What are TILs and why are they important ?
INTRODUCTION AND RATIONALE
1. Anti-tumor immune response
a) Evading immune response, an emerging hallmark of cancer
Human tumor pathogenesis has been traditionally seen as a multistep process through which normal cells progressively acquire the capacity to transform, the so-called “hallmarks of cancer” (Hanahan et al., 2000) (Figure 1). Thus, tumor pathogenesis is generally considered to derive from genetic and epigenetic alterations in malignant cells. Recently, this reductionist cancer cell-centric (or cell-autonomous) view of the disease evolved to integrate the ability of cancer cells to actively evade the immune system, along with the tumor-promoting effects associated with an inflammatory state driven by infiltrating immune cells (Hanahan et al., 2011).
Figure 1 – Hallmarks of cancer.
This illustration encompasses the six hallmarks of cancer originally proposed in 2000: the two emerging hallmarks, including the ability of cancer cells to evade immunological destruction, and the enabling characteristics, encompassing tumor-promoting inflammation induced by infiltrating immune cells (Hanahan et al., 2011).
The recognition of the immune system’s role as an emerging hallmark accommodates the concept of “immunosurveillance”, which postulates that nascent tumor cells are eliminated by the immune system until malignant cells escape the immune system detection or actively suppress immune responses (Zitvogel et al., 2006). Evidence of cancer immunosurveillance comes from both animal models and clinical epidemiology. For instance, an increased incidence of malignancies has been observed in immunodeficient mice (spontaneous and carcinogen-induced) (Koebel et al., 2007) as well as in immunocompromised patients (Swinnen et al., 1990; Dal Maso et al., 2001; Spano et al., 2008).
However, immunocompetent individuals also develop cancers in spite of this immunosurveillance. It is hypothesized that this occurs after a phase of equilibrium (or dormancy) during which the immune response tries to control neoplastic progression; if this fails, malignant cells escape immune control and a tumor develops (Dunn et al., 2004). This process, known as “immunoediting”, exerts a selective pressure on cancer cells, selecting poorly immunogenic malignant clones. Figure 2 illustrates the central concept that multistep carcinogenesis results from crosstalk of cancer-cell intrinsic factors and host immune system (cell-extrinsic) effects (Zitvogel et al., 2006). The mechanisms used by tumors for this process of “immunoediting” are further detailed in section 3 of the introduction.
Figure 2 – Immunoediting process.
Relationship between cell-intrinsic and cell-extrinsic aspects of tumor progression (Zitvogel et al., 2006).
b) The immune contexture
In solid tumors, the tumor microenvironment includes the extracellular matrix and various cellular components, namely, stromal, endothelial and immune cells (Grivennikov et al., 2010). Some tumors are highly infiltrated by immune cells, while in others, only subtle infiltrations are detectable. The density, location and organization of these immune cells is referred to as the “immune contexture”(Fridman et al., 2012). The immune contexture determined at diagnosis reflects the immune response, and an increased number of immune cells correlates with a favorable clinical outcome in various malignancies, such as melanoma, colorectal, lung and breast cancers (Fridman et al., 2012).
The prognostic value of immune cells has been further refined with the development of more accurate methods to identify the functional status and distinct phenotypes of immune cells. Immune cells such as cytotoxic T cells, helper T cells, natural killer (NK) cells or dendritic cells (DC) contribute to the anti-tumor response, while other cells such as FOXP3 regulatory T cells (Treg) and myeloid-derived suppressor cells (MDSC) have a pro-tumorigenic effect that promotes cancer growth and invasion by producing factors sustaining angiogenesis and promoting cancer cell proliferation (DeNardo et al., 2010) (Figure 3). Consistent with their anti- or pro-tumorigenic effect, immune cells have distinct prognostic significance (Fridman et al., 2011). These observations confirm that immune cells in solid tumors are able to exert influence on the behavior of cancer cells and that the development of malignant disease results, to a certain extent, from the bidirectional cross-talk between cancer cells and their immune microenvironment. Characterization of the immune landscape and quantification of tumor infiltrating immune cells is therefore used as a surrogate to evaluate a patient’s immune response and tumor immunogenicity.
Figure 3 – Dichotomous roles of the immune system.
This figure illustrates that some immune cells antagonize and others enhance tumor progression. (Salgado et al., 2015).
Breast cancer was not initially considered to be an immunogenic tumor, but evidence from both pre-clinical and clinical studies have recently recognized the “immune contexture” in breast cancer as prognosis biomarkers and targets for immunotherapeutic treatments (Becht et al., 2015; Kroemer et al., 2015).
c) Immune response to cancer therapy
The immune system not only modulates tumor progression, but also plays a role in the response to cancer therapy. The presence of tumor immune infiltrates and expression of immune gene signatures have been associated with a good response to treatment in different malignancies (Fridman et al., 2012). In the metastatic setting, heterogenous tumor immune microenvironment within the same patient have been linked to distinct behaviors post chemotherapy (Jiménez-Sánchez et al., 2017). A growing body of evidence shows that conventional anticancer agents, besides their cytotoxic effects, mediate robust immunostimulatory effects by either inducing immunogenic tumor cell death or by engaging immune effector mechanisms that substantially contribute to the therapeutic effect (Galluzzi et al. 2016).
Although it is often argued that chemotherapy has immunosuppressive effects, several chemotherapeutic agents have been demonstrated to promote immune responses by releasing tumor antigen, inducing the so-called immunogenic cell death (ICD) and also by decreasing the number of immunosuppressive cells (L. Galluzzi et al., 2016). ICD relies on three components: i) translocation of calreticulin to the cell-surface, ii) release of the Toll-like receptor (TLR) agonist HMGB1, and iii) release of ATP into the extracellular milieu. Consequently, ICD promotes DC activation, presentation of tumor-associated antigens and production of inflammatory cytokines (Zitvogel et al., 2008). This process increases the immunogenicity of cancers and primes the immune system by stimulating innate immune effectors and inducing cytotoxic T cell responses (Zitvogel et al., 2013). In breast cancer, ICD induced by chemotherapy has been able to modulate the immune contexture by increasing T cell infiltration and improving the CD8/FOXP3 ratio (Ladoire et al., 2011; Dieci et al., 2014).
Targeted therapies with monoclonal antibodies mediate their anti-tumor activities through various mechanisms, including direct-cell toxicity, by interfering with signaling pathways but also by stimulating immune responses through antibody-dependent cellular cytotoxicity (ADCC) (Ferris et al., 2010; Scott et al., 2012). Trastuzumab, a monoclonal antibody directed against HER2 receptor, has been demonstrated to activate various immune effectors involving both innate and adaptive immune responses (Park et al. 2010; Stagg et al. 2011; Charlebois et al. 2016). In breast cancer patients, benefit from trastuzumab has been correlated with increased tumor-infiltrating immune cells (Loi et al. 2014; Heppner et al. 2016). These data suggest that the ability to mount anti-tumor immune responses may play a role in the outcome of patients with HER2-positive breast cancer treated with trastuzumab.
2. Anti-tumor immune response in breast cancer
a) Breast cancer taxonomy and immune gene signatures
Breast cancer (BC) is the most common cancer, and the leading-cause of cancer related mortality, among women worldwide (Youlden et al., 2012). Nevertheless, while the incidence of BC is increasing, mortality is decreasing, and this has been attributed to improvements in cancer treatment and early detection (Berry et al., 2005).
Invasive breast tumors are conventionally classified into four main, distinct molecular subtypes relying on specific gene expression profiles: Luminal A, Luminal B, HER2-enriched and basal-like (Perou et al., 2000; Sorlie et al., 2003). These subtypes represent biologically distinct diseases and demonstrate characteristic immunohistochemical features. Luminal A tumors are characterized by the expression of hormonal receptors, lack of HER2 receptor and low ki67, whereas luminal B tumors are characterized by the expression of hormonal receptors with either high ki67 or with HER2 expression (Goldhirsch et al., 2013). HER2-enriched tumors are mainly hormonal receptor negative and overexpress the HER2 receptor, while basal-like tumors are characterized by the lack of hormonal receptors (estrogen and progesterone) and HER2 receptors and are therefore categorized as “triple-negative” BC (TNBC). Intrinsic genomic subtypes and corresponding clinicopathological surrogates are described in Table 1.
Table 1 – Breast cancer classification.
aIHC expression of ER, PR, HER2. GEP: gene-expression profiling. Table adapted from Inwald et al. 2015; Kroemer et al. 2015 and from http://www.pathophys.org/breast-cancer/ .
Importantly, a disparity in prognosis and response to therapy remains within each disease entity, which has led investigators to continue searching for further refinements. The first generation of molecular analyses in BC based on gene expression profiling identified several gene signatures that improved breast cancer prognosis prediction, e.g., GGI, MammaprintÒ, Oncotype DXÒ (Sotiriou et al. 2009). Since the prognostic value of these signatures is mainly driven by proliferating genes (Sotiriou et al. 2009; Sotiriou et al. 2006), these signatures have failed to predict clinical outcome in patients with high proliferative ER-negative or HER2-positive BC. The prognostic value of two of them – Oncotype DXÒ and MammaprintÒ – has been recently validated through two prospective randomized clinical trials (Sparano et al. 2015; Cardoso et al. 2016). These results provided 1A level of evidence, proving that both signatures add prognostic information to conventional clinicopathological criteria and thus are clinically useful in identifying low-risk patients who can be spared adjuvant chemotherapy.
Originally, high expression of immune-related genes was considered to be a confounding variable within microarray-based gene expression analyses. However, in recent years immune-related gene expression has been shown to be a major molecular process associated with good prognosis, particularly in the HER2-positive and TNBC subgroups, where proliferation-driven prognostic signatures are not informative. In 2007, Teschendorff et al. was the first to report an immune response related seven-gene module associated with favorable clinical outcome in ER-negative basal BC and identified a subclass of tumors with a better prognosis (Teschendorff et al., 2007). In the following years, several investigations reported other immune-related gene expression signatures associated with improved patient outcome, particularly in high proliferative subtypes such as TNBC and HER2-positive BC (Desmedt et al. 2008; Lehmann et al. 2011; Rody et al. 2009; Schmidt et al. 2008). In a large meta-analysis, immune response signatures were also associated with an increased rate of pathological complete response (pCR, defined as the absence of invasive or in situ residuals in the breast and lymph nodes after completion of a neoadjuvant treatment) (Ignatiadis et al., 2012). pCR rate is recognized as a suitable surrogate end point for long term clinical benefit (Cortazar et al., 2014). Today, it is well recognized that immune gene signatures are able to signal the relative abundance of tumor-infiltrating immune cells and subpopulations of immune cells (T and B cell metagene, Tfh signature) (Desmedt et al., 2008; Schmidt et al., 2008; Rody et al., 2009; Gu-Trantien et al., 2013). A recent computational approach based on tumor transcriptomes (CIBERSORT) was able to infer leukocyte representation and the proportion of leukocytes subsets within a tumor, and it was reported to be predictive of clinical outcome in BC (H. Raza Ali et al., 2016; Bense et al., 2017).
b) Tumor-infiltrating lymphocytes
Prognostic and predictive significance
The association between tumor-infiltrating lymphocytes (TIL) and outcome in BC was first reported by Aaltomaa and colleagues in the early 1990s (Aaltomaa et al., 1992). Since this report, many other investigators have studied and, very recently, validated the prognostic and predictive value of TIL using material collected from several randomized clinical trials including thousands of patients (Savas et al., 2016).
In 2010, Denkert and colleagues demonstrated that the lymphocytic infiltrate on pre-therapeutic core biopsies predicted pCR in response to anthracycline/taxanes neoadjuvant chemotherapy (Denkert et al., 2010). The association between response to neoadjuvant chemotherapy and TIL has been confirmed in numerous other reports (Issa-Nummer et al. 2013; Ali et al. 2016; Ono et al. 2012; Solinas et al. 2017).
In the adjuvant setting, the prognostic value of TIL was investigated retrospectively using material from a phase III trial (BIG 2-98) including more than 2,000 patients. In this study, stromal and intratumoral TIL were more abundant in ER-negative/HER2-negative and HER2-positive tumors than in ER-positive/HER2-negative tumors. TIL were shown to be associated with a better prognosis in ER-negative/HER2-negative BC (Loi et al. 2013). The prognostic impact in this BC subtype was later confirmed in two other reports from phase III adjuvant chemotherapy trials involving anthracycline-containing regimens, supporting TIL as an independent prognostic biomarker in TNBC (Adams et al., 2014; Loi et al., 2014). TIL were evaluated as a continuous variable, and each 10% increment was associated with a reduction in the risk of distant recurrence varying between 13% to 18%, depending on the study. Tumors with the highest TIL infiltration (³ 50% lymphocytic infiltration of either tumor epithelium or stroma) were categorized as lymphocytic-predominant BC (LPBC). Although this subgroup included a lower number of patients, it was associated with the best outcome, highlighting the very good prognosis for patients with extensive TIL infiltration. The association between TIL and a better outcome after anthracycline-based chemotherapy supports the hypothesis that a pre-existing host immune response might enhance the effect of immunogenic chemotherapy such as anthracyclines (Galluzzi et al. 2016). The prognostic value of TIL in adjuvant randomized controlled BC trials is listed in table 2.
Although the prognostic value of immune signatures has been shown in systemically untreated ER-negative/HER2-negative and HER2-positive tumors from several gene expression datasets (Desmedt et al., 2008; Bense et al., 2017), the prognostic role of TIL in untreated patients had been unexplored. This area has now been investigated in one study aiming to compare the prognostic value of TIL in adjuvant chemotherapy-treated (anthracycline-based regimen) and untreated patients. TIL were associated with improved survival in HER2-positive and TNBC, but no significant interaction between TIL and anthracycline chemotherapy was observed (Dieci et al., 2015).
In HER2-positive BC, higher stromal TIL were demonstrated to predict benefit of anthracycline-only adjuvant chemotherapy in the BIG2-98 trial, and to trastuzumab in the FinHER trial (Loi et al. 2013; Loi et al. 2014). This latter observation suggested that an immune response contributes to trastuzumab’s anti-tumor effect (Bianchini et al. 2014), also supported by gene-expression analysis (Perez et al., 2015). A recent large phase III adjuvant trial including women with HER2-positive BC patients confirmed the prognostic value of stromal TIL, but did not observe trastuzumab benefit in patients with high TIL levels (Perez et al., 2016). These conflicting results could be due to the low number of events in the trastuzumab arm in Perez et al.’s study, which was therefore likely to be underpowered to confirm the results of the FinHER trial (Loi et al., 2014; Perez et al., 2016). The presence of increased stromal TIL has been very recently reported to be associated to longer overall survival in HER2-positive metastatic BC, suggesting that the prognostic value of TIL assessed in primary tumors extends to the advanced setting (Luen et al., 2016).
All studies agree with the lack of prognostic value of immune parameters for ER-positive (luminal) tumors. However, a recent meta-analysis performed in the neoadjuvant setting demonstrated that increased stromal TIL in 13% of luminal tumors was significantly associated with a higher pCR rate after neoadjuvant chemotherapy (Denkert et al. 2016). Another report using molecular profiling observed that inflamed tumors had poorer response to endocrine treatment (Dunbier et al., 2013). The immune response is probably not a key aspect in ER-positive BC biology, but its role could still be relevant in a subset of patients with luminal tumors, and thus needs further investigation.
Most of these studies assessed TIL as a continuous parameter on a single hematoxylin and eosin (H&E)-stained tumor section and used the criteria described by Denkert et al. to evaluate TIL infiltration (Denkert et al., 2010). These criteria define intratumoral TIL as intraepithelial mononuclear cells within tumor cell nests or in direct contact with tumor cells, and stromal TIL as lymphocytes in the tumor stroma without direct contact with tumor cells. The cooperative efforts of an international immuno-oncology biomarker working group of pathologists, oncologists and immunologists are ongoing to harmonize TIL assessment and maximize reproducibility (Salgado et al. 2015). The first guidelines advocate to quantify only stromal TIL on H&E-stained tumor sections. Stromal and intratumoral TIL are generally correlated, but intratumoral TIL are far less abundant and more difficult to identify on H&E sections (Salgado et al. 2015). A recent ring study conducted by the working group demonstrated that a software-guided image evaluation approach could improve inter-observer variability (Denkert et al. 2016). These collaborative efforts aim to establish TIL as a predictive and prognostic biomarker that could help to tailor therapy in the clinical management of BC.
TIL composition and organization
In BC, TIL have been found to be mainly composed of CD4+ and CD8+ T cells (Ruffell et al., 2012). CD8+ T cells are generally cytotoxic T cells able to directly kill cancer cells. In one study increased presence of CD8+ T cells assessed using immunohistochemistry on tissue microarrays has been associated with improved survival in patients, regardless of BC subtype (Ali et al., 2014). However, in other studies, the association was only observed in TNBC (Mahmoud et al., 2011; Liu et al., 2012).
CD4+ T cells are generally helper T cells (Th) or regulatory T cells (Treg), the functions of which are mediated largely by secreted cytokines. For Th cells, secreted cytokines recruit leukocytes and stimulate phagocytes and cytotoxic T cells to kill tumor cells or help B cells to produce antibodies. Follicular CD4+ Th cells (Tfh) found in B cell follicles (named tertiary lymphoid structures [TLS] in tumors) were demonstrated to predict improved survival and response to treatment in BC (Gu-Trantien et al., 2013). CD4+ Tregs (which express the transcription factor FOXP3) function mainly by mediating immunotolerance through the inhibition of effector T cells via cytokines such as TGF-β and IL-10, or metabolites such as adenosine. Consistent with this, the presence of CD4+ FOXP3+CD25hi Tregs in BC tumors is associated with a worse prognosis (Shou et al., 2016).
In addition to the presence of specific immune subsets, the organization of immune cells may also influence and refine the prognostic value of TIL. TIL can cluster in aggregates or in TLS found mainly in the peri-tumoral stroma of BC (Gu-Trantien et al., 2013). TLS could reflect the generation of an effective immune response (e.g., a priming of the response) in direct proximity of the tumor and their presence has been associated with improved survival in lung (Dieu-Nosjean et al., 2008) and colon cancer (Coppola et al., 2011).
B cells, when present, are located principally in these TLS. As B cells represent a heterogeneous population, their role is still controversial in antitumor immunity. However, it is suggested that they may contribute to humoral anti-tumor immune response by producing antibodies directed against tumor-associated antigens (Tsou et al., 2016) and to cellular immunity through the production of cytokines and increased activation of antigen-presenting cells (Siliņa et al., 2014). In a recent study, B cells identified in a tissue microarray including 1902 cases of primary BC were associated with a favorable prognosis (Mahmoud et al., 2012).
The extent and type of TIL, in conjunction with their level of organization, may be an important biomarker to improve the stratification of patients for immune-based treatment selection. Importantly, such a comprehensive analysis has yet to be performed in BC.
Impact of chemotherapy
Neoadjuvant chemotherapy increased TIL infiltration (Demaria et al., 2001) and improved the ratio of CD8+ T cell to FOXP3+ T cells among TIL in HER2-positive BC (Ladoire et al., 2011). Higher TIL infiltration in residual disease after chemotherapy and a favorable CD8/FOXP3 ratio predict a better outcome (Ladoire et al., 2011; Dieci et al., 2014). In peripheral blood, an increased proportion of a subset of CD4 (CD25–CD127–CD4+)T cells has been observed after neoadjuvant chemotherapy, which correlated with the response to treatment (Péguillet et al., 2014). These findings support the notion that chemotherapy induces changes that enhance anti-tumor immune response and that some portion of the clinical effect of chemotherapy depends on its immunological effects.
3. Negative immune regulation induced by tumors
The presence of immune effector cells does not always correlate with clinical benefit. Indeed, multiple negative regulatory mechanisms are able to inhibit the anti-tumor immune response. Under physiologic conditions, these mechanisms control immune responses to avoid auto-immunity and limit damage from excessive or chronic inflammation (Viganò et al., 2012; Abbas et al., 2015). In cancer, these mechanisms provide pathways by which tumors can evade the immune system, and they are involved in the “immunoediting” process that sculpts the tumor and its microenvironment. These negative regulatory mechanisms include overexpression of inhibitory checkpoint molecules, recruitment of immunosuppressive cells, production of suppressive factors in the tumor microenvironment, and decreased antigen presentation. These mechanisms are considered to be of therapeutic interest in cancer therapy because their manipulation may provide a means to enhance antitumor immune responses.
a) Checkpoint molecules
Activation of T cells requires engagement of the TCR (T cell receptor) and CD28 (a co-stimulatory receptor) on MHC and B7 molecules, respectively, expressed by antigen-presenting cells. If there is no co-stimulation or if there is a co-inhibitory signal (instead of the co-stimulatory one), immune cells become anergic or die by apoptosis (Abbas et al., 2015). Receptors delivering co-inhibitory signals function as an “immune checkpoint” and their role is to maintain peripheral tolerance and to prevent auto-immunity (Boussiotis, 2016). In cancers, co-inhibitory signals impede the anti-tumor immune response both at the level of activation of immune effectors by antigen presenting cells and in the phase of immune recognition of tumor cells by immune effectors. Co-inhibitory receptors are upregulated on T cells due to persistent and continuous antigen stimulation induced by the chronic inflammation associated with tumors (Pauken et al., 2015).
Tumor cells can also express several of these co-inhibitory receptor molecules. The upregulation of these receptors is linked to an adaptive process (called “adaptive immune resistance”) induced by immune cell infiltration and used by tumors to limit inflammation and to protect against immune attack (Ribas, 2015). Increased expression of co-inhibitory receptors can also be driven by oncogenic pathways (Akbay et al., 2013).
One of the most studied co-inhibitory receptors, or immune checkpoints, is the receptor programmed death-1 (PD-1, CD279). PD-1 is a T cell inhibitory receptor that belongs to the B7/CD28/CTLA-4 superfamily. Interaction between PD-1 and its ligand programmed death-ligand 1 (PD-L1, B7-H1) inhibits activated T cells and downregulates T cell response and by inhibiting TCR-mediated activation and CD28 co-stimulation (Dong et al. 2002; Hui et al. 2017) (Figure 4).
PD-L1 may be expressed by tumor cells and tumor infiltrating immune cells and is expressed in multiple solid tumor types, including melanoma, non-small cell lung cancer, and ovarian carcinoma (Gatalica et al. 2014; Patel et al. 2015). In BC, high PD-L1 expression has been associated with higher histological grade, higher proliferation, hormonal receptor negativity and higher TIL infiltration (Muenst et al., 2014; Sabatier et al., 2014; Ali et al., 2015; Bae et al., 2016). Despite its association with poor clinicopathological features, PD-L1 expression has been associated with a better outcome in several reports, mainly in the ER-negative subgroup (Ali et al., 2015; Baptista et al., 2016). Other studies demonstrated that PD-L1 expression contributes to poor prognosis (Zhang et al., 2017). PD-1/PD-L1 axis is the target of immune checkpoint inhibitors that have been evaluated in early phase BC clinical trials (discussed in section 4 of this introduction).
Figure 4 – PD-1 mediated inhibition of T cells.
T cells recognizing tumor antigens can be activated to proliferate, secrete inflammatory cytokines, and resist cell death. Prolonged TCR stimulation during an ongoing immune response can upregulate PD-1 expression. Tumor cells can express PD-L1 (and PD-L2, not shown) as a consequence of inflammatory cytokines and/or oncogenic signaling pathways. PD-1/PD-L1 binding inhibits TCR-mediated positive signaling, leading to reduced proliferation, reduced cytokine secretion, and reduced survival. (Buchbinder et al., 2016).
IFN-γ indicates interferon-γ; MHC, major histocompatibility complex; PD-1, programmed death protein 1; PD-L1, programmed death ligand 1; PD-L2, programmed death ligand 2; TCR, T-cell receptor.
b) Immunosuppressive microenvironment
Recruitment of immunosuppressive cells, and the production of immunosuppressive factors in the tumor microenvironment, are further negative-feedback mechanisms used by tumors to prevent excessive inflammation. Suppressive immune cells including tumor-associated macrophages (TAM) (Biswas et al., 2013), MDSC (Kumar et al., 2016) and Tregs act through cell-cell interactions and production of immunosuppressive factors to inhibit immune effectors cells (Lindau et al., 2013).
Tumors are also able to produce immunosuppressive metabolites. Among them, extracellular adenosine has multiple anti-inflammatory and tumor promoting effects and is therefore recognized as an “immune checkpoint mediator” involved in the mechanism of tumor immune escape (Allard et al. 2016). Adenosine levels are increased in the tumor microenvironment through the conversion of adenosine triphosphate (ATP), released by dying tumor cells. ATP is considered to be a “danger signal” able to elicit an anticancer immune response (Aymeric et al., 2010). Two ecto-nucleotidases: CD39 and CD73, expressed by tumor cells, regulatory immune cells, stromal cells, and by endothelial cells are involved in the conversion of ATP in the tumor microenvironment. CD39 (ENTPD1) hydrolyzes ATP and adenosine diphosphate (ADP) into adenosine monophosphate (AMP), which is then converted into adenosine by CD73 (NT5E) (Yegutkin, 2008). CD73-derived adenosine is recognized as orchestrating the interplay between tumor and stroma to promote cancer growth as illustrated in Figure 5 (Allard et al., 2012; Antonioli, Blandizzi, et al., 2016). Extracellular adenosine, through the activation of A2 (A2a and/or A2b) adenosine receptors, impedes the antitumor immune response by disabling cytotoxic effector cells while enhancing the proliferation and polarization of immunosuppressive cells (Young et al. 2014; Allard et al. 2016), (Figure 6). In addition, extracellular adenosine enhances tumor angiogenesis through the production of VEGF by tumor cells and the regulation of endothelial cell functions (Allard et al. 2014). Finally, adenosine also promotes tumor progression, acting directly on cancer cells by stimulating A2a and/or A2b receptors, leading to activation of cell proliferation, metastatic properties, and chemotactic response (Allard et al., 2012).
Figure 5 – The role of CD73 in the tumor microenvironment.
CD73-derived adenosine shapes the cancer milieu, leading to the generation of a marked immunosuppressive and pro-angiogenic environment that paves the way for neoplastic development. ATP: adenosine triphosphate; AMP: adenosine monophosphate; DC: dendritic cell; MSDC: myeloid-derived suppressor cells; NK cell: natural killer cell; Treg cell: regulatory T cell. : increases; ¯: decreases. (Antonioli et al. 2016)
Figure 6 – Adenosine and tumor-immune infiltrating cells.
CD73-derived adenosine in the tumor microenvironment shapes the phenotype of immune infiltrating cells, impeding the immune response by disabling cytotoxic effector cells while enhancing the proliferation and polarization of immunosuppressive cells.
4. Promising success of cancer immunotherapy
a) Immune checkpoint blockade
The improved understanding of cancer cells’s ability to exploit immune mechanisms and to evade immune surveillance led to the development of immune therapeutic strategies to induce or enhance anti-tumor immunity. The use of monoclonal antibodies (mAbs) targeting immune checkpoints to enhance the function of effector T cells has been shown to be one of the most promising approaches to date. The goal of these treatments is to harness and enhance the immune system by disrupting negative immune regulation. Checkpoint blockade with anti-PD-1 and anti-CTLA-4 mAbs represents one of the most encouraging advances in oncology in recent years and has demonstrated impressive antitumor activity and durable clinical benefit in diverse advanced malignancies (Hodi et al., 2010; Brahmer et al., 2012; Topalian et al., 2012).
Based on these encouraging results, multiple PD-1 or PD-L1 inhibitors have entered into clinical development, and some have been approved by the FDA and EMA for several indications such as melanoma, non-small cell lung cancer (NSCLC), renal cell carcinoma, urothelial carcinoma, head and neck cancer, Hodgkin’s lymphoma and are under investigation in other solid tumors including BC.
Melanoma was the first cancer indicated for checkpoint blockade with ipilimumab (anti-CTLA-4), approved in 2011 (Hodi et al., 2010). Later both pembrolizumab and nivolumab, two antibodies targeting PD-1, were also demonstrated to improve survival in metastatic melanoma (Larkin et al. 2015; Robert et al. 2014). PD-1 blockade was even demonstrated to be more effective than ipilimumab in advanced melanoma (Robert et al., 2015).
PD-1 blockade also demonstrated remarkable benefit in NSCLC, which comprises approximately 85% of all lung cancers (Govindan et al., 2006). Two anti-PD-1 antibodies (pembrolizumab and nivolumab) and more recently an anti-PD-L1 antibody (atezolizumab) have been approved for therapeutic use based on the improved survival and decreased toxicity they demonstrated when compared to chemotherapy in numerous randomized clinical trials (Borghaei et al., 2015; Brahmer et al., 2015; Fehrenbacher et al., 2016; Herbst et al., 2016; Reck et al., 2016). Checkpoint blockade mAbs are well tolerated and associated with fewer side-effects when compared to chemotherapy; however, they are associated with substantial inflammatory effects that can resemble autoimmune diseases (van der Vlist et al., 2016).
b) Biomarkers of response to immune checkpoint blockade
Patient response to checkpoint blockade has been highly variable with some patients experiencing exceptionally long-lasting responses and others having only short, partial responses, or disease stabilization, with a large proportion of non-responding patients. Although mutational landscape and PD-L1 expression have both been recognized as determinants of response to PD-1 blockade, effective biomarkers are still lacking (Topalian et al., 2012; Herbst et al., 2014; Rizvi et al., 2015; Daud et al., 2016).
PD-L1 expression has shown some predictive value in PD-1 blockade trials but its use as a biomarker is still controversial, as responses have also been observed in 5-20% of PD-L1 negative cases (Fusi et al., 2015). Additionally, PD-L1 negative patients were excluded from many trials making it difficult to properly define the predictive significance of the biomarker. Moreover, different antibodies, staining platforms, thresholds of positivity on different type of cells have been used across different clinical trials to assess PD-L1 expression (Hirsch et al., 2016). A model combining PD-L1 expression with TIL infiltration has been proposed to classify the tumor microenvironment and to discriminate tumors that are most likely to respond to a PD-1 blockade (Smyth et al., 2015; Teng et al., 2015). Combining multiple biomarkers could be a rational approach to tailor immunotherapeutic treatment and should be integrated in future clinical trials (Blank et al., 2016).
The mutational load (as a marker for tumor foreignness) is another parameter that has been associated with checkpoint blockade response (Snyder et al., 2014; Rizvi et al., 2015). NSCLC and melanoma, two cancers caused by chronic exposure to exogenous mutagens (ultraviolet light (Pfeifer et al., 2005) and cigarette smoke (Pfeifer et al., 2002) respectively) are tumors with a high mutational burden and have been associated with increased response rates and clinical benefit from immune checkpoint blockade (Figure 7). The higher mutational load of these tumors results in a higher immunogenicity due to the expression of mutated antigens (referred as neoantigens) that can be recognized by specific T cells (Gros et al., 2016). These observations suggest that the immune system’s ability to recognize neoantigens is important for checkpoint inhibitor activity. However, there is still a lack of valid assays to predict the immunogenicity or to monitor relevant antigen specific immune responses.
Figure 7 – Estimate of the neoantigen repertoire in human cancer.
Data depict the number of somatic mutations in individual tumors. Categories on the right indicate current estimates of the likelihood of neoantigen formation in different tumor types. (Schumacher et al., 2015).
c) Breast cancer immunotherapy
The increasing evidence with regard to the prognostic influence of TIL in BC, and the expression of PD-L1 in a non-negligible proportion of BC (mainly TNBC) coupled with the recent breakthroughs of immunotherapy in other cancers, has led to the initiation of several clinical trials testing checkpoint blockade in BC.
Early phases trials have demonstrated encouraging results in the metastatic setting, with higher response rates in TNBC and PD-L1 positive cases. One of these trials included 32 patients with metastatic TNBC patients having PD-L1 positive tumors (58% of the 111 screened patients) and demonstrated that pembrolizumab provided a manageable toxicity profile and long-lasting responses in heavily pre-treated patients (Nanda et al., 2016). The overall response rates of early phase trials evaluating monotherapy PD-1 blockade are listed in Table 3.
Table 3 – Early phase trials with PD-1 axis blocking agents in BC.
TC indicates tumor cells, IC; immune cells, ORR; overall response rate, CR; complete response, PR; partial response, Nb pts; number of patients.
d) Synergistic combinations
Combining of immunotherapeutic agents with conventional cancer treatments is a conceptually promising strategy, given the demonstrated scientific rationale in several preclinical models (Melero et al., 2015). In human melanoma, the combination of nivolumab and ipilumumab has already shown a higher response rate for patients with untreated advanced melanoma; however, side-effects were substantially increased compared to monotherapies (Postow et al. 2015; Larkin et al. 2015). This combination was approved by FDA in 2015.
Many ongoing studies in BC are investigating the role of PD-1 blockade in combination with other agents, such as other immunotherapeutic agents, conventional chemotherapy and radiotherapy, or targeted agents (Stagg et al., 2012). Chemotherapy has been shown to induce immunogenic cell death, reducing tumor burden and therefore allowing immunotherapy to be more effective (Melero et al., 2015). The combination of an anti-PD-L1 (atezolizumab) with nab-paclitaxel already demonstrated promising activity and an acceptable safety profile in metastatic TNBC (Adams et al., 2016). Two phase III trials combining PD-1 blockade agents with chemotherapy in first line metastatic TNBC are ongoing (NCT02819518, NCT02425891). Another interesting approach that has demonstrated synergy and improved therapeutic activity in preclinical models, is the combination of trastuzumab and anti-PD1 (Stagg et al. 2011). The safety and efficacy of this combination is currently being investigated in the phase Ib/II PANACEA trial in HER2-positive metastatic BC resistant to trastuzumab (NCT02129556). The hypothesis is that the addition of an immunotherapy targeting PD-1 could reverse trastuzumab resistance in patients who were previously progressing on trastuzumab.
Efforts are also underway to improve the efficacy of immunotherapies by developing combined synergistic strategies and by studying mechanisms of response to identify he patients who are most likely to respond to checkpoint inhibitors.
5. CD73 as a promising immune target
Targeting the immunosuppressive tumor microenvironment is another strategy to potentiate the immunostimulatory activity of the checkpoint-blockade. Multiple therapeutic strategies to eliminate or reprogram immunosuppressive cells are under active pre-clinical and clinical development. One of these strategies is to target the CD73 pathway to relieve adenosine-mediated immunosuppression. The ecto-5’-nucleotidase CD73 is an adenosine-generating enzyme and is overexpressed in several cancers, including ovarian carcinoma, head and neck cancer, BC, melanoma, glioblastoma, colon cancer, kidney and pancreatic carcinoma (Allard et al. 2016; Antonioli et al. 2016). This up-regulation is partly driven by hypoxia and has been associated mostly with a poorer outcome (Turcotte et al. 2015; Loi et al. 2013; Ren et al. 2016). Accumulation of extracellular adenosine in the tumor microenvironment promotes tumor immune escape, metastasis, and resistance to immune checkpoint inhibitors. In vivo studies have demonstrated that CD73-deficient mice display delayed tumor growth in multiple tumor models and are resistant to lung metastasis development (Stagg et al. 2011; Yegutkin et al. 2011), while CD73 overexpression in tumor cells has been demonstrated to confer resistance to chemotherapy (Loi et al. 2013). Targeting CD73 or adenosine receptors in pre-clinical models has resulted in favorable anti-tumor effects; therefore, drugs targeting adenosine-mediated immunosuppression via CD73 and A2 adenosine receptors are now under clinical development in phase I clinical trials (NCT02503774, NCT02403193, NCT02655822).
The potential of adenosine targeting treatment was enhanced when combined with other immunomodulatory treatments such as checkpoint blockade (Allard et al., 2013). This synergistic activity has already been demonstrated with the combination of either anti-CD73 or A2a antagonists with anti-PD1 or anti-CTLA-4 antibodies in mouse models (Allard et al., 2013; Iannone et al., 2014; Mittal et al., 2014; Beavis et al., 2015). The combination of PD-1 blockade with CD73 or A2a blockade is now under clinical investigation in humans.
Targeting the adenosine pathway could improve anti-tumor immunity and is one of the most promising next generation target in immuno-oncology (Allard et al. 2016).
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