Immuno-Oncology: Emerging Targets and Combination Therapies

Abstract

Host immunity recognizes and eliminates most early tumor cells, yet immunological checkpoints, exemplified by CTLA-4, PD-1, and PD-L1, pose a significant obstacle to effective antitumor immune responses. T-lymphocyte co-inhibitory pathways influence intensity, inflammation and duration of antitumor immunity. However, tumors and their immunosuppressive microenvironments exploit them to evade immune destruction. Recent PD-1 checkpoint inhibitors yielded unprecedented efficacies and durable responses across advanced-stage melanoma, showcasing potential to replace conventional radiotherapy regimens. Neverthless, many clinical problems remain in terms of efficacy, patient-to-patient variability, and undesirable outcomes and side effects. In this review, we evaluate recent advances in the immuno-oncology field and discuss ways forward. First, we give an overview of current immunotherapy modalities, involving mainy single agents, including inhibitor monoclonal antibodies (mAbs) targeting T-cell checkpoints of PD-1 and CTLA-4. However, neoantigen recognition alone cannot eliminate tumors effectively in vivo given their inherent complex micro-environment, heterogeneous nature and stemness. Then, based mainly upon CTLA-4 and PD-1 checkpoint inhibitors as a "backbone," we cover a range of emerging ("second-generation") therapies incorporating other immunotherapies or non-immune based strategies in synergistic combination. These include targeted therapies such as tyrosine kinase inhibitors, co-stimulatory mAbs, bifunctional agents, epigenetic modulators (such as inhibitors of histone deacetylases or DNA methyltransferase), vaccines, adoptive-T-cell therapy, nanoparticles, oncolytic viruses, and even synthetic "gene circuits." A number of novel immunotherapy co-targets in pre-clinical development are also introduced. The latter include metabolic components, exosomes and ion channels. We discuss in some detail of the personalization of immunotherapy essential for ultimate maximization of clinical outcomes. Finally, we outline possible future technical and conceptual developments including realistic in vitro and in vivo models and inputs from physics, engineering, and artificial intelligence. We conclude that the breadth and quality of immunotherapeutic approaches and the types of cancers that can be treated will increase significantly in the foreseeable future.

Keywords: biomarkers; checkpoint blockade; combination immunotherapy; personalized therapy; tumor microenvironment.

Figure 1

The cellular make-up of the tumor microenvironment (TME). The tumor niche possesses a dynamic structural topography with significant spatial variability in vascular supply, growth factor and cytokine accessibility, ECM-derived structural support and interactions with immune cells. TME hence contributes to tumor heterogeneity as a “rogue organ,” formed by normal-malignant cell associations. Created using information from Balkwill et al. (2) and Tang et al. (3).

Figure 2

Immunosuppressive mechanisms of the TME. Treg (regulatory T-) cells generate IL-10 and TGF-β angiogenic cytokines to suppress CTL (cytotoxic T-lymphocyte) activity. Myeloid-derived suppressor cells (MDSCs) produce reactive oxygen species (ROS), arginase (ARG) and nitric oxide (NO) that inhibit T-cell activation. Tumor-associated macrophages (TAMs) similarly block CTL and natural killer (NK) T-cells, immature dendritic cells cause T-cell anergy via IDO enzyme secretion, while cancer-associated fibroblasts (CAFs) and endothelial cells (tumor, lymphatic, and vascular) produce TGF-β and stimulate T-cell apoptosis by FasL-Fas binding (5, 6). MHC I is downregulated in tumor cells to inhibit T-cell recognition. FasL is expressed by tumors, killing T-cells (7). Tumors secrete VEGF to sustain tumor endothelial cells, and lactate and FGF to promote CAF development (8). Immunosuppressive TAMs are maintained by a suite of tumor secretions: CCL2, CXCL12, and IL-1β (8). NK cell inhibition by tumors is accomplished by release of IL6/10, IDO, and TGF-β. CAFs suppress NK cells via cytokines and growth factors including PGE2, TGF-β, and IDO (6). Tumors recruit immunosuppressive to the TME via TNF-α and CCL2 (9). IDO, indoleamine 2,3-dioxygenase; CD80, cluster of differentiation 80; M-CSF, macrophage colony-stimulating factor; CCL2, chemokine ligand 2; PGE2, prostaglandin E2; CXCL2, chemokine (C-X-C motif) ligand 2; TGF, transforming growth factor; IL, interleukin. Figure created by combining information from Jeanbart and Swartz (5), Hargadon et al. (10), Derbal et al. (8), Hasmim et al. (6), and Baginska et al. (9). See Abbreviations list for further definitions.

Figure 3

T-cell activation and cell-surface therapeutic targets. T-cell activation by APC/DCs and impact upon the tumor cell is driven by many integrated signals. Depicted are immune receptor-ligand pairings amenable to pharmacological manipulation by immunomodulatory mAbs. HVEM, herpes virus-entry mediator; LIGHT, lymphocyte activation gene 3 protein; GITR, glucocorticoid-induced TNFR family-related protein; ICOS, inducible T-cell costimulatory; LAG-3, lymphocyte activation gene 3 protein; TIGIT, T-cell immunoreceptor with Ig and ITIM domains; TIM-3, T-cell Ig mucin domain-containing 3; BTLA, B-lymphocyte and T-lymphocyte attenuator; VISTA, V-domain Ig suppressor of T-cell activation; TNF, tumor necrosis factor. Figure created by combining information from Mahoney et al. (13), Melero et al. (14), and Khalil et al. (15). See Abbreviations list for further definitions.

Figure 4

T-cell activation, inhibition and anti-CTLA-4/anti-PD-1 blockade mechanisms. (A) T-cell activation is initiated by TCR-MHCI-antigen interaction (signal 1). Full activation and effector activity demand additional CD28-CD80/86 binding (signal 2). Both signals cause T-cells to secrete IL-2 that drives T-cell proliferation and differentiation. (B) T-cell activation is limited by CTLA-4, upregulated on activated T-cells. CTLA-4 outcompetes CD28 for CD80/86 ligands, thus stopping signal 2 needed for T-cell activation. Contrarily, later coinhibitory PD-1 checkpoint interacts with its ligand to diminish T-cell cytotoxic activity in tumors expressing PD-L1. (C) Dual checkpoint anti-CTLA-4/PD-1 blockade mAbs block inhibitory CTLA-4 and PD-1 checkpoints, enabling release of cytokines involved in sustaining activated T-cells. CD28 can now bind its ligand to enable signal 2. (D) Activated T-cells can now join the antitumor T-cell effector response to destroy tumor cells. Adapted from Mellman et al. (17). See Abbreviations list for further definitions.

Figure 5

T-cell targets for mAb-based immunotherapy. Inhibitory and stimulatory receptors expressed in the TME may be targeted for therapeutic intervention. Agonistic antibodies, such as anti-OX40 or anti-CD28, target and activate co-stimulatory molecules, while blocking or antagonist antibodies, including anti-PD-1 or anti-CTLA-4, block T-cell inhibitory molecules. In either case, T-cells are stimulated and tumor destruction promoted. Adapted from Mellman et al. (17) and Vasaturo et al. (18). See Abbreviations list for further definitions.

Figure 6

Schematic comparison of patient survival associated with different therapies and improved survival with combination immunotherapy. Graph shows significantly improved survival for immunotherapies relative to conventional chemotherapy. First generation immunotherapies entail anti-CTLA-4 ipilimumab and the therapeutic vaccine Sipuleucel-T that defined the initiating wave of modern immunotherapies. Second generation immunotherapies are exemplified by anti-PD-1 nivolumab and pembrolizumab, and anti-PD-L1 agents of durvalumab and atezolizumab, that deliver effective responses in 40% of patients across many clinical trials (42). Combinations, such as dual-checkpoint CTLA-4/PD-1 blockade, produce strong effects in 60–70% of patients and alongside multifunctional single-agent modalities, represent the “third generation” of immunotherapies (40, 41). Dashed lines indicate projected survival rates based upon preclinical and clinical trials.

Figure 7

Exosome contributions to cancer facilitated by transport of oncogenic nucleic acids and proteins. Exosomes have major and diverse roles in tumorigenesis, including: (i) promoting an immunosuppressive TME by dampening NK and T-cells, while expanding inhibitory Treg and MDSC populations, (ii) mobilizing neutrophils, and thus skewing marcophages toward their M2 immunosuppressive form (iii) maintaining tumor drug resistance by exporting antitumor drugs and shuttling multi-drug-resistant proteins (iv) support tumor thrombosis and angiogenesis by activating endothelial cells (v) promoting metastasis by converting fibroblasts into myofibroblasts. CAF, cancer-associated fibroblasts; MDSC, myeloid-derived suppressor cell. Created using information from Zhang et al. (168).

Figure 8

Ion channels as cancer immunotherapy targets. The impact of ion channel dysregulation upon tumor-immune system interactions is depicted. In both immune and tumor cells, ion channels are involved in regulating Ca²⁺ influx and downstream signaling pathways. Dysregulation of ion channels can directly fuel carcinogenesis, and immune cell cytotoxicity is dampened by alterations in Ca²⁺ signaling. Cancer hallmarks are boxed in red. Created using information from Bose et al. (200), Litan and Langhans (201), and Panyi et al. (193).

Figure 9

Personalized immunotherapy. (A) Multifactorial biomarker panels. The three most widely established biomarkers of response to anti-PD-L1 immunotherapies have strong functional overlap, hence all three will soon be used together to provide stronger predictive value of therapeutic outcome than single biomarkers. Adapted from Topalian et al. (203). (B) TME stratification into 4 categories based upon PD-L1 expression and presence of TILs (tumor infiltrating lymphocytes) by Teng et al. (204). This promises to enable prediction of patients that respond to checkpoint blockades, namely anti-PD-1 and anti-PD-L1 (204).

Figure 10

New neoantigen discovery pipeline to facilitate personalized immunotherapy. Neoantigens are mutated antigens that are unique to cancer cells, foreign to the immune system and severely limit immunotherapy efficacy. Checkpoint inhibitors stimulate and enable host immunity to detect neoantigens and destroy tumors. Following collection of patient tumor and blood samples, whole exosome sequencing on malignant and non-transformed cells derived from the same patient reveal tumor-specific mutations. Subsequently, whole-transcriptome RNA-sequencing establishes mutation expression levels, before in silico tools can identify neoepitopes (tumor-specific mutation-derived peptides presented on tumor cells via the MHC-I and recognized by T-lymphocytes). To deduce the most immunogenic subset of neoepitopes, and hence most promising of immunotherapy targets, T-cell assays are then run. Neoantigen discovery will therefore drive the design of neoantigen-specific vaccines and effective combination immunotherapies, and enable estimation of patient clinical response to treatments. Furthermore, neoantigen load estimation will therefore enable improved, personalized immunotherapies. Created using information from Schumacher and Schreiber (224), and Kvistborg et al. (225).