptides have important roles in the early diagnosis of breast cancer, and consequently, in decreased mortality.

Dendritic cell vaccines
Dendritic cells are principally the major regulators of the immune response. They are known as the major Ag-presenting cells for the preparation and activation of the immature T cells against tumor cells, at which many researchers have been motivated to develop several dendritic cell-based immunotherapies. 59 The dendritic cell vaccines are constructed from the immune-stimulating white cells of patients -a type of personalized medicine.  A patient with non-lactating female human is immunized by this method against a human α-lactalbumin. The process of inducing an immune response against α-lactalbumin is comprised by the method by a step-by-step managing an immunogenic composition, the immunogenic composition comprising an adjuvant; and a polypeptide comprising a human α-lactalbumin sequence

US5922836A Washington University
Mamma globin The invention proposes a vaccine for treatment of breast cancers expressing mammaglobin. It consists of at least one B cell mammaglobin antigen, one T C mammaglobin antigen for inducing antibody and/or cell-mediated immune responses against mammaglobin-expressing tumors.
Jul. 13, 1999 55 In other words, the cells are taken out of the patient's blood, activated in the lab away from the tumor influence, and then, injected back into the patient. It has previously proven that the immune cells have less ability to recognize and target HER2 as a most frequently overexpressed protein in the breast cancer (20-25%). 29 Thus, the DCbased vaccines can be an effective solution to re-stimulate the immune system. This strategy has previously been developed by researchers in the form of HER2 targeted vaccine on the breast cancer cells. These kinds of vaccines seem to be well-tolerated, and by use of which patients might only experience trivial toxicity.

DNA vaccine
The DNA vaccines represent an attractive immunotherapeutic method for the cancer treatment regarding its simplicity, and stability. Norell et al 60 developed a vaccine to target the HER2 Ag and to serve as an immunotherapy modality against breast cancer. In a primary clinical test, a DNA plasmid encoding full-length version of HER2 was used along with low doses of IL-2 and granulocyte-macrophage-colony-stimulating factor (GM-CSF) in an experimentally handled vaccine in the metastatic HER2-expressing breast cancer patients. In another preclinical attempt, 61 the human mammaglobin-A (Mam-A/MGBA) cDNA encoding vaccine was engineered to activate the Mam-A-specific CD8 T-cell immune responses. Several clinical studies have proven that DNA vaccines can be safely used without concerning clinical toxicity or autoimmunity. 62, 63 DNA vaccines can also be designed for long-term cancer protection, in large part because of being very cost-effective. Although DNA vaccines display some advantages, the induction of powerful Ag-specific cellular immune responses against endogenous self-antigens of solid tumors remains as a major striking challenge. 22

Viral vector-based vaccine
The viral vectors have also been used for the vaccination, in part due to their ability in an efficient delivery of genes. They show high in vitro and in vivo transgene expression and elicit low toxicity, yet are often immunogenic. They have been used to serve as Ag-specific vaccines for the activation of the immune response in the tumor microenvironment. Mam-A, as one of the most frequently overexpressed proteins in the primary and metastatic breast cancers in the human, has been exploited for the production of the viral vector-based vaccines. In a study, the Mam-A was inserted into a replication-deficient adenovirus vector, by which the dendritic cells prepared from the healthy female individuals were transduced resulting in the stimulation of CD8 + CTLs in vitro. The transfection with adenoviral vectors encoding Mam-A was shown to improve the maturation of DCs and secretion of IL-12. 64

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.

Cancer vaccine modeling and design by computational approaches
The computational models can principally guide biologists from a quantitative data to a qualitative and thus predictive knowledge. In particular, the therapeutic effects at the organism level can be efficiently studied, predicted and optimized by the mathematical modeling of the immune system. These models can be used to simulate the behavior of the immune system in response to various pathogens and immunosurveillance of cancer. Furthermore, insilico simulation of physiological and pathophysiological interactions at the cellular and molecular levels can be used to provide key information prior to laboratory-based experiments.
In computational biology, designing an effective vaccine for cancer therapy is one of the most exciting challenges regarding the highly complex system of tumors, where various interactions may occur, including events and conditions leading to the initiation, progression, invasion and metastasis of cancer cells. It seems that adaptive learning techniques might be useful for the development of the models at the hierarchy of organism, organ, cell, and molecule levels. The developed models simplify understanding of the general behavior of the system such as cellular and molecular interactions, the course of a disease and the effects of treatment through computational simulation of the system.
During the last couple of years, a number of models have been proposed for the computational modeling of the cancer vaccines. SimTriplex is an agent-based approach for the modeling of cancer vaccine, which has especially been designed for the simulation of triplex tumor-preventive cell vaccines and its effects on HER2 transgenic mice prone to the breast cancer development. 66 MetastaSim is another model inspired by the SimTriplex model sharing a similar framework for modeling the biological mechanisms. 67 Furthermore, several types of research have been done to investigate the response of the immune system against the cancer cells through computational modeling. 68 To this end, a mathematical model 69 has been developed to simulate the dynamic growth of an immunogenic tumor in which an active immune response exists. The model especially focuses on the interactions between cancer cells and cytotoxic T cells and antigen presenting cells in small multicellular tumors. The above developed computational models provided the ability to design and establish efficient therapeutic strategies for the cancer treatment.
Besides the above tools for the computational modeling of cancer vaccines, a number of tools are available for the development of cancer vaccines. The tools can be used in a step-by-step in-silico procedure to design and construct a targeted vaccine. First, several analyses are done over the primary sequence of the target protein to investigate its structural and biochemical characteristics. Then, a set of tools are utilized to predict and select the appropriate B and T-cell epitopes using epitope databases. Finally, the selected epitopes are fused together via proper linkers to construct the final cancer vaccine sequence. The constructed vaccine sequence is submitted to a homology modeling software to build its 3D structure and validate its functional behavior.

B-and T-cell epitope prediction
An important research field in immunoinformatics is the development of algorithms for analyzing potential B-and T-cell epitopes. The algorithm has the ability to efficiently analyze the potential binding sites within the sequences of B-and T-cell epitopes instead of the laboratory analysis of pathogen gene products. The anti-tumor behavior of the immune system is potentially stimulated when a short deletion or insertion or substitution in DNA is discriminated as non-self. A mutation imposes novel peptide(s), which are presented by the MHC molecules on the surface of the cell and can be detected by T helper or cytotoxic T lymphocytes (CTL). Thus, it is required to look for a set of potentially immunogenic peptides, and then, analyze these sequences by the epitope prediction tools. Results of the analysis lead an immunologist to in silico design and development of new vaccines. This strategy is called reverse vaccinology, where the pathogen genome is analyzed to identify the potential antigenic proteins. 70 Additionally, a major goal in the epitope prediction is to analyze the binding affinity of antigenic peptides to the MHC molecules. The most fruitful proposed methods for the prediction of T-cell epitopes have been designed based on data-driven approaches. The methods first look for the peptide-binding property of particular class I or class II MHC alleles, and then, in-silico prediction of epitopes is done. A variety of tools and techniques has been proposed for the epitope prediction and investigation, including (i) homology modeling, (ii) protein threading, (iii) docking techniques, and (iv) identification of structural binding motifs. Alternatively, the role of interactions between antigen and antibody in the human immune system can be studied by analyzing whole mutated antigen sequence. Conventional methods may face different issues and limitations, including the use of high cost and timedemanding procedures for the pathogen cultivation and subsequent protein extraction and testing. 71 Taken all, computational tools provide the ability for the design of efficient vaccines by saving time and resources.

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 tumorrelated 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, Bcipep 78 and CED 79 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.

Molecular docking
Protein-protein interactions (PPIs) provide important underlying mechanisms of the biological systems. 62,80 The structure and mechanism of protein-protein complexes provide enormously valuable knowledge for several biological applications especially for various groups of medicines, including chemotherapies, antimicrobial drugs and vaccine design. In silico molecular docking is known as a promising approach for the structural investigation and systematic analysis of PPI mechanism at the atomic level. A docking algorithm generally predicts the 3D-structure of PPIs through analyzing its molecular component structures. The algorithm uses various computational methods over the structural profile of proteins to decipher potentially suitable binders. Some methods have previously been developed to investigate the PPIs through molecular docking of proteins. The methods are available in the form of the standalone programs, including Autodock, 81 Gold, 82 ZDOCK, 83 RosettaDock, 84 and some others. Besides, some of the programs are also available via public Web servers that may display some limited options for the simulation of a single proteinprotein docking, while they are not relevant for large-scale studies. Docking tools can be used to analyze the MHC molecules by investigating peptides. The EPIDOCK is a structure-based Web server that is broadly employed for the prediction of MHC binding peptides. 85 The output of a docking tool is deemed to be very useful for the prediction and binding of a T-cell epitope. Consequently, for in-silico search for the possible antigenic determinant of the MHC molecule binding that can be used to successfully design and develop cancer vaccines.

Protein 3D structure prediction
Computational prediction of protein 3D structure is an essential and critical step in different bioinformatics studies. The methods generally work based on the fact that similar sequences are folded into similar 3D structures. Thus, they look at sequences with the known 3D structure for high identical sequences of the protein of interest. Then, a 3D model is constructed for the novel sequence using the structure of the most similar sequence as a template. Finally, the constructed model is refined to minimize its energy level and increase its quality. 86 In the field of vaccine design and construction via multiepitope peptides, it is required to predict an appropriate 3D structure for the designed vaccine sequence. To this end, the sequence can be submitted to online homology modeling web servers such as Phyre2, 87 I-TASSER, 88 and SWISS-MODEL, 89 or use standalone programs such as MODELLER. 90 The quality of the constructed model is evaluated using different online tools, including ERRAT, 91 Verify 3D, 92 and ProCheck. 93

Molecular dynamics simulation
The computational process of molecular motions is called molecular dynamics. In molecular dynamics simulation, the physical movements of atoms and molecules are studied through a computational method. During the simulation, the interaction between atoms and molecules are modeled for a fixed period of time. The trajectories of atoms and molecules are simulated using Newton's equations for interacting particles motion within a system. Furthermore, the potential energies of particles and their interatomic forces are computed via molecular mechanics force fields. Such approaches were initially proposed by Fermi et al 94 and Alder et al, 95 and then, widely used in related fields of biomolecules, materials science and chemical physics.
Furthermore, in the field of vaccine design, it is highly important to minimize the energy level of the constructed vaccine and to exclude poor molecular contacts. In this line, the vaccine must be submitted to an MD simulation software such as GROningen MAchine for Chemical Simulations (GROMACS) 96 or Chemistry at Harvard Macromolecular Mechanics (CHARMM) 97 or Assisted Model Building with Energy Refinement (AMBER). 98

Machine learning applications in immunoinformatics
The increasing growth and complexity of the generated biological data prove the importance of the design and development of intelligent computational methods for understanding and analyzing these data. Today, machine learning techniques play an important role in different applications of the computational biology. The techniques have been widely utilized in the development of different immunoinformatics tools for in-silico modeling, prediction, and simulation of the immune system mechanisms. 99 Specifically, the machine learning methods can be efficiently used to simulate the classical experiments to select vaccine targets with a reduced time and cost. 74 Accordingly, researchers have recently paid high attention to the value of machine learning approaches in immunology.
To date, machine learning techniques have been used in a variety of immunoinformatics applications including analysis of antibodies and their mechanisms, 100 investigations of antigens, 101 studies of allergenicity, 102 classifications of immunological data 103 and establishment of protocols for vaccine design. 104 The design, development, and optimization of vaccines are facilitated and improved using these practical applications. The task is highly complex and involves with the selection of components for the formulation of vaccines through a significant number of experiments and in silico analysis. Machine learning methods have recently been given a privileged role in proper selection of targets and reduction in the number of required experiments over a large domain of possible combinations of components. Several machine learning techniques have been employed for development of immunoinformatics tools, including artificial neural networks (ANNs), 105, 106 support vector machines (SVMs), 107 hidden Markov models (HMMs), 108, 109 and many others. The techniques give to computers the ability to learn without explicitly developing a computer program. They commonly construct a model based on a set of training samples as input observations in order to produce an output for data-driven prediction or decision making. These data-driven algorithms are relevant for a range of computing tasks which programming a set of strict instructions is infeasible or highly complex. The useful tools in immunoinformatics are summarized in Table 3.

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