Processes 2019, 7,37
T-regulatory cells (Treg) play an important role in immunological tolerance. In the case of cancer, in
addition to normal antigens (self), cancer cells express antigens unique to the tumor, which can result
in an immune response. The computational models discussed in this review will largely focus on the
adaptive immune system.
Although the immune system is well equipped to eliminate abnormal cells, cancer cells have
several ways to evade the immune system. For example, cancer cells can become invisible to the
immune system by downregulating major histocompatibility complex (MHC) class-I receptors on their
cell surface, and in turn, not presenting the mutation associated antigen for detection by T-cell receptors.
Cancer cells attract regulatory T-cells (Treg) [14] and myeloid-derived suppressor cells (MDSC) [15]
to the tumors; an abundance of these cell types results in an immunosuppressive environment [16].
Furthermore, receptors such as CTLA-4 on Treg can bind to CD80 and CD86 on T-cells and antigen
presenting cells (APC), which inhibits co-stimulation of these cells, and results in T-cell suppression.
Treg can further inhibit the adaptive immune response by interfering with B-cell function and releasing
immunosuppressive cytokines such as IL-10. In fact, under certain conditions, the immune response
can contribute to tumor growth instead of inhibiting it [17]. Another way that tumors escape detection
is through chronic inflammation [18]. During chronic inflammation, T-cells eventually lose their
effectiveness over the course of the infection, called T-cell exhaustion [19]. T-cell exhaustion is also
frequently found in the tumor microenvironment through the PD-L1/PD-1 pathway [20]. PD-1
blockage enhances T-cell and NK (Natural Killer) cell activity in tumors [21,22]. Several immune
checkpoint inhibitors are currently being used in treatments of patients with cancer [23,24].
Innate immune cells, such as macrophages, neutrophils, and eosinophils, have also been shown
to decrease or enhance tumor growth depending on their polarization state. Macrophages have a great
deal of plasticity but are usually classified in one of two types: an M1-type that is immuno-enhancing,
and an M2-type which is immuno-suppressing; though there is a spectrum of states between M1
and M2 [25]. Studies have shown that high numbers of tumor-associated macrophages (TAM)
can lead to a worse clinical outcome [26]. TAM have been shown to promote tumor growth by
increasing vascularization, cancer cell migration, cancer cell survival, and immuno-suppression [27].
Macrophages are recruited to hypoxic areas of the tumor [28], and aid in tumor progression [26,29].
Cancer cells recruit macrophages through Colony Stimulating Factor 1 (CSF1), and high CSF1
concentrations are correlated with poor prognoses [30]. Macrophages can be converted to TAM within the
tumor by secreted factors, such as c-c chemokine receptor type-2 (CCR2) [31], which causes them to exhibit
an M2-like phenotype [32]; this conversion of macrophages leads to a distinct subpopulation [33]. TAM
secretion of c-c chemokine ligand type-18 (CCL18) can enable the epithelial-to-mesenchymal transition of
breast cancer cells [34,35]. These TAM have also been shown to be associated with invasion, extravasation
and metastasis [36–38]. Thus, M2-type macrophages are currently being targeted with therapeutics for
tumor treatment [39]. Neutrophils are less abundant in tumors, but they are becoming more recognized
for their duel role in the immune response to cancer [40]. Eosinophils are also commonly found within
tumors [41], and have been found to enhance T-cell infiltration [42]. These cells also have a duel role in
the immune response, and can promote or suppress tumor growth [43]. There is a complex interaction
between cancer cells and the immune system. Thus, it is important to understand the conditions in which
tumors are eliminated or enhanced by the immune system.
As is evident from the complex mechanisms of immune response and immune evasion described
above, modeling the immune system is a challenging task [44]. For the specific case of cancer, immune
cells can be found within the TME, the lymphatic system and the lymph nodes, resulting in spatial
complexity. Molecular and cellular components themselves are complex and have patient specific features
such as unique lymphocyte antigen receptors. In addition, different functions of the immune system
occur at different time scales, ranging from minutes to years. For example, intracellular signaling occurs
in minutes, whereas memory cells exist on the order of years. Revealing this complexity across different
scales as discussed above is very challenging or impossible to achieve in an experimental setting. Thus,
computational modeling platforms provide a powerful tool to complement experimental measurements
43
T-regulatory cells (Treg) play an important role in immunological tolerance. In the case of cancer, in
addition to normal antigens (self), cancer cells express antigens unique to the tumor, which can result
in an immune response. The computational models discussed in this review will largely focus on the
adaptive immune system.
Although the immune system is well equipped to eliminate abnormal cells, cancer cells have
several ways to evade the immune system. For example, cancer cells can become invisible to the
immune system by downregulating major histocompatibility complex (MHC) class-I receptors on their
cell surface, and in turn, not presenting the mutation associated antigen for detection by T-cell receptors.
Cancer cells attract regulatory T-cells (Treg) [14] and myeloid-derived suppressor cells (MDSC) [15]
to the tumors; an abundance of these cell types results in an immunosuppressive environment [16].
Furthermore, receptors such as CTLA-4 on Treg can bind to CD80 and CD86 on T-cells and antigen
presenting cells (APC), which inhibits co-stimulation of these cells, and results in T-cell suppression.
Treg can further inhibit the adaptive immune response by interfering with B-cell function and releasing
immunosuppressive cytokines such as IL-10. In fact, under certain conditions, the immune response
can contribute to tumor growth instead of inhibiting it [17]. Another way that tumors escape detection
is through chronic inflammation [18]. During chronic inflammation, T-cells eventually lose their
effectiveness over the course of the infection, called T-cell exhaustion [19]. T-cell exhaustion is also
frequently found in the tumor microenvironment through the PD-L1/PD-1 pathway [20]. PD-1
blockage enhances T-cell and NK (Natural Killer) cell activity in tumors [21,22]. Several immune
checkpoint inhibitors are currently being used in treatments of patients with cancer [23,24].
Innate immune cells, such as macrophages, neutrophils, and eosinophils, have also been shown
to decrease or enhance tumor growth depending on their polarization state. Macrophages have a great
deal of plasticity but are usually classified in one of two types: an M1-type that is immuno-enhancing,
and an M2-type which is immuno-suppressing; though there is a spectrum of states between M1
and M2 [25]. Studies have shown that high numbers of tumor-associated macrophages (TAM)
can lead to a worse clinical outcome [26]. TAM have been shown to promote tumor growth by
increasing vascularization, cancer cell migration, cancer cell survival, and immuno-suppression [27].
Macrophages are recruited to hypoxic areas of the tumor [28], and aid in tumor progression [26,29].
Cancer cells recruit macrophages through Colony Stimulating Factor 1 (CSF1), and high CSF1
concentrations are correlated with poor prognoses [30]. Macrophages can be converted to TAM within the
tumor by secreted factors, such as c-c chemokine receptor type-2 (CCR2) [31], which causes them to exhibit
an M2-like phenotype [32]; this conversion of macrophages leads to a distinct subpopulation [33]. TAM
secretion of c-c chemokine ligand type-18 (CCL18) can enable the epithelial-to-mesenchymal transition of
breast cancer cells [34,35]. These TAM have also been shown to be associated with invasion, extravasation
and metastasis [36–38]. Thus, M2-type macrophages are currently being targeted with therapeutics for
tumor treatment [39]. Neutrophils are less abundant in tumors, but they are becoming more recognized
for their duel role in the immune response to cancer [40]. Eosinophils are also commonly found within
tumors [41], and have been found to enhance T-cell infiltration [42]. These cells also have a duel role in
the immune response, and can promote or suppress tumor growth [43]. There is a complex interaction
between cancer cells and the immune system. Thus, it is important to understand the conditions in which
tumors are eliminated or enhanced by the immune system.
As is evident from the complex mechanisms of immune response and immune evasion described
above, modeling the immune system is a challenging task [44]. For the specific case of cancer, immune
cells can be found within the TME, the lymphatic system and the lymph nodes, resulting in spatial
complexity. Molecular and cellular components themselves are complex and have patient specific features
such as unique lymphocyte antigen receptors. In addition, different functions of the immune system
occur at different time scales, ranging from minutes to years. For example, intracellular signaling occurs
in minutes, whereas memory cells exist on the order of years. Revealing this complexity across different
scales as discussed above is very challenging or impossible to achieve in an experimental setting. Thus,
computational modeling platforms provide a powerful tool to complement experimental measurements
43