Tissue-resident memory T cells in tumor immunity and immunotherapy.

Tissue-resident memory T cells (TRM) represent a heterogeneous T cell population with the functionality of both effector and memory T cells. TRM express residence gene signatures. This feature allows them to traffic to, reside in, and potentially patrol peripheral tissues, thereby enforcing an efficient long-term immune-protective role. Recent studies have revealed TRM involvement in tumor immune responses. TRM tumor infiltration correlates with enhanced response to current immunotherapy and is often associated with favorable clinical outcome in patients with cancer. Thus, targeting TRM may lead to enhanced cancer immunotherapy efficacy. Here, we review and discuss recent advances on the nature of TRM in the context of tumor immunity and immunotherapy.

Introduction

Tissue-resident memory T cells (TRM) reside in peripheral tissues, patrol their surroundings, and rapidly respond to alarming signals (Jiang et al., 2012; Schenkel et al., 2014; Park and Kupper, 2015; Clark, 2015; Dijkgraaf et al., 2019). These features enable them to potentially serve as critical players in antitumor surveillance and immunity. Early studies in viral infections have revealed that T cells were retained in tissues, including human and murine skin as well as murine small intestine and lung tissues (Zhu et al., 2007; Gebhardt et al., 2009; Masopust et al., 2010; Teijaro et al., 2011). In support of TRM tissue retention, TRM down-regulate the expression of markers for tissue egress and express surface molecules that enable their retention in tissues (Kumar et al., 2017; Park et al., 2019; Raphael et al., 2020; Byrne et al., 2020). TRM differentiation is guided by transcription programs common to both effector and memory T cells (Milner and Goldrath 2018), consistent with their persistence in tissues as memory T cells yet retaining rapid effector function for immune protection. TRM are defined in multiple infection models to be retained in tissues, as shown in parabiosis experiments in mice, antibody in vivo labeling, and in other approaches that mark and monitor tissue T cells (Szabo et al., 2019; Masopust and Soerens, 2019). Of note, the transcriptome signature of TRM suggests a “tissue-tailored” retention model rather than a “one size fits all” model, highlighting tissue- and organ-specific immune regulation, also named tissue- and organ-specific “immunostats” (Amsen et al., 2018; Pao et al., 2018). Tissue-specific adaptations may enable TRM to maintain homeostasis in a site-specific fashion. Intriguingly, transcriptionally and functionally distinct TRM subsets and TRM precursors have been observed in the murine small intestine during infection (Kurd et al., 2020). This suggests a high heterogeneity and complexity of TRM populations.

Compared with their protective role in infectious diseases (Wu et al., 2018; Wilk and Mills, 2018; Muruganandah et al., 2018; O’Hara et al., 2020), the importance and significance of TRM in tumor immunity are not adequately characterized. Nevertheless, recent studies in cancer-bearing mouse models have indicated a pivotal role of TRM in antitumor immunity in several tumor types (Nizard et al., 2017; Milner et al., 2017; Malik et al., 2017; Enamorado et al., 2017). Moreover, human studies show an association between tumor TRM and improved responses to immunotherapy and favorable clinical outcome in patients with cancer (Table 1). These findings fuel interest in TRM research in cancer immunology and immunotherapy. Here, we review the current understanding of TRM from mouse infection models and human studies and how these findings have been used to guide studies of TRM in tumor immunity, as well as implications for the role of TRM in immune surveillance and immunotherapies.

Table 1.

Clinical relevance of TRM in patients with cancer

Cancer types	TRM phenotype	Correlation with clinical features	References
Breast cancer	CD8+, CD103+	Intratumoral CD103+CD8+ T cells are positively associated with RFS and OS in estrogen receptor–negative basal-like breast cancer.	Wang et al., 2016
		CD8+ TRM gene signature is associated with improved DFS and OS in patients with TNBC.	Savas et al., 2018
		Levels of CD103+CD8+ T cells are higher in TNBCs in patients without tumor relapse than with tumor relapse.	Egelston et al., 2019
Bladder cancer	CD8+, CD103+, CD69+, CD49a+, PD-1+	Levels of CD103+CD8+ T cells are negatively associated with tumor size and are positively associated with OS and RFS.	Wang et al., 2015
		High TRM tumor infiltration is associated with lower tumor stage.	Hartana et al., 2018
Cervical cancer	CD8+, CD103+, PD-1+, GzmB+	CD103 expression is associated with improved DSS.	Komdeur et al., 2017
		Prognostic benefit of increased CD103 expression is observed in patients treated with radiotherapy.
		Abundance of intraepithelial CD103+ cells is associated with improved DSS and DFS in patients with or without radio(chemo)therapy.
Colorectal cancer	CD8+, CD103+	High density of CD103+ cells is associated with DFS.	Huang et al., 2017
		High density of CD103+ cells is negatively associated with OS and DFS in patients with KRAS WT tumors.
Endometrial cancer	CD8+, CD103+, PD-1+	CD103+ cells are associated with prolonged DSS.	Workel et al., 2016
Esophageal cancer	CD8+, CD103+, PD-1+, TIM3+	High density of CD103hiCD8hi cells is associated with improved OS.	Han et al., 2020
Gastric cancer	CD103+, CD69+, PD-1+, TIGIT+, CD39+	Presence of CD8+CD103+ cells is associated with improved OS.	Lin et al., 2020
Hepatocellular carcinoma	CD8+, CD103+	High density of CD8+CD103+ cells is associated with improved OS.	Lim et al., 2019
Head and neck cancer	CD8+, CD103+, CD39+	High frequencies of CD103+CD39+ cells are associated with improved OS.	Duhen et al., 2018
		High expression of CD103 is associated with improved OS.
Lung cancer	CD8+, CD103+ CD69+, CD49a+, PD-1+, 4-1BB+, CXCR6+ CD39+, TIM3+	High levels of CD103+ cells are associated with improved OS in total tumor and stromal region.	Djenidi et al., 2015
		High levels of CD103+ cells are associated with improved DFS in total tumor, epithelial tumor islets, and stromal region.	Ganesan et al., 2017
		High density of CD103+ cells is associated with improved OS.	Duhen et al., 2018
		High expression of both CD103 and CD39, as well as CD103 alone, is associated with improved OS.	Koh et al., 2017
		High numbers of intratumoral, but not stromal, CD103+ cells are associated with prolonged DFS and OS.	Clarke et al., 2019
		PD-1+TIM3+ cells are enriched in responders to anti–PD-1 therapy.	Nizard et al., 2017
		High intratumoral CD103+ cells are associated with improved OS.
Melanoma	CD8+, CD103+, CD69+, CD49a+, PD-1+, LAG-3+, GzmB+	High levels of CD49a expression are associated with improved DFS and OS.	Murray et al., 2016; Edwards et al., 2018; Menares et al., 2019
		Increased numbers of CD69+CD103+CD8+ cells are associated with improved OS in immunotherapy-naive patients.
		Enrichment of TRM gene signature is associated with improved OS.
Ovarian cancer	CD8+, CD103+, PD-1+, CD3+	Presence of CD103+ cells is associated with increased DSS in HGSOC and mucinous cancers.	Webb et al., 2014
		Patients with HGSOC, containing both CD103+ and PD-1+ cells, are associated with increased DSS.	Webb et al., 2015
		CD103+ cell numbers are associated with improved DSS in patients treated with PS.	Bösmüller et al., 2016
		High infiltration of CD103+ cells in tumor parenchyma of primary tumors is associated with improved 10-yr OS.	Komdeur et al., 2016
			Santoiemma et al., 2016
Pancreatic cancer	CD8+, CD103+	Increased ratio of CD8+CD103+ cells to CD8+CD103− cells is associated with improved DFS.	Lohneis et al., 2017

DFS, disease-free survival; DSS, disease-specific survival; HGSOC, high-grade serous ovarian cancer; OS, overall survival; PS, primary surgery and adjuvant chemotherapy; RFS, relapse-free survival; TIGIT, T cell immunoreceptor with immunoglobulin and ITIM domains; TNBC, triple-negative breast cancer.

Identifying TRM

TRM are defined based on the expression of specific markers, a distinct transcriptional profile, and their functional retention in tissues, all properties that distinguish TRM from circulating memory T cells, including effector memory T cells (TEM) and central memory T cells (TCM). Based on this definition, CD8+ TRM are found to be a component of tumor-infiltrating lymphocytes (TILs) in patients with solid tumors (Byrne et al., 2020). However, it is important to note that TRM are loosely defined in different studies in the context of tumor immunity.

TRM express tissue retention markers, including CD69 (C-type lectin), αE(CD103)β7 (E-cadherin receptor), and α1(CD49a)β1 (VLA-1), and exhibit reduced expression of migration and tissue egress markers, such as CCR7, CD62L, and sphingosine-1-phosphate receptor 1 (S1PR1; Schenkel and Masopust, 2014; Kumar et al., 2017; Szabo et al., 2019). Other core markers identified in both human and mouse TRM include the chemokine receptor CXCR6, the inhibitory receptor programmed death receptor 1 (PD-1), and CD101, which have inhibitory function (Mackay et al., 2016; Kumar et al., 2017; Wein et al., 2019). The expression of TRM-associated markers differs between organs and tissue sites. CD103 is most highly expressed by CD8+ TRM in mucosal and barrier sites, is variably expressed by TRM in lung and skin, and is not expressed by TRM in lymphoid sites (Kumar et al., 2017; Steinert et al., 2015). PD-1 is highly expressed by human TRM in exocrine sites, such as the pancreas, and to a lesser extent by lymphoid TRM, but is not highly expressed by intestinal TRM (Weisberg et al., 2019). In human tumors, TRM phenotype can also vary according to the tumor type (Table 1). Interestingly, human TRM associated with multiple tumor types in different sites express CD103, possibly due to the epithelial origin of many solid tumors, while lung tumor–associated TRM also express CXCR6 mRNA (Ganesan et al., 2017). PD-1 is expressed by TRM in tumor tissues and healthy tissues. Tumor-associated myeloid APCs, including macrophages and myeloid dendritic cells (DCs), highly express programmed death-ligand 1 (PD-L1; B7-H1), engage PD-1+ T cells, and mediate immune suppression in spontaneous and immunotherapy-induced tumor immunity (Curiel et al., 2004; Zou et al., 2016; Lin et al., 2018). It is unknown if PD-L1+ APCs interact with PD-1+ TRM, resulting in TRM functional alteration in the tumor-draining LNs and tumor microenvironment. Additionally, whether tumor-associated TRM in a particular tissue site differ phenotypically from TRM in the identical healthy tissue site requires further investigation.

Transcriptome profiling of the population and single-cell RNA sequencing (scRNAseq) have elucidated core features of TRM in mouse models and humans and further revealed TRM heterogeneity and tissue-specific signatures. Population-level RNAseq of mouse and human TRM has revealed conserved signatures that include genes encoding the surface and intracellular molecules described above, as well as transcriptional regulators (Mackay et al., 2016; Kumar et al., 2016; Hombrink et al., 2016). In mice, the transcription factors (such as Hobit, Blimp-1, Runx3, and Id2 family members) have been reported to play a role in TRM biology (Mackay et al., 2016; Milner et al., 2017; Milner et al., 2020). Human TRM do not express elevated levels of Hobit, although Notch expression is up-regulated and is related to TRM establishment (Hombrink et al., 2016; Kumar et al., 2017). A “master regulator” defining human TRM development has not yet been identified. However, single-cell transcriptome profiling of TRM has begun to reveal new insights into distinguished features of TRM, including their heterogeneity and tissue-specific variations. In humans, scRNAseq analysis of T cells from lung and lymphoid sites has revealed a TRM-associated tissue gene signature, including cell–cell communication, cell structure, and cell–cell matrix interactions (Szabo et al., 2019). This suggests that cell structure and cell interaction may regulate TRM formation and maintenance. Transcriptome analysis has also indicated considerable heterogeneity among mouse TRM populations (Milner et al., 2020).

Transcriptome analysis has likewise identified tumor-associated TRM subsets in the tumor microenvironment. For example, single-cell sequencing suggests different TRM subsets in human breast cancer (Savas et al., 2018). Interestingly, PD-1–expressing TRM may possess superior functionality when compared with PD-1–expressing non-TRM, as suggested in transcriptome profiling of human lung cancer (Clarke et al., 2019). Based on our current understanding of PD-L1 and PD-1 in tumor immunity (Curiel et al., 2004; Zou et al., 2016; Lin et al., 2018), this finding is unexpected. Transcriptionally distinct subsets of TRM, including Blimp1hi and Id3hi subpopulations, were also identified in a mouse melanoma model (Fig. 1; Milner et al., 2020). Although transcriptome studies have generated some insight into TRM in tumors, it is important and critical to phenotypically and functionally define the different subsets of tumor-associated TRM, including PD-1+ TRM. Notably, although tumor-infiltrating lymphocytes show a TRM-like signature, there are no specific phenotypic and functional markers to define TRM among different memory T cell subsets in the tumor microenvironment (Sasson et al., 2020). Thus, functional studies following single-cell phenotyping are critical to resolve discrepancies regarding different tumor-infiltrating T cell subsets, including TRM.

Figure 1.

T (1 and 2) Before specific antigen priming, CD8+ naive T cells may interact with DCs in the presence of TGF-β. This process may prepare TRM formation. (3) During memory phase, TSCM may give rise to different memory subsets, including TRM. IL-2, IL-15, and TGF-β, which may provide optimal signaling for TRM formation. (4) TRM precursors may undergo a transcription factor–driven generation program. (5) TRM can traffic into the tumor microenvironment, and they are maintained in situ without (or with minimal) recirculation. (6) TRM retention in tumors may be related to certain integrins, including CD44, α1(CD49a)β1, αE(CD103)β7, and CD69. CD44 and α1(CD49a)β1 tether TRM to the extracellular matrix. αE(CD103)β7 anchors TRM via interacting with E-cadherin on the epithelial cell surface. CD69 blocks S1PR1-mediated “exit” signaling. Blockade of TRM egress from the tissue may be promoted by up-regulation of some transcription factors (including Blimp, Hobit, Notch, and Runx) and by down-regulation of Krüppel-like factor 2 (KLF2). Microbiome-derived SCFA may promote long-term maintenance of TRM. FABP and FFA uptake may favor TRM survival and antitumor functionality. TRM may express immune checkpoint proteins, suggesting that TRM likely responds to checkpoint blockade. (7) TRM may release perforin and GzmB and directly kill target tumor cells. TRM-derived cytokines (IFN-γ, IL-2, and TNF-α) promote infiltration of DCs, T and B cells, and natural killer cells, indirectly boosting antitumor immunity.

TRM retention

TRM were originally identified in mice using parabiosis models and in vivo antibody labeling (Anderson et al., 2014; Szabo et al., 2019). Multiple molecules (including CD44, CD69, integrins [CD49a, CD103]), some transcription factors (i.e., Notch, Runx3, Blimp1, Hobit), fatty acid–binding proteins (FABPs), and microbiome-derived metabolites (such as short-chain fatty acid [SCFA]) are reported to be involved in TRM retention in the tissue. CD44 is a receptor for extracellular matrix and assists TRM interaction with epithelial cells and collagen (Amsen et al., 2018). CD69 restrains the function of S1PR1 signaling and blocks TRM egress from tissue (Skon et al., 2013; Mackay et al., 2015). Similarly, CD49a and CD103 function as “anchors” to arrest TRM within the tissues (Chen and Shen, 2020; Byrne et al., 2020).

The pathways controlling TRM retention in tumors are poorly defined. Different levels of CD8+CD103+ TRM are found in tumor epithelium and tumor stroma in different tumor types in patients (Cresswell et al., 2001; Ling et al., 2007; Djenidi et al., 2015; Wang et al., 2015; Workel et al., 2016; Nizard et al., 2017), suggesting that CD103 may be particularly important for TRM retention in tumors. Antibody blockade of CD103 or genetic deficiency of CD103 results in reduced tumor-infiltrating T cells and accelerated tumor progression in mice (Sandoval et al., 2013; Murray et al., 2016; Sun et al., 2016; Malik et al., 2017), supporting a role for CD103 in T cell tumor retention. Notch plays a role in controlling maximal CD103 expression in tumor-associated TRM (Hombrink et al., 2016). Transcription factors, including Runx3, Blimp, Hobit, and KLF2, have been shown to down-regulate homing receptor expression for egress in TRM and promote TRM tissue retention in mouse infection models (Milner et al., 2017; Mackay et al., 2016; Skon et al., 2013). The potential role of these transcription factors in TRM tumor retention remains to be established.

It appears that TRM retention is subject to their adaptation to and regulation by their tissue of residency. Consistent with this concept, tissue-tailored, variable, and malleable profiles of FABP isoforms are found in murine TRM after viral infection (Frizzell et al., 2020). Interestingly, microbiota-derived SCFA favors CD8+ T cell long-term survival and memory (Bachem et al., 2019). Altogether, these early studies suggest that it is crucial to decode molecular mechanisms by which TRM gain tissue-tailored “labels” and to characterize the “area code” to control TRM memory, survival, and function in the tumor microenvironment in different tumor types.

Generation and maintenance of TRM

Several models (including linear, asymmetrical, self-renewal, simultaneous, “one cell, one fate,” and “one cell, multiple fates” programs) are proposed to explain TRM differentiation (Enamorado et al., 2018; Raphael et al., 2020). Moreover, TRM differentiation is influenced by different factors at the earliest priming stage in the LN, the cytokine environment during differentiation and activation, and finally through tissue-specific influences. At the level of priming, murine Batf3-dependent DCs and human CD1c+/CD163+ TGF-β–producing DCs can prime T cells for TRM generation in lymphoid tissues (Mami-Chouaib et al., 2018; Amsen et al., 2018; Bourdely et al., 2020). DC-specific DNGR-1 (CLEC9A) provides optimal signal for murine TRM generation (Iborra et al., 2016). Even before antigen encounter, naive T cells can undergo “training” in the LNs via interacting with migratory αVβ8+ DCs. These DCs activate and present TGF-β to naive CD8+ T cells, resulting in TRM-like features, including up-regulation of CD103 expression and epigenetic modifications of TRM-related genes (Mani et al., 2019). It is speculated that DCs in the tumor-draining LNs may similarly affect TRM development in the tumor microenvironment.

TRM undergo a unique, hybrid effector cell–memory cell differentiation program driven by transcription factors associated with both memory and effector cell characteristics. For example, Blimp1 and Notch are required for TRM and favor TEM, whereas Runx3 and Nr4a1 promote TRM and support TCM (Milner and Goldrath, 2018). Conversely, T-bet and Eomes inhibit TRM formation but promote TEM and TCM differentiation, respectively. Furthermore, TRM seem to be reminiscent effector cells via expression of PD-1, IFNγ, perforin, and granzyme B (GzmB) on both mRNA and protein levels (Szabo et al., 2019; Milner and Goldrath, 2018; Ganesan et al., 2017), and they share properties of stem cells, as they may be long-lived and not terminally differentiated (Milner and Goldrath, 2018). Overall, coexistence of these memory-, effector-, and stem-like properties may reinforce the antitumor functionality of TRM.

Cytokines at the tissue site or during priming can influence TRM formation

Notably, TGF-β promotes CD103 expression and is critical for formation of TRM in the gut, skin, and lungs (Zhang and Bevan, 2013; Raphael et al., 2020). TGF-β can be highly expressed in the tumor microenvironment and may promote TRM establishment. Tissue-specific factors are also important for TRM establishment at specific sites. The cutaneous lymphocyte antigen and chemokine receptors, including CCR4, CCR8, CCR10, and CCR19, are expressed on TRM resident in the skin, whereas CCR9, CXCR3, and integrin α4β7 are revealed in intestinal-resident TRM (Farber et al., 2014; Amsen et al., 2018; Sun et al., 2019). Murine TRM in the kidney have enhanced expression of E-selectin and P-selectin (Ma et al., 2017). CXCR6 controls TRM trafficking to murine lungs (Wein et al., 2019) and is expressed in TRM in human lung cancer (Ganesan et al., 2017), while murine TRM migrate to the brain via CCR5 and CXCL10 (Glass et al., 2005; Klein et al., 2005). However, α4β7+ TRM are also present in the murine skin (Ohmatsu et al., 2010). CXCR3 can “navigate” TRM to the lungs, skin, and vagina, whereas P-selectin plays a role in migration of CD4+ TRM to the intestine (Haddad et al., 2003; Jeyanathan et al., 2017; Chen and Shen, 2020). Future studies are required to precisely characterize TRM-related functional states in different tumors.

Once established in the tissues, there is evidence that TRM can be maintained for a long period of time. Mouse studies in a parabiosis model show that CD4+ and CD8+ TRM generated from site-specific infection do not emerge into the circulation and enter peripheral organs during homeostasis for several weeks (Teijaro et al., 2011; Jiang et al., 2012; Steinert et al., 2015). Additionally, TRM are identified from intravenous antibody labeling due to their presence in tissues, not in the vasculature (Anderson et al., 2012; Turner et al., 2014). In humans, long-term persistence of CD4+ and CD8+ TRM has been demonstrated in transplanted organs in which donor TRM in lungs and intestines were maintained for over a year after transplant but were not detected in blood (Snyder et al., 2019; Bartolomé-Casado et al., 2019; Bartolomé-Casado et al., 2020; Pallett et al., 2020). Moreover, TRM frequencies in multiple mucosal barriers and lymphoid sites are stably maintained throughout decades of adult life (Kumar et al., 2018). Together, these findings suggest that TRM maintenance is tissue specific and integral for tissue homeostasis.

Studies in mice suggest that TRM persistence and stability in tissues can be variable

Lung CD8+ TRM generated after infection tend to wane over time, unless persistent antigen stimulation is provided (Uddbäck et al., 2021). Over time, lung TRM were also found to migrate out of the lung to the associated lymphoid tissue (Stolley et al., 2020). Recent studies have found low levels of potential TRM in healthy human peripheral blood (Klicznik et al., 2019; Guggino et al., 2019) and in the synovial fluid of individuals with spondyloarthritis (Guggino et al., 2019; Qaiyum et al., 2019). In line with this, scRNAseq analysis identifies a tissue gene signature in a minor fraction of peripheral blood T cells (Szabo et al., 2019). Thus, it seems that TRM emergence into the circulation may occur, but it may be a rare event. In mice, reactivation of TRM in secondary infection can result in migration of effector progeny to the local draining LNs (Fonseca et al., 2020; Stolley et al., 2020). Adoptive transfer of purified mouse TRM can enable TRM to migrate to and populate different tissue sites in response to systemic virus challenge (Fonseca et al., 2020). While these results suggest TRM plasticity for reactivation, further studies are needed to investigate the trafficking and retention properties of TRM in different sites; this is an important research area in TRM biology.

Tumor-infiltrating T cells have been studied before the identification of TRM

Tumor-associated TRM may consist of TRM already in the original tissue site before tumorigenesis and T cells that migrate from the periphery, which become TRM due to certain tumor environmental factors such as TGF-β (Fig. 1). Based on results in murine infection models, we propose that tumor-specific TRM reside primarily in the tumor milieu, where they may locally proliferate in response to antigen encounter in situ without or with minimal exiting from the tumor site. This concept is in agreement with the data that tumor-associated antigen-specific T cells are largely found in tumor tissue rather than in the circulation (Webb et al., 2014; Smazynski and Webb, 2018). Recall immune responses could be initiated at nonlymphoid tissues, such as tumor tissues (Zou, 2005). If so, tumor-infiltrating TRM may be locally activated to proliferate and combat tumor cells in the tumor microenvironment. Subsequently, they may exit the primary tumor niche and inhabit new sites within the tissue of origin—for instance, metastatic tumor tissues. Given that the majority of cancer patients die from tumor metastases, boosting TRM-mediated long-term local and systemic memory would be meaningful. Nonetheless, the potential mechanisms controlling tumor-associated TRM maintenance, replenishment, and function remain to be dissected.

Role of TRM in tumor immune surveillance and immunity

Due to their long-term retention in multiple tissues, TRM can play an important role in both tumor immune surveillance and immunity at diverse sites, analogous to their role in immune protection to pathogens (Amsen et al., 2018; Gebhardt et al., 2018; Byrne et al., 2020). Recent studies in mice have pointed toward the functional importance of TRM in immune responses (Park et al., 2018; Beura et al., 2018a; Klicznik et al., 2019; Fonseca et al., 2020). One intriguing possibility is that TRM may eliminate transformed cells in situ, thereby preventing tumor initiation. However, once a tumor is established, tumor cells outcompete tumor-infiltrating T cells for nutrients, resulting in impaired T cell functionality (Zhang et al., 2017; Bian et al., 2020). Correspondingly, human gastric cancer cells outcompete TRM for lipid uptake, resulting in TRM death (Lin et al., 2020). The data suggest that fatty acids may be required for TRM survival in the tumor niche.

TRM mediate antitumor immunity directly through production of effector and cytolytic mediators and through the release of cytokines and chemokines for immune cell recruitment and activation. CD8+ TRM can be reactivated by both hematopoietic and nonhematopoietic APCs within the sites, which can shape their functionality (Low et al., 2020). Once stimulated, TRM release lytic granules containing perforin and GzmB and kill tumor cells, similarly to effector CD8+ T cells (Amsen et al., 2018). Furthermore, TRM can support immune equilibrium in a melanoma mouse model and contribute to tumor control (Park et al., 2019a).

Interestingly, tumor-infiltrating CD8+CD39+CD103+ TRM elicit more potent cytotoxic and effector functions compared with CD103− counterparts (Franciszkiewicz et al., 2013; Djenidi et al., 2015; Enamorado et al., 2017; Malik et al., 2017; Nizard et al., 2017; Duhen et al., 2018; Park et al., 2019; Sasson et al., 2020). In line with this, CD8+CD103+ TRM in tumor, but not in tumor stroma, are a better prognostic factor in patients with cancer (Koh et al., 2017; Dhodapkar, 2018). Human tumor-infiltrating T helper type 17 (Th17) cells are affected by TGFβ in the tumor microenvironment, express CD49, are long-lived memory cells, and mediate potent antitumor immunity (Kryczek et al., 2009; Kryczek et al., 2011). These features suggest that human tumor-infiltrating Th17 cells exhibit and/or gain a TRM phenotype. Correspondingly, CD4+ TRM with a Th17 signature have been observed in patients with autoimmune disease (Krebs et al., 2020). Interestingly, CD49a+ TRM are the most effective tumor killers among T cells in a melanoma mouse model (Le Floc’h et al., 2007; Djenidi et al., 2015; Murray et al., 2016). It is tempting to speculate that TRM may possess superior antitumor activity compared with other tumor-associated lymphocytes.

In addition to tumor killing via their direct cytotoxic activity, TRM function as an immune stimulator. TRM-derived effector cytokines stimulate local DCs, natural killer cells, and T cells to boost antitumor immune responses (Schenkel et al., 2013; McMaster et al., 2015; Hombrink et al., 2016; Glasner et al., 2018). Furthermore, TRM more rapidly respond to antigen reexposure compared with circulating memory T cells (Mackay et al., 2012; Schenkel et al., 2014; Ariotti et al., 2014). Therefore, TRM may play a crucial role against tumor recurrence. Collectively, activated TRM initiate a system of a rapid, tissue-wide state of alarm for optimal immune protection. Unexpectedly, although TRM express inhibitory checkpoint receptors, their cytotoxic and effector functionalities are maintained (Ganesan et al., 2017; Savas et al., 2018; Boddupalli et al., 2016). Treatment with PD-1 and PD-L1 blockade results in TRM proliferation in patients with melanoma (Edwards et al., 2018), and production of high levels of GzmB, TNF-α, and IFN-γ (Djenidi et al., 2015; Ganesan et al., 2017; Behr et al., 2019). However, it has also been reported that TRM isolated from normal human lung tissue may be more effective in effector cytokine production than their counterparts isolated from tumor lung tissue (Bengsch et al., 2018). A tolerogenic signature has been observed in CD8+CD103+ T cells with high expression of IL-10 and CTLA-4 and low expression of TNF-α, IFN-γ, and GzmB in a melanoma mouse model (Gabriely et al., 2017). Nevertheless, TRM are associated with favorable prognosis in many types of human cancer (Table 1). Intriguingly, mouse infection models have recently shown that lung TRM can migrate to the draining LNs (Beura et al., 2018b) and that stimulation of lung TRM led to enhanced responses in the lung-draining LNs (Paik and Farber, 2021). These studies suggest that TRM may coordinate local immunity through fortification of the immune response in the neighboring LNs. If TRM existed in the tumor-draining LNs, TRM would be an ideal cell population to inhibit tumor lymphatic spread and metastases. Collectively, all of the above multifunctional TRM activities make them ideal effector T cells in antitumor immune responses.

TRM in tumor immunotherapy

There is evidence that TRM are involved in tumor immunotherapy. Checkpoint therapy boosts TRM formation in melanoma-bearing mice (Enamorado et al., 2017). PD-1 blockade in combination with TCM transfer results in 10-fold increase in TRM and inhibits B16 and MC38 tumor growth (Enamorado et al., 2017). Consistent with mouse studies, PD-1 blockade of TRM from human non–small cell lung cancer promotes ex vivo cytolysis of TRM to autologous tumors (Djenidi et al., 2015). Anti–PD-1 therapy leads to potent proliferation of CD8+CD103+ TRM in patients with melanoma, and the levels of CD8+CD103+ TRM are associated with improved patient survival (Edwards et al., 2018). Therefore, checkpoint blockade supports the notion that targeting TRM may be therapeutically meaningful. Multiple strategies have been suggested to modulate TRM to enhance cancer therapy efficacy (Fig. 2 and Table 2).

Figure 2.

TSeveral strategies have been proposed to enhance TRM activity. Checkpoint blockade may enhance intratumoral proliferation of TRM. DCs may be used as a vaccine to induce functional TRM. Targeting specific transcription factors and cytokines may drive TRM formation and may boost induction of active TRM. Targeting particular metabolites (e.g., FFA and FABP) may promote TRM pool and antitumor activity. Adoptive transfer of preprogramed TRM or TSCM may improve TRM seeding or differentiation, thereby enhancing antitumor immunity. PPAR, peroxisome proliferator–activated receptor.

Table 2.

Role of TRM in cancer immunotherapy

Tumor types	Treatment strategy	Effects	References
Cervical cancer	Vaccination + radiotherapy	HPV E6/E7-targeted therapeutic vaccination in combination with radiotherapy results in increased intratumoral number of CD8+CD103+ cells.	Komdeur et al., 2017
Colorectal cancer	Anti–TGF-β mAb + radiotherapy	TGF-β contributes to TRM radioresistance.	Arina et al., 2019
Esophageal cancer	Anti–PD-L1 mAb	PD-L1 blockade increases the number of CD8+CD103+ cells in the tumor.	Han et al., 2020
Gastric cancer	Anti–PD-L1 mAb	PD-L1 blockade increases FABP4 and 5 expression in TRM, favoring lipid uptake by TRM and resulting in improved cell survival.	Lin et al., 2020
		PD-L1 blockade unleashes TRM in the PDX mice.
		Non-responder PDX mice to PD-L1 blockade have less TRM than responders.
Head and neck cancer	Vaccination, anti–TGF-β mAb	STxB-E7 vaccination induces TRM and inhibits tumor growth.	Nizard et al., 2017
		TFG-β blockade inhibits TRM formation after vaccine immunization, resulting in lower vaccine efficacy.
Melanoma	“Prime-boost” immunization (CpG ODN 1826, OVA)	Subcutaneous antigen injection and epicutaneous CpG ODN adjuvant administration correlate with enhanced numbers of CD103+CD8+ cells in the skin, enhance TCIRC in the blood, and prevent tumor development.	Lai et al., 2019
	Vaccination (pVAX-OVA/DNA-OVA and pcDNA-GP100/DNA-GP100)	Vaccination-induced TRM strongly suppress the growth of melanoma cells independently of TCIRC.	Gálvez-Cancino et al., 2018
	Anti–PD-1 mAb (nivolumab, pembrolizumab)	CD103+ cells significantly expand early during treatment.	Edwards et al., 2018
	Adoptive T cell transfer	Runx3-deficient CD8+ cells fail to infiltrate the tumor, resulting in higher tumor growth and mortality.	Milner et al., 2017
		Runx3 overexpression enhances CD8+ cell tumor infiltration, inhibits tumor growth, and prolongs OS.
	T reg depletion and tumor removal	Skin-resident TRM are necessary for rejection of tumor rechallenge and long-lived melanoma immune protection.	Malik et al., 2017
	Adoptive T cell transfer, anti–PD-1 mAb	Anti–PD-1 boosts TRM tumor infiltration and improves antitumor immunity after TCM transfer.	Enamorado et al., 2017
	Anti-CD103, anti–VLA-1 mAb	Blockade of CD103 or VLA-1 on TRM impairs tumor control.	Murray et al., 2016
	Adoptive T cell transfer, anti-CD103 mAb	Transfer of CD8+CD103+ cells enhances tumor growth, whereas CD103 blockade inhibits tumorigenesis.	Gabriely et al., 2017

HPV, human papillomavirus; ODN, oligodeoxynucleotide; OS, overall survival; PDX, patient-derived xenograft; STxB, B subunit of Shiga toxin; TCIRC, circulating memory T cells; VLA-1, very late antigen 1.

Targeting TRM priming, differentiation, and survival

DC priming is essential for TRM formation in different models, including tumors (Yu et al., 2013; Wu et al., 2014; Iborra et al., 2016; Shin et al., 2016; Enamorado et al., 2017). CD103+ DCs are required for optimal generation of TRM (Iborra et al., 2016). Both CD1c+ DCs (Yu et al., 2013) and CD301b+ DCs (Shin et al., 2016) promote TRM generation from effector CD8+ T cells. Given the importance of DCs in TRM formation, DCs may be used as a vaccine to induce TRM. Furthermore, IL-15 promotes CD8+ TRM in situ (Sowell et al., 2017). Cytokine nanogel allows transference of high doses of IL-15 to the tumor microenvironment (Tang et al., 2018; Xie et al., 2019) and enhancement of the TRM pool in the tumor. Interestingly, Bhlhe40 (a transcription factor) orchestrates TRM survival and functionality and is critical for immunotherapy efficacy (Li et al., 2019). Thus, targeting Bhlhe40 may be an option for cancer immunotherapy. In addition, TGF-β supports TRM formation (Zhang and Bevan, 2013) and promotes radiation resistance of TRM (Arina et al., 2019). Blockade of TGF-β results in reduced numbers of TRM after vaccination (Nizard et al., 2017). Given the general immune-suppressive role of TGF-β, it is challenging to specifically target TGF-β-signaling in TRM to improve cancer therapy. Future studies will determine whether critical and specific potential TGF-β downstream gene(s) for TRM formation can be identified.

Adoptive TRM transfusion

Adoptive transfusion of preprogrammed TRM may be a cancer immunotherapy strategy. Adoptive transfer of tumor-infiltrating T cells with overexpression of Runx3 promotes TRM development, inhibits tumor growth, and improves mouse survival in a melanoma murine model (Milner et al., 2017). T memory stem cells (TSCM) differentiate into TRM (Kondo et al., 2017). Adoptive transfusion of TSCM and chimeric antigen receptor TSCM (Kondo et al., 2020) may lead to increased TRM in cancers. Furthermore, in murine models of melanoma, the presence of both TRM and circulating T cells offers improved protection against tumor challenge compared with only one alone (Enamorado et al., 2017). Thus, adoptive TRM transfusion may be a reasonable approach in combination with other cancer therapies.

Targeting metabolites

T cells compete with tumor cells for glucose, amino acids, and free fatty acids (FFAs; Zhao et al., 2016; Molodtsov and Turk, 2018; Chen and Huang, 2019; Bian et al., 2020, Wang and Zou, 2020). TRM express high levels of FFA-binding proteins FABP4 and FABP5 (Pan et al., 2017; Lin et al., 2020). Peroxisome proliferator–activated receptor agonists promote FFA catabolism, accelerate T cell–mediated antitumor immunity, and sensitize anti–PD-1 treatment (Zhang et al., 2017; Chowdhury et al., 2018). The antitumor effect may be partially attributed to TRM responses. Checkpoint blockade promotes FABP4 and FABP5 expression in TRM, resulting in higher lipid uptake by TRM and enhancing TRM survival (Lin et al., 2020). As such, targeting FFA and FABP can affect TRM and improve antitumor immunity. Future studies will generate insight into TRM metabolic features and how to target TRM metabolism for cancer therapy.

Conclusions

Current evidence shows that TRM can evoke potent antitumor immune responses. However, our understanding of their phenotype, differentiation, trafficking, tissue retention, and effector function remains in its infancy. This is particularly the case in patients with cancer. Substantial human studies rely on expression of CD49, CD69, and CD103 to define TRM. A number of mouse tumor-bearing models and human samples have revealed the presence of TRM phenotype cells within tumors. These TRM may arise de novo from the tissue or infiltrate as part of the antitumor immunosurveillance. Evidence thus far shows that TRM presence in a tumor or in response to immunotherapy can be a useful prognostic indicator for improved outcomes. Using high-throughput technologies, including single-cell sequencing, microscopic tissue spatial analysis, and multi-omics studies, we may be able to fruitfully study limited clinical materials to gain critical and novel information on TRM, leading to developing new cancer treatments via targeting TRM.