Average human lifespan has dramatically increased across the globe. For example, life expectancy in the United States (U.S.) has risen by more than 30 years since the beginning of the 20^th century, from 47 to 79 years (Arias and Xu, 2019). More recently, many parts of the world have seen a sharp increase in obesity rates, with now over 40% of adults in the U.S. being obese (defined as a body mass index (BMI) ≥30 kg/m²). These profound demographic changes are also reflected in the diseases that impose the largest burden on present-day societies. Notably, cancer is the leading cause of death in the U.S. for individuals between 55 and 74 years of age and the second leading cause of death overall, behind only cardiovascular disease (Kochanek et al., 2019).

Age is among the most well-known cancer risk factors, and the incidence of most cancer types increases with age (Siegel et al., 2019). Illustratively, the chance for an individual in the U.S. to develop any cancer in the first 50 years of life is 4–5%, whereas this is close to 30% over the age of 70 (Siegel et al., 2019). Similarly, obesity is a risk factor for at least 13 cancer types, including common cancers like colorectal and postmenopausal breast cancer (Lauby-Secretan et al., 2016). Due to the increasingly high prevalence of obesity in the western world, obesity-related cancers constitute a significant health problem: between 2011 and 2015, almost 5% and almost 10% of cancer cases in men and women respectively of 30 years and older in the U.S. were attributable to excess body weight (Islami et al., 2018). In contrast to aging and obesity, dietary restriction is thought to provide widespread health benefits and increased lifespan (Fontana and Partridge, 2015; López-Otín et al., 2016; McCay et al., 1935), and the limited evidence available, largely derived from animal studies, suggests that cancer incidence may also be lowered with dietary restriction (Colman et al., 2009; Michels and Ekbom, 2004; Ross and Bras, 1965; Rous, 1914; Weindruch and Walford, 1982).

For both aging and obesity, a plethora of systemic and local factors have been suggested to directly promote cancer incidence and growth (Fane and Weeraratna, 2020; Khandekar et al., 2011). Although aging and excess body weight are distinct physiological entities, both conditions are also associated with reduced immune function, as evidenced by increased susceptibility to infections and suboptimal antibody titers following vaccination (Nikolich-Žugich et al., 2012; Painter et al., 2015; Sheridan et al., 2012), and both are among the main risk factors for a severe disease course in the ongoing COVID-19 pandemic, which is characterized by immune dysregulation and dysfunction (Lighter et al., 2020; Vabret et al., 2020; Wu and McGoogan, 2020). Given the recent appreciation of the importance of anti-cancer immune responses, this poses the question whether altered immunity may impact cancer rates and outcomes in systemic conditions like aging, obesity, and dietary restriction.

The first evidence for cancer immunotherapy was provided by the American surgeon William Coley in the late 19^th century (McCarthy, 2006). Dr. Coley injected sarcoma patients with streptococcal bacteria or bacterial products, inducing an immune response and, in some cases, tumor regressions. Not much later, the notion that defense mechanisms must exist in the body to limit the frequency of malignancies was first suggested by Ehrlich, 1909. However, Coley’s approach fell out of favor, and it was not until the second half of the 20^th century that experimentation with immune-mediated cancer therapies, including the first cancer vaccines, resumed (Decker et al., 2017; Graham and Graham, 1959). Around the same time, the concept of cancer immunosurveillance was described by Burnet and Thomas but could not yet be experimentally proven given the limited understanding of the immune system and the tools available at the time (Burnet, 1957; Dunn et al., 2002). The field of immunology was then propelled by several seminal discoveries, including the existence of distinct T and B lymphocyte populations (Cooper et al., 1965; Miller et al., 1967) as well as the concept of major histocompatibility complex (MHC) restriction in T cell-mediated immunity (Zinkernagel and Doherty, 1974). Definitive evidence for a role of immune cells in suppressing malignant tumors was provided by the laboratory of Robert Schreiber, who observed that tumors generated in immunodeficient mice could be cleared upon transplantation into immunocompetent animals (Shankaran et al., 2001), confirming the existence of cancer immunosurveillance and immunoediting (Dunn et al., 2002). These findings led to a renewed interest and confidence in the field of cancer immunotherapy, which would ultimately revolutionize cancer care (see ‘Cancer immunotherapies’ section below).

CD8⁺ T cells play a central role in anti-tumor immune responses by recognizing tumor cell antigens that differ from normal tissue and deploying cytolytic machinery to kill cancer cells directly. However, CD8⁺ T cell functions are often suppressed in the context of anti-tumor immunity. One reason for reduced T cell function in this context is the potently immunosuppressive nature of the tumor microenvironment (TME), where inhibitory signaling pathways are activated and immunosuppressive cell populations are present (Nakamura and Smyth, 2020). Moreover, extensive research has highlighted the importance of cell-intrinsic metabolic pathways for T cell activation, differentiation, and function (Buck et al., 2017). Since the TME is often depleted of nutrients, due to limited blood supply and competition with metabolically active tumor cells, nutrient unavailability provides another layer of immunosuppression (Lim et al., 2020).

In addition to local factors, the macroenvironment in which anti-tumor T cell responses take place also warrants consideration. Interestingly, aging and obesity both induce metabolic (e.g. insulin resistance) and immunologic (e.g. chronic inflammation) systemic alterations (Hotamisligil, 2017; López-Otín et al., 2013). However, how these organism-level changes are reflected in the TME and impact the anti-tumor immune response both systemically and locally are incompletely understood. Given that average age and obesity prevalence are rising in many parts of the world, a better understanding of these mechanisms is important to inform therapeutic strategies aiming to enhance the anti-tumor immune response in patients of any age and body composition. Here, we review the current knowledge of the shared and distinct effects of age, obesity, and various forms of dietary restriction on anti-tumor T cell responses with an emphasis on the metabolic pathways involved and the CD8⁺ T cell compartment. For each of these conditions, we will address alterations in (1) the anti-tumor T cell response, (2) metabolism and signaling, (3) TME-specific immunosuppression, and (4) cancer immunotherapy responsiveness.

Anti-tumor T cell responses

T lymphocytes are a part of the adaptive immune system, which is characterized by the ability to mount immune responses that are highly specific to certain antigens and result in the formation of immunological memory (Chaplin, 2010). To facilitate the recognition of any pathogenic antigen, T cells are endowed with unique antigen receptors known as T cell receptors (TCRs). Since TCRs recognize antigenic peptides in the context of MHC molecules, they rely on other cells to take up, process, and present antigens to them. To facilitate the interaction of the TCR with peptide-MHC complexes, T cells express a coreceptor, CD8 or CD4, which binds to MHC class I (MHC-I) or MHC class II (MHC-II) respectively (Rossjohn et al., 2015).

Naïve CD8⁺ and CD4⁺ T cells are generated from bone marrow-derived progenitor cells in the thymus and circulate through secondary lymphoid organs in the body, such as lymph nodes and spleen, until they encounter their cognate antigen. For initial T cell activation, an antigen needs to be presented by a professional antigen-presenting cell (APC), usually a cell of the myeloid lineage like a dendritic cell or macrophage, in the context of the appropriate costimulatory signals, most importantly ligation of the costimulatory receptor CD28 (Esensten et al., 2016; Pennock et al., 2013). Upon activation, a T cell undergoes clonal expansion, differentiates into an effector cell, and migrates toward the relevant peripheral tissue, for example a site of infection or a tumor (Smith-Garvin et al., 2009). Cytotoxic effector CD8⁺ T cells can directly kill cells that present their cognate antigen through cytotoxic molecules, like granzyme B (GzmB) and perforin, and also secrete pro-inflammatory cytokines, like interferon (IFN)-γ and tumor necrosis factor (TNF)-α (Farhood et al., 2019; Figure 1). CD8⁺ T cells therefore play a central role in the effectuation of immune responses against intracellular pathogens and cancer. Activated CD4⁺ T cells take on more diverse roles. Conventional CD4⁺ T cells (Tconvs) can differentiate into various types of immune helper T cells that support an immune response. A distinct lineage of CD4⁺ T cells, so-called regulatory T cells (Tregs), suppresses immune responses and is important for preventing autoimmunity (van der Vliet and Nieuwenhuis, 2007). After a successful immune response, most effector T cells undergo apoptosis, while some remain as memory T cells, which are poised to be reactivated more quickly than naïve T cells (Pennock et al., 2013).

Figure 1

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In addition to the TCR and costimulatory receptors such as CD28, which promote T cell activation, T cells also express co-inhibitory receptors, including cytotoxic T lymphocyte-associated protein 4 (CTLA-4) and programmed death-1 (PD-1). Inhibitory signaling through these receptors is important to maintain tolerance to self-antigens and for the resolution of immune responses (Nishimura et al., 1999; Tivol et al., 1995). However, enhanced expression of co-inhibitory receptors develops when T cells are exposed to persistent antigen stimulation (e.g. during chronic viral infection or in tumors) and correlates with an exhausted, dysfunctional state in T cells (Barber et al., 2006; Blackburn et al., 2009). Moreover, tumor cells and other cell populations in the TME suppress T cell function by expressing ligands of these receptors, such as PD-L1 (Dong et al., 2002).

Several other immune cell types can also play important roles in anti-tumor immunity. Natural killer (NK) lymphocytes, like CD8⁺ T cells, can acquire a cytotoxic phenotype and kill cancer cells (Miller and Lanier, 2019). Some myeloid cells in the TME can promote the anti-tumor immune response, for example macrophages with a pro-inflammatory phenotype often referred to as ‘M1’, but many tumor-associated macrophages have an ‘M2’-like phenotype and have pro-tumor effects, including suppression of anti-tumor immunity (Zhou et al., 2020). Importantly, the M1 and M2 designations do not describe distinct populations, but rather reflect opposite ends of the wide spectrum of functional states that macrophages can assume (Murray, 2017). Additionally, myeloid-derived suppressor cells (MDSCs), a heterogenous population of immature myeloid cells, inhibit T cell function in the TME through a variety of mechanisms, including depletion of extracellular arginine, expression of PD-L1, and recruitment of Tregs (Kumar et al., 2016).

Anti-tumor T cell responses with aging

Aging negatively impacts CD8⁺ T cell immunity, in cancer and other contexts, through a combination of T cell-intrinsic and -extrinsic mechanisms. First, the generation of new naïve T cells declines with age, as the thymus involutes and produces fewer naïve CD4⁺ and CD8⁺ T cells (Bains et al., 2009). Maintenance of the naïve T cell pool, both in terms of numbers and TCR diversity, thus becomes dependent on homeostatic proliferation of existing T cells, which leads to a reduction in naïve T cell numbers, especially of CD8⁺ T cells (Goronzy and Weyand, 2019). In addition, aging-associated adipocyte accumulation in the bone marrow also contributes to reduced hematopoiesis with age (Ambrosi et al., 2017), and hematopoiesis becomes skewed toward myeloid and away from lymphoid lineages with age (Beerman et al., 2010).

While the number of naïve T cells decreases with age, there are increased numbers of circulating T cells that express markers of antigen experience, for example CD45RO in humans or CD44 in mice (Lages et al., 2010; Li et al., 2019). Adoptive transfer experiments in mice have shown that these cells are dysfunctional (Decman et al., 2010). Some antigen-experienced cells express co-inhibitory receptors, including PD-1, and have been likened to exhausted T cells (Decman et al., 2012; Lages et al., 2010), whereas others are virtual memory T cells (Tvm), which develop from naïve T cells without full antigenic stimulation, and display some features of cellular senescence – an aging-associated phenotype of growth arrest (Chiu et al., 2013; Quinn et al., 2018; Renkema et al., 2014). However, although Tvm cells display a strongly reduced proliferation capacity, especially after TCR stimulation, they can still proliferate after stimulation with interleukin (IL)−15 (Quinn et al., 2018; Renkema et al., 2014), and thus do not have the irreversible cell cycle block that is characteristic of true cellular senescence (Goronzy and Weyand, 2019). In humans the number of T effector memory CD45RA (T_EMRA) cells increases with age (Quinn et al., 2018). T_EMRA cells develop upon viral infections and display some, but not all, properties of cellular senescence (Goronzy and Weyand, 2019). Finally, a loss of the co-stimulatory receptor CD28, which is essential for normal T cell activation, has been observed on CD8⁺ T cells during aging (Lages et al., 2010), and CD8⁺ CD28^– T cells may even play an immunosuppressive role in tumors (Filaci et al., 2007).

T cells rely on antigen-presenting cells, especially dendritic cells, to display antigens and provide costimulatory signals to drive T cell activation in secondary lymphoid organs. In addition to the described changes in the T cell compartment itself, dendritic cells also have reduced functionality with aging, including in the context of murine cancer models (Grolleau-Julius et al., 2008; Grolleau-Julius et al., 2006). Overall, a reduction in the naïve CD8⁺ T cell compartment, the existence of multiple dysfunctional T cell populations and reduced antigen presentation by dendritic cells likely all contribute to reduced T cell-mediated immunity in aging (Figure 2).

Figure 2

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Few studies have analyzed the anti-tumor T cell response without any immune-boosting therapies in aged animals, but the existing studies predominantly show reduced T cell function in this context. For example, anti-tumor T cell responses are decreased with age in a mouse model of oral cancer, and this is accompanied by increased numbers of MDSCs (Sekido et al., 2019). Similarly, MDSCs inhibit anti-tumor T cell function with age in a murine breast cancer model (Grizzle et al., 2007). Defects in anti-tumor CD8⁺ T cell responses were also observed in a hematological cancer model as well as a mesothelioma model (Jackaman et al., 2019; Lustgarten et al., 2004). Paradoxically, one study found that the growth of several tumor types was slower in aged mice, and this difference in growth rate was CD8⁺ T cell-dependent (Oh et al., 2018). It is not clear what explains this discrepancy, but differences in experimental setup (e.g. mouse strain, tumor model, duration of aging) and environmental factors (e.g. microbiome) between studies could play a role. More research carefully examining the anti-tumor immune response in aged animals and humans is thus needed. At this point, most of the available evidence points toward a reduction of anti-tumor immunity with advanced age.

Metabolism and signaling

Metabolism plays crucial roles in regulating T cell activation, differentiation, and function (Buck et al., 2017; Lim et al., 2020; O'Sullivan et al., 2019). When naïve T cells are activated by ligation of the TCR and costimulatory signals, they initiate a phase of rapid growth, differentiation, and proliferation (Smith-Garvin et al., 2009). This transition is accompanied by a switch from a quiescent metabolic state, characterized by mostly oxidative metabolism, to a highly metabolically active state, characterized by the use of anabolic pathways such as anaerobic glycolysis, glutaminolysis, and one-carbon metabolism (Chang et al., 2013; Ho et al., 2015; Ron-Harel et al., 2016; Wang et al., 2011). These pathways not only provide the molecules necessary for cell growth and proliferation, but also support activation and differentiation in different ways. For example, glycolysis directly impacts translation of IFN-γ mRNA (Chang et al., 2013), calcium signaling (Ho et al., 2015) and epigenetic state (Peng et al., 2016) in activated T cells.

In contrast to effector T cells, memory T cells and Tregs rely on oxidative metabolic pathways, including fatty acid oxidation (FAO) (Michalek et al., 2011; Pearce et al., 2009; van der Windt et al., 2012). In many cases, manipulating metabolism is sufficient to change the differentiation of T cells toward distinct phenotypes, indicating that certain metabolic pathways lie upstream of fate decisions in T cells (Buck et al., 2016; Michalek et al., 2011; Pearce et al., 2009; Pollizzi et al., 2016; Verbist et al., 2016).

Several transcription factors and nutrient-sensitive signaling pathways coordinate the metabolic profiles of distinct T cell subsets. The anabolic metabolic program characteristic of effector T cells is orchestrated by mammalian target of rapamycin (mTOR), Myc, and hypoxia-inducible factor (HIF)−1α, which are engaged during T cell activation (Pollizzi and Powell, 2014). In contrast, memory T cell metabolism is promoted by AMP-activated protein kinase (AMPK) – a nutrient sensor that is activated during low energy conditions and opposes signaling through mTOR, which is activated by high nutrient levels (Pollizzi and Powell, 2014; Figure 3). Accordingly, memory T cell generation is promoted by metformin-induced AMPK activation as well as rapamycin-mediated mTOR inhibition (Araki et al., 2009; Pearce et al., 2009).

Figure 3

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Although effector T cell responses have predominantly been associated with a shift toward anabolic metabolic pathways, a more oxidative metabolic profile may be beneficial for T cell survival and functionality in the context of a nutrient-depleted TME (see section on ‘Immune regulation in the tumor microenvironment’). Various interventions that promote oxidative, mitochondrial metabolic pathways in T cells enhance their anti-tumor function, for example promoting mitochondrial fusion, enhancing mitochondrial biogenesis, supplementing arginine, inducing fatty acid metabolism, and inhibiting glutamine metabolism (Buck et al., 2016; Chowdhury et al., 2018; Geiger et al., 2016; Leone et al., 2019; Scharping et al., 2016; Zhang et al., 2017).

Since the nutrient sensors mTOR and AMPK are affected by systemic metabolic state, an interesting question is how the balance between mTOR- and AMPK-mediated metabolism may impact anti-tumor T cell function downstream of systemic metabolism. While mTOR and AMPK are generally activated in different energy conditions, both signaling pathways are engaged during T cell activation (Pollizzi and Powell, 2014). Given the importance of mTOR signaling in T cell activation and acquisition of effector functions, the mTOR inhibitor rapamycin is generally viewed as immunosuppressive, and mTOR signaling has indeed been shown to support anti-tumor T cell responses (Chaoul et al., 2015; Pollizzi et al., 2015). However, rapamycin can also promote anti-tumor immunity in certain contexts, such as the MOC1 oral cancer model (Cash et al., 2015; Moore et al., 2016). On the other hand, T cell-intrinsic AMPK signaling is also required for T cell responses, including anti-tumor immunity, by promoting metabolic fitness and survival (Blagih et al., 2015; Rao et al., 2015), which may be of particular relevance in the context of a nutrient-depleted TME. Indeed, the AMPK activator metformin promotes anti-tumor T cell responses through several mechanisms, including AMPK activation in T cells (Eikawa et al., 2015), AMPK-dependent degradation of inhibitory ligand PD-L1 (Cha et al., 2018), and reduced oxygen consumption in tumor cells, likely resulting from AMPK-independent inhibition of the electron transport chain by metformin (Scharping et al., 2017). These findings show that the net result of the balance between effector function-promoting mTOR and metabolic fitness-promoting AMPK is context-dependent and can affect CD8⁺ T cell function both directly through T cell-intrinsic metabolic effects and indirectly through other cell types. Cellular nutrient sensors like mTOR and AMPK thus provide an example of how organismal metabolic state may interact with cellular metabolism and T cell function.

Immune modulation in the tumor microenvironment

Both cellular and metabolic factors contribute to the immunosuppressive nature of the TME. Cancer cells, Tregs, macrophages, MDSCs, and fibroblasts cultivate a highly immunosuppressive microenvironment, where CD8⁺ T cell responses are impaired by signaling through inhibitory receptors, suppressive cytokines, and reduced antigen presentation (Anderson et al., 2017; Figure 4). Metabolic challenges in the TME constitute another layer of immunosuppression (Lim et al., 2020). For example, T cells compete with tumor cells and other intratumoral cell populations for nutrients, which are limited by the tumor vasculature and high density of metabolically active tumor cells. This has been shown most specifically for glucose (Chang et al., 2015; Ho et al., 2015), but may apply to other nutrients as well. Accordingly, immunosuppressive cells in the TME express the enzymes arginase and indoleamine 2,3-dioxygenase, which degrade the amino acids arginine and tryptophan respectively, resulting in suppression of T cell responses (Rodriguez et al., 2004; Uyttenhove et al., 2003). Moreover, metabolic waste products, such as lactate, accumulate in the TME and suppress T cells (Brand et al., 2016). Finally, tumors often induce cell-intrinsic metabolic changes in T cells, like reduced mitochondrial mass and function, which impair their anti-tumor functionality (Kumar et al., 2020; Scharping et al., 2016).

Figure 4

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Cancer immunotherapies with aging

Several immunotherapeutic modalities, such as engagement of immunostimulatory receptors (e.g. TLRs or OX40) and cancer vaccination strategies (using DNA, tumor cells or dendritic cells), are less effective in aged animals (Castro et al., 2009; Dominguez and Lustgarten, 2008; Grolleau-Julius et al., 2008; Provinciali et al., 2003; Provinciali et al., 2000; Ruby and Weinberg, 2009). Fewer T cells are recovered following adoptive transfer of young T cells into aged compared to young tumor-bearing mice (Farazi et al., 2014), suggesting that adoptive cell therapies may also be less effective with aging, in a manner dependent on the aged host environment. These results have contributed to the general notion of decreased anti-tumor immune function with age.

In contrast, most available data suggest that the efficacy of ICB is intact in aged human cancer patients and in animal tumor models (Figure 5). Kugel et al. found that responsiveness to anti-PD-1 was even enhanced in older human melanoma patients and aged mice, and this was associated with increased CD8⁺ T cell infiltration (Kugel et al., 2018). Others have also found good ICB responsiveness in elderly patients with melanoma (Ben-Betzalel et al., 2019; Betof et al., 2017) or non-small cell lung cancer (Lichtenstein et al., 2019), and across multiple cancer types (Corbaux et al., 2019). Overall, most human studies show at least similar efficacy of anti-CTLA-4 and anti-PD-(L)1 therapies in older compared to younger patients (Huang et al., 2020; Pawelec, 2019; Poropatich et al., 2017).

Figure 5

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In murine studies, the responsiveness to ICB varies with cancer type and the immunotherapy used. Anti-CTLA-4, anti-PD-1 and anti-PD-L1 monotherapies are all highly effective in aged animals with an oral cancer model (Sekido et al., 2019). Aged and young mice respond similarly to anti-PD-L1 immunotherapy in a hematological malignancy model (Mirza et al., 2010). However, in the B16 melanoma model, anti-PD-1 is equally effective between aged and young mice, whereas anti-CTLA-4 works less well and anti-PD-L1 does not work at all (Padrón et al., 2018). Responsiveness to anti-CTLA-4 and anti-PD-L1 is also reduced in aged breast cancer models, but efficacy can be improved by enhancing interferon signaling in the TME (Sceneay et al., 2019). Thus, while ICB appears to remain efficacious with aging in mice and humans, further studies are needed to delineate the (determinants of) efficacy of different immunotherapeutic modalities in aged populations.

While many outstanding questions remain, age and systemic metabolic state impact anti-tumor CD8⁺ T cell responses. Systemic and TME-specific factors in the host environment as well as CD8⁺ T cell-intrinsic factors play a role. There are striking similarities in the changes observed with aging and obesity: both induce a chronic inflammatory state, reduce naïve CD8⁺ T cell generation, promote dysfunctional T cell states, and increase immunosuppressive myeloid cell populations in tumors. Together, these factors significantly reduce the anti-tumor CD8⁺ T cell response. In contrast, dietary restriction promotes anti-tumor immunity by opposing most of these age- and obesity-related mechanisms. Dietary restriction also causes cancer cell-intrinsic metabolic changes, such as induction of autophagy and ER stress, which benefit the anti-tumor T cell response. A dietary restriction-induced systemic shift toward AMPK-mediated oxidative metabolism likely contributes to the development of an environment that is more conducive to anti-tumor CD8⁺ T cell function by reducing systemic inflammation and improving the TME cellular landscape. Additionally, enhanced AMPK signaling in CD8⁺ T cells themselves may promote their survival and function in the TME.

The parallels between aging and obesity, and how they contrast with dietary restriction, are further emphasized by research looking at a combination of these conditions. For example, the observed increase in expression of PD-1 and other exhaustion markers on T cells in melanoma tumors with high body weight was more pronounced in patients over 60 years of age (Wang et al., 2019). Moreover, dietary restriction can restore some of the age-related decline in anti-tumor function of CD4⁺ T cells and responsiveness to OX40 immunotherapy (Farazi et al., 2014). Aged and obese mice display a similar immune dysregulation, leading to a harmful cytokine storm upon administration of immunotherapy consisting of a costimulatory receptor CD40 agonist combined with IL-2, which caloric restriction protects against (Mirsoian et al., 2014). These findings suggest that aging and obesity activate common pathways to impair the anti-tumor immune response, and these can be counteracted by dietary restriction.

The observation that responsiveness to ICB immunotherapies, like anti-PD-1, is intact or even enhanced in aged or obese individuals is surprising. Typically, having a better pre-existing anti-tumor CD8⁺ T cell response, as evidenced by intratumoral CD8⁺ T cell infiltration, increases the chances of responding to ICB (Ji et al., 2012). The mechanisms underlying this paradox in the context of age and obesity are unclear. Perhaps some of the dysfunctional T cell populations in aging and obesity are more prone to (re)activation by blockade of coinhibitory receptor signaling. If so, that would also explain why, in contrast to ICB, other immunotherapy modalities yield reduced responses with age and obesity in animal models. Intratumoral exhausted CD8⁺ T cells can be divided into several subpopulations including ‘terminally exhausted’ and ‘progenitor exhausted’ populations, the former having higher cytolytic function and the latter being more important for responsiveness to anti-PD-1 therapy (Beltra et al., 2020; Hudson et al., 2019; Im et al., 2016; Miller et al., 2019). It would be interesting to investigate whether an altered distribution of intratumoral CD8⁺ T cells between these populations with aging or obesity could contribute to the observed paradox of reduced baseline anti-tumor immunity with intact ICB responses.

Despite much progress over recent years, many important questions remain unanswered. First, how conditions of obesity or dietary restriction impact nutrient availability in the TME is largely a black box. Dietary changes can alter the metabolic composition of the TME (Maddocks et al., 2017; Sullivan et al., 2019), but this has not been analyzed for HFD or dietary restriction. Given the limited blood supply and high local metabolic rates in a tumor, alterations in metabolite concentrations in the circulation may not be proportionally reflected in the TME (Sullivan et al., 2019). Moreover, diet can change cancer cell metabolism (Bose et al., 2020; Lien and Vander Heiden, 2019), further shaping the metabolic conditions in the TME and in some cases benefitting specific intratumoral cell populations (Kumagai et al., 2020). Second, it is largely unknown what the intracellular fates of nutrients in the TME are and how these may differ between cell types. Third, there are various systemic conditions in addition to the ones discussed here for which the effects on anti-tumor immunity are unknown, and it would be interesting to explore those. Fourth, since mouse research studying obesity often involves the feeding of a HFD, the question arises whether the observed effects are due to the diet itself or the obese state. Delineating the contributions of diet and obese state to both systemic and TME-specific metabolic alterations will be an exciting avenue for future studies with potential therapeutic implications. And finally, preclinical findings with dietary restriction, suggesting enhanced anti-tumor T cell responses, look promising. It needs to be determined whether combination of dietary restriction and CRMs with immunotherapies can boost anti-tumor immunity, and some such trials are already underway (Lévesque et al., 2019). To further facilitate this, it would be important to assess which exact dietary interventions are most beneficial in which context and what metabolic changes underly any therapeutic benefit.

Research investigating the impact of age and metabolic state on both cancer biology and anti-tumor immunity also has methodological implications and highlights shortcomings of the currently available technologies. For one, it may be important to use aged and/or metabolically unhealthy animals for investigations into the anti-tumor immune response, in addition to the lean, young animals that are most commonly used, to better model cancer patients and improve the translatability of any findings. In that regard, it is noteworthy that there is a high degree of congruency between existing human and animal data concerning anti-tumor immunity and ICB responsiveness with age, obesity, and dietary restriction. Moreover, despite the value and practicality of using implantable tumor models for animal studies, they have some limitations, including (1) they do not assess effects of the ‘agedness’ of cancer cells themselves, and (2) they study cancer progression, whereas most human studies quantify cancer incidence. There may be value in using animal models of spontaneous tumor growth to assess how age and diet alter immune surveillance, although there are challenges to this approach, as genetic cancer models are typically not very immunogenic. Finally, it remains very challenging to determine the activity intracellular metabolic pathways in distinct cell populations in a tissue, for example a tumor, in vivo. Mass spectrometry-based approaches are complicated, given that only limited CD8⁺ T cell numbers can be obtained from a tumor and extensive processing and sorting procedures are required for their isolation, which are bound to impact metabolite pool sizes. Recently, several groups have made progress in this area, for example using in vivo infusion of isotopically labeled glucose to assess T cell metabolism (Ma et al., 2019) or tracing isotopically labeled nutrients into macromolecules with a slow turnover rate in distinct intratumoral cell populations, specifically cancer cells and fibroblasts (Lau et al., 2020). Others have developed advanced flow cytometric strategies for reading out metabolic characteristics at a single-cell level (Ahl et al., 2020).

Cancer represents a huge public health burden, and the cancer risk factors of old age and obesity are increasing in prevalence. It is therefore increasingly critical to consider these factors in the design of cancer immunotherapies. Expanding our knowledge of the macroenvironmental determinants of anti-tumor immunity should reveal new ways to extend the benefit of cancer immunotherapies to more patients.

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