Open access peer-reviewed chapter

Mimetic Vaccines in Immuno-Oncology

Written By

Anastas Pashov and Thomas Kieber-Emmons

Submitted: 04 January 2019 Reviewed: 04 March 2019 Published: 15 April 2019

DOI: 10.5772/intechopen.85593

From the Edited Volume

Cancer Immunotherapy and Biological Cancer Treatments

Edited by Hilal Arnouk

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Abstract

While the interest in cancer vaccines is renewed by some results in vaccine-based clinical trials, the premise still suffers from the incomplete concept of a successful vaccine. Future progress may come from matching preclinical data with clinical expectations while taking a step back to understand the systems perspective. A field that benefits most from this bird’s eye view is tumor immunology. For instance, the accumulation over the last three decades of clear associations of T and B cell cross-reactivity between a set of host targets of autoimmunity and microbial antigens strongly supports a pathogenic role for molecular mimicry. Mimicry on its turn invites the concept of networks of molecular interactions. The intentional and rational approach to exploit mimicry in cancer vaccine development, while littered with failure, has provided also some insight into success. Here, we visit successes and underlying rationale to lend to future development of mimetic vaccines in immune-oncology.

Keywords

  • vaccine
  • anti-idiotype
  • peptide
  • tumor associated carbohydrate antigens
  • carbohydrate mimetic peptide

1. Introduction

Targeting malignancies through manipulating the immune system has seen success in a variety approaches ranging from whole cell vaccination, to autologous dendritic cell based vaccines and therapeutic immune-modulation [1, 2, 3, 4, 5]. But a number of opportunities and challenges remain. While tumor antigen identification from sequencing the cancer genome continues to be a high priority we now know that tumor antigens arise from multiple mechanisms that include somatic mutations, translocations, and amplifications and post-translational modifications. The role of post-translational modification with tumor associated carbohydrate antigens (TACA) in the generation of novel cancer antigens is in particular an opportunity to be explored [6, 7, 8, 9].

Characterizing and overcoming the immunosuppressive environment of the tumors has led to a focus on downstream checkpoints that regulate activated T cells, or on vaccination and T cell adoptive transfer to expand the T cell pool [10, 11, 12]. However, it is well known that cancer-signaling pathways play pivotal roles in the biologic behavior of tumor cells that creates an opportunity to rethink cancer in general [13] and rethink cancer targeting strategies with small molecules [14, 15], with monoclonal antibodies [16] and induced antibodies [17, 18]. By the same token such pathways are also involved in developing therapeutic resistance, which requires alternative immunotherapeutic strategies. One such strategy is to develop polyclonal humoral immune responses by active immunotherapy. Itself, this concept can have multiple approaches and an orchestra of potential mechanisms that encompass a dynamic systems immunology perspective (Figure 1). On the one hand the effect can be pursued by formulating a platform with multiple epitopes of target antigens [19]. On the other—making use of polyspecific (pan-antigen) mimetics to target simultaneously multiple antigens on cancer cells [17, 18]. A polyclonal antibody approach would target more than two antigens on a single tumor cell, which is expected to have even higher potential. This latter idea is a part of the conceptual evolution in immune-oncology harnessing polyclonal responses to cancer cells.

Figure 1.

The concept of mimetic vaccines in oncology. On the one end the spectrum of B cell subsets includes high affinity/specificity clones generated by somatic hypermutation in B2 follicular cells under conditions of strict tolerance to self. On the other, are the innate like B1a cells producing constitutively poly/autospecific natural antibodies. Carbohydrate specificity and anti-idiotype interactions are related more to the later compartment. Polyspecific vaccines based on carbohydrate mimotopes or idiotypes recruit B cell clones across that spectrum but their novel properties are related mostly to their capacity to elicit diversified responses from MZ and B1 cells including idiotypically connected clones. In addition, the mimotopes capture only the most salient features of the carbohydrate epitopes and induce diversified responses targeting multiple antigens (illustrated by diverse words sharing only partially the topology of the mimotope as compared to highly specific responses that match the shape of the epitope). Thus, mimetic vaccines both target polyspecific compartments of the B cell repertoire as well as they themselves function as polyspecific antigens.

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2. Setting the stage: systems concepts

Systems immunology is now in focus to understand the immune system [20], especially in the context of vaccinology [21]. This perspective is ushering in a new era in vaccine development [22]. For the future, it is argued that successful approaches will depend on the elucidation of the entire network of immune signaling pathways that regulate immune responses with an eye toward integrating advances in computational and systems biology, genomics, immune monitoring, bioinformatics and machine learning [22, 23].

Systems immunology also teaches us that one antigen can substitute for another having the potential to regulate tolerance [24, 25, 26, 27, 28]. However, it is unclear why an immune system that is tolerant of its own self-antigens would respond to a self-antigen mimic in a vaccine. Antibodies referred to as anti-idiotypic are produced during the process of tolerization and demonstrated in tolerant animals [29, 30] and in patients [31]. These antibodies may prevent a B cell receptor from interacting with the antigen. Jerne envisioned the immune system as a web of immunoglobulin V domains constituting an idiotypic network. Inherent to the idiotype network is that antibodies recognize antibodies. Jerne thought that regulatory processes governed by idiotypic interactions could explain the generation of the various immune states that include tolerance.

An extension of the network theory was that antibodies, by virtue of being recognized by antibodies, might function as mimics of antigens that would break tolerance instead of maintaining it—the so-called Ab2, used as antigen surrogates [32]. Thus a new context of molecular mimicry was born—one highlighted by the functionality of idiotypic antibodies in the context of the idiotype network theory [33, 34, 35, 36, 37, 38, 39].

Smaller fragments (peptides) of anti-idiotypes proved to translate successfully to vaccines too [40]. Peptides as mimics of antigens were clearly defined with the advent of phage screening technology [41, 42] growing in its application in biomedical sciences [43]. Peptide mimics are well defined as B and T cell epitopes [44]. Now there is an unprecedented opportunity to unravel the intricacies of the human immune response to immunization. Yet, fundamentally, vaccine strategies across susceptible disease depend on the identification of immunogenic antigens that can serve as the best targets [45, 46, 47].

Tumor antigens present a special challenge. Except for small details defined by mutations or altered post-translational modifications, generally they are self-antigens and this poses a barrier to effective vaccination. Tolerance is different from non-specific immunosuppression, and immunodeficiency. Like immune response, tolerance is specific existing both for T-cell and B cells and, like immunological memory tolerance is lasting longer at the T cell level than at the B cell level. Maintenance of immunological tolerance requires persistence of antigen. Tolerance can be broken naturally or artificially [48, 49]. Mimicry might impact on an already existing autoimmune process rather than precipitate novel disease by breaking of tolerance from the beginning [50]. While molecular mimicry is proposed as a basis for potential pathogenesis of some human disease, there are examples also of its exploitation in vaccine development.

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3. Polyclonal activation

Now it is acknowledged that the natural antibody repertoire is created in the absence of exogenous antigens and/or germinal center maturation [51, 52]. It is also acknowledged that these preexisting antibodies can be affected by the presence of exogenous antigen since they recognize in a polyspecific manner evolutionarily fixed epitopes present in foreign antigens as well as on self-antigens [53]. Because of their constitutive expression, responses by natural antibodies are generally excluded from vaccine strategies. Among approaches that can modulate the natural antibody repertoire are immunizations affecting idiotypic interactions. When possible, an “idiotypic vaccination” could be a little explored way to activate the B and T cell cascades involving the natural responses against antigens.

Once acclaimed, idiotypy—the theory that the B lymphocyte repertoire forms a highly connected network of mutually recognizing and stimulating clones [54, 55]—unfortunately predated the discovery of many more levels of immune system complexity. The daunting task of attuning to the new knowledge prevented this theory from maintaining a support that would match its intellectual attractiveness. The first significant update, which almost rehabilitated it, stated that only the compartment of the B cell repertoire characterized by germline variable regions and the prerequisite physiological poly/autoreactivity forms this network [56]. Almost, because many immune system phenomena like a self-assertive rather than ignorant tolerance or the immune memory ultimately do not need to be explained by emergent properties of the immune network. Now it is accepted that specific cell populations and genetic programs rather than the dynamics of a network of functionally equivalent agents (clones) are responsible for almost all of the observed immune phenomena. In fact, recent development in our understanding of swarms of simple agents uncovers the limits of such systems where complexity of the behavior of the agents and the size of the system have Goldilocks conditions for optimal behavior [57]. No wonder, evolution has used the “swarm” solution rarely and ultimately replaced it (complemented it) in most cases by centralized systems and specialized components at a higher level of organization.

Another reason the intellectually attractive “second generation network” hypothesis also fails is may be the fact that the compartment producing natural antibodies is defined in many ways and even 25 years later is still rather a fuzzy set [53]. While the natural antibodies in a strict sense are produced by a particular subset of B1 cell derived plasma cells in the bone marrow without external stimulus there are B1 cells (e.g. B1b) and marginal zone cells that produce antibodies with many “natural” characteristics like polyspecificity in response to stimulation [58]. Thus, focusing on the naturally autoreactive compartment of the repertoire did not answer all questions but also added another dimension of uncertainty.

Natural antibodies are known to bind to a variety of antigens that are both self and exogenous and thereby providing one of the first lines of defense against both bacterial and viral pathogens [53, 59]. Antibodies reactive to self-antigens play a key role in both healthy individuals and patients with autoimmune disorders [60, 61, 62]. Hence, such antibodies are intrinsically multifaceted in their regulatory roles in immune responses and tolerance. While the immune response activated against self can be detrimental when triggered in an autoimmune genetic background, tuning immune activity with natural antibodies is a potential therapeutic strategy. One conceptual approach in this tuning is using naturally occurring anti-idiotype (anti-Id) antibodies to stimulate multifaceted natural antibodies.

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4. Anti-idiotypic antibodies as mimics

Anti-idiotypic based vaccines have a long history of generating immune responses in experimental animals and in humans [27, 28, 63, 64, 65]. One of the first demonstrations for the basis of molecular mimicry observed between proteins and anti-idiotypes for proteins was dissected in the TEPC-15 idiotype system [66]. Vasta et al. [66] illustrated that mimicry could be at the sequence level. They suggested that the minimal stretch of homology (8–10 amino acid residues) was responsible for the cross-reactive nature of the TEPC-15 idiotype and the acute-phase protein C-reactive protein (CRP) from the horseshoe crab Limulus polyphemus (limulin). Of no less importance, it was shown that T helper cells could recognize a shared determinant that is present on idiotypically different myeloma proteins [67]. These findings collectively showed that T helper cells, induced by priming with antigen, can recognize shared idiotypic determinates, suggesting that peptides derived from anti-idiotypes can be processed as immunogens [40, 68].

The early studies of anti-idiotypes made clear the idea that functional mimicry of ligands of biological receptors is a matter of just binding to an antibody-binding site. This functional or antigenic mimicry ushered in concepts and a technology. It was evident that structural and immunological rules governing molecular mimicry require definition for its successful exploitation whether anti-idiotypes, small fragments derived from them or peptide mimetics [69, 70]. It was suggested that ligand-based pharmacophore design principles could be applied to designing peptides that can mimic ligands reactive with antibodies [69, 71]. Often times it was stated that there were no observable structural correlations to explain the mimicry [72]. Yet it seemed that antibodies could mimic antigens at the molecular level whereby the antigen and anti-idiotype could bind essentially the same combining-site residues of the Ab1 antibody [73].

Historically, clinical trials with anti-idiotypes in the cancer space have proved to be of mixed success [74, 75] but, clearly showing that humoral and cellular immune reactivity against a tumor can be enhanced upon active anti-id vaccination [76]. Other studies with anti-ids in humans have included those associated with tumor associated carbohydrate antigens (TACAs), [77, 78, 79, 80]. An anti-Id vaccine, Racotumomab, raised against the murine anti-ganglioside N-glycolyl (NGc) GM3 (NGcGM3) has shown efficacy [81] in several phase I trials in melanoma, breast and lung cancers [82, 83]. These examples are representative for other anti-Id vaccine trials. In sum they indicate the induction of B and T-cell immune responses against a tumor.

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5. Mimetic peptides in immuno-oncology

From a technology perspective the concept of developing and screening combinatorial or random peptide phage display became an effective means of identifying peptides that can bind target molecules and regulate their function [41, 42]. Phage-displayed peptide libraries have proved effective for (i) mapping of B and T cells epitopes, (ii) defining bioactive peptides that bind to receptors, (iii) selection of cell/organ specific binding peptides, and (iv) identification and development of peptide-mediated drug delivery systems to mention a few applications [43]. Among concepts emphasized by phage screening technology was that of the mimotope. The term mimotope, coined by Mario Geysen in 1986 [84] described a peptide mimicking a discontinuous antigenic determinant on foot and mouth virus. Phage screening technology has evolved, giving us unparalleled access to tight binding peptides to significantly accelerate identification of new leads for drug discovery [85].

The ability to produce combinatorial peptide libraries with a highly diverse pool of randomized ligands has transformed phage display into a straightforward, versatile and high throughput screening methodology for the identification of potential vaccine candidates against different diseases that include cancer [86, 87, 88]. While most studies with mimotopes identified by phage screening are still in preclinical studies, immunization results do provide insight for future development of novel mimotope-based tumor vaccines [89, 90, 91, 92]. Starting from phage screening, we have developed carbohydrate-mimetic peptide (CMP) vaccines that target carbohydrate antigens [70, 71]. We brought CMPs from preclinical assessments of mimicking peptides of TACA [93, 94, 95] to clinical studies [17, 18] where one peptide can induce polyclonal responses to two or more antigens, which do or do not share epitopes.

Clinically we have shown that CMPs can achieve this multi-epitope targeting. The peptide P10s is a CMP designed to mimic both LeY and GD2 antigens using anti-LeY (BR55-2) and anti-GD2 (ME36.1) antibodies as templates [95]. Therefore, vaccination with P10s may lead to targeting various molecular entities associated with glycoproteins and with glycolipids, reducing the possibility of immune editing and escape. Moreover, the P10s vaccine has the potential to activate cellular responses [96]. We completed a phase I clinical trial of the P10s vaccine in breast cancer patients and showed its feasibility, safety and immune efficacy. The data indicates induction of anti-peptide and anti-glycan antibodies [17, 18]. Antibodies of immunized subjects mediated cytotoxicity on human breast cancer cell lines through currently unknown mechanisms independent of complement-mediated cell cytotoxicity, but had no effect on normal breast cell line MCF-10A [17, 18]. Serum antibodies parallel the effect of anti-LeY and anti-GD2 monoclonal antibodies. After more than 6 years of follow up, 4 out of 6 vaccinated subjects are still alive with 3 of them in remission. Our clinical data suggest that vaccination of breast cancer patient’s results in tumor regression and survival benefit.

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6. Linking signaling with polyclonal response

The complexity of glycans found on the cell surface argues for their informational role involved in regulating multiple cellular processes essential for tumor development or its metastases. Since TACAs are expressed on glycoproteins and glycolipids that regulate multiple cellular pathways, TACAs are by definition pan-targets. Many glycoproteins and glycolipids are associated with signaling cascades through Focal Adhesion Kinase (FAK) with its activation hypothesized to play an important role in the pathogenesis of human cancers [97, 98]. FAK is a non-receptor tyrosine kinase that plays an important role in signal transduction pathways that are initiated at sites of integrin-mediated cell adhesion and by growth factor receptors [99]. FAK is also linked to oncogenes at both a biochemical and functional level. Moreover, overexpression and/or increased activity of FAK are common in a wide variety of human cancers, implicating a role for FAK in carcinogenesis. It is therefore a key regulator of survival, proliferation, migration and invasion: signaling cascades and processes that are all involved in the development and progression of cancer. FAK localized at focal contact sites and communicates with TACA-expressing molecules. Coordinated and localized stimulation of these cascades influences focal contact turnover and actin cytoskeleton dynamic addition to expression of motility- and invasion-associated proteins such as matrix metalloproteinases. FAK-dependent regulation of chemokine’s and cytokines in cancer cells can drive elevated levels of regulatory T cells into the tumor environment resulting in suppression of the anti-tumor CD8+ T-cell response [100].

FAK is associated with several mechanisms to regulate cell migration and invasion through its phosphorylation. These include interactions with Src, P13K, Grb7, N-WASP and EndoII. Interaction with integrin also mediates FAK association with extracellular matrix, triggering the binding of adaptor molecules leading to the modulation of small GTPases, Ack, ERK2/MAP and JNK/SAP kinase cascades. The convergence of signaling by FAK plays an important role in tumor-cell survival and in drug resistance, as these pathways overlap. Given the important role of FAK in a large number of processes involved in tumorigenesis, metastasis, and survival signaling, Akt/FAK pathways are now regarded as a potential target to overcome drug resistance.

A variety of results suggest that GD2 and LeY play a role in the migration and survival of cancer cells, since (i) anti-GD2 antibodies [101] and natural anti-TACA antibodies [102] can mediate anoikis; (ii) apoptosis signals are transduced via reduction in the phosphorylation levels of FAK, the activation of a MAPK family members, p38 and c-Jun terminal kinase (JNK), upon binding of such antibodies [101, 103]; (iii) P10s reacts with anti-GD2 and anti-LeY monoclonal antibodies; (iv) anti-P10s antibodies block cell migration and (v) anti-P10s antibodies from P10s immunized subjects are cytotoxic to human breast cancer cell lines.

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7. Synergism of chemo- and immunotherapy

Combining agents with distinct or perhaps overlapping mechanisms of action can potentially result in synergistic anticancer effects. Numerous preclinical studies have established the synergistic relationships between modulation of Tregs and differential expression of immune effector ligands on tumor cells [104, 105]. Consequently, combinatorial anticancer therapy is now a well-established paradigm due to a number of clinical trials demonstrating therapeutic success. However, the mechanisms associated with successful application are not well understood [106]. Standard cancer chemotherapy can promote tumor immunity in two major ways: (i) inducing immunogenic cell death as part of its intended therapeutic effect leading to epitope spreading [107, 108]; and (ii) disrupting strategies that tumors use to evade the immune response [109, 110]. In particular, epitope spreading ensures a polyclonal, polyfunctional immune response that promises to keep the tumor in check indefinitely [111]. Cancer patients can display tumor-reactive antibodies at baseline, which can increase in both breadth and quantity after immunotherapy [112]. IgG antibodies, produced by B cells, are indicative of CD4 helper T cells of linked specificity. Activation of tumor-specific CD8 T cells result from the same processes that generate activated CD4 T cells.

Checkpoint inhibitors have changed the face of immunotherapy with objective responses observed in some patients based on combinatorial regimes involving CTLA4 agent ipilimumab and the PD-1-specific checkpoint inhibitor nivolumab [113]. Nevertheless, a sizeable fraction of patients do not respond to checkpoint inhibitor combination. This could be for several reasons that include but are not limited to not having the correct T cell precursors to target the tumor associated antigens, dysfunctional T cell receptors and down regulation of MHC complexes [114, 115]. Interestingly, FAK inhibition has been noted to increase the activity of checkpoint inhibitors [116]. This work suggests that FAK inhibition increases immune surveillance and renders tumors responsive to immunotherapy.

In our own work when combining chemotherapy with CMPs we primarily focused on immune effector synergistic relationships. A particular synergistic action requires apoptotic tumor cell death, and does not occur as a consequence of perturbations in immunological regulatory circuits. The resistance of many types of cancer to conventional chemotherapies is problematic and a major factor undermining successful cancer treatment. Again FAK plays a role with its silencing augments docetaxel-mediated apoptosis of cancer cells [117]. We have shown that immunization with P10s can overcome resistance to taxanes and coupled with other studies indicating that targeting GD2 antigen is associated with FAK silencing we have come to another important therapeutic option of inducing multiple responses that go through the FAK gatekeeper to improve upon immunotherapy strategies.

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8. Conclusion

Harnessing the body’s own immune system to kill cancer cells has shown promise for a growing number of cancers, revolutionizing the clinical management of multiple tumors. The success of checkpoint antagonists heralds the dawn of a new age in cancer therapy, in which immunotherapy is becoming a key strategy for clinical management. Checkpoint inhibitors have taught us that they can unleash natural responses to tumor cells. The goal of cancer vaccines should be rethought in terms of boosting those natural responses. Combination therapies that integrate distinct therapeutic modalities that include vaccines, small molecules, radiotherapy and checkpoint inhibitors are under investigation. Yet understanding the cellular and molecular underpins are essential for effective translation to the clinic. Polyclonal activation of the immune system should lend to epitope spreading phenomena, which will further effectiveness of cancer therapy. Yet this concept had been for the most part limited to the idea of presenting multiple epitopes of a particular target associated with T cell activation. This viewpoint needs to be reassessed to include the idea of extending to humoral responses since antibodies have proved to be essential to the cancer treating armament.

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Conflict of interest

The authors declare that this research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. TKE and AP are named as inventors on an institutional patent application filled by UAMS that is related to the CMP vaccine briefly described in this manuscript. Therefore, TKE and AP and UAMS have a potential financial interest in the vaccine briefly described. No financial or other support of any kind has resulted from this patent application. These financial interests have been reviewed by approved supervision in accordance with the UAMS conflict of interest policies.

References

  1. 1. Pardoll D. Cancer and the immune system: Basic concepts and targets for intervention. Seminars in Oncology. 2015;42(4):523-538. DOI: 10.1053/j.seminoncol.2015.05.003. Epub 2015 Jun 3
  2. 2. Sabado RL, Bhardwaj N. Directing dendritic cell immunotherapy towards successful cancer treatment. Immunotherapy. 2010;2(1):37-56. DOI: 10.2217/imt.09.43
  3. 3. Gardner A, Ruffell B. Dendritic cells and cancer immunity. Trends in Immunology. 2016;37(12):855-865. DOI: 10.1016/j.it.2016.09.006. Epub 2016 Oct 25
  4. 4. Mackiewicz J et al. Re-induction using whole cell melanoma vaccine genetically modified to melanoma stem cells-like beyond recurrence extends long term survival of high risk resected patients—Updated results. Journal for ImmunoTherapy of Cancer. 2018;6(1):134. DOI: 10.1186/s40425-018-0456-1
  5. 5. Kwiatkowska-Borowczyk E et al. Whole cell melanoma vaccine genetically modified to stem cells like phenotype generates specific immune responses to ALDH1A1 and long-term survival in advanced melanoma patients. Oncoimmunology. 2018;7(11):e1509821. DOI: 10.1080/2162402X.2018.1509821. eCollection 2018
  6. 6. Monzavi-Karbassi B, Pashov A, Kieber-Emmons T. Tumor-associated glycans and immune surveillance. Vaccines (Basel). 2013;1(2):174-203. DOI: 10.3390/vaccines1020174
  7. 7. Sadraei SI, Reynolds MR, Trant JF. The synthesis and biological characterization of acetal-free mimics of the tumor-associated carbohydrate antigens. Advances in Carbohydrate Chemistry and Biochemistry. 2017;74:137-237. DOI: 10.1016/bs.accb.2017.10.003. Epub 2017 Nov 20
  8. 8. Liu CC, Ye XS. Carbohydrate-based cancer vaccines: Target cancer with sugar bullets. Glycoconjugate Journal. 2012;29(5-6):259-271. DOI: 10.1007/s10719-012-9399-9. Epub 2012 Jun 6
  9. 9. Wei MM, Wang YS, Ye XS. Carbohydrate-based vaccines for oncotherapy. Medicinal Research Reviews. 2018;38(3):1003-1026. DOI: 10.1002/med.21493. Epub 2018 Mar 7
  10. 10. Hahn AW et al. The future of immune checkpoint cancer therapy after PD-1 and CTLA-4. Immunotherapy. 2017;9(8):681-692. DOI: 10.2217/imt-2017-0024
  11. 11. Emerson DA, Redmond WL. Overcoming tumor-induced immune suppression: From relieving inhibition to providing costimulation with T cell agonists. BioDrugs. 2018;32(3):221-231. DOI: 10.1007/s40259-018-0277-2
  12. 12. Du X et al. A reappraisal of CTLA-4 checkpoint blockade in cancer immunotherapy. Cell Research. 2018;28(4):416-432. DOI: 10.1038/s41422-018-0011-0. Epub 2018 Feb 22
  13. 13. Pashov A et al. Thinking cancer. Monoclonal Antibodies in Immunodiagnosis and Immunotherapy. 2018;37(3):117-125. DOI: 10.1089/mab.2018.0014
  14. 14. Gross S et al. Targeting cancer with kinase inhibitors. The Journal of Clinical Investigation. 2015;125(5):1780-1789. DOI: 10.1172/JCI76094. Epub 2015 May 1
  15. 15. Rodon J et al. Development of PI3K inhibitors: Lessons learned from early clinical trials. Nature Reviews. Clinical Oncology. 2013;10(3):143-153. DOI: 10.1038/nrclinonc.2013.10. Epub 2013 Feb 12
  16. 16. Tsao CY et al. Anti-proliferative and pro-apoptotic activity of GD2 ganglioside-specific monoclonal antibody 3F8 in human melanoma cells. Oncoimmunology. 2015;4(8):e1023975. DOI: 10.1080/2162402X.2015.1023975. eCollection 2015 Aug
  17. 17. Hutchins LF et al. Targeting tumor-associated carbohydrate antigens: A phase I study of a carbohydrate mimetic-peptide vaccine in stage IV breast cancer subjects. Oncotarget. 2017;8(58):99161-99178. DOI: 10.18632/oncotarget.21959. eCollection 2017 Nov 17
  18. 18. Makhoul I et al. Moving a carbohydrate mimetic peptide into the clinic. Human Vaccines & Immunotherapeutics. 2015;11(1):37-44. DOI: 10.4161/hv.34300. Epub 2014 Nov 1
  19. 19. Clay TM et al. Polyclonal immune responses to antigens associated with cancer signaling pathways and new strategies to enhance cancer vaccines. Immunologic Research. 2011;49(1-3):235-247. DOI: 10.1007/s12026-010-8186-6
  20. 20. Cohn M. Ten experiments that would make a difference in understanding immune mechanisms. Cellular and Molecular Life Sciences. 2012;69(3):405-412. DOI: 10.1007/s00018-011-0869-1. Epub 2011 Nov 1
  21. 21. Davis MM, Tato CM, Furman D. Systems immunology: Just getting started. Nature Immunology. 2017;18(7):725-732. DOI: 10.1038/ni.3768
  22. 22. Villani AC, Sarkizova S, Hacohen N. Systems immunology: Learning the rules of the immune system. Annual Review of Immunology. 2018;36:813-842. DOI: 10.1146/annurev-immunol-042617-053035
  23. 23. Wooden SL, Koff WC. The human vaccines project: Towards a comprehensive understanding of the human immune response to immunization. Human Vaccines & Immunotherapeutics. 2018;14(9):2214-2216. DOI: 10.1080/21645515.2018.1476813. Epub 2018 Jun 28
  24. 24. Coutinho A. The self-nonself discrimination and the nature and acquisition of the antibody repertoire. Annales d’immunologie. 1980;131D(3):235-253
  25. 25. Isenberg D, Shoenfeld Y. Autoantibodies, idiotypes, anti-idiotypes and autoimmunity. Acta Haematologica. 1986;76(2-3):95-100
  26. 26. Vertosick FT, Kelly RH. The immune system as a neural network: A multi-epitope approach. Journal of Theoretical Biology. 1991;150(2):225-237
  27. 27. Lopez-Requena A, Burrone OR, Cesco-Gaspere M. Idiotypes as immunogens: Facing the challenge of inducing strong therapeutic immune responses against the variable region of immunoglobulins. Frontiers in Oncology. 2012;2:159. DOI: 10.3389/fonc.2012.00159. eCollection 2012
  28. 28. Kieber-Emmons T et al. The promise of the anti-idiotype concept. Frontiers in Oncology. 2012;2(196):1-12. DOI: 10.3389/fonc.2012.00196. eCollection 02012
  29. 29. Adib M et al. IgG autoantibody activity in normal mouse serum is controlled by IgM. Journal of Immunology. 1990;145(11):3807-3813
  30. 30. Hurez V et al. Pooled normal human polyspecific IgM contains neutralizing anti-idiotypes to IgG autoantibodies of autoimmune patients and protects from experimental autoimmune disease. Blood. 1997;90(10):4004-4013
  31. 31. Quaglia S et al. A functional idiotype/anti-idiotype network is active in genetically gluten-intolerant individuals negative for both celiac disease–related intestinal damage and serum autoantibodies. The Journal of Immunology. 2019;202(4):1079-1087
  32. 32. Kohler H. The servant idiotype network. Scandinavian Journal of Immunology. 1991;33(5):495-497 discussion 498
  33. 33. Augustin AA, Sim GK, Bona CA. Internal images of antigens within the immune network. Survey of Immunologic Research. 1983;2(1):78-87
  34. 34. Bona CA. Internal image concept revisited. Proceedings of the Society for Experimental Biology and Medicine. 1996;213(1):32-42
  35. 35. Bona CA et al. Epibody: The image of the network created by a single antibody. Immunological Reviews. 1986;90:115-127
  36. 36. Geha RS. Idiotypic-antiidiotypic interactions in humans. Journal of Biological Response Modifiers. 1984;3(6):573-579
  37. 37. Kennedy RC, Melnick JL, Dreesman GR. Anti-idiotypes and immunity. Scientific American. 1986;255(1):48-56
  38. 38. Cheng HL et al. Structural basis of stimulatory anti-idiotypic antibodies. Molecular Immunology. 1988;25(1):33-40
  39. 39. Kohler H et al. Idiotypic networks and nature of molecular mimicry: An overview. Methods in Enzymology. 1989;178:3-35
  40. 40. Westerink MA et al. Peptide mimicry of the meningococcal group C capsular polysaccharide. Proceedings of the National Academy of Sciences of the United States of America. 1995;92(9):4021-4025
  41. 41. Smith G. Filamentous fusion phage: Novel expression vectors that display cloned antigens on the virion surface. Science. 1985;228(4705):1315-1317
  42. 42. Smith GP. Surface presentation of protein epitopes using bacteriophage expression systems. Current Opinion in Biotechnology. 1991;2(5):668-673
  43. 43. Wu CH et al. Advancement and applications of peptide phage display technology in biomedical science. Journal of Biomedical Science. 2016;23(8):1-14. DOI: 10.1186/s12929-016-0223-x
  44. 44. Pinilla C, et al. Identification of B cell and T cell epitopes using synthetic peptide combinatorial libraries. Current Protocols in Immunology. 2012;Chapter(9): p. Unit9.5. DOI: 10.1002/0471142735.im0905s99
  45. 45. Finn OJ. Human tumor antigens yesterday, today, and tomorrow. Cancer Immunology Research. 2017;5(5):347-354. DOI: 10.1158/2326-6066.CIR-17-0112
  46. 46. Wang RF, Wang HY. Immune targets and neoantigens for cancer immunotherapy and precision medicine. Cell Research. 2017;27(1):11-37. DOI: 10.1038/cr.2016.155. Epub 2016 Dec 27
  47. 47. Siniard RC, Harada S. Immunogenomics: Using genomics to personalize cancer immunotherapy. Virchows Archiv. 2017;417:209-219
  48. 48. Prinz JC. Disease mimicry—A pathogenetic concept for T cell-mediated autoimmune disorders triggered by molecular mimicry? Autoimmunity Reviews. 2004;3(1):10-15. DOI: 10.1016/S1568-9972(03)00059-4
  49. 49. Oldstone MB. Molecular mimicry: Its evolution from concept to mechanism as a cause of autoimmune diseases. Monoclonal Antibodies in Immunodiagnosis and Immunotherapy. 2014;33(3):158-165. DOI: 10.1089/mab.2013.0090. Epub 2014 Apr 2
  50. 50. Rojas M et al. Molecular mimicry and autoimmunity. Journal of Autoimmunity. 2018;95:100-123. DOI: 10.1016/j.jaut.2018.10.012. Epub 2018 Oct 26
  51. 51. Hooijkaas H et al. Isotypes and specificities of immunoglobulins produced by germ-free mice fed chemically defined ultrafiltered “antigen-free” diet. European Journal of Immunology. 1984;14(12):1127-1130
  52. 52. Baumgarth N. How specific is too specific? B-cell responses to viral infections reveal the importance of breadth over depth. Immunological Reviews. 2013;255(1):82-94
  53. 53. Holodick NE, Rodríguez-Zhurbenko N, Hernández AM. Defining natural antibodies. Frontiers in Immunology. 2017;8:872
  54. 54. Jerne NK. Towards a network theory of the immune system. Annales d’Immunologie (Paris). 1974;125C(1-2):373-389
  55. 55. Jerne NK. The immune system: A web of V-domains. Harvey Lectures. 1974;70:93-110
  56. 56. Varela F, Coutinho A. Second generation immune networks. Immunology Today. 1991;12:159-166
  57. 57. Manrique PD et al. Getting closer to the goal by being less capable. Science Advances. 2019;5(2):eaau5902
  58. 58. Savage HP, Baumgarth N. Characteristics of natural antibody-secreting cells. Annals of the New York Academy of Sciences. 2015;1362:132-142
  59. 59. Vale AM et al. The global self-reactivity profile of the natural antibody repertoire is largely independent of germline DH sequence. Frontiers in Immunology. 2016;7:296. DOI: 10.3389/fimmu.2016.00296. eCollection 2016
  60. 60. Lundkvist I et al. Evidence for a functional idiotypic network among natural antibodies in normal mice. Proceedings of the National Academy of Sciences of the United States of America. 1989;86:5074-5078
  61. 61. Baumgarth N et al. The role of B-1 and B-2 cells in immune protection from influenza virus infection. Current Topics in Microbiology and Immunology. 2000;252:163-169
  62. 62. Nguyen TT, Elsner RA, Baumgarth N. Natural IgM prevents autoimmunity by enforcing B cell central tolerance induction. Journal of Immunology. 2015;194(4):1489-1502
  63. 63. Kohler H et al. Revised immune network concepts. Clinical Immunology and Immunopathology. 1989;52(1):104-116
  64. 64. Ruffini PA et al. Idiotypic vaccination for B-cell malignancies as a model for therapeutic cancer vaccines: From prototype protein to second generation vaccines. Haematologica. 2002;87(9):989-1001
  65. 65. de Cerio AL et al. Anti-idiotype antibodies in cancer treatment. Oncogene. 2007;26(25):3594-3602
  66. 66. Vasta GR, Marchalonis JJ, Kohler H. Invertebrate recognition protein cross-reacts with an immunoglobulin idiotype. The Journal of Experimental Medicine. 1984;159(4):1270-1276
  67. 67. Gleason K, Kohler H. Regulatory idiotypes. T helper cells recognize a shared VH idiotope on phosphorylcholine-specific antibodies. The Journal of Experimental Medicine. 1982;156(2):539-549
  68. 68. Raychaudhuri S et al. Tumor idiotype vaccines. VII. Analysis and correlation of structural, idiotypic, and biologic properties of protective and nonprotective Ab2. Journal of Immunology. 1990;145(2):760-767
  69. 69. Kieber-Emmons T et al. Structural considerations in idiotype vaccine design. Monographs in Allergy. 1987;22:126-133
  70. 70. Cunto-Amesty G et al. Exploiting molecular mimicry: Defining rules of the game. International Reviews of Immunology. 2001;20(2):157-180
  71. 71. Luo P et al. A molecular basis for functional peptide mimicry of a carbohydrate antigen. The Journal of Biological Chemistry. 2000;275(21):16146-16154
  72. 72. Bentley GA et al. Three-dimensional structure of an idiotope-anti-idiotope complex. Nature. 1990;348(6298):254-257
  73. 73. Fields BA et al. Molecular basis of antigen mimicry by an anti-idiotope. Nature. 1995;374(6524):739-742
  74. 74. Herlyn D et al. Anti-idiotype cancer vaccines: Past and future. Cancer Immunology, Immunotherapy. 1996;43(2):65-76
  75. 75. Foon KA et al. Immune response to the carcinoembryonic antigen in patients treated with an anti-idiotype antibody vaccine. The Journal of Clinical Investigation. 1995;96(1):334-342
  76. 76. Herlyn D et al. Anti-idiotype immunization of cancer patients: Modulation of the immune response. Proceedings of the National Academy of Sciences of the United States of America. 1987;84(22):8055-8059
  77. 77. McCaffery M et al. Immunization of melanoma patients with BEC2 anti-idiotypic monoclonal antibody that mimics GD3 ganglioside: Enhanced immunogenicity when combined with adjuvant. Clinical Cancer Research. 1996;2(4):679-686
  78. 78. Yao TJ et al. Immunization of melanoma patients with BEC2-keyhole limpet hemocyanin plus BCG intradermally followed by intravenous booster immunizations with BEC2 to induce anti-GD3 ganglioside antibodies. Clinical Cancer Research. 1999;5(1):77-81
  79. 79. Grant SC et al. Long survival of patients with small cell lung cancer after adjuvant treatment with the anti-idiotypic antibody BEC2 plus Bacillus Calmette-Guerin. Clinical Cancer Research. 1999;5(6):1319-1323
  80. 80. Foon KA et al. Clinical and immune responses in advanced melanoma patients immunized with an anti-idiotype antibody mimicking disialoganglioside GD2. Journal of Clinical Oncology. 2000;18(2):376-384. DOI: 10.1200/JCO.2000.18.2.376
  81. 81. Perez A et al. A monoclonal antibody against NeuGc-containing gangliosides contains a regulatory idiotope involved in the interaction with B and T cells. Molecular Immunology. 2002;39(1-2):103-112
  82. 82. Diaz A et al. Immune responses in breast cancer patients immunized with an anti-idiotype antibody mimicking NeuGc-containing gangliosides. Clinical Immunology. 2003;107(2):80-89
  83. 83. Neninger E et al. Active immunotherapy with 1E10 anti-idiotype vaccine in patients with small cell lung cancer: Report of a phase I trial. Cancer Biology & Therapy. 2007;6(2):145-150
  84. 84. Geysen HM, Rodda SJ, Mason TJ. A priori delineation of a peptide which mimics a discontinuous antigenic determinant. Molecular Immunology. 1986;23(7):709-715
  85. 85. Obexer R, Walport LJ, Suga H. Exploring sequence space: Harnessing chemical and biological diversity towards new peptide leads. Current Opinion in Chemical Biology. 2017;38:52-61. DOI: 10.1016/j.cbpa.2017.02.020. Epub 2017 Mar 17
  86. 86. Aghebati-Maleki L et al. Phage display as a promising approach for vaccine development. Journal of Biomedical Science. 2016;23(1):66
  87. 87. Goulart LR, Santos Pde S. Strategies for vaccine design using phage display-derived peptides. Methods in Molecular Biology. 2016;1404:423-435. DOI: 10.1007/978-1-4939-3389-1_28
  88. 88. Huang J, He B, Zhou P. Mimotope-based prediction of B-cell epitopes. Methods in Molecular Biology. 2014;1184:237-243. DOI: 10.1007/978-1-4939-1115-8_13
  89. 89. Orlandi R et al. Antigenic and immunogenic mimicry of the HER2/neu oncoprotein by phage-displayed peptides. European Journal of Immunology. 1994;24(11):2868-2873
  90. 90. Zhao L, Liu Z, Fan D. Overview of mimotopes and related strategies in tumor vaccine development. Expert Review of Vaccines. 2008;7(10):1547-1555. DOI: 10.1586/14760584.7.10.1547
  91. 91. Ashok BT et al. Peptide mimotopes of oncoproteins as therapeutic agents in breast cancer. International Journal of Molecular Medicine. 2003;11(4):465-471
  92. 92. Sherev T, Wiesmuller KH, Walden P. Mimotopes of tumor-associated T-cell epitopes for cancer vaccines determined with combinatorial peptide libraries. Molecular Biotechnology. 2003;25(1):53-61
  93. 93. Agadjanyan M et al. Peptide mimicry of carbohydrate epitopes on human immunodeficiency virus. Nature Biotechnology. 1997;15(6):547-551. DOI: 10.1038/nbt0697-547
  94. 94. Kieber-Emmons T et al. Vaccination with carbohydrate peptide mimotopes promotes anti-tumor responses. Nature Biotechnology. 1999;17(7):660-665. DOI: 10.1038/10870
  95. 95. Monzavi-Karbassi B et al. Preclinical studies of carbohydrate mimetic peptide vaccines for breast cancer and melanoma. Vaccine. 2007;25(16):3022-3031. DOI: 10.1016/j.vaccine.2007.01.072. Epub 2007 Jan 26
  96. 96. Wondimu A et al. Peptides mimicking GD2 ganglioside elicit cellular, humoral and tumor-protective immune responses in mice. Cancer Immunology, Immunotherapy. 2008;57(7):1079-1089
  97. 97. Zhao X, Guan JL. Focal adhesion kinase and its signaling pathways in cell migration and angiogenesis. Advanced Drug Delivery Reviews. 2011;63(8):610-615. DOI: 10.1016/j.addr.2010.11.001. Epub 2010 Nov 29
  98. 98. Tai YL, Chen LC, Shen TL. Emerging roles of focal adhesion kinase in cancer. BioMed Research International. 2015;2015:690690. DOI: 10.1155/2015/690690. Epub 2015 Mar 31
  99. 99. Guan JL. Integrin signaling through FAK in the regulation of mammary stem cells and breast cancer. IUBMB Life. 2010;62(4):268-276. DOI: 10.1002/iub.303
  100. 100. Serrels A et al. Nuclear FAK controls chemokine transcription, Tregs, and evasion of anti-tumor immunity. Cell. 2015;163(1):160-173. DOI: 10.1016/j.cell.2015.09.001
  101. 101. Aixinjueluo W et al. Mechanisms for the apoptosis of small cell lung cancer cells induced by anti-GD2 monoclonal antibodies: Roles of anoikis. The Journal of Biological Chemistry. 2005;280(33):29828-29836. Epub 2005 May 26
  102. 102. Vollmers HP, Brandlein S. Natural antibodies and cancer. New Biotechnology. 2009;25(5):294-298. Epub 2009 Apr 11
  103. 103. Yoshida S et al. An anti-GD2 monoclonal antibody enhances apoptotic effects of anti-cancer drugs against small cell lung cancer cells via JNK (c-Jun terminal kinase) activation. Japanese Journal of Cancer Research. 2002;93(7):816-824
  104. 104. Ribas A. Anti-CTLA4 antibody clinical trials in melanoma. Update on Cancer Therapeutics. 2007;2(3):133-139
  105. 105. Asano T et al. PD-1 modulates regulatory T-cell homeostasis during low-dose interleukin-2 therapy. Blood. 2017;129(15):2186-2197
  106. 106. Emens LA, Middleton G. The interplay of immunotherapy and chemotherapy: Harnessing potential synergies. Cancer Immunology Research. 2015;3(5):436-443. DOI: 10.1158/2326-6066.CIR-15-0064
  107. 107. Tesniere A et al. Molecular characteristics of immunogenic cancer cell death. Cell Death and Differentiation. 2008;15(1):3-12
  108. 108. Pfirschke C et al. Immunogenic chemotherapy sensitizes tumors to checkpoint blockade therapy. Immunity. 2016;44(2):343-354
  109. 109. Baitsch L et al. The three main stumbling blocks for anticancer T cells. Trends in Immunology. Jul 2012;33(7):364-372. DOI: 10.1016/j.it.2012.02.006
  110. 110. Takeuchi Y, Nishikawa H. Roles of regulatory T cells in cancer immunity. International Immunology. 2016;28(8):401-409
  111. 111. Heckler M, Dougan SK. Unmasking pancreatic cancer: Epitope spreading after single antigen chimeric antigen receptor T-cell therapy in a human phase I trial. Gastroenterology. 2018;155(1):11-14. DOI: 10.1053/j.gastro.2018.06.023. Epub 2018 Jun 6
  112. 112. Gnjatic S et al. Identifying baseline immune-related biomarkers to predict clinical outcome of immunotherapy. Journal for ImmunoTherapy of Cancer. 2017;5:44. DOI: 10.1186/s40425-017-0243-4. eCollection 2017
  113. 113. Kroemer G, Galluzzi L. Combinatorial immunotherapy with checkpoint blockers solves the problem of metastatic melanoma—An exclamation sign with a question mark. Oncoimmunology. 2015;4(7):e1058037. DOI: 10.1080/2162402X.2015.1058037. eCollection 2015 Jul
  114. 114. De Sousa Linhares A et al. Not all immune checkpoints are created equal. Frontiers in Immunology. 31 Aug 2018;9(1909):1-15. DOI: 10.3389/fimmu.2018.01909. eCollection 2018
  115. 115. Park JA, Cheung N-KV. Limitations and opportunities for immune checkpoint inhibitors in pediatric malignancies. Cancer Treatment Reviews. 2017;58:22-33
  116. 116. Jiang H et al. Targeting focal adhesion kinase renders pancreatic cancers responsive to checkpoint immunotherapy. Nature Medicine. 2016;22(8):851-860. DOI: 10.1038/nm.4123. Epub 2016 Jul 4
  117. 117. Halder J et al. Focal adhesion kinase silencing augments docetaxel-mediated apoptosis in ovarian cancer cells. Clinical Cancer Research. 2005;11(24 Pt 1):8829-8836. DOI: 10.1158/1078-0432.CCR-05-1728

Written By

Anastas Pashov and Thomas Kieber-Emmons

Submitted: 04 January 2019 Reviewed: 04 March 2019 Published: 15 April 2019