Pixabay

Vaccines are the most effective and cost-efficient weapons that can be used to prevent (preventive vaccines) or cure (therapeutic vaccines) diseases caused by infectious agents or cancer cells. Usually, when one thinks about the word vaccine, the first thought that comes into the mind is related to an artificial administration of a stimulus that instructs the immune system to fight against the cause of a particular pathological state (the pathogen). However, in the case of cancer vaccines, the main view, still unknown to the majority of the people not working in the field,  is represented by the exploitation of the host’s immune system to treat or prevent cancer. The idea, however, dates back decades.

In the same way a “traditional” vaccine works, a cancer vaccine can promote the eradication of malignant cells during their initial transformation from safe to harmful cells. This eradication process, commonly referred to as immune surveillance of tumors [1], is carried out by the immune system and, most of the time, it happens without any external intervention. Tumors are the result of a particular combination of factors related to genetic and epigenetic changes that enable immortality.

In the same way a “traditional” vaccine works, a cancer vaccine can promote the eradication of malignant cells during their initial transformation from safe to harmful cells.

This is not a completely undetectable process: during the transformation of a normal cell into a malignant one, foreign antigens (neo-antigens or, to be more specific, onco-antigens) are created;  these should render neoplastic cells visible by the immune system that can target them for elimination. Tumors cells, like every living organisms, want, nevertheless, to live. Hence, tumors try to become resistant and invisible to immune system attacks by developing multiple resistance mechanisms that include local immune evasion, induction of tolerance and systemic interference of T cell signaling. Besides, mimicking the metaphor of Darwin’s natural selection, immune recognition of cancer cells enforces a selective pressure on developing ones. This favors the development of less immunogenic and more apoptosis-resistant neoplastic cells, through a mechanism well known as immune editing [2].

Due to the fact that cancer cells are particularly good at evading any action from the immune system, most anti-cancer treatments are based on other means like surgery, radiation therapy, and chemotherapy. Nowadays, however, it is clear that the various arms of the immune system play an essential role in protecting humans from cancer. After unsatisfactory efforts and explicit clinical failures, the field of cancer immunotherapy has received a significant boost, thanks mainly to the development in 2010 of an autologous cellular immunotherapy, sipuleucel-T, for the treatment of prostate cancer [3] and the approval of the anti-cytotoxic T lymphocyte-associated protein 4 (CTLA-4) antibody ipilimumab (2011) and anti-programmed cell death protein 1 (PD1) antibodies (2014) for the treatment of melanoma [4]. These achievements have renovated the field and brought attention to the opportunities that immunotherapeutic approaches can offer [5,6].

There are still, however, some difficulties to be overcome when developing effective immunotherapy strategies against cancer. The general lack of understanding of the mechanisms of immunization, the role of dendritic cells, the ability of cancer to induce tolerance, and the identification of the most suitable antigens to use are just some examples of how the development of effective strategies is still problematic [7-10]. There are several biotechnological methodologies, based on both in silico and in vivo techniques, that study and suggest possible candidates for use in immunotherapies. However, they are not able, on their own, to quantify and analyze the immune system response globally. Moreover, there are now several computational techniques to predict T cell epitopes (and, to some extent, also B cell epitopes) [11,12]. Computational simulations may help in solving these issues, but these need to be integrated with the in vitro and in silico molecular analyses [13,14]. So, a complete computational/biological pipeline that allow the best integration of  in silico, in vitro and in vivo methodologies may potentially  boost and improve cancer immunotherapy development and effectiveness.

The aim of the thematic series is to bring together the latest advances in both biological and computational research, looking broadly at the basic biological aspects of immunotherapy, emerging immunotherapies (both prophylactic and preventive) and different vaccination approaches. The novel, and, at the same time, established character of computation in immunology greatly improves and speeds-up the development of novel vaccination strategies, both therapeutic and preventive, against cancer. We welcome original research, methodology, software, and database article submissions.

The deadline for submission of manuscripts is 30th November 2017. For more information, visit the BMC Immunology website.

References

1. Sharma P, Wagner K, Wolchok JD, Allison JP. Novel cancer immunotherapy agents with survival benefit: recent successes and next steps. Nat Rev Cancer. 2011;11:805–12.
2. Mahoney KM, Rennert PD, Freeman GJ. Combination cancer immunotherapy and new immunomodulatory targets. Nat Rev Drug Discov. 2015;14:561–84.
3. Topalian SL, Weiner GJ, Pardoll DM. Cancer immunotherapy comes of age. J Clin Oncol. 2011;29:4828–36.
4. Sharma P, Allison JP. Immune checkpoint targeting in Cancer Therapy: toward combination strategies with curative potential. Cell. 2015;161:205–14.
5. Mellman I, Coukos G, Dranoff G. Cancer immunotherapy comes of age. Nature. 2011;480:480–9.
6. Sofia Farkona, Eleftherios P. Diamandis and Ivan M. Blasutig. Cancer immunotherapy: the beginning of the end of cancer? BMC Medicine 2016;14:73.
7. Rosenberg SA, Yang JC, Restifo NP. Cancer immunotherapy. Moving beyond current vaccines. Nat Med. 2004;10:909–15.
8. Palucka K, Banchereau J. Dendritic-cell-based therapeutic cancer vaccines. Immunity. 2013;39:38–48.
9. Palucka K, Banchereau J. Cancer immunotherapy via dendritic cells. Nat Rev Cancer. 2012;12:265–77.
10. Chen DS, Mellman I. Oncology meets immunology: the cancer-immunity Cycle. Immunity. 2013;39:1–10.
11. Sheikh, QM, Gatherer, D, Reche, PA & Flower, DR 2016, ‘Towards the knowledge-based design of universal influenza epitope ensemble vaccines’ Bioinformatics, vol. 32, no. 21, pp. 3233-3239.
12. Sivalingam, Ganesh N., and Adrian J. Shepherd. “An Analysis of B-Cell Epitope Discontinuity.” Molecular Immunology 51.3-4 (2012): 304–309.
13. Pappalardo, F, Fichera, E, Paparone, N, et al. A computational model to predict the immune system activation by citrus derived vaccine adjuvants. Bioinformatics, 32(17):2672–2680, 2016.
14. Pappalardo, F, Pennisi, M, Ricupito, A, et al. Induction of T cell memory by a dendritic cell vaccine: a computational model. Bioinformatics, 30(13):1884–1891, 2014.
15. Palladini, A, Nicoletti, G, Pappalardo, F, et al. In silico modeling and in vivo efficacy of cancer-preventive vaccinations. Cancer Research, 70(20):7755–7763, 2010.