Inmunología y cáncer

Immunity
Volume 39, Issue 1, 25 July 2013, Pages 1–10

Review
Oncology Meets Immunology: The Cancer-Immunity Cycle
• Daniel S. Chen1, 3,
• Ira Mellman2, 3, ,

doi:10.1016/j.immuni.2013.07.012
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The genetic and cellular alterations that define cancer provide the immune system with the means to generate T cell responses that recognize and eradicate cancer cells. However, elimination of cancer by T cells is only one step in the Cancer-Immunity Cycle, which manages the delicate balance between the recognition of nonself and the prevention of autoimmunity. Identification of cancer cell T cell inhibitory signals, including PD-L1, has prompted the development of a new class of cancer immunotherapy that specifically hinders immune effector inhibition, reinvigorating and potentially expanding preexisting anticancer immune responses. The presence of suppressive factors in the tumor microenvironment may explain the limited activity observed with previous immune-based therapies and why these therapies may be more effective in combination with agents that target other steps of the cycle. Emerging clinical data suggest that cancer immunotherapy is likely to become a key part of the clinical management of cancer.
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Main Text
Introduction
The development of cancer immunotherapy has reached an important inflection point in the history of cancer therapy (reviewed in Mellman et al., 2011). Durable monotherapy responses are consistently being reported for a broad range of human cancers with several different agents (Hamid et al., 2013a, Herbst et al., 2013, Hodi et al., 2010 and Topalian et al., 2012b), providing a compelling argument that cancer immunotherapy is active in a range of indications beyond melanoma, a disease often thought to be atypically immunogenic (Jacobs et al., 2012). In addition to encouraging activity, many of the cancer immunotherapy approaches report safety profiles that are milder and more manageable than traditional or targeted (i.e., oncogene-centric) cancer therapies.
Cancer is characterized by the accumulation of a variable number of genetic alterations and the loss of normal cellular regulatory processes (Tian et al., 2011). These events have long been known to result in the expression of neoantigens, differentiation antigens, or cancer testis antigens, which can lead to presentation of peptides bound to major histocompatibility class I (MHCI) molecules on the surface of cancer cells, distinguishing them from their normal counterparts. Since the work of Boon and colleagues, we have known that these cancer-specific peptide-MHCI complexes can be recognized by CD8+ T cells produced spontaneously in cancer patients (Boon et al., 1994). However, even when T cell responses occurred, they rarely provided protective immunity nor could they be mobilized to provide a basis for therapy.
As demonstrated by elegant analyses of cancer in mice, the continued deletion of cancer cells expressing T cell targets (immune editing) may enable cancers to evolve to avoid attack (Dunn et al., 2002). Despite these findings, recent results from human cancer have demonstrated that overcoming negative regulators to T cell responses in lymphoid organs (checkpoints) and in the tumor bed (immunostat function) are likely to explain the failure of immune protection in many patients (Mullard, 2013). Factors in the tumor microenvironment can act to modulate the existing activated antitumor T cell immune response, acting as an immune rheostat or “immunostat.” This class of molecules, including PD-L1:PD-1 (reviewed in Chen et al., 2012 and Topalian et al., 2012a), emphasizes that the immune response in cancer reflects a series of carefully regulated events that may be optimally addressed not singly but as a group. The challenge now is to use this new understanding to develop new drugs and implement clinical strategies.
The articles contained in this issue each address key aspects of how the immune response can control or be manipulated to enhance anticancer immunity (Galon et al., 2013, Kalos and June, 2013, Motz and Coukos, 2013, Palucka and Banchereau, 2013, van den Boorn and Hartmann, 2013 and Zitvogel et al., 2013). Here, we will integrate this information and consider how it might best be used in clinical development.