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Review

Impact of Tumor and Immunological Heterogeneity on the Anti-Cancer Immune Response

1
Department of Clinical Pathology, Victorian Comprehensive Cancer Centre, The University of Melbourne, Melbourne, VIC 3000, Australia
2
Centre for Cancer Research, The University of Melbourne, Melbourne, VIC 3000, Australia
3
Department of Biochemistry and Molecular Biology, Biomedicine Discovery Institute, Monash University, Clayton, VIC 3800, Australia
*
Author to whom correspondence should be addressed.
Cancers 2019, 11(9), 1217; https://doi.org/10.3390/cancers11091217
Submission received: 17 July 2019 / Revised: 15 August 2019 / Accepted: 16 August 2019 / Published: 21 August 2019
(This article belongs to the Special Issue Metastatic Progression and Tumour Heterogeneity)

Abstract

:
Metastatic tumors are the primary cause of cancer-related mortality. In recent years, interest in the immunologic control of malignancy has helped establish escape from immunosurveillance as a critical requirement for incipient metastases. Our improved understanding of the immune system’s interactions with cancer cells has led to major therapeutic advances but has also unraveled a previously unsuspected level of complexity. This review will discuss the vast spatial and functional heterogeneity in the tumor-infiltrating immune system, with particular focus on natural killer (NK) cells, as well as the impact of tumor cell-specific factors, such as secretome composition, receptor–ligand repertoire, and neoantigen diversity, which can further drive immunological heterogeneity. We emphasize how tumor and immunological heterogeneity may undermine the efficacy of T-cell directed immunotherapies and explore the potential of NK cells to be harnessed to circumvent these limitations.

1. Introduction

Recent advances in our understanding of cancer, driven by the development of sophisticated biochemical and molecular techniques, have highlighted the complex and heterogenous nature of this disease. Within individual tumors, significant differences in the molecular and phenotypic profiles may arise from tumor cell-intrinsic or extrinsic factors. Genomics has provided the most extensive insights to date about tumor-intrinsic variations, with sequencing studies revealing a large extent of clinically-relevant intra-tumor heterogeneity [1,2,3]. Thus, next generation sequencing of multiple tumor types identifying the association between increased clonal heterogeneity and higher pathological stage and/or worse prognosis [4]. Moreover, genetic heterogeneity has also been identified across patients, and the incidence of clinically actionable mutations differs significantly between tumors arising from different tissue or cell types, amongst patients with the same class of tumor, and between matched primary and metastatic tumors within the same patient [5,6,7,8]. Non-genetic determinants of heterogeneity have also garnered significant interest, as even genetically identical cells may harbor unique chemosensitivity profiles [9]. This points towards the role epigenetic modifications [10,11,12] and metabolic reprogramming [13], in dictating the functional variation observed within individual populations.
Tumor cell extrinsic factors, such as the cellular and structural elements of the tumor microenvironment (TME), are also known to influence tumor heterogeneity. For instance, the spatial arrangement of cells with receptor tyrosine kinase amplifications in glioblastoma has been shown to correspond with degree of vascularization in the local TME [14]. Similarly, in melanoma patients, the extent of subclonal divergence from the mutational profile of the primary tumor is dependent on the metastatic site [15], suggesting an influence of the local microenvironment. Perhaps the most important component of the TME are the immune cells. Whilst the tumor-sculpting role of the anti-cancer immune response has long been recognised, conflicting reports exist on the impact of this immunoediting on tumor heterogeneity. The selective pressure of the immune response has been shown to profoundly reduce sub-clonal diversity via the targeted elimination of immunogenic cell variants [16], recent evidence indicates that the adaptive immune response may in fact potentiate genomic instability [17], thus promoting the rise of novel subclones thereby increasing tumor heterogeneity. As heterogeneity within the immune or tumor cell compartments could conceivably impact the efficacy of immunotherapies, there is a vital need to improve our understanding of the relationship between the two.

2. Spatial, Functional, and Temporal Heterogeneity of Immune Cell Infiltrates

Tumor cells develop in a dynamic niche; individual tumor cell subpopulations not only compete and cooperate with each other, but also with the surrounding TME and its constituent immune cells. Single-cell sequencing studies have confirmed that tumors may be populated by a vast and diverse array of immune components: innate leukocytes, such as natural killer (NK) cells and mast cells; phagocytes, such as macrophages, neutrophils, and dendritic cells; and adaptive effectors, including naïve, memory, and effector B- and T-lymphocytes [18]. It is clear that the degree of immune infiltration and the composition of this infiltrate can vary markedly across tumor types [19] and stages [18], as well as between patients with the same tumor type [20,21]. Similarly, whether synchronous metastases within a single patient regress or progress has been associated with their distinct immune profile [22]. Within a tumor, complexity is further compounded by the differing spatial distribution of immune effectors between the core and invasive fronts, as well as within the adjacent tertiary lymphoid structures [23,24].
However, as increased infiltration of CD8+ T-cells is prognostic for better outcome in numerous tumor types [25,26,27,28], traditional scoring of tumor immunogenicity has been based upon the degree of T-cell inflammation alone. Immunologically ’hot’ tumors, such as melanoma and non-small cell lung cancer (NSCLC), present with a high degree of T-cell permeation, whereas tumor-infiltrating lymphocytes (TILs) are scarcely observed in ’cold’ tumors, such as ovarian, prostate, and pancreatic cancers. More recently, a third immunologically ’altered’ phenotype has been proposed, denoting cases where peri-tumoral sites are densely inflamed with immune cells which lack the ability to infiltrate into the tumor [29]. As the T-cell inflamed gene expression profiles of ’hot’ tumors have been strongly linked with positive response to checkpoint blockade therapies [30,31], significant attention has been focused on developing therapeutic strategies which can convert immunologically ’cold’ or ’altered’ phenotypes into ’hot’ environments [32,33,34]. Yet, as the immune contexture may vary across non-adjacent tumor regions, it should be emphasized that many tumors may not be universally ’hot’ nor ’cold’.
To address this disparity, the Immunoscore method proposed by the Galon group incorporates spatial context into its immunological quantitation metric, computed by the ratio of memory CD3+ and cytotoxic CD8+ TILs at the tumor centre and invasive margins. In colorectal cancer (CRC), this index has been validated as an independent prognostic marker which performs better than both Tumor-Node-Metastasis (TNM) staging and microsatellite instability (MSI) status [35,36,37] and an in vitro diagnostic assay has been made clinically available for assessing relapse risk in Stage II and III CRC. Yet, there is substantial evidence that quantification varies between non-adjacent areas of tumor biopsies [38,39,40], suggesting that single biopsies may not be representative of the broader infiltrating immune landscape. Pertinently, a phenomenon termed Immunoskew has been documented, whereby a minority of tumor regions are densely infiltrated with TILs despite an otherwise barren TME [41]. Determining whether Immunoskew extends to other immune cell populations, and identifying the specific intra-tumor differences which drive this differential infiltration pattern, are worthy areas for future study.
Beyond TILs, the contribution of other cell types to tumor immunogenicity should not be overlooked. NK cells are inversely correlated with cancer incidence [42,43] and intra-tumoral NK cell infiltrates have been identified as a positive prognostic marker in multiple solid cancers [44,45,46,47,48] and haematological malignancies [49]. Additionally, NK cells are supremely important in the control of metastasis. A wealth of in vivo studies have demonstrated that mice depleted of NK cells via pharmacological inhibition [50,51,52] or genetic knockout [53] are more vulnerable to metastasis than their NK cell-proficient counterparts. The same is true for mice reconstituted with NK cells deficient in cytotoxic molecules, such as perforin and interferon-γ (IFNγ) [54,55] or activating receptors [56,57,58,59]. This notion has been confirmed in a clinical study of CRC liver metastases, where increased frequency of intra-tumoral NK cells was the variable most significantly (p = 0.01) associated with better overall survival, performing better than other clinical parameters including TNM stage, number/size of metastases, and frequency of infiltrating CD3+ lymphocytes [60]. Additionally, there is strong evidence supporting the role of NK cells in the clearance of putative cancer stem cells [61,62,63,64], suggesting that NKs may promote long-term recurrence-free survival.
The discordance in immune infiltrate between primary and metastatic tumors is more pronounced in metachronous than synchronous tumors [24,65,66] indicating that temporal changes also contribute to tumor heterogeneity. The composition of immune infiltrates is also known to change as tumors progress, with one study in CRC reporting an increased prevalence of innate immune cells and decreased number of most T-cell lineages in more advanced tumor stages [18]. The latter may be particularly important when considering the age-associated decline in lymphocyte number and function [67], particularly given that the majority of new cancer diagnoses are made in those over the age of 65.
Above all, a limitation of current techniques that quantify immune infiltrates is that they frequently do not assess functionality. Recent evidence suggests that infiltration alone may not be sufficient to elicit anti-tumor responses, as effector cells can be relegated to the peritumoral stroma and therefore lack the direct cell contact required for target cell destruction [68,69,70]. Similarly, the efficacy of each immune cell population may be influenced by the immunoregulatory cytokines produced by neighboring cell types. For example, infiltrating cytotoxic lymphocytes may be restrained by various immunosuppressive cell types, including myeloid-derived suppressor cells [71], Tregs [33,72,73,74], and tumor-associated (TA) fibroblasts [68,75,76,77], which are diversely distributed across cancer types. Conversely, traditionally immunosuppressive cells can act beneficially depending on the surrounding tumor context [78,79].

3. Tumor Cell-Driven Immunological Heterogeneity

The observation that increased TIL fractions have positive prognostic value in numerous tumor types has culminated in the harnessing of this subset for immunotherapy, primarily in the form of immune checkpoint inhibitors. Whilst strikingly effective in tumor types, such as melanoma, renal cell carcinoma (RCC), and NSCLC, the efficacy of immune checkpoint therapies is highly variable across solid malignancies. For example, in CRC, positive therapeutic responses to T-cell directed checkpoint inhibitors are limited to approximately 30% of patients with MSI, which represents 5% of all patients [80]. Whilst the exact molecular mechanisms which underpin this resistance remain elusive, emerging evidence suggests that broad spectrum of clinical responses could be partially attributable to immunological heterogeneity. As well as differences in immune infiltration and interaction of immune cell types, there are multiple tumor cell intrinsic factors, such as the secretome, receptor–ligand profile, and neoantigen repertoire, which can drive immunological heterogeneity (Figure 1).

3.1. Secretome Heterogeneity

Infiltrating immune cells can be conditioned by the soluble factors secreted by nearby tumor cells. Tumor cells can directly foster an immunosuppressive TME via the production of enzymes and metabolites including indolamine 2, 3-dioxygenase (IDO) [81,82], lactic acid [83] and prostaglandin E2 [68,84]. As metabolically heterogeneous regions are detectable within discrete tumors [13], it is conceivable that these immunosuppressive metabolites may be irregularly distributed. Although such mediators are directly implicated in the dampening of T- and NK cell activity, their immunomodulatory effects are not reflected in routine clinical immunohistochemistry, where the focus is on assessing the presence or absence of lymphocytes, not their activation state.
There are multiple reports of tumor-derived cytokines, such as transforming growth factor-β1 (TGF-β1) suppressing cytotoxic effector functions [85,86,87], frequently acting via the downregulation of activating receptors [88,89,90]. As TGF-β1 production is exacerbated in hypoxic conditions, it follows that hypoxic tumor cells show heightened resistance to NK cell-mediated killing [91,92,93]. In response to hypoxia, accumulation of immunosuppressive adenosine and subsequent signaling via the A2A adenosine receptor has been shown to potently inhibit T- and NK cells [94,95]. This tumor-protective effect is abrogated in hyperoxic conditions [96,97], suggesting that supplemental oxygen could be a useful co-adjuvant for immunotherapy. Due to the disorganized vascularization of growing tumors, tumor cells may be irregularly exposed to hypoxia [98]. Interestingly, this intermittent hypoxic conditioning has been shown to enhance inflammatory responses as compared with chronic hypoxia [99,100,101]. However, this phenomenon has also been shown to enhance tumor growth and promote radiotherapy resistance in in vitro and in vivo models [102]. Thus, more research interrogating the role of intermittent hypoxia in the context of the TME would be valuable.
Additionally, there is mounting evidence that different immune cell subtypes, particularly NK cells, may exhibit tropisms for different tumor types. Human NK cells develop from CD34+ hematopoietic progenitors in the bone marrow and critically rely on interleukin-15 (IL-15) transpresentation for maturation into two functionally distinct mature NK cell subsets in the periphery [103,104], divided based on CD56 expression. Approximately 90% of circulating NK cells exhibit the CD56dim phenotype, which primarily function as cytolytic effectors via production of perforin and granzyme B. Conversely, the immunoregulatory CD56bright subset is charged with production of type I pro-inflammatory cytokines (IFNγ, Tumor necrosis factor (TNF)α, GM-CSF, IL-10, IL-13) and preferentially reside in the secondary lymphoid organs. In breast cancer [105] and gastrointestinal stromal tumours (GIST) [44], tumor-infiltrating NK cells are primarily of the poorly cytotoxic CD56bright subtype, whereas glioblastomas are preferentially infiltrated by CD56dim NK cells [106], and conflicting tropisms have been reported in NSCLC [107,108]. Such differences in NK cell homing may also be associated with the extent of hypoxia in the TME, as hypoxia-induced upregulation of chemokines C-X-C chemokine receptor type 4 (CXCR4) and CCR7 has been shown to favor migration of the CD56bright subset [109]. Intriguingly, this is unlikely to be explained by chemokine profile alone, as NK cell infiltration in CRC is scarce despite elevated expression of chemokines that attract CD56bright (CXCL9, CXCL10, CCL3, CCL4) and CD56dim (CXCL8, CXCL1, CXCL5, and CXCL12) subsets in tumor tissue as compared with adjacent normal mucosa [69].

3.2. Receptor–Ligand Heterogeneity

Through somatic recombination, the adaptive immune system is able to generate immunoglobulin and T-cell receptor (TCR) repertoires which span millions of antigens. Disparate receptor repertoires also exist within the NK cell compartment and underpin their functional heterogeneity. NK cell effector functions are tightly controlled by a complex network of activating and inhibitory receptors, and the ability of NK cells to eliminate target cells and produce cytokines relies upon the integration of signals from both types. Activating receptors, such as the natural cytotoxicity receptors (NKp30, NKp44, and NKp46) and NKG2D, recognise stress-induced ligands which are upregulated in response to DNA damage or viral transformation (“induced-self” recognition) [110,111]. Conversely, inhibitory receptors comprising the highly polymorphic killer cell immunoglobulin-like receptor (KIR) family work to prevent the aberrant targeting of healthy host cells by engaging “self” molecules, such as major histocompatibility complex class I (MHC-I), glycoproteins, and cadherins, and accordingly targeting those that have lost expression of these molecules (“missing-self” recognition).
Whilst NK cell receptors are preformed and, therefore, do not undergo the rearrangements characteristic of B- and T-cell receptors, a remarkable degree of NK cell diversity is conferred by the combinatorial expression of different NK receptors. Utilising a mass cytometry panel of 28 NK cell receptors, Horowitz et al. successfully detected up to 30,000 distinct NK cell phenotypes within a healthy individual. Such heterogeneity may in part be explained by the multiple factors which can regulate NK cell receptor repertoires, including host-genetics [112,113], epigenetic regulation [114] and previous viral infection [115,116,117].
The KIRs are the most heterogeneously expressed family of receptors. KIRs are encoded by 15 highly polymorphic genes clustered in the leukocyte receptor complex on chromosome 19q13.4 [118]. CD56dim NK cells express between 7 and 11 KIR family members; the presence or absence of individual KIR genes in each haplotype generates considerable genotypic diversity, which is compounded by differing allelic frequencies within each gene. Such heterogeneity is of clinical importance, as KIR-mismatch is a prerequisite for the graft-versus-leukaemia effect of allogenic NK cell transfer [119,120,121] and specific KIR genotypes have been associated with better responses to combination immunotherapies in neuroblastoma patients [122,123]. Similarly, three splice variants of the activating receptor NKp30 have been identified and the relative abundance of activating versus inhibitory isoforms has been associated with clinical outcome in neuroblastoma [124] and gastrointestinal sarcoma [44]. In the latter study, expression of inhibitory NKp30c as the most abundant isoform was the only independent prognostic factor for overall survival, whose overexpression was traced to a single nucleotide polymorphism in the natural cytotoxicity receptor-3 (NCR3) gene [44].
Importantly, numerous in vitro studies have demonstrated the ability of tumor cell lines to differentially regulate the receptor repertoires of NK cells [62,63,125]. Coordinated patterns of receptor dysregulation have similarly been documented in tumor-infiltrating as compared with peripheral NK cells. Reduced expression of activating receptors (including NKp30, NKp46, NKp80, CD16, DNAX accessory molecule-1 (DNAM-1) and NKG2D) has been documented in lung carcinoma [108], breast cancer [105] and acute myeloid leukaemia [126]. In each case, functional analysis of these patient-derived NK cells revealed that tumor-associated NK cells are poor producers of IFNγ and have an impaired ability to degranulate, although these studies did not investigate whether these defects impacted clinical outcome. Conversely, upregulation of the CD96/NKG2A inhibitory receptor complex has been observed in renal cell [127] and associated with poor prognosis in hepatocellular [128] carcinomas.
Immune cell responsiveness is not only determined by the balance of receptors present on a given cell, but also by the various ligands expressed by the target cell. For instance, a recognised mechanism of tumor escape in is the shedding of soluble ’decoy’ ligands for NK cell activating receptors, including BCL2-associated athanogene 6 (BAG-6) [129,130] and B7-H6 [131]. Interestingly, a genome-wide knockout screen performed by Klein and colleagues [132] identified loss of B7-H6 as the sole event which increased resistance of the chronic myeloid leukaemia cells to NK cell killing. Yet, recent studies investigating the functional consequences of NKG2D ligand shedding have challenged the idea that soluble ligands are exclusively immunosuppressive; in human cancers, shedding of MHC class I polypeptide related sequence A (MIC-A), a low-affinity NKG2D ligand, facilitates immune evasion [133,134]; however, shedding of the high-affinity murine analogue, MULT-1, enhances NK cell activation and tumor rejection [135].
Another major mechanism by which tumors evade immune destruction is up-regulation of immune checkpoint ligands, such as CD80/86, 4-1BBL, and OX40-L. Immune checkpoints are a broad group of inhibitory pathways and co-receptors with the primary purpose to restrict the duration and amplitude of an immune response, thereby minimizing collateral damage to healthy tissues [136]. Immune checkpoints primarily regulate T-cell responses, although checkpoint expression has been documented in B cells, NK cells and professional antigen-presenting cells (APCs) [136]. In the context of cancer, chronic antigen exposure coupled with engagement of inhibitory immune checkpoint ligands on tumor cells results in effector T-cell exhaustion, wherein T-cells undergo profound impairment of proliferation, cytokine production and cytotoxicity. Even in hostile immune environments densely infiltrated with cytotoxic T-lymphocytes, checkpoint ligand expression impinges upon tumor clearance [137]. Programmed Death Ligand 1 (PD-L1) has attracted particular attention in that its expression is associated with poor prognosis in multiple cancers [138,139,140,141]. Indeed, six of the seven FDA-approved immune checkpoint inhibitors target the PD-1/PD-L1 inhibitory axis [142]. PD-L1 expression by tumor cells is a strong predictive biomarker for response to PD-L1 blockade [143], although positive therapeutic responses to have been reported PD-L1-knockout mice [144] PD-L1-negative patients [145]. This suggests that whilst PD-L1 positivity enriches for responders, combining PD-L1 expression with other predictive factors, such as MSI status, may increase our confidence in patient selection. Indeed, even in tumors classed as PD-L1-positive, individual tumor cells vary widely in terms of PD-L1 expression [66,146]. Individual research groups set thresholds for ligand positivity ranging from 1–50% [147] and in tumors classed as checkpoint-positive, negative-staining cells may be ignored during clinical decision making despite their likely influence on treatment efficacy. Likewise, ligand profiles are labile in response to therapy; conventional chemotherapeutics increase expression ligands for the NK cell activating receptors NKG2D and DNAM-1 in multiple myeloma [148] and ovarian cancer [149] cells.
There is also some degree of binding promiscuity involved in receptor–ligand interactions. An array of NK cell receptors with opposing functional roles compete for binding of CD155 (PVR) ligand, including activating DNAX accessory molecule-1 (DNAM-1) and inhibitory T-cell immunoreceptor with Ig and ITIM domains (TIGIT) [150]. CD96-CD155 ligation is primarily considered an inhibitory checkpoint in the NK-mediated control of metastasis [151], however an activating role for CD96 has also been reported via promoting target adhesion [152]. Such complexity demonstrates how the interplay between immune cell receptors and ligands should be assessed as a network rather than at the single molecule level, and how such assessment should take into account spatial heterogeneity rather than focus on limited areas.

3.3. Neoantigenic Heterogeneity; A Challenge for T-cell Directed Immunotherapies

Just as the ability of the immune system to recognise and destroy invading pathogens or foreign particles relies on the ability to distinguish self from non- or altered-self, the genetic marks carried by tumor cells provide a diverse set of antigens that the immune system can use to detect malignant cells amongst their normal counterparts. Accordingly, T-cell directed immunotherapies have currently proven most efficacious in cancer types with high average tumor mutation burden (TMB) [153,154]. Whilst clinical responses to immune checkpoint blockade in cancer types with traditionally low TMB have been reported, these are generally restricted to virally-induced cancers, such as Merkel cell carcinoma and human papilloma virus-positive head and neck squamous cell carcinoma (HPV+ HNSCC), which show enhanced T-cell infiltration due to the presence of viral antigen [155,156]. Similarly, MSI has been identified as a pan-cancer predictive marker for checkpoint inhibitors [157,158], as MSI tumors harbour DNA mismatch-repair defects and thus present with 10–100 fold greater TMB than genomically stable tumors [159]. MSI tumors also have higher TIL density as compared with their microsatellite stable (MSS) counterparts, due primarily to their increased frequency of mutated neo-epitopes recognisable as non-self [160]. Neoepitope load is predictive of clinical outcome in bladder cancer [161], multiple myeloma [162], melanoma [163], and ovarian cancer [163,164], and there several reports of cytotoxic T-cells recognising epitopes derived from single point mutations [165,166,167]. Accordingly, heightened TMB is associated with more diversified expansion of T-cells [168] and greater infiltration of neoantigen-specific clonotypes [169].
Neoepitope targeting is an appealing therapeutic avenue in that the lack of neoepitope expression in healthy cells ensures that neoepitope-specific T-cells are not impinged by central tolerance, thereby conferring greater specificity and less toxicity. To this end, multiple studies are currently investigating the possibility of targeting neoepitopes with for personalised immunotherapy (see Türeci et al. [170] 2016 for a complete list of completed and ongoing trials). Yet, a barrier to the clinical applicability of these strategies inheres in the tremendously diverse range of antigenome landscapes observed between patients. In a recent pan-cancer analysis where almost one million unique neoantigens were identified, only 24 were conserved in at least 5% of patients in one or more cancer types [171]. Similar results have been reported in analyses of individual cancer types [172,173]; of note, a cohort study from The Cancer Genome Atlas (TCGA) in CRC (n = 598) revealed that only 4% of predicted neoepitopes were shared by at least two patients [174]. This complexity is compounded by the substantial diversity across patients with respect to human leukocyte antigen (HLA) haplotypes required for antigen presentation. This may be particularly important as, unlike membrane-associated checkpoint molecules, the majority of tumorigenic mutations affect genes which encode for intracellular proteins [175] and are therefore only recognizable by CD8+ T-cells following antigen processing and presentation in the context of MHC-I.
There is also strong evidence supporting the existence of neoantigenic heterogeneity within individual tumors. In lung adenocarcinoma, post-surgical recurrence has been associated with an increased proportion of branched neoantigens, defined as those not homogenously detected throughout the tumor [21]. Importantly, TCR sequencing of 45 tumor regions in these patients demonstrated that the majority of T-cell clones were topographically restricted, and that intra-tumor heterogeneity in TCR repertoires positively correlated with predicted neoantigen variety. Together, these findings suggest that regional differences in T-cell infiltration may be driven by spatially distinct neoantigen profiles, which may have important consequences for the development of therapies which target single neoantigens. There is also accumulating data suggesting that neoantigens are not equally ’potent’ in their ability to elicit T-cell effector functions, highlighting that assessing neoantigen quality may be more important than their quantity. Recent work has demonstrated that qualitative neoantigen prediction models, where fitness is conferred by a higher probability of TCR-recognition, have surpassed quantitative models in their ability to stratify for survival [176,177].

4. Neoantigen-Independent Strategies for Immunotherapy

Evidently, neoantigenic heterogeneity presents a formidable challenge in the development of T-cell based immunotherapies. To circumvent this striking degree of variability, clinical attention has been directed towards targeting non-mutated antigens that show heightened tumor specificity, including cancer germline antigens (CGAs). Unlike patient-specific neoepitopes, non-mutated antigens arise from comparatively well-defined mechanisms and are thus more likely to be conserved across patients. CG antigens are proteins that are exclusively expressed by germ cells which can be aberrantly re-expressed in multiple cancers, including the archetypal melanoma antigen (MAGE), synovial sarcoma X-chromosome breakpoint (SSX), and oesophageal squamous cell carcinoma (ESO) families. Expression of CG antigens is epigenetically modulated, being frequently induced following hypomethylation of CpG islands and covalent histone modifications [178]. Due to their absence on healthy somatic cells, CGAs have garnered substantial interest as therapeutic targets. However, development of CGA-directed therapies has been hampered by their low prevalence. Indeed, Kerkar et al. [179] report that only 2–3% of common epithelial cancers uniformly express New York-ESO-1 (NY-ESO-1).
An alternate strategy has been to target TA antigens that, despite basal expression in healthy cells, are preferentially expressed by transformed cells. One class of TA antigens are the differentiation antigens, which are homogenously expressed by cells of a given tissue type or cell lineage and consequently, by all malignant cells arising therefrom. Given that these antigens are concomitantly expressed in healthy tissues, therapeutic efficacy is generally accompanied by ’on-target’ toxicity. For example, adoptive cell transfer directed against the metastatic melanoma differentiation antigens gp1000 and melanoma-associated antigen recognised by T cells (MART-1) resulted in regression in 30% of patients, though these individuals frequently experienced uveitis and hearing loss due to destruction of melanocytes in the eye and ear [180]. Similarly, targeting carcinoembryonic antigen (CEA) overexpression in metastatic CRC induced regression but also severe inflammatory colitis [181].

Harnessing NK Cells for Innate Immunotherapy

In recent years, NK cells have emerged as alternative candidates for immunotherapeutic development. Certainly, the MHC-I unrestricted manner of NK cell responses may render this subset a more promising candidate for immunotherapy, as they may overcome the restricted benefit of antigen-specific T-cells in tumors with high mutational diversity. NK-based therapies may prove a new frontier in the treatment of immunologically ’cold’ or refractory tumors, given that the one of the most common mechanisms of immune escape employed by tumor cells is downregulation of MHC-I machinery [182]. Similarly, defects in genes implicated in antigen processing and presentation have recently been identified as key drivers of acquired resistance to immune checkpoint therapies [183]. Additionally, NK cell cytotoxicity may be triggered following engagement of ligands upregulated by transformed cells in response to epithelial-mesenchymal transition, such as MIC-A/B and ULBP1-3 [184]. The latter renders NK cells particularly apt in the eradication of early metastatic cells. Importantly, NK cell receptors are preformed and thus do not require prior sensitisation, clonal expansion and co-stimulatory signalling required for T-cell responsiveness, thus allowing for more rapid cytotoxic responses. Whilst adoptive transfer of HLA-mismatched NK cells induces graft-versus-tumor effects, these cells do not contribute to dose-limiting graft-versus-host disease (GvHD) and may even play a protective role by dampening alloreactive T-cell responses [185,186].
Although no therapies directed specifically at NK cells have been approved in the clinic to date, such promising data suggests that a next wave of therapeutic advances could come from targeting this cell type (Table 1). In phase I/II clinical trials, monoclonal antibodies targeting NK cell inhibitory receptors, such as NKG2A [187] and the KIR family [188], have been shown to bolster NK cell-mediated cytotoxicity. Chimeric antigen receptor (CAR) NK cells directed against CD19 [189,190], CD2 subset-1 (CS-1) [191] and epidermal growth factor receptor (EGFR) [192,193] have also shown efficacy in xenograft models. To improve specificity, Bi-Specific Killer cell Engagers (BiKEs) have been developed which co-target the CD16 low affinity IgG receptor (FcγRIII) and epitopes expressed by malignant cells, such as CD33 [194] and EpCAM [195]. BiKEs have been shown to mediate NK cell cytotoxicity, which is markedly enhanced following the incorporation of a modified human IL-15 crosslinker to generate a tri-specific moity (TriKE; [196]. Yet, these approaches all still rely on tumor cell expression of the selected target and may therefore show limited success in eliminating heterogenous tumor cell populations. Addressing this challenge, CAR T-cells have been engineered to co-express members of the natural cytotoxicity receptor (NCR) family of NK cell activating receptors (including NKp46 [197], NKp44 [198], and NKp30 [199]. These ’hybrid’ CARs avoid the obstacle of MHC-restriction but retain the long-term persistence of adoptively transferred T-cells, endowing cytotoxic T-cells with an NK cell-like pattern of recognition. It is through such innovations, which consider the complexity of tumor cell heterogeneity and acknowledge that immunotherapy may not be a ’one size fits all’ approach, that we may draw the greatest clinical benefit.

5. Concluding Remarks

Whether driven by immune cell-intrinsic or tumor-induced factors, it is clear that a vast scope of immunological heterogeneity exists across human cancers. Incorporating our understanding of this heterogeneity into clinical studies may improve our ability to further stratify patients who are candidates for immunotherapy and aid in the design of rational combination therapies directed against heterogeneously expressed targets thereby complementing existing therapeutic strategies, such as those targeting PD-L1. Additionally, further research exploring the influence of TMB on the infiltration and effector functions of non-antigen restricted mediators, specifically NK cells, could inform new therapeutic strategies harnessing the innate immune compartment.

Author Contributions

Writing—original draft preparation, C.S.; Writing—review and editing, F.H. and N.D.H.

Funding

This research was funded by the National Health and Medical Research Council of Australia, grants GNT1069024 and GNT1164081 to FH.

Conflicts of Interest

N.D.H. has ownership interest (including patents) in oNKo-innate Pty Ltd and receives research funding from Servier and Paranta Biosciences.

Abbreviations

Common cell types and acronyms used throughout this manuscript.
CD56bright natural killer (NK) cellImmunoregulatory subset (~10%) of NK cells producing type I pro-inflammatory cytokines
CD56dim NK cell Cytotoxic subset (~90%) of NK cells characterized by high production of perforin and granzyme B.
Chimeric antigen receptor (CAR)Chimeric proteins that fuse an extracellular tumor antigen-targeting domain with a lymphocyte (T- or NK cell)-activating intracellular moiety.
Bi-/Tri-specific killer cell engager (Bi-/TriKE)Advanced biologicals engineered to express antibody domains capable of binding multiple unique antigens (e.g., 2 antigens /Bi- or 3 antigens/Tri on NK cells and tumor cells to promote NK cell activation and binding to tumor cells)
Killer cell immunoglobulin-like receptor (KIR) Large family of highly polymorphic NK cell receptors (also expressed in a subset of T-cells) which regulate cytotoxicity by engaging “self” molecules, such as MHC-I.
Major histocompatibility complex class I (MHC-I) Multi-protein complex expressed by all nucleated cells in mammals. MHC-I presents peptide fragments (derived from self, non-self and neo-antigens) to cytotoxic T-cells.
Natural cytotoxicity receptor (NCR)Family of type I transmembrane proteins which, when stimulated, trigger NK cell degranulation and cytotoxicity; most tumor-associated NCR ligands are unknown.
Neoantigen-dependent killingPeptides arising from tumor mutations are presented to T-cells in the context of MHC-I, triggering clonal expansion of cytotoxic T-cells which specifically target tumor cells expressing the cognate neoantigen.
Neoantigen-independent killingCytotoxicity which does not require priming by a specific antigen; NK cell cytotoxicity is antigen-independent and therefore not restricted to tumor cells that express the cognate neoantigen.
Microsatellite instability (MSI)Type of genetic instability arising from defective DNA mismatch repair, resulting in a hypermutated phenotype.
Tumor-infiltrating lymphocyte (TIL)Lymphocyte which has migrated from the peripheral blood into a solid tumor; This term often refers to tumor-infiltrating cytotoxic CD8+ T-cells.
Tumor mutation burden (TMB)Number of mutations per coding area of a tumor genome; high TMB is associated with better responses to checkpoint immunotherapy.

References

  1. Eirew, P.; Steif, A.; Khattra, J.; Ha, G.; Yap, D.; Farahani, H.; Gelmon, K.; Chia, S.; Mar, C.; Wan, A.; et al. Dynamics of genomic clones in breast cancer patient xenografts at single-cell resolution. Nature 2015, 518, 422–426. [Google Scholar] [CrossRef] [PubMed]
  2. Kim, T.M.; Jung, S.H.; An, C.H.; Lee, S.H.; Baek, I.P.; Kim, M.S.; Park, S.W.; Rhee, J.K.; Lee, S.H.; Chung, Y.J. Subclonal Genomic Architectures of Primary and Metastatic Colorectal Cancer Based on Intratumoral Genetic Heterogeneity. Clin. Cancer Res. 2015, 21, 4461–4472. [Google Scholar] [CrossRef]
  3. Khalique, L.; Ayhan, A.; Weale, M.E.; Jacobs, I.J.; Ramus, S.J.; Gayther, S.A. Genetic intra-tumor heterogeneity in epithelial ovarian cancer and its implications for molecular diagnosis of tumors. J. Pathol. 2007, 211, 286–295. [Google Scholar] [CrossRef] [PubMed]
  4. Oh, B.Y.; Shin, H.T.; Yun, J.W.; Kim, K.T.; Kim, J.; Bae, J.S.; Cho, Y.B.; Lee, W.Y.; Yun, S.H.; Park, Y.A.; et al. Intratumor heterogeneity inferred from targeted deep sequencing as a prognostic indicator. Sci. Rep. 2019, 9, 4542. [Google Scholar] [CrossRef] [PubMed]
  5. Liu, G.; Zhan, X.; Dong, C.; Liu, L. Genomics alterations of metastatic and primary tissues across 15 cancer types. Sci. Rep. 2017, 7, 13262. [Google Scholar] [CrossRef] [PubMed]
  6. Yang, J.; Luo, H.; Li, Y.; Li, J.; Cai, Z.; Su, X.; Dai, D.; Du, W.; Chen, T.; Chen, M. Intratumoral Heterogeneity Determines Discordant Results of Diagnostic Tests for Human Epidermal Growth Factor Receptor (HER) 2 in Gastric Cancer Specimens. Cell Biochem. Biophys. 2012, 62, 221–228. [Google Scholar] [CrossRef] [PubMed]
  7. Kalikali, A.; Voutsinaa, A.; Koutsopoulosb, A.; Pallis, A.; Trypaki, M.; Souglakos, J.; Stathopoulos, E.; Mavroudis, D.; Georgoulias, V. P3-157: Correlation of EGFR mutation status between primary tumor and metastases in NSCLC. J. Thorac. Oncol. 2008, 2, S746. [Google Scholar] [CrossRef]
  8. Echeverria, G.V.; Powell, E.; Seth, S.; Ge, Z.; Carugo, A.; Bristow, C.; Peoples, M.; Robinson, F.; Qiu, H.; Shao, J.; et al. High-resolution clonal mapping of multi-organ metastasis in triple negative breast cancer. Nat. Commun. 2018, 9, 5079. [Google Scholar] [PubMed]
  9. Kreso, A.; O’Brien, C.A.; Van Galen, P.; Gan, O.I.; Notta, F.; Brown, A.M.K.; Ng, K.; Jing, M.; Wienholds, E.; Dunant, C.; et al. Variable clonal repopulation dynamics influence chemotherapy response in colorectal cancer. Science 2013, 339, 543–548. [Google Scholar] [CrossRef] [PubMed]
  10. Cheow, L.F.; Courtois, E.T.; Tan, Y.; Viswanathan, R.; Xing, Q.; Tan, R.Z.; Tan, D.S.W.; Robson, P.; Loh, Y.H.; Quake, S.R.; et al. Single-cell multimodal profiling reveals cellular epigenetic heterogeneity. Nat. Methods 2016, 13, 833–836. [Google Scholar] [CrossRef]
  11. Henrique, R. Epigenetic Heterogeneity of High-Grade Prostatic Intraepithelial Neoplasia: Clues for Clonal Progression in Prostate Carcinogenesis. Mol. Cancer Res. 2006, 4, 1–8. [Google Scholar] [CrossRef] [Green Version]
  12. Sigalotti, L.; Fratta, E.; Coral, S.; Tanzarella, S.; Danielli, R.; Colizzi, F.; Fonsatti, E.; Traversari, C.; Altomonte, M.; Maio, M. Intratumor Heterogeneity of Cancer/Testis Antigens Expression in Human Cutaneous Melanoma Is Methylation-Regulated and Functionally Reverted by 5-Aza-2′-deoxycytidine. Cancer Res. 2004, 64, 9167–9171. [Google Scholar] [CrossRef]
  13. Hensley, C.T.; Faubert, B.; Yuan, Q.; Jin, E.; Kim, J.; Jiang, L.; Ko, B.; Skelton, R.; Loudat, L.; Wodzak, M.; et al. Metabolic Heterogeneity in Human Lung Tumors. Cell 2016, 164, 681–694. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  14. Little, S.E.; Popov, S.; Jury, A.; Bax, D.A.; Doey, L.; Al-Sarraj, S.; Jurgensmeier, J.M.; Jones, C. Receptor Tyrosine Kinase Genes Amplified in Glioblastoma Exhibit a Mutual Exclusivity in Variable Proportions Reflective of Individual Tumor Heterogeneity. Cancer Res. 2012, 72, 1614–1620. [Google Scholar] [CrossRef] [Green Version]
  15. Colombino, M.; Capone, M.; Lissia, A.; Cossu, A.; Rubino, C.; De Giorgi, V.; Massi, D.; Fonsatti, E.; Staibano, S.; Nappi, O.; et al. BRAF/NRAS Mutation Frequencies Among Primary Tumors and Metastases in Patients with Melanoma. J. Clin. Oncol. 2012, 30, 2522–2529. [Google Scholar] [CrossRef]
  16. Milo, I.; Bedora-Faure, M.; Garcia, Z.; Thibaut, R.; Périé, L.; Shakhar, G.; Deriano, L.; Bousso, P. The immune system profoundly restricts intratumor genetic heterogeneity. Sci. Immunol. 2018, 3, 29. [Google Scholar] [CrossRef] [PubMed]
  17. Takeda, K.; Nakayama, M.; Hayakawa, Y.; Kojima, Y.; Ikeda, H.; Imai, N.; Ogasawara, K.; Okumura, K.; Thomas, D.M.; Smyth, M.J. IFN-γ is required for cytotoxic T cell-dependent cancer genome immunoediting. Nat. Commun. 2017, 8, 14607. [Google Scholar] [CrossRef] [PubMed]
  18. Bindea, G.; Mlecnik, B.; Tosolini, M.; Kirilovsky, A.; Waldner, M.; Obenauf, A.C.; Angell, H.; Fredriksen, T.; Lafontaine, L.; Berger, A.; et al. Spatiotemporal dynamics of intratumoral immune cells reveal the immune landscape in human cancer. Immunity 2013, 39, 782–795. [Google Scholar] [CrossRef] [PubMed]
  19. Gentles, A.J.; Newman, A.M.; Liu, C.L.; Bratman, S.V.; Feng, W.; Kim, D.; Nair, V.S.; Xu, Y.; Khuong, A.; Hoang, C.D.; et al. The prognostic landscape of genes and infiltrating immune cells across human cancers. Nat. Med. 2015, 21, 938–945. [Google Scholar] [CrossRef] [PubMed]
  20. Doucette, T.; Rao, G.; Rao, A.; Shen, L.; Aldape, K.; Wei, J.; Dziurzynski, K.; Gilbert, M.; Heimberger, A.B. Immune Heterogeneity of Glioblastoma Subtypes: Extrapolation from the Cancer Genome Atlas. Cancer Immunol. Res. 2013, 1, 112–122. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  21. Reuben, A.; Gittelman, R.; Gao, J.; Zhang, J.; Yusko, E.C.; Wu, C.J.; Emerson, R.; Zhang, J.; Tipton, C.; Li, J.; et al. TCR Repertoire Intratumor Heterogeneity in Localized Lung Adenocarcinomas: An Association with Predicted Neoantigen Heterogeneity and Postsurgical Recurrence. Cancer Discov. 2017, 7, 12–17. [Google Scholar] [CrossRef]
  22. Jiménez-Sánchez, A.; Memon, D.; Pourpe, S.; Veeraraghavan, H.; Li, Y.; Vargas, H.A.; Gill, M.B.; Park, K.J.; Zivanovic, O.; Konner, J.; et al. Heterogeneous Tumor-Immune Microenvironments among Differentially Growing Metastases in an Ovarian Cancer Patient. Cell 2017, 170, 927–938. [Google Scholar] [CrossRef] [PubMed]
  23. Hu, Z.; Sun, R.; Curtis, C. A population genetics perspective on the determinants of intra-tumor heterogeneity. Biochim. Biophys. Acta Rev. Cancer 2017, 1867, 109–126. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  24. Obeid, J.M.; Hu, Y.; Erdag, G.; Leick, K.M.; Slingluff, C.L. The heterogeneity of tumor-infiltrating CD8+ T cells in metastatic melanoma distorts their quantification. Melanoma Res. 2017, 27, 211–217. [Google Scholar] [CrossRef] [PubMed]
  25. Gao, Y.; Souza-Fonseca-Guimaraes, F.; Bald, T.; Ng, S.S.; Young, A.; Ngiow, S.F.; Rautela, J.; Straube, J.; Waddell, N.; Blake, S.J.; et al. Tumor immunoevasion by the conversion of effector NK cells into type 1 innate lymphoid cells. Nat. Immunol. 2017, 18, 1004–1015. [Google Scholar] [CrossRef] [PubMed]
  26. Hiraoka, K.; Miyamoto, M.; Cho, Y.; Suzuoki, M.; Oshikiri, T.; Nakakubo, Y.; Itoh, T.; Ohbuchi, T.; Kondo, S.; Katoh, H. Concurrent infiltration by CD8 + T cells and CD4 + T cells is a favourable prognostic factor in non-small-cell lung carcinoma. Br. J. Cancer 2006, 94, 275–280. [Google Scholar] [CrossRef] [PubMed]
  27. Leffers, N.; Gooden, M.J.M.; De Jong, R.A.; Hoogeboom, B.N.; Ten Hoor, K.A.; Hollema, H.; Boezen, H.M.; Van Der Zee, A.G.J.; Daemen, T.; Nijman, H.W. Prognostic significance of tumor-infiltrating T-lymphocytes in primary and metastatic lesions of advanced stage ovarian cancer. Cancer Immunol. Immunother. 2009, 58, 449–459. [Google Scholar] [CrossRef]
  28. Naito, Y.; Saito, K.; Shiiba, K.; Ohuchi, A.; Saigenji, K.; Nagura, H.; Ohtani, H. CD8+ T cells infiltrated within cancer cell nests as a prognostic factor in human colorectal cancer. Cancer Res. 1998, 58, 3491–3494. [Google Scholar] [PubMed]
  29. Camus, M.; Tosolini, M.; Mlecnik, B.; Pagè, F.; Kirilovsky, A.; Berger, A.; Costes, A.; Bindea, G.; Charoentong, P.; Bruneval, P.; et al. Coordination of Intratumoral Immune Reaction and Human Colorectal Cancer Recurrence. Cancer Res 2009, 69, 2685–2693. [Google Scholar] [CrossRef] [Green Version]
  30. Ott, P.A.; Bang, Y.J.; Piha-Paul, S.A.; Abdul Razak, A.R.; Bennouna, J.; Soria, J.C.; Rugo, H.S.; Cohen, R.B.; O’Neil, B.H.; Mehnert, J.M.; et al. T-cell–inflamed gene-expression profile, programmed death ligand 1 expression, and tumor mutational burden predict efficacy in patients treated with pembrolizumab across 20 cancers: KEYNOTE-028. J. Clin. Oncol. 2019, 37, 318–327. [Google Scholar] [CrossRef]
  31. Ji, R.R.; Chasalow, S.D.; Wang, L.; Hamid, O.; Schmidt, H.; Cogswell, J.; Alaparthy, S.; Berman, D.; Jure-Kunkel, M.; Siemers, N.O.; et al. An immune-active tumor microenvironment favors clinical response to ipilimumab. Cancer Immunol. Immunother. 2012, 61, 1019–1031. [Google Scholar] [CrossRef] [PubMed]
  32. Prendergast, G.C.; Mondal, A.; Dey, S.; Laury-Kleintop, L.D.; Muller, A.J. Inflammatory Reprogramming with IDO1 Inhibitors: Turning Immunologically Unresponsive ’Cold’ Tumors ’Hot’. Trends Cancer 2018, 4, 38–58. [Google Scholar] [CrossRef] [PubMed]
  33. Ghiringhelli, F.; Ménard, C.; Terme, M.; Flament, C.; Taieb, J.; Chaput, N.; Puig, P.E.; Novault, S.; Escudier, B.; Vivier, E.; et al. CD4 + CD25 + regulatory T cells inhibit natural killer cell functions in a transforming growth factor–β–dependent manner. J. Exp. Med. 2005, 202, 1075–1085. [Google Scholar] [CrossRef] [PubMed]
  34. Galluzzi, L.; Buqué, A.; Kepp, O.; Zitvogel, L.; Kroemer, G. Immunological Effects of Conventional Chemotherapy and Targeted Anticancer Agents. Cancer Cell 2015, 28, 690–714. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  35. Mlecnik, B.; Tosolini, M.; Kirilovsky, A.; Berger, A.; Bindea, G.; Meatchi, T.; Bruneval, P.; Trajanoski, Z.; Fridman, W.H.; Pagès, F.; et al. Histopathologic-based prognostic factors of colorectal cancers are associated with the state of the local immune reaction. J. Clin. Oncol. 2011, 29, 610–618. [Google Scholar] [CrossRef] [PubMed]
  36. Mlecnik, B.; Bindea, G.; Angell, H.K.; Valge-Archer, V.; Latouche, J.B.; Maby, P.; Angelova, M.; Tougeron, D.; Church, S.E.; Lafontaine, L.; et al. Integrative Analyses of Colorectal Cancer Show Immunoscore Is a Stronger Predictor of Patient Survival Than Microsatellite Instability. Immunity 2016, 44, 698–711. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  37. Pagès, F.; Mlecnik, B.; Marliot, F.; Bindea, G.; Ou, F.S.; Bifulco, C.; Lugli, A.; Zlobec, I.; Rau, T.T.; Berger, M.D.; et al. International validation of the consensus Immunoscore for the classification of colon cancer: A prognostic and accuracy study. Lancet 2018, 391, 2128–2139. [Google Scholar] [CrossRef]
  38. Gong, C.; Anders, R.A.; Zhu, Q.; Taube, J.M.; Green, B.; Cheng, W.; Bartelink, I.H.; Vicini, P.; Wang, B.; Popel, A.S. Quantitative Characterization of CD8+ T Cell Clustering and Spatial Heterogeneity in Solid Tumors. Front. Oncol. 2019, 8, 649. [Google Scholar] [CrossRef]
  39. Shi, L.; Zhang, Y.; Feng, L.; Wang, L.; Rong, W.; Wu, F.; Wu, J.; Zhang, K.; Cheng, S.; Shi, L.; et al. Multi-omics study revealing the complexity and spatial heterogeneity of tumor-infiltrating lymphocytes in primary liver carcinoma. Oncotarget 2017, 8, 34844–34857. [Google Scholar] [CrossRef]
  40. Heindl, A.; Sestak, I.; Naidoo, K.; Cuzick, J.; Dowsett, M.; Yuan, Y. Relevance of Spatial Heterogeneity of Immune Infiltration for Predicting Risk of Recurrence After Endocrine Therapy of ER+ Breast Cancer. Jnci J. Natl. Cancer Inst. 2018, 110, 166–175. [Google Scholar] [CrossRef]
  41. Khan, A.M.; Yuan, Y. Biopsy variability of lymphocytic infiltration in breast cancer subtypes and the ImmunoSkew score. Sci. Rep. 2016, 6, 36231. [Google Scholar] [CrossRef] [PubMed]
  42. Orange, J.S. Natural Killer Cell Deficiency. Stiehm’s Immune Defic. 2014, 132, 765–774. [Google Scholar]
  43. Miyake, S.; Suga, K.; Matsuyama, S.; Nakachi, K.; Imai, K. Natural cytotoxic activity of peripheral-blood lymphocytes and cancer incidence: An 11-year follow-up study of a general population. Lancet 2002, 356, 1795–1799. [Google Scholar]
  44. Delahaye, N.F.; Rusakiewicz, S.; Martins, I.; Ménard, C.; Roux, S.; Lyonnet, L.; Paul, P.; Sarabi, M.; Chaput, N.; Semeraro, M.; et al. Alternatively spliced NKp30 isoforms affect the prognosis of gastrointestinal stromal tumors. Nat. Med. 2011, 17, 700–707. [Google Scholar] [CrossRef]
  45. Gannon, P.O.; Poisson, A.O.; Delvoye, N.; Lapointe, R.; Mes-Masson, A.-M.; Saad, F. Characterization of the intra-prostatic immune cell infiltration in androgen-deprived prostate cancer patients. J. Immunol. Methods 2009, 348, 9–17. [Google Scholar] [CrossRef] [PubMed]
  46. Donskov, F.; von der Maase, H. Impact of immune parameters on long-term survival in metastatic renal cell carcinoma. J. Clin. Oncol. 2006, 24, 1997–2005. [Google Scholar] [CrossRef] [PubMed]
  47. Coca, S.; Perez-Piqueras, J.; Martinez, D.; Colmenarejo, A.; Saez, M.A.; Vallejo, C.; Martos, J.A.; Moreno, M. The prognostic significance of intratumoral natural killer cells in patients with colorectal carcinoma. Cancer 1997, 79, 2320–2328. [Google Scholar] [CrossRef]
  48. Cursons, J.; Souza-Fonseca Guimaraes, F.; Foroutan, M.; Anderson, A.; Hollande, F.; Hediyeh-Zadeh, S.; Behren, A.; Huntington, N.D.; Davis, M.J. A gene signature predicting natural killer cell infiltration and improved survival in melanoma patients. Cancer Immunol. Res. 2018, 2019. [Google Scholar] [CrossRef]
  49. Ilander, M.; Olsson-Strömberg, U.; Schlums, H.; Guilhot, J.; Brück, O.; Lähteenmäki, H.; Kasanen, T.; Koskenvesa, P.; Söderlund, S.; Höglund, M.; et al. Increased proportion of mature NK cells is associated with successful imatinib discontinuation in chronic myeloid leukemia. Leukemia 2017, 31, 1108–1116. [Google Scholar] [CrossRef]
  50. Saijo, N.; Ozaki, A.; Beppu, Y.; Takahashi, K.; Fujita, J.; Sasaki, Y.; Nomori, H.; Kimata, M.; Shimizu, E.; Hoshi, A. Analysis of metastatic spread and growth of tumor cells in mice with depressed natural killer activity by anti-asialo GMl antibody or anticancer agents. J. Cancer Res. Clin. Oncol. 1984, 107, 157–163. [Google Scholar] [CrossRef]
  51. Yano, S.; Nishioka, Y.; Izumi, K.; Tsuruo, T.; Tanaka, T.; Miyasaka, M.; Sone, S. Novel metastasis model of human lung cancer in SCID mice depleted of NK cells. Int. J. Cancer 1996, 67, 211–217. [Google Scholar] [CrossRef]
  52. Kasai, M.; Yoneda, T.; Habu, S.; Maruyama, Y.; Okumura, K.; Tokunaga, T. Okumura; Tokunaga, T. In vivo effect of anti-asialo GM1 antibody on natural killer activity. Nature 1981, 291, 334–335. [Google Scholar] [CrossRef]
  53. Sathe, P.; Delconte, R.B.; Souza-Fonseca-Guimaraes, F.; Seillet, C.; Chopin, M.; Vandenberg, C.J.; Rankin, L.C.; Mielke, L.A.; Vikstrom, I.; Kolesnik, T.B.; et al. Innate immunodeficiency following genetic ablation of Mcl1 in natural killer cells. Nat. Commun. 2014, 5, 4539. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  54. Smyth, M.J.; Thia, K.Y.T.; Cretney, E.; Kelly, J.M.; Snook, M.B.; Forbes, C.A.; Scalzo, A.A. Perforin Is a Major Contributor to NK Cell Control of Tumor Metastasis. J. Immunol. 1999, 162, 6658–6662. [Google Scholar]
  55. Street, S.E.A.; Cretney, E.; Smyth, M.J. Perforin and interferon-activities independently control tumor initiation, growth, and metastasis. Blood 2001, 97, 192–197. [Google Scholar] [CrossRef] [PubMed]
  56. Glasner, A.; Ghadially, H.; Gur, C.; Stanietsky, N.; Tsukerman, P.; Enk, J.; Mandelboim, O. Recognition and Prevention of Tumor Metastasis by the NK Receptor NKp46/NCR1. J. Immunol. 2012, 188, 2509–2515. [Google Scholar] [CrossRef] [Green Version]
  57. Iguchi-Manaka, A.; Kai, H.; Yamashita, Y.; Shibata, K.; Tahara-Hanaoka, S.; Honda, S.; Yasui, T.; Kikutani, H.; Shibuya, K.; Shibuya, A. Accelerated tumor growth in mice deficient in DNAM-1 receptor. J. Exp. Med. 2008, 205, 2959–2964. [Google Scholar] [CrossRef] [Green Version]
  58. Chan, C.J.; Andrews, D.M.; McLaughlin, N.M.; Yagita, H.; Gilfillan, S.; Colonna, M.; Smyth, M.J. DNAM-1/CD155 Interactions Promote Cytokine and NK Cell-Mediated Suppression of Poorly Immunogenic Melanoma Metastases. J. Immunol. 2010, 184, 902–911. [Google Scholar] [CrossRef]
  59. Takeda, K.; Hayakawa, Y.; Smyth, M.; Kayagaki, N.; Yamaguchi, N.; Kakuta, S.; Iwakura, Y.; Yagita, H.; Okumura, K. Involvement of tumor necrosis factor-related apoptosis-inducing ligand in surveillance of tumor metastasis by liver natural killer cells. Nat. Med. 2001, 7, 94–100. [Google Scholar] [CrossRef] [PubMed]
  60. Donadon, M.; Hudspeth, K.; Cimino, M.; Di Tommaso, L.; Preti, M.; Tentorio, P.; Roncalli, M.; Mavilio, D.; Torzilli, G. Increased Infiltration of Natural Killer and T Cells in Colorectal Liver Metastases Improves Patient Overall Survival. J. Gastrointest. Surg. 2017, 21, 1–11. [Google Scholar] [CrossRef] [PubMed]
  61. Tallerico, R.; Todaro, M.; Di Franco, S.; Maccalli, C.; Garofalo, C.; Sottile, R.; Palmieri, C.; Tirinato, L.; Pangigadde, P.N.; La Rocca, R.; et al. Human NK cells selective targeting of colon cancer-initiating cells: A role for natural cytotoxicity receptors and MHC class I molecules. J. Immunol. 2013, 190, 2381–2390. [Google Scholar] [CrossRef] [PubMed]
  62. Pietra, G.; Manzini, C.; Vitale, M.; Balsamo, M.; Ognio, E.; Boitano, M.; Queirolo, P.; Moretta, L.; Mingari, M.C. Natural killer cells kill human melanoma cells with characteristics of cancer stem cells. Int. Immunol. 2009, 21, 793–801. [Google Scholar] [CrossRef] [PubMed]
  63. Castriconi, R.; Daga, A.; Dondero, A.; Zona, G.; Poliani, P.L.; Melotti, A.; Griffero, F.; Marubbi, D.; Spaziante, R.; Bellora, F.; et al. NK Cells Recognize and Kill Human Glioblastoma Cells with Stem Cell-Like Properties. J. Immunol. 2009, 182, 3530–3539. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  64. Ames, E.; Canter, R.J.; Grossenbacher, S.K.; Mac, S.; Chen, M.; Smith, R.C.; Hagino, T.; Perez-Cunningham, J.; Sckisel, G.D.; Urayama, S.; et al. NK Cells Preferentially Target Tumor Cells with a Cancer Stem Cell Phenotype. J. Immunol. 2015, 195, 4010–4019. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  65. Mansfield, A.S.; Aubry, M.C.; Moser, J.C.; Harrington, S.M.; Dronca, R.S.; Park, S.S.; Dong, H. Temporal and spatial discordance of programmed cell death-ligand 1 expression and lymphocyte tumor infiltration between paired primary lesions and brain metastases in lung cancer. Ann. Oncol. 2016, 27, 1953–1958. [Google Scholar] [CrossRef]
  66. Li, M.; Li, A.; Zhou, S.; Xu, Y.; Xiao, Y.; Bi, R.; Yang, W. Heterogeneity of PD-L1 expression in primary tumors and paired lymph node metastases of triple negative breast cancer. BMC Cancer 2018, 18. [Google Scholar] [CrossRef] [PubMed]
  67. Linton, P.J.; Dorshkind, K. Age-related changes in lymphocyte development and function. Nat. Immunol. 2004, 5, 133–139. [Google Scholar] [CrossRef]
  68. Li, T.; Yang, Y.; Hua, X.; Wang, G.; Liu, W.; Jia, C.; Tai, Y.; Zhang, Q.; Chen, G. Hepatocellular carcinoma-associated fibroblasts trigger NK cell dysfunction via PGE2 and IDO. Cancer Lett. 2012, 318, 154–161. [Google Scholar] [CrossRef]
  69. Halama, N.; Braun, M.; Kahlert, C.; Spille, A.; Quack, C.; Rahbari, N.; Koch, M.; Weitz, J.; Kloor, M.; Zoernig, I.; et al. Natural Killer Cells are Scarce in Colorectal Carcinoma Tissue Despite High Levels of Chemokines and Cytokines. Clin. Cancer Res. 2011, 17, 678–689. [Google Scholar] [CrossRef]
  70. Rosenberg, S.A.; Sherry, R.M.; Morton, K.E.; Scharfman, W.J.; Yang, J.C.; Topalian, S.L.; Royal, R.E.; Kammula, U.; Restifo, N.P.; Hughes, M.S.; et al. Tumor progression can occur despite the induction of very high levels of self/tumor antigen-specific CD8+ T cells in patients with melanoma. J. Immunol. 2005, 175, 6169–6176. [Google Scholar] [CrossRef]
  71. Hoechst, B.; Voigtlaender, T.; Ormandy, L.; Gamrekelashvili, J.; Zhao, F.; Wedemeyer, H.; Lehner, F.; Manns, M.P.; Greten, T.F.; Korangy, F. Myeloid derived suppressor cells inhibit natural killer cells in patients with hepatocellular carcinoma via the NKp30 receptor. Hepatology 2009, 50, 799–807. [Google Scholar] [CrossRef] [PubMed]
  72. Hanke, T.; Melling, N.; Simon, R.; Sauter, G.; Bokemeyer, C.; Lebok, P.; Terracciano, L.M.; Izbicki, J.R.; Marx, A.H. High intratumoral FOXP3+ T regulatory cell (Tregs) density is an independent good prognosticator in nodal negative colorectal cancer. Int. J. Clin. Exp. Pathol. 2015, 8, 8227–8235. [Google Scholar]
  73. Heimberger, A.B.; Abou-Ghazal, M.; Reina-Ortiz, C.; Yang, D.S.; Sun, W.; Qiao, W.; Hiraoka, N.; Fuller, G.N. Incidence and prognostic impact of FoxP3+ regulatory T cells in human gliomas. Clin. Cancer Res. 2008, 14, 5166–5172. [Google Scholar] [CrossRef]
  74. Shen, Z.; Zhou, S.; Wang, Y.; Li, R.L.; Zhong, C.; Liang, C.; Sun, Y. Higher intratumoral infiltrated Foxp3+ Treg numbers and Foxp3+/CD8+ ratio are associated with adverse prognosis in resectable gastric cancer. J. Cancer Res. Clin. Oncol. 2010, 136, 1585–1595. [Google Scholar] [CrossRef] [PubMed]
  75. Li, T.; Yi, S.; Liu, W.; Jia, C.; Wang, G.; Hua, X.; Tai, Y.; Zhang, Q.; Chen, G. Colorectal carcinoma-derived fibroblasts modulate natural killer cell phenotype and antitumor cytotoxicity. Med. Oncol. 2013, 30, 663. [Google Scholar] [CrossRef] [PubMed]
  76. Zhang, R.; Qi, F.; Zhao, F.; Li, G.; Shao, S.; Zhang, X.; Yuan, L.; Feng, Y. Cancer-associated fibroblasts enhance tumor-associated macrophages enrichment and suppress NK cells function in colorectal cancer. Cell Death Dis. 2019, 10, 273. [Google Scholar] [CrossRef]
  77. Balsamo, M.; Scordamaglia, F.; Pietra, G.; Manzini, C.; Cantoni, C.; Boitano, M.; Queirolo, P.; Vermi, W.; Facchetti, F.; Moretta, A.; et al. Melanoma-associated fibroblasts modulate NK cell phenotype and antitumor cytotoxicity. Proc. Natl. Acad. Sci. 2009, 106, 20847–20852. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  78. West, N.R.; Kost, S.E.; Martin, S.D.; Milne, K.; Deleeuw, R.J.; Nelson, B.H.; Watson, P.H. Tumor-infiltrating FOXP3 + lymphocytes are associated with cytotoxic immune responses and good clinical outcome in oestrogen receptor-negative breast cancer. Br. J. Cancer 2013, 108, 155–162. [Google Scholar] [CrossRef] [PubMed]
  79. Salama, P.; Phillips, M.; Grieu, F.; Morris, M.; Zeps, N.; Joseph, D.; Platell, C.; Iacopetta, B. Tumor-infiltrating FOXP3+ T regulatory cells show strong prognostic significance in colorectal cancer. J. Clin. Oncol. 2009, 27, 186–192. [Google Scholar] [CrossRef] [PubMed]
  80. Birenda, K.; Hwang, J.J. Advances in Immunotherapy in the treatment of Colorectal Cancer. Am. J. Hematol. Oncol. 2017, 13, 4–8. [Google Scholar]
  81. Peng, Y.P.; Zhang, J.J.; Liang, W.; Tu, M.; Lu, Z.P.; Wei, J.S.; Jiang, K.R.; Gao, W.T.; Wu, J.L.; Xu, Z.K.; et al. Elevation of MMP-9 and IDO induced by pancreatic cancer cells mediates natural killer cell dysfunction. Bmc Cancer 2014, 14, 738. [Google Scholar] [CrossRef]
  82. Song, H.; Park, H.; Kim, J.; Park, G.; Kim, Y.S.; Kim, S.M.; Kim, D.; Seo, S.K.; Lee, H.K.; Cho, D.; et al. IDO metabolite produced by EBV-transformed B cells inhibits surface expression of NKG2D in NK cells via the c-Jun N-terminal kinase (JNK) pathway. Immunol. Lett. 2011, 136, 187–193. [Google Scholar] [CrossRef]
  83. Brand, A.; Singer, K.; Koehl, G.E.; Kolitzus, M.; Schoenhammer, G.; Thiel, A.; Matos, C.; Bruss, C.; Klobuch, S.; Peter, K.; et al. LDHA-Associated Lactic Acid Production Blunts Tumor Immunosurveillance by T and NK Cells. Cell Metab. 2016, 24, 657–671. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  84. Holt, D.; Ma, X.; Kundu, N.; Fulton, A. Prostaglandin E2 (PGE2) suppresses natural killer cell function primarily through the PGE2 receptor EP4. Cancer Immunol. Immunother. 2011, 60, 1577–1586. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  85. Lee, J.C.; Lee, K.; Kim, D.; Heo, D.S. Elevated TGF- 1 Secretion and Down-Modulation of NKG2D Underlies Impaired NK Cytotoxicity in Cancer Patients. J. Immunol. 2004, 172, 7335–7340. [Google Scholar] [CrossRef]
  86. Laouar, Y.; Sutterwala, F.S.; Gorelik, L.; Flavell, R.A. Transforming growth factor-β controls T helper type 1 cell development through regulation of natural killer cell interferon-γ. Nat. Immunol. 2005, 6, 600–607. [Google Scholar] [CrossRef]
  87. Jun, E.; Song, A.Y.; Choi, J.W.; Ko, D.H.; Kang, H.J.; Kim, S.W.; Bryceson, Y.; Kim, S.C.; Kim, H.S. Progressive impairment of NK cell cytotoxic degranulation is associated with TGF-b1 deregulation and disease progression in pancreatic cancer. Front. Immunol. 2019, 10, 1354. [Google Scholar] [CrossRef]
  88. Crane, C.A.; Han, S.J.; Barry, J.J.; Ahn, B.J.; Lanier, L.L.; Parsa, A.T. TGF-β downregulates the activating receptor NKG2D on NK cells and CD8+ T cells in glioma patients. Neuro. Oncol. 2010, 12, 7–13. [Google Scholar] [CrossRef]
  89. Castriconi, R.; Dondero, A.; Bellora, F.; Moretta, L.; Castellano, A.; Locatelli, F.; Corrias, M.V.; Moretta, A.; Bottino, C. Transforming growth factor 1 inhibits expression of NKp30 and NKG2D receptors: Consequences for the NK-mediated killing of dendritic cells. Proc. Natl. Acad. Sci. 2003, 100, 4120–4125. [Google Scholar] [CrossRef]
  90. Balsamo, M.; Manzini, C.; Pietra, G.; Raggi, F.; Blengio, F.; Mingari, M.C.; Varesio, L.; Moretta, L.; Bosco, M.C.; Vitale, M. Hypoxia downregulates the expression of activating receptors involved in NK-cell-mediated target cell killing without affecting ADCC. Eur. J. Immunol. 2013, 43, 2756–2764. [Google Scholar] [CrossRef] [PubMed]
  91. Baginska, J.; Viry, E.; Berchem, G.; Poli, A.; Noman, M.Z.; van Moer, K.; Medves, S.; Zimmer, J.; Oudin, A.; Niclou, S.P.; et al. Granzyme B degradation by autophagy decreases tumor cell susceptibility to natural killer-mediated lysis under hypoxia. Proc. Natl. Acad. Sci. 2013, 110, 17450–17455. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  92. Sceneay, J.; Chow, M.T.; Chen, A.; Halse, H.M.; Wong, C.S.F.; Andrews, D.M.; Sloan, E.K.; Parker, B.S.; Bowtell, D.D.; Smyth, M.J.; et al. Primary Tumor Hypoxia Recruits CD11b þ /Ly6C med /Ly6G þ Immune Suppressor Cells and Compromises NK Cell Cytotoxicity in the Premetastatic Niche. Cancer Res. 2012, 72, 3906–3911. [Google Scholar] [CrossRef] [PubMed]
  93. Berchem, G.; Noman, M.Z.; Bosseler, M.; Paggetti, J.; Baconnais, S.; Le cam, E.; Nanbakhsh, A.; Moussay, E.; Mami-Chouaib, F.; Janji, B.; et al. Hypoxic tumor-derived microvesicles negatively regulate NK cell function by a mechanism involving TGF-β and miR23a transfer. Oncoimmunology 2015, 24, e1062968. [Google Scholar] [CrossRef] [PubMed]
  94. Häusler, S.F.M.; Montalbán Del Barrio, I.; Strohschein, J.; Anoop Chandran, P.; Engel, J.B.; Hönig, A.; Ossadnik, M.; Horn, E.; Fischer, B.; Krockenberger, M.; et al. Ectonucleotidases CD39 and CD73 on OvCA cells are potent adenosine-generating enzymes responsible for adenosine receptor 2A-dependent suppression of T cell function and NK cell cytotoxicity. Cancer Immunol. Immunother. 2011, 60, 1405–1418. [Google Scholar] [CrossRef] [PubMed]
  95. Young, A.; Foong Ngiow, S.; Gao, Y.; Patch, A.M.; Barkauskas, D.S.; Messaoudene, M.; Lin, G.; Coudert, J.D.; Stannard, K.A.; Zitvogel, L.; et al. Tumor Biology and Immunology A2AR Adenosine Signaling Suppresses Natural Killer Cell Maturation in the Tumor Microenvironment. Cancer Res. 2018, 78, 1003–1016. [Google Scholar] [CrossRef] [PubMed]
  96. Hatfield, S.M.; Kjaergaard, J.; Lukashev, D.; Belikoff, B.; Schreiber, T.H.; Sethumadhavan, S.; Abbott, R.; Philbrook, P.; Thayer, M.; Shujia, D.; et al. Systemic oxygenation weakens the hypoxia and hypoxia inducible factor 1α-dependent and extracellular adenosine-mediated tumor protection. J. Mol. Med. 2014, 92, 1283–1292. [Google Scholar] [CrossRef] [PubMed]
  97. Hatfield, S.M.; Kjaergaard, J.; Lukashev, D.; Schreiber, T.H.; Belikoff, B.; Abbott, R.; Sethumadhavan, S.; Philbrook, P.; Ko, K.; Cannici, R.; et al. Immunological mechanisms of the antitumor effects of supplemental oxygenation. Sci. Transl. Med. 2015, 7. [Google Scholar] [CrossRef] [PubMed]
  98. Michiels, C.; Tellier, C.; Feron, O. Cycling hypoxia: A key feature of the tumor microenvironment. Biochim. Biophys. Acta - Rev. Cancer 2016, 1866, 76–86. [Google Scholar] [CrossRef] [PubMed]
  99. Serebrovskaya, T.V.; Nikolsky, I.S.; Nikolska, V.V.; Mallet, R.T.; Ishchuk, V.A. Intermittent Hypoxia Mobilizes Hematopoietic Progenitors and Augments Cellular and Humoral Elements of Innate Immunity in Adult Men. High Alt. Med. Biol. 2011, 1, 243–252. [Google Scholar] [CrossRef]
  100. Ryan, S.; Taylor, C.T.; McNicholas, W.T. Selective Activation of Inflammatory Pathways by Intermittent Hypoxia in Obstructive Sleep Apnea Syndrome. Circulation 2005, 112, 2660–2667. [Google Scholar] [CrossRef] [Green Version]
  101. Taylor, C.T.; Kent, B.D.; Crinion, S.J.; McNicholas, W.T.; Ryan, S. Human adipocytes are highly sensitive to intermittent hypoxia induced NF-kappaB activity and subsequent inflammatory gene expression. Biochem. Biophys. Res. Commun. 2014, 447, 660–665. [Google Scholar] [CrossRef] [PubMed]
  102. Martinive, P.; Defresne, F.; Bouzin, C.; Saliez, J.; Lair, F.; Grégoire, V.; Michiels, C.; Dessy, C.; Feron, O. Preconditioning of the tumor vasculature and tumor cells by intermittent hypoxia: Implications for anticancer therapies. Cancer Res. 2006, 66, 11736–11744. [Google Scholar] [CrossRef] [PubMed]
  103. Ranson, T.; Vosshenrich, C.A.J.; Corcuff, E.; Richard, O.; Müller, W.; di Santo, J.P. IL-15 is an essential mediator of peripheral NK-cell homeostasis. Blood 2003, 101, 4887–4893. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  104. Huntington, N.D.; Legrand, N.; Alves, N.L.; Jaron, B.; Weijer, K.; Plet, A.; Corcuff, E.; Mortier, E.; Jacques, Y.; Spits, H.; et al. IL-15 trans-presentation promotes human NK cell development and differentiation in vivo. J. Exp. Med. 2008, 206, 25–34. [Google Scholar] [CrossRef] [PubMed]
  105. Mamessier, E.; Sylvain, A.; Thibult, M.L.; Houvenaeghel, G.; Jacquemier, J.; Castellano, R.; Gonçalves, A.; André, P.; Romagné, F.; Thibault, G.; et al. Human breast cancer cells enhance self tolerance by promoting evasion from NK cell antitumor immunity. J. Clin. Invest. 2011, 121, 3609–3622. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  106. Kmiecik, J.; Poli, A.; Brons, N.H.C.; Waha, A.; Eide, G.E.; Enger, P.Ø.; Zimmer, J.; Chekenya, M. Elevated CD3+ and CD8+ tumor-infiltrating immune cells correlate with prolonged survival in glioblastoma patients despite integrated immunosuppressive mechanisms in the tumor microenvironment and at the systemic level. J. Neuroimmunol. 2013, 264, 71–83. [Google Scholar] [CrossRef] [Green Version]
  107. Carrega, P.; Bonaccorsi, I.; Di Carlo, E.; Morandi, B.; Paul, P.; Rizzello, V.; Cipollone, G.; Navarra, G.; Mingari, M.C.; Moretta, L.; et al. CD56brightPerforinlow Noncytotoxic Human NK Cells Are Abundant in Both Healthy and Neoplastic Solid Tissues and Recirculate to Secondary Lymphoid Organs via Afferent Lymph. J. Immunol. 2014, 192, 3805–3815. [Google Scholar] [CrossRef] [PubMed]
  108. Platonova, S.; Cherfils-Vicini, J.; Damotte, D.; Crozet, L.; Vieillard, V.; Validire, P.; André, P.; Dieu-Nosjean, M.C.; Alifano, M.; Régnard, J.F.; et al. Profound coordinated alterations of intratumoral NK cell phenotype and function in lung carcinoma. Cancer Res. 2011, 71, 5412–5422. [Google Scholar] [CrossRef]
  109. Parodi, M.; Raggi, F.; Cangelosi, D.; Manzini, C.; Balsamo, M.; Blengio, F.; Eva, A.; Varesio, L.; Pietra, G.; Moretta, L.; et al. Hypoxia modifies the transcriptome of human NK cells, modulates their immunoregulatory profile, and influences NK cell subset migration. Front. Immunol. 2018, 9, 2358. [Google Scholar] [CrossRef]
  110. Kärre, K.; Ljunggren, H.G.; Piontek, G.; Kiessling, R. Selective rejection of H-2-deficient lymphoma variants suggests alternative immune defence strategy. Nature 1986, 319, 675–678. [Google Scholar] [CrossRef]
  111. Karlhofer, F.M.; Ribaudo, R.K.; Yokoyama, W.M. MHC class I alloantigen specificity of Ly-49+ IL-2-activated natural killer cells. Nature 1992, 358, 66–70. [Google Scholar] [CrossRef]
  112. Horowitz, A.; Strauss-Albee, D.M.; Leipold, M.; Kubo, J.; Nemat-Gorgani, N.; Dogan, O.C.; Dekker, C.L.; Mackey, S.; Maecker, H.; Swan, G.E.; et al. Genetic and environmental determinants of human NK cell diversity revealed by mass cytometry. Sci. Transl. Med. 2013, 5. [Google Scholar] [CrossRef]
  113. Martin, A.M.; Freitas, E.M.; Witt, C.S.; Christiansen, F.T. The genomic organization and evolution of the natural killer immunoglobulin-like receptor (KIR) gene cluster. Immunogenetics 2000, 51, 268–280. [Google Scholar] [CrossRef] [PubMed]
  114. Cichocki, F.; Miller, J.S.; Anderson, S.K.; Bryceson, Y.T. Epigenetic regulation of NK cell differentiation and effector functions. Front. Immunol. 2013, 4, 55. [Google Scholar] [CrossRef] [Green Version]
  115. Béziat, V.; Liu, L.L.; Malmberg, J.A.; Ivarsson, M.A.; Sohlberg, E.; Björklund, A.T.; Retière, C.; Sverremark-Ekström, E.; Traherne, J.; Ljungman, P.; et al. NK cell responses to cytomegalovirus infection lead to stable imprints in the human KIR repertoire and involve activating KIRs. Blood 2013, 121, 2678–2688. [Google Scholar] [CrossRef] [PubMed]
  116. Debre, P.; Petitdemange, C.; Becquart, P.; Wauquier, N.; Be, V.; Leroy, E.M.; Vieillard, V.; Béziat, V.; Debré, P.; Leroy, E.M.; et al. Unconventional repertoire profile is imprinted during acute chikungunya infection for natural killer cells polarization toward cytotoxicity. PLoS Pathog. 2011, 7, e1002268. [Google Scholar]
  117. Charoudeh, H.N.; Terszowski, G.; Czaja, K.; Gonzalez, A.; Schmitter, K.; Stern, M. Modulation of the natural killer cell KIR repertoire by cytomegalovirus infection. Eur. J. Immunol. 2013, 43, 480–487. [Google Scholar] [CrossRef] [PubMed]
  118. Middleton, D.; Gonzelez, F. The extensive polymorphism of KIR genes. Immunology 2010, 129, 8–19. [Google Scholar] [CrossRef]
  119. Ruggeri, L.; Capanni, M.; Casucci, M.; Volpi, I.; Tosti, A.; Perruccio, K.; Urbani, E.; Negrin, R.S.; Martelli, M.F.; Velardi, A. Role of natural killer cell alloreactivity in HLA-mismatched hematopoietic stem cell transplantation. Blood 1999, 94, 333–339. [Google Scholar]
  120. Giebel, S.; Locatelli, F.; Lamparelli, T.; Velardi, A.; Davies, S.; Frumento, G.; Maccario, R.; Bonetti, F.; Wojnar, J.; Martinetti, M.; et al. Survival advantage with KIR ligand incompatibility in hematopoietic stem cell transplantation from unrelated donors. Blood 2003, 2, 814–819. [Google Scholar] [CrossRef]
  121. Pfeiffer, M.; Schumm, M.; Feuchtinger, T.; Dietz, K.; Handgretinger, R.; Lang, P. Intensity of HLA class I expression and KIR-mismatch determine NK-cell mediated lysis of leukaemic blasts from children with acute lymphatic leukaemia. Br. J. Haematol. 2007, 138, 97–100. [Google Scholar] [CrossRef] [PubMed]
  122. Delgado, D.C.; Hank, J.A.; Kolesar, J.; Lorentzen, D.; Gan, J.; Seo, S.; Kim, K.; Shusterman, S.; Gillies, S.D.; Reisfeld, R.A.; et al. Genotypes of NK Cell KIR Receptors, Their Ligands, and Fc Receptors in the Response of Neuroblastoma Patients to Hu14.18-IL2 Immunotherapy. Cancer Res. 2010, 70, 9554–9561. [Google Scholar] [CrossRef] [PubMed]
  123. Erbe, A.K.; Wang, W.; Carmichael, L.; Kim, K.M.; Mendoņca, E.A.; Song, Y.; Hess, D.; Reville, P.K.; London, W.B.; Naranjo, A.; et al. Neuroblastoma patients’ KIR and KIR-ligand genotypes influence clinical outcome for dinutuximab-based immunotherapy: A report from the children’s oncology group. Clin. Cancer Res. 2018, 24, 189–196. [Google Scholar] [CrossRef] [PubMed]
  124. Semeraro, M.; Rusakiewicz, S.; Minard-Colin, V.; Delahaye, N.F.; Enot, D.; Vély, F.; Marabelle, A.; Papoular, B.; Piperoglou, C.; Ponzoni, M.; et al. Clinical impact of the NKp30/B7-H6 axis in high-risk neuroblastoma patients. Sci. Transl. Med. 2015, 7, ra55–ra283. [Google Scholar] [CrossRef] [PubMed]
  125. Rocca, Y.S.; Roberti, M.P.; Arriaga, J.M.; Amat, M.; Bruno, L.; Pampena, M.B.; Huertas, E.; Loria, F.S.; Pairola, A.; Bianchini, M.; et al. Altered phenotype in peripheral blood and tumor-associated NK cells from colorectal cancer patients. Innate Immun. 2013, 19, 76–85. [Google Scholar] [CrossRef] [PubMed]
  126. Costello, R.T.; Sivori, S.; Marcenaro, E.; Lafage-Pochitaloff, M.; Mozziconacci, M.J.; Reviron, D.; Gastaut, J.A.; Pende, D.; Olive, D.; Moretta, A. Defective expression and function of natural killer cell-triggering receptors in patients with acute myeloid leukemia. Blood 2002, 99, 3661–3667. [Google Scholar] [CrossRef] [PubMed]
  127. Schleypen, J.S.; Von Geldern, M.; Weiß, E.H.; Kotzias, N.; Rohrmann, K.; Schendel, D.J.; Falk, C.S.; Pohla, H. Renal cell carcinoma-infiltrating natural killer cells express differential repertoires of activating and inhibitory receptors and are inhibited by specific HLA class I allotypes. Int. J. Cancer 2003, 106, 905–912. [Google Scholar] [CrossRef]
  128. Sun, C.; Xu, J.; Huang, Q.; Huang, M.; Wen, H.; Zhang, C.; Wang, J.; Song, J.; Zheng, M.; Sun, H.; et al. High NKG2A expression contributes to NK cell exhaustion and predicts a poor prognosis of patients with liver cancer. Oncoimmunology 2017, 6. [Google Scholar] [CrossRef]
  129. Reiners, K.S.; Topolar, D.; Henke, A.; Simhadri, V.R.; Kessler, J.; Sauer, M.; Bessler, M.; Hansen, H.P.; Tawadros, S.; Herling, M.; et al. Soluble ligands for NK cell receptors promote evasion of chronic lymphocytic leukemia cells from NK cell anti-tumor activity. Blood 2013, 121, 3658–3665. [Google Scholar] [CrossRef]
  130. Binici, J.; Hartmann, J.; Herrmann, J.; Schreiber, C.; Beyer, S.; Güler, G.; Vogel, V.; Tumulka, F.; Abele, R.; Mäntele, W.; et al. A soluble fragment of the tumor antigen BCL2-associated athanogene 6 (BAG-6) is essential and sufficient for inhibition of NKp30 receptor-dependent cytotoxicity of natural killer cells. J. Biol. Chem. 2013, 288, 34295–34303. [Google Scholar] [CrossRef]
  131. Schlecker, E.; Fiegler, N.; Arnold, A.; Altevogt, P.; Rose-John, S.; Moldenhauer, G.; Sucker, A.; Paschen, A.; Von Strandmann, E.P.; Textor, S.; et al. Metalloprotease-mediated tumor cell shedding of B7-H6, the ligand of the natural killer cell-activating receptor NKp30. Cancer Res. 2014, 74, 3429–3440. [Google Scholar] [CrossRef] [PubMed]
  132. Klein, K.; Wang, T.; Lander, E.S.; Altfeld, M.; Garcia-Beltran, W.F. Applying CRISPR-based genetic screens to identify drivers of tumor-cell sensitivity towards NK-cell attack. bioRxiv 2019. [Google Scholar] [CrossRef]
  133. Groh, V.; Wu, J.; Yee, C.; Spies, T. Tumor-derived soluble MIC ligands impair expression of NKG2D and T-cell activation. Nature 2002, 419, 734–738. [Google Scholar] [CrossRef] [PubMed]
  134. Steinle, A.; Wu, J.D.; Higgins, L.M.; Cosman, D.; Plymate, S.R.; Haugk, K. Prevalent expression of the immunostimulatory MHC class I chain–related molecule is counteracted by shedding in prostate cancer. J. Clin. Invest. 2008, 114, 560–568. [Google Scholar]
  135. Deng, W.; Gowen, B.G.; Zhang, L.; Wang, L.; Lau, S.; Iannello, A.; Xu, J.; Rovis, T.L.; Xiong, N.; Raulet, D.H. A shed NKG2D ligand that promotes natural killer cell activation and tumor rejection. Science 2015, 348, 136–139. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  136. Pardoll, D.M. The blockade of immune checkpoints in cancer immunotherapy. Nat. Rev. Cancer 2012, 12, 252–264. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  137. Llosa, N.J.; Cruise, M.; Tam, A.; Wicks, E.C.; Hechenbleikner, E.M.; Taube, J.M.; Blosser, R.L.; Fan, H.; Wang, H.; Luber, B.S.; et al. The vigorous immune microenvironment of microsatellite instable colon cancer is balanced by multiple counter-inhibitory checkpoints. Cancer Discov. 2015, 5, 43–51. [Google Scholar] [CrossRef] [PubMed]
  138. Shi, S.J.; Wang, L.J.; Wang, G.D.; Guo, Z.Y.; Wei, M.; Meng, Y.L.; Yang, A.G.; Wen, W.H. B7-H1 Expression Is Associated with Poor Prognosis in Colorectal Carcinoma and Regulates the Proliferation and Invasion of HCT116 Colorectal Cancer Cells. PLoS ONE 2013, 8, e76012. [Google Scholar] [CrossRef] [PubMed]
  139. Mu, C.Y.; Huang, J.A.; Chen, Y.; Chen, C.; Zhang, X.G. High expression of PD-L1 in lung cancer may contribute to poor prognosis and tumor cells immune escape through suppressing tumor infiltrating dendritic cells maturation. Med. Oncol. 2011, 28, 682–688. [Google Scholar] [CrossRef] [PubMed]
  140. Muenst, S.; Schaerli, A.R.; Gao, F.; Däster, S.; Trella, E.; Droeser, R.A.; Muraro, M.G.; Zajac, P.; Zanetti, R.; Gillanders, W.E.; et al. Expression of programmed death ligand 1 (PD-L1) is associated with poor prognosis in human breast cancer. Breast Cancer Res. Treat. 2014, 146, 15–24. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  141. Nakanishi, J.; Wada, Y.; Matsumoto, K.; Azuma, M.; Kikuchi, K.; Ueda, S. Overexpression of B7-H1 (PD-L1) significantly associates with tumor grade and postoperative prognosis in human urothelial cancers. Cancer Immunol. Immunother. 2007, 56, 1173–1182. [Google Scholar] [CrossRef] [PubMed]
  142. Hargadon, K.M.; Johnson, C.E.; Williams, C.J. Immune checkpoint blockade therapy for cancer: An overview of FDA-approved immune checkpoint inhibitors. Int. Immunopharmacol. 2018, 62, 29–39. [Google Scholar] [CrossRef]
  143. Patel, S.P.; Kurzrock, R. PD-L1 Expression as a Predictive Biomarker in Cancer Immunotherapy. Mol. Cancer Ther. 2015, 14, 847–856. [Google Scholar] [CrossRef]
  144. Kleinovink, J.W.; Marijt, K.A.; Schoonderwoerd, M.J.A.; van Hall, T.; Ossendorp, F.; Fransen, M.F. PD-L1 expression on malignant cells is no prerequisite for checkpoint therapy. Oncoimmunology 2017, 6. [Google Scholar] [CrossRef] [PubMed]
  145. Shen, X.; Zhao, B. Efficacy of PD-1 or PD-L1 inhibitors and PD-L1 expression status in cancer: Meta-analysis. BMJ 2018. [Google Scholar] [CrossRef] [PubMed]
  146. McLaughlin, J.; Han, G.; Schalper, K.A.; Carvajal-Hausdorf, D.; Pelekanou, V.; Rehman, J.; Velcheti, V.; Herbst, R.; LoRusso, P.; Rimm, D.L. Quantitative assessment of the heterogeneity of PD-L1 expression in non-small-cell lung cancer. JAMA Oncol 2016, 2, 46–54. [Google Scholar] [CrossRef]
  147. Ilie, M.; Long-Mira, E.; Bence, C.; Butori, C.; Lassalle, S.; Bouhlel, L.; Fazzalari, L.; Zahaf, K.; Lalvée, S.; Washetine, K.; et al. Comparative study of the PD-L1 status between surgically resected specimens and matched biopsies of NSCLC patients reveal major discordances: A potential issue for anti-PD-L1 therapeutic strategies. Ann. Oncol. 2016, 27, 147–153. [Google Scholar] [CrossRef]
  148. Soriani, A.; Zingoni, A.; Cerboni, C.; Iannitto, M.L.; Ricciardi, M.R.; Di Gialleonardo, V.; Cippitelli, M.; Fionda, C.; Petrucci, M.T.; Guarini, A.; et al. ATM-ATR dependent up-regulation of DNAM-1 and NKG2D ligands on multiple myeloma cells by therapeutic agents results in enhanced NK cell susceptibility and is associated with a senescent phenotype. Blood 2009, 113, 3503–3511. [Google Scholar] [CrossRef]
  149. Siew, Y.Y.; Neo, S.Y.; Yew, H.C.; Lim, S.W.; Ng, Y.C.; Lew, S.M.; Seetoh, W.G.; Seow, S.V.; Koh, H.L. Oxaliplatin regulates expression of stress ligands in ovarian cancer cells and modulates their susceptibility to natural killer cell-mediated cytotoxicity. Int. Immunol. 2015, 27, 621–632. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  150. Guillerey, C.; Huntington, N.D.; Smyth, M.J. Targeting natural killer cells in cancer immunotherapy. Nat. Immunol. 2016, 17, 1025–1036. [Google Scholar] [CrossRef]
  151. Blake, S.J.; Stannard, K.; Liu, J.; Allen, S.; Yong, M.C.R.; Mittal, D.; Aguilera, A.R.; Miles, J.J.; Lutzky, V.P.; de Andrade, L.F.; et al. Suppression of metastases using a new lymphocyte checkpoint target for cancer immunotherapy. Cancer Discov. 2016, 4, 446–459. [Google Scholar] [CrossRef] [PubMed]
  152. Fuchs, A.; Cella, M.; Giurisato, E.; Shaw, A.S.; Colonna, M. Cutting edge: CD96 (tactile) promotes NK cell-target cell adhesion by interacting with the poliovirus receptor (CD155). J. Immunol. 2004, 172, 3994–3998. [Google Scholar] [CrossRef] [PubMed]
  153. Goodman, A.M.; Kato, S.; Bazhenova, L.; Patel, S.P.; Frampton, G.M.; Miller, V.; Stephens, P.J.; Daniels, G.A.; Kurzrock, R. Tumor Mutational Burden as an Independent Predictor of Response to Immunotherapy in Diverse Cancers. Mol. Cancer 2017, 16, 2598–2608. [Google Scholar] [CrossRef] [PubMed]
  154. Rizvi, N.A.; Hellmann, M.D.; Snyder, A.; Kvistborg, P.; Makarov, V.; Havel, J.J.; Lee, W.; Yuan, J.; Wong, P.; Ho, T.S.; et al. Cancer immunology. Mutational landscape determines sensitivity to PD-1 blockade in non-small cell lung cancer. Science 2015, 348, 124–128. [Google Scholar] [CrossRef] [PubMed]
  155. Yarchoan, M.; Hopkins, A.; Jaffee, E.M. Tumor Mutational Burden and Response Rate to PD-1 Inhibition. N. Engl. J. Med. 2017, 377, 2500–2501. [Google Scholar] [CrossRef] [PubMed]
  156. Kaufman, H.L.; Russell, J.S.; Hamid, O.; Bhatia, S.; Terheyden, P.; D’Angelo, S.P.; Shih, K.C.; Lebbé, C.; Milella, M.; Brownell, I.; et al. Updated efficacy of avelumab in patients with previously treated metastatic Merkel cell carcinoma after ≥1 year of follow-up: JAVELIN Merkel 200, a phase 2 clinical trial. J. Immunother. Cancer 2018, 6. [Google Scholar] [CrossRef] [PubMed]
  157. Dudley, J.C.; Lin, M.T.; Le, D.T.; Eshleman, J.R. Microsatellite Instability as a Biomarker for PD-1 Blockade. Clin. Cancer Res. 2016, 22, 813–820. [Google Scholar] [CrossRef] [Green Version]
  158. Le, D.T.; Durham, J.N.; Smith, K.N.; Wang, H.; Bartlett, B.R.; Aulakh, L.K.; Lu, S.; Kemberling, H.; Wilt, C.; Luber, B.S.; et al. Mismatch repair deficiency predicts response of solid tumors to PD-1 blockade. Science 2017, 357, 409–413. [Google Scholar] [CrossRef] [Green Version]
  159. Timmermann, B.; Kerick, M.; Roehr, C.; Fischer, A.; Isau, M.; Boerno, S.T.; Wunderlich, A.; Barmeyer, C.; Seemann, P.; Koenig, J.; et al. Somatic Mutation Profiles of MSI and MSS Colorectal Cancer Identified by Whole Exome Next Generation Sequencing and Bioinformatics Analysis. PLoS ONE 2010, 5. [Google Scholar] [CrossRef]
  160. Germano, G.; Lamba, S.; Rospo, G.; Barault, L.; Magri, A.; Maione, F.; Russo, M.; Crisafulli, G.; Bartolini, A.; Lerda, G.; et al. Inactivation of DNA repair triggers neoantigen generation and impairs tumor growth. Nature 2017, 552, 1–5. [Google Scholar] [CrossRef]
  161. Choudhury, N.J.; Kiyotani, K.; Yap, K.L.; Campanile, A.; Antic, T.; Yew, P.Y.; Steinberg, G.; Park, J.H.; Nakamura, Y.; O’Donnell, P.H. Low T-cell Receptor Diversity, High Somatic Mutation Burden, and High Neoantigen Load as Predictors of Clinical Outcome in Muscle-invasive Bladder Cancer. Eur. Urol. Focus 2016, 2, 445–452. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  162. Miller, A.; Asmann, Y.; Cattaneo, L.; Braggio, E.; Keats, J.; Auclair, D.; Lonial, S.; Russell, S.J.; Stewart, A.K. High somatic mutation and neoantigen burden are correlated with decreased progression-free survival in multiple myeloma. Blood Cancer J. 2017, 7. [Google Scholar] [CrossRef] [PubMed]
  163. Lauss, M.; Donia, M.; Harbst, K.; Andersen, R.; Mitra, S.; Rosengren, F.; Salim, M.; Vallon-Christersson, J.; Törngren, T.; Kvist, A.; et al. Mutational and putative neoantigen load predict clinical benefit of adoptive T cell therapy in melanoma. Nat. Commun. 2017, 8. [Google Scholar] [CrossRef] [PubMed]
  164. Strickland, K.C.; Howitt, B.E.; Shukla, S.A.; Rodig, S.; Ritterhouse, L.L.; Liu, J.F.; Garber, J.E.; Chowdhury, D.; Wu, C.J.; D’Andrea, A.D.; et al. Association and prognostic significance of BRCA1/2-mutation status with neoantigen load, number of tumor-infiltrating lymphocytes and expression of PD-1/PD-L1 in high grade serous ovarian cancer. Oncotarget 2016, 7, 13587–13598. [Google Scholar] [CrossRef] [PubMed]
  165. Coulie, P.G.; Lehmann, F.; Lethe, B.; Herman, J.; Lurquin, C.; Andrawiss, M.; Boon, T. A mutated intron sequence codes for an antigenic peptide recognized by cytolytic T lymphocytes on a human melanoma. Proc. Natl. Acad. Sci. 2006, 92, 7976–7980. [Google Scholar] [CrossRef]
  166. Takenoyama, M.; Baurain, J.F.; Yasuda, M.; So, T.; Sugaya, M.; Hanagiri, T.; Sugio, K.; Yasumoto, K.; Boon, T.; Coulie, P.G. A point mutation in the NFYC gene generates an antigenic peptide recognized by autologous cytolytic T lymphocytes on a human squamous cell lung carcinoma. Int. J. Cancer 2006, 118, 1992–1997. [Google Scholar] [CrossRef] [PubMed]
  167. Lennerz, V.; Fatho, M.; Gentilini, C.; Frye, R.A.; Lifke, A.; Ferel, D.; Wolfel, C.; Huber, C.; Wolfel, T. The response of autologous T cells to a human melanoma is dominated by mutated neoantigens. Proc. Natl. Acad. Sci. USA 2005, 102, 16013–16018. [Google Scholar] [CrossRef] [Green Version]
  168. Jia, Q.; Wu, W.; Wang, Y.; Alexander, P.B.; Sun, C.; Gong, Z.; Cheng, J.N.; Sun, H.; Guan, Y.; Xia, X.; et al. Local mutational diversity drives intratumoral immune heterogeneity in non-small cell lung cancer. Nat. Commun. 2018, 9, 5361. [Google Scholar] [CrossRef]
  169. McGranahan, N.; Furness, A.J.S.; Rosenthal, R.; Ramskov, S.; Lyngaa, R.; Saini, S.K.; Jamal-Hanjani, M.; Wilson, G.A.; Birkbak, N.J.; Hiley, C.T.; et al. Clonal neoantigens elicit T cell immunoreactivity and sensitivity to immune checkpoint blockade. Science 2016, 351, 1463–1469. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  170. Türeci, Ö.; Vormehr, M.; Diken, M.; Kreiter, S.; Huber, C.; Sahin, U. Targeting the heterogeneity of cancer with individualized neoepitope vaccines. Clin. Cancer Res. 2016, 22, 1885–1896. [Google Scholar] [CrossRef] [PubMed]
  171. Charoentong, P.; Finotello, F.; Angelova, M.; Mayer, C.; Efremova, M.; Rieder, D.; Hackl, H.; Trajanoski, Z. Pan-cancer Immunogenomic Analyses Reveal Genotype-Immunophenotype Relationships and Predictors of Response to Checkpoint Blockade. Cell Rep. 2017, 18, 248–262. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  172. Tran, E.; Robbins, P.F.; Rosenberg, S.A.; Author, N.I. Final common pathway’ of human cancer immunotherapy: Targeting random somatic mutations A brief survey of non-mutant tumor antigens HHS Public Access Author manuscript. Nat Immunol. 2017, 18, 255–262. [Google Scholar] [CrossRef] [PubMed]
  173. Mcgranahan, N.; Swanton, C. Review Clonal Heterogeneity and Tumor Evolution: Past, Present, and the Future. Cell 2017, 168, 613–628. [Google Scholar] [CrossRef] [PubMed]
  174. Angelova, M.; Charoentong, P.; Hackl, H.; Fischer, M.L.; Snajder, R.; Krogsdam, A.M.; Waldner, M.J.; Bindea, G.; Mlecnik, B.; Galon, J.; et al. Characterization of the immunophenotypes and antigenomes of colorectal cancers reveals distinct tumor escape mechanisms and novel targets for immunotherapy. Genome Biol. 2015, 16, 64. [Google Scholar] [CrossRef] [PubMed]
  175. Brown, S.D.; Warren, R.L.; Gibb, E.A.; Martin, S.D.; Spinelli, J.J.; Nelson, B.H.; Holt, R.A. Neo-antigens predicted by tumor genome meta-analysis correlate with increased patient survival. Genome Res. 2014, 24, 743–750. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  176. Łuksza, M.; Riaz, N.; Makarov, V.; Balachandran, V.P.; Hellmann, M.D.; Solovyov, A.; Rizvi, N.A.; Merghoub, T.; Levine, A.J.; Chan, T.A.; et al. A neoantigen fitness model predicts tumor response to checkpoint blockade immunotherapy. Nature 2017, 551, 517–520. [Google Scholar] [CrossRef] [PubMed]
  177. Balachandran, V.P.; Luksza, M.; Zhao, J.N.; Makarov, V.; Moral, J.A.; Remark, R.; Herbst, B.; Askan, G.; Bhanot, U.; Senbabaoglu, Y.; et al. Identification of unique neoantigen qualities in long-term survivors of pancreatic cancer. Nature 2017, 551, S12–S16. [Google Scholar] [CrossRef] [PubMed]
  178. Akers, S.N.; Odunsi, K.; Karpf, A.R. Regulation of cancer germline antigen gene expression: Implications for cancer immunotherapy. Future Oncol. 2010, 6, 717–732. [Google Scholar] [CrossRef] [PubMed]
  179. Kerkar, S.P.; Wang, Z.F.; Lasota, J.; Park, T.; Patel, K.; Groh, E.; Rosenberg, S.A.; Miettinen, M.M. MAGE-A is more highly expressed than NY-ESO-1 in a systematic immunohistochemical analysis of 3668 cases. J. Immunother. 2016, 39, 181–187. [Google Scholar] [CrossRef] [PubMed]
  180. Johnson, L.A.; Morgan, R.A.; Dudley, M.E.; Cassard, L.; Yang, J.C.; Hughes, M.S.; Kammula, U.S.; Royal, R.E.; Sherry, R.M.; Wunderlich, J.R.; et al. Gene therapy with human and mouse T-cell receptors mediates cancer regression and targets normal tissues expressing cognate antigen. Blood 2009, 114, 535–546. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  181. Parkhurst, M.R.; Yang, J.C.; Langan, R.C.; Dudley, M.E.; Nathan, D.A.N.; Feldman, S.A.; Davis, J.L.; Morgan, R.A.; Merino, M.J.; Sherry, R.M.; et al. T cells targeting carcinoembryonic antigen can mediate regression of metastatic colorectal cancer but induce severe transient colitis. Mol. Ther. 2011, 19, 620–626. [Google Scholar] [CrossRef] [PubMed]
  182. Garrido, F.; Algarra, I. MHC antigens and tumor escape from immune surveillance. Adv. Cancer Res. 2001, 83, 117–158. [Google Scholar] [PubMed]
  183. Patel, S.J.; Sanjana, N.E.; Kishton, R.J.; Eidizadeh, A.; Vodnala, S.K.; Cam, M.; Gartner, J.J.; Jia, L.; Steinberg, S.M.; Yamamoto, T.N.; et al. Identification of essential genes for cancer immunotherapy. Nature 2017, 548, 537–542. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  184. Lopez-Soto, A.; Huergo-Zapico, L.; Galvan, J.A.; Rodrigo, L.; de Herreros, A.G.; Astudillo, A.; Gonzalez, S. Epithelial-Mesenchymal Transition Induces an Antitumor Immune Response Mediated by NKG2D Receptor. J. Immunol. 2013, 190, 4408–4419. [Google Scholar] [CrossRef] [PubMed]
  185. Ruggeri, L. Effectiveness of Donor Natural Killer Cell Alloreactivity in Mismatched Hematopoietic Transplants. Science 2002, 295, 2097–2100. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  186. Ruggeri, L.; Mancusi, A.; Burchielli, E.; Capanni, M.; Carotti, A.; Aloisi, T.; Aversa, F.; Martelli, M.F.; Velardi, A. NK cell alloreactivity and allogeneic hematopoietic stem cell transplantation. Blood Cells Mol. Dis. 2008, 40, 84–90. [Google Scholar] [CrossRef] [PubMed]
  187. André, P.; Denis, C.; Soulas, C.; Bourbon-Caillet, C.; Lopez, J.; Arnoux, T.; Bléry, M.; Bonnafous, C.; Gauthier, L.; Morel, A.; et al. Anti-NKG2A mAb Is a Checkpoint Inhibitor that Promotes Anti-Tumor Immunity by Unleashing Both T and NK Cells. Cell 2018, 175, 1731–1743. [Google Scholar] [CrossRef]
  188. Vey, N.; Karlin, L.; Sadot-Lebouvier, S.; Broussais, F.; Berton-Rigaud, D.; Rey, J.; Charbonnier, A.; Marie, D.; André, P.; Paturel, C.; et al. A phase 1 study of lirilumab (antibody against killer immunoglobulin-like receptor antibody KIR2D; IPH2102) in patients with solid tumors and hematologic malignancies. Oncotarget 2018, 9, 17675–17688. [Google Scholar] [CrossRef] [Green Version]
  189. Romanski, A.; Uherek, C.; Bug, G.; Seifried, E.; Klingemann, H.; Wels, W.S.; Ottmann, O.G.; Tonn, T. CD19-CAR engineered NK-92 cells are sufficient to overcome NK cell resistance in B-cell malignancies. J. Cell. Mol. Med. 2016, 20, 1287–1294. [Google Scholar] [CrossRef]
  190. Liu, E.; Tong, Y.; Dotti, G.; Shaim, H.; Savoldo, B.; Mukherjee, M.; Orange, J.; Wan, X.; Lu, X.; Reynolds, A.; et al. Cord blood NK cells engineered to express IL-15 and a CD19-targeted CAR show long-term persistence and potent antitumor activity. Leukemia 2018, 32, 520–531. [Google Scholar] [CrossRef]
  191. Chu, J.; Deng, Y.; Benson, D.M.; He, S.; Hughes, T.; Zhang, J.; Peng, Y.; Mao, H.; Yi, L.; Ghoshal, K.; et al. CS1-specific chimeric antigen receptor (CAR)-engineered natural killer cells enhance in vitro and in vivo antitumor activity against human multiple myeloma. Leukemia 2014, 28, 917–927. [Google Scholar] [CrossRef] [PubMed]
  192. Chen, X.; Han, J.; Chu, J.; Zhang, L.; Zhang, J.; Chen, C.; Chen, L.; Wang, Y.; Wang, H.; Yi, L.; et al. A combinational therapy of EGFR-CAR NK cells and oncolytic herpes simplex virus 1 for breast cancer brain metastases. Oncotarget 2006, 7, 27764–27777. [Google Scholar] [CrossRef] [PubMed]
  193. Han, J.; Chu, J.; Keung Chan, W.; Zhang, J.; Wang, Y.; Cohen, J.B.; Victor, A.; Meisen, W.H.; Kim, S.H.; Grandi, P.; et al. CAR-engineered NK cells targeting wild-type EGFR and EGFRvIII enhance killing of glioblastoma and patient-derived glioblastoma stem cells. Sci. Rep. 2015, 5. [Google Scholar] [CrossRef] [PubMed]
  194. Gleason, M.K.; Ross, J.A.; Warlick, E.D.; Lund, T.C.; Verneris, M.R.; Wiernik, A.; Spellman, S.; Haagenson, M.D.; Lenvik, A.J.; Litzow, M.R.; et al. CD16xCD33 bispecific killer cell engager (BiKE) activates NK cells against primary MDS and MDSC CD33+ targets. Blood 2014, 123, 3016–3026. [Google Scholar] [CrossRef] [PubMed]
  195. Vallera, D.A.; Zhang, B.; Gleason, M.K.; Oh, S.; Weiner, L.M.; Kaufman, D.S.; McCullar, V.; Miller, J.S.; Verneris, M.R. Heterodimeric bispecific single-chain variable-fragment antibodies against EpCAM and CD16 induce effective antibody-dependent cellular cytotoxicity against human carcinoma cells. Cancer Biother. Radiopharm. 2013, 28, 274–282. [Google Scholar] [CrossRef]
  196. Vallera, D.A.; Felices, M.; McElmurry, R.; McCullar, V.; Zhou, X.; Schmohl, J.U.; Zhang, B.; Lenvik, A.J.; Panoskaltsis-Mortari, A.; Verneris, M.R.; et al. IL15 Trispecific Killer Engagers (TriKE) Make Natural Killer Cells Specific to CD33+Targets while Also Inducing Persistence, in Vivo Expansion, and Enhanced Function. Clin. Cancer Res. 2016, 22, 3440–3450. [Google Scholar] [CrossRef]
  197. Tal, Y.; Yaakobi, S.; Horovitz-Fried, M.; Safyon, E.; Rosental, B.; Porgador, A.; Cohen, C.J. An NCR1-based chimeric receptor endows T-cells with multiple anti-tumor specificities. Oncotarget 2014, 5, 10949–10958. [Google Scholar] [CrossRef] [Green Version]
  198. Eisenberg, V.; Shamalov, K.; Meir, S.; Hoogi, S.; Sarkar, R.; Pinker, S.; Markel, G.; Porgador, A.; Cohen, C.J. Targeting Multiple Tumors Using T-Cells Engineered to Express a Natural Cytotoxicity Receptor 2-Based Chimeric Receptor. Front. Immunol. 2017, 29. [Google Scholar] [CrossRef]
  199. Zhang, T.; Wu, M.; Sentman, C.L. An NKp30-Based Chimeric Antigen Receptor Promotes T Cell Effector Functions and Antitumor Efficacy In Vivo. J. Immunol. 2012, 189, 2290–2299. [Google Scholar] [CrossRef]
  200. Arai, S.; Meagher, R.; Swearingen, M.; Myint, H.; Rich, E.; Martinson, J.; Klingemann, H. Infusion of the allogeneic cell line NK-92 in patients with advanced renal cell cancer or melanoma: A phase I trial. Cytotherapy 2008, 10, 625–632. [Google Scholar] [CrossRef] [PubMed]
  201. Tonn, T.; Schwabe, D.; Klingemann, HG.; Becker, S.; Esser, R.; Koehl, U.; Suttorp, M.; Seifried, E.; Ottmann, OG.; Bug, G. Treatment of patients with advanced cancer with the natural killer cell line NK-92. Cytotherapy 2013, 15, 1563–1570. [Google Scholar] [CrossRef] [PubMed]
Figure 1. Tumor and immunological heterogeneity. Tumor-intrinsic drivers of heterogeneity (upper left) include diversity in: the degree of tumor vascularization or hypoxia (1), which determines whether the local tumor microenvironment (TME) will support or suppress anti-tumor immune cells; the variable expression of neoantigens (2) and ligands (3), which facilitate interaction with various immune cell types; and the secretion of soluble factors (4) (which may also be produced by the immune cells themselves) that may promote or restrain the action of nearby immune cells. Immune cell contributions to heterogeneity (bottom right) include: the type and density of infiltrating versus excluded immune cells (5); modulatory interactions between co-localised immune cell types (6); the balance of activating versus inhibitory receptors (7); effector cell distribution between the invasive margin (IM) and central tumor (CT) (8); and the overall balance between pro- and anti-tumor effectors (9).
Figure 1. Tumor and immunological heterogeneity. Tumor-intrinsic drivers of heterogeneity (upper left) include diversity in: the degree of tumor vascularization or hypoxia (1), which determines whether the local tumor microenvironment (TME) will support or suppress anti-tumor immune cells; the variable expression of neoantigens (2) and ligands (3), which facilitate interaction with various immune cell types; and the secretion of soluble factors (4) (which may also be produced by the immune cells themselves) that may promote or restrain the action of nearby immune cells. Immune cell contributions to heterogeneity (bottom right) include: the type and density of infiltrating versus excluded immune cells (5); modulatory interactions between co-localised immune cell types (6); the balance of activating versus inhibitory receptors (7); effector cell distribution between the invasive margin (IM) and central tumor (CT) (8); and the overall balance between pro- and anti-tumor effectors (9).
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Table 1. Completed and currently active clinical trials of NK cell-based immunotherapies.
Table 1. Completed and currently active clinical trials of NK cell-based immunotherapies.
ApproachTargetIndicationPhaseClinical Trial ID(s)
Adoptive cell transfer:Allogenic PBMCs (non-targeted)Leukemias and lymphomasPhase I/IINCT00569283; NCT00799799;
NCT00823524; NCT00303667;
NCT00187096; NCT00274846;
NCT01106950; NCT005626292;
NCT01390402; NCT02395822;
NCT00586690; NCT00586703;
NCT00145626; NCT01386619;
NCT00945126; NCT00354172;
NCT01313897; NCT01181258
Solid cancersPhase I/IINCT01287104; NCT01212341;
NCT01105650; UMIN000013378;
NCT01147380; 2005-005125-58
Autologous PBMCs (non-targeted)Multiple myelomaPhase INCT02481934
Advanced digestive cancerPhase IUMIN000007527
Advanced melanoma or kidney cancerPhase IINCT00328861
NK-92 (NK cell line; non-targeted)Advanced renal cell cancer or melanomaPhase IN/A [200]
End-stage chemotherapy resistant cancerPhase IN/A [201]
Hematologic malignancies NCT00990717
Relapsed acute myeloid leukemiaPhase INCT00900809
Stage IIIB or Stage IV Merkel cell carcinomaPhase IINCT02465957
Chimeric Antigen ReceptorsCD19Solid and hematological malignanciesPhase I/IINCT03690310; NCT03679927;
NCT03056339; NCT01974479;
NCT00995137; NCT02892695
ROBO1Solid tumorsPhase I/IINCT03940820
BCMARelapsed and refractory multiple myelomaPhase I/IINCT03940833
PSMACastration-resistant prostate cancerPhase INCT03692663
NKG2DMetastatic solid tumorsPhase INCT03415100
MesothelinEpithelial ovarian cancerNot yet recruitingNCT03692637
CD33 CAR NK-92Acute myeloid leukemiaPhase I/IINCT02944162
CD7Lymphoma and leukemiaPhase I/IINCT02742727
MUC1Solid tumorsPhase I/IINCT02839954
HER2GlioblastomaPhase I/IINCT03383978
NKG2D ligandsSolid tumorsPhase INCT03415100
Bi- and Tri- specific Killer cell Engagers (BiKe/TriKes)CD16 × CD33Myelodysplastic syndromesPre-clinicalN/A [194]
AFM13 (CD30 × CD16A) BiKeHodgkin lymphomaPhase INCT01221571
AFM13 (CD30 × CD16A) BiKeRelapsed/refractory cutaneous lymphomasPhase I/IINCT03192202
CD16 × IL-15 × CD33 TriKeAML & high-risk myelodysplastic syndromesPhase I/IINCT03214666
Anti-NKG2AMonalizumab (anti-NKG2A) + cetuximab (anti-NKG2A)Squamous cell carcinoma of the head and neckPhase IINCT02643550
Monalizumab (anti-NKG2A) + durvalumab (anti-NKG2A)Advanced or metastatic solid cancersPhase I/II
Anti-KIRKIR3DL2
IPH4102Cutaneous T-cell lymphomaPhase INCT02593045
IPH4102 +/− gemcitabine +/− oxaliplatinAdvanced T-cell lymphomaPhase IINCT03902184
KIR2DL1-,2-,3-:
Lirilumab (IPH2102/BMS-986015)Smoldering multiple myelomaPhase IINCT01222286
Lirilumab (IPH2102/BMS-986015)Acute myeloid leukemiaPhase IINCT01687387
Lirilumab (IPH2102/BMS-986015) + ipilimumab (anti-PD-1)Advanced solid tumorsPhase I/IINCT01714739 & NCT03203876
Lirilumab (IPH2102/BMS-986015) + ipilimumab (anti-CTLA-4)NSCLC, Castration Resistant Prostate Cancer, MelanomaPhase INCT01750580
Lirilumab (IPH2102/BMS-986015) + ipilimumab (anti-CD20)Chronic lymphocytic leukemiaPhase IINCT02481297
1-7F9 (IPH2101)Multiple myelomaPhase INCT0055296 & NCT00999830
1-7F9 (IPH2101)Acute myeloid leukemiaPhase INCT01256073

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Shembrey, C.; Huntington, N.D.; Hollande, F. Impact of Tumor and Immunological Heterogeneity on the Anti-Cancer Immune Response. Cancers 2019, 11, 1217. https://doi.org/10.3390/cancers11091217

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Shembrey C, Huntington ND, Hollande F. Impact of Tumor and Immunological Heterogeneity on the Anti-Cancer Immune Response. Cancers. 2019; 11(9):1217. https://doi.org/10.3390/cancers11091217

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Shembrey, Carolyn, Nicholas D. Huntington, and Frédéric Hollande. 2019. "Impact of Tumor and Immunological Heterogeneity on the Anti-Cancer Immune Response" Cancers 11, no. 9: 1217. https://doi.org/10.3390/cancers11091217

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