Advances in the understanding and therapeutic manipulation of cancer immune responsiveness: a Society for Immunotherapy of Cancer (SITC) review.

Introduction

Immunotherapy for cancer scored two “home runs” through the development of immune checkpoint inhibition (ICI) and adoptive cell therapy (ACT). These two modalities can offer long-term remission as well as a potential “cure” for cancers that are refractory to standard or alternate experimental therapies. Yet, most patients with cancer do not receive benefits from immunotherapy. While mechanisms determining cancer immune responsiveness (CIR) are gradually becoming more understood, much remains to be learned. Here, experts from the Society for Immunotherapy of Cancer’s (SITC) Cancer Immune Responsiveness Committee summarizes salient advancements in emerging areas spanning a broad range of factors related to inherited or acquired patient characteristics and intrinsic cancer biology ( figure 1 ). We highlight advances in overcoming unmodifiable factors such as germline and somatic determinants of CIR as well as correcting modifiable factors related to the behavioral, environmental, and anamnestic environment of individual patients. The current status of clinical progress is presented with particular attention to the two prominent modalities, ICI and ACT, with the belief that much can be learned by studying large cohorts of patients undergoing clinical trials, or real-world data sets, given these therapies are now approved as standard of care.

Unmodifiable host factors: germline genetics

Genome-wide germline studies demonstrated the impact of rare and common genetic variants on immunologic disorders, 1 immune homeostasis, 2 and infectious diseases including COVID-19, 3 and hepatitis C virus infection. 4 Thus, it is plausible that germline variants might influence antitumor immune responses, although their relative weight is difficult to estimate due to the plurality of other contributing factors. Large sample sizes are usually required to identify the modest effects of a single genetic variant. For instance, it is estimated that achieving sufficient statistical power to identify low-penetrance genetic influences on ICI responsiveness requires a sample of >5,000 subjects, thus necessitating large collaborative efforts between different institutions and the integration of diverse competences by Genome-wide association studies (GWAS). 5

Germline genetic variants and response to immunotherapy

Cancer immunologists historically focused on analyses of tumor cells and their interaction with the immune system. Data regarding the contribution of germline genetics are scarce for several reasons. Before the broad adoption of ICI, ICI-treated patient samples were accessible mostly through enrollment in early-phase clinical trials that are generally monocentric, enroll a limited number of patients, and are not sufficiently powered for germline analyses. Moreover, integrated expertize in cancer immunology, medical oncology, bioinformatics, and genomics is needed to address the relevance of individual genetic background in CIR. Finally, the potential for re-identification of patients restricted access to germline data. Therefore, germline genetic studies have been predominantly focused on targeted genes to minimize the multiple testing burden, and to avoid genetic fingerprinting.

Studies in patients receiving immunotherapy reported associations between treatment responsiveness and candidate immune-related genes, such as IRF5, 6 CCR5, 7 8 CTLA-4, 9 Fc-gamma receptor, 10 and interleukin (IL)-2 or IL-21 11 in the context of CTLA-4 or PD-1 blockade. It remains to be seen if these candidate-gene studies replicate in larger, hypothesis-free GWAS settings. HLA heterozygosity, affecting the ability to present a broader spectrum of neoantigens, was found to be associated with better outcomes in patients receiving ICI, 12 although these results were not confirmed in a large meta-analysis. 13 The association of a specific HLA allele (HLA-A*03) with ICI response to different checkpoint inhibitors 14 could also not be replicated in an independent study focusing on patients treated with the anti-PD-1 antibody pembrolizumab. 13 Recently, another study reported the association of natural killer (NK)-cell educating KIR/HLA combinations with survival in anti-PD-L1 treated patients with lung cancer. 15

As ICI is now standardly used in clinical practice across multiple tumor types, “real world data” might represent a great opportunity to explore the germline role, although clinical outcomes are difficult to capture. Nevertheless, large sample sizes can mitigate the problem by increasing the signal-to-noise ratio. While sharing individual-level germline genetic data for large-scale analyses might be challenging because of ethical restrictions, pooled analyses can be performed by sharing summary statistics unrestricted by confidentiality issues. The Cancer Immunology Germline Genomics Initiative (CIGGI) is currently being formed to conduct large-scale mega-analyses and meta-analyses to identify new and validate known variants co-determining outcome and safety in patients with cancer treated with immunotherapies. Members of CIGGI commit to open data sharing and contributing to the analyses to create a hub for the generation and exchange of novel hypotheses, followed by a functional investigation of potentially actionable variants (“variant-to-function”).

Recently, a multi-institutional immuno-oncology melanoma germline genetic consortium (IO-GEM) was founded by pooling resources from about 45 centers across USA, Europe, and Australia. The primary analyses of IO-GEM focus on discovering germline variations modulating efficacy (response/survival) and toxicity of ICI across treatment regimens. Initial findings suggest the presence of several variants previously linked to autoimmunity are significantly associated with ICI toxicity. The critical advantage of disease-focused efforts is the deeper sophistication in collection and analysis of clinical information allowing for multiparameter analysis of additional putative biomarkers besides germline data. Moving forward, it will be critical to increase awareness about the importance of germline genetic data to incentivize patients to contribute by consenting to data sharing in public repositories.

Germline genetic variants and functional orientation of the tumor microenvironment

A recent pan-cancer analysis of the entire TCGA solid tumor cohort (>9,500 subjects with either array genotyping or whole-exome sequencing data), supported by the SITC, explored the impact of germline genetics on tumor microenvironment (TME) phenotypes. 16

Sayaman et al performed GWAS, heritability analysis (for common variants), and pathway burden analysis (for rare pathogenic cancer variants) using 139 immune traits compiled by the TCGA pan-immune working group 17 18 to address whether genotypic differences can explain microenvironmental variations. One-fourth of the traits showed significant heritability. The abundance or gene-expression estimates of CD8 T cells, Th-1 cells, and the activation of interferon (IFN) signaling, which has been previously associated with favorable prognosis and responsiveness to immunotherapy, were among the traits displaying the highest heritability (15–20%). 16

In addition to associations driven by known expression quantitative trait loci (eQTL) of HLA and IL-17RA genes, 19 the GWAS analysis identified 21 loci significantly associated with 17 immune traits modulating the immune microenvironment. 16 Colocalization analysis using orthogonal data sets, including TCGA and the Genotype-Tissue Expression Data set, identified variants associated with different abundance of T cells with genes involved in immune functions or oncogenic processes. Additionally, rare variant analysis identified associations between WNT-β catenin pathway mutations, BRCA1, and telomere stabilizing genes with differential intratumoral immune response. The study also demonstrated that the impact of germline mutations in mismatch-repair genes on leukocyte infiltration relied on the development of a microsatellite instable/hypermutated phenotype at the somatic level. This implies that the germline deficiency of mismatch-repair genes functions alone (ie, in the absence of microsatellite instability) and may not be a reliable indicator of ICI responsiveness.

Remarkably, top loci identified in the GWAS analysis using intertumoral IFN signatures as phenotypes included genes critical for the IFN signaling, such as IFN induced with helicase C domain 1 (IFIH1) and TMEM173 (STING). Those IFIH1 and TMEM173 variants overlap with the ones identified in the context of auto-immune diseases (IFIH1 variants), 20 or response to vaccine for infectious diseases (TMEM173 variants), 21 corroborating the existence of a common pathway between different forms of immune-mediated tissue rejection. Furthermore, another recent study on TCGA and immune-checkpoint inhibitor data sets identified 1,084 genetic variants (eQTLs) affecting the tumor immune microenvironment (TIME) and showed that polygenic risk scores based on these eQTLs can be used to stratify cancer risk, survival, and response to immune checkpoint blockad. 22 Furthermore, by analyzing a large cohort of breast tumors (primary, metastases and in situ), Houlahan et al demonstrated that a patient’s germline variations in an oncogene, such as HER2, together with their human leukocyte antigens (Ags), influence which tumors are likely to persist and which are more prone to undergo T-cell mediated elimination. 23 These recent data have added to the growing body of evidence supporting the important role of the host’s genetic background in shaping CIR.

Genetics of immune-related adverse events

ICI toxicities are the result of immune activation against host tissues and are termed immune-related adverse events (irAEs). 24 In the registrational randomized trials of these agents, Grade 3 or 4 irAEs occur in~20% of anti-CTLA-4 treated patients, 25 ~12% with anti-PD-1, and~50% with CTLA-4/PD-1 combinations. 26 irAEs can involve any organ system and include dermatitis, colitis, hypothyroidism, hypophysitis, type I diabetes, 27 arthritis, hepatitis, pneumonitis, neurological toxicities, and myocarditis, among others. 24 A recent GWAS study identified three loci including one in the IL-7 gene associated with both improved survival and susceptibility to irAEs (as toxicity and response are often coupled clinically). 28 In some cases, irAEs may unmask pre-existing autoimmune susceptibilities, for example, many patients who develop treatment-related hypothyroidism carried thyroid antibodies prior to the initiation of therapy. 29

GWAS identified numerous variants associated with autoimmune disorders in the general population. Polygenic Risk Scores (PRS) integrate the risk associated with individual common variants with low-to-moderate effects in the general population to identify high-risk individuals. Indeed, PRS for autoimmune hypothyroidism in the general population predicted the incidence of hypothyroidism among patients receiving ICI. 30 31 PRS for type 1 diabetes is a strong predictor of disease, mostly driven by HLA associations and preliminary studies in patients receiving ICI, which suggests that the same HLA associations are also predictive of therapy-induced type 1 diabetes. 32 Dermatological irAEs are associated with PRS for psoriasis. 33 To date, no large studies are available on genetic predictors of gastrointestinal complications. Moreover, high PRS for autoimmune disorders predicts benefit from immunotherapy in some 31 33 but not all 30 studies.

Chimeric antigen receptor (CAR)-T is another form of immunotherapy currently used in clinical practice for the treatment of B-cell malignancies in which the contribution of the host’s genetic background on toxicity warrants investigations. For instance, CAR-T cell-induced cytokine release syndrome (CRS) has analogies with hemophagocytic lymphohistiocytosis (HLH), a rare hyperinflammatory condition. Preliminary data suggests an association between rare variants in genes predisposing to HLH and severe CRS following anti-CD19 CAR-T cell treatment. 34 Box 1 summarizes take-home messages, challenges, and unanswered questions regarding germline genetic contributions to CIR.

Take home messages and challenges for germline genetic contributions to cancer immune responsiveness (CIR)

Unanswered questions for germline genetic contributions to CIR

Can PRS be clinically validated to predict response to ICI or irAEs to be used together with other biomarkers (ie, tumor mutational burden, gene signatures) to identify patients most suitable to immunotherapeutic approaches?

Do germline genetic associations differ across different immunotherapeutic approaches and cancer types?

Can rare genetic variants explain (at least in part) extreme phenotypes such as rapid progressors or long-lasting complete responders to ICI?

Unmodifiable host factors: somatic alterations and the tumor microenvironment

Somatic alterations of cancer cells are the major, though not the only, orchestrators of the microenvironment in which neoplasia strives 35 36 and mold the multicellular network of distinct cancer immunophenotypes with prognostic, predictive and therapeutic significance. 37 38 Thus a new area of investigation is emerging defined as synthetic lethality addressing how targeting of intrinsic cancer pathways can result not only in cytotoxic effects but at the same time broaden the mechanism of action of therapeutics but increasingly engaging endogenous anticancer mechanisms. 39 40

Nevertheless, CIR is influenced by a plethora of additional determinants that mold TME immunobiology, including its vasculature, the composition and phenotypes of tumor, stromal, and immune cells and additional metabolic modifiers. Investigation of the TME has revealed pivotal interactions among cell types regulating the CIR. 41 42 Transcriptional and proteomic profiling from clinical samples generated signatures predictive of response, which may support clinical decision-making, although the real-world application of such signatures is complicated by challenges associated with sample acquisition, assay validation, execution, and cost. 43 44 Single-cell RNA-seq (scRNA-seq) has been quite useful in informing the distribution of gene expression in each cell subpopulation, but it fails to preserve the spatial information. 45 Spatial transcriptomics (ST) resolves the distribution of transcripts and proteins within different immune phenotypes and, in some cases, preserved cell type assignment. 46 Table 1 summarizes the main advantages and disadvantages of commonly used transcriptional profiling platforms.

The biology determining Immunophenotypes and their prognostic, predictive and therapeutic significance

Transcriptional profiling of a tumor’s immunological state and subsequent response to immunotherapy was originally employed more than 20 years ago when IL-2 responsive melanomas were characterized by an immune-active TME. 47 48 From that work and other seminal studies emerged the concept of the immunological constant of rejection (ICR), 48 49 a molecular signature that describes a state of heightened immunological T helper 1/cytotoxic activation associated with better prognosis in primary tumors 3536 50 , 52 and enhanced responsiveness to immunological interventions. 35 51 53 54 However, by concurrently assessing the T helper 1/cytotoxic immune response within the TME (using the ICR or a similar marker such as the tumor-inflammation signature/tumor inflammation signature (TIS) described by Cristescu et al in 2018), along with the level of tumor mutational burden (TMB), it is possible to more accurately categorize patients based on their potential responsiveness to ICI. 15 55 Recently, an emerging area of interest is immunogenic cell death, which has been tightly associated with an immune-active TME and may drive some of the transcriptional patterns associated with ICI response. 37 56

Subsequent investigations using bulk or scRNA-seq dissected the composition and functional activity of immune and stromal infiltrates in different types of tumors, and their impact on regulating the extracellular matrix composition and tumor metabolic activity. 4357 , 59 Assessing the prognostic value of immune cell subsets in the TME also led to the publication of a variety of predictive and prognostic immune signatures, mostly overlapping with each other in terms of gene content. 60 , 65

In several studies, scRNA-seq was used to identify factors associated with tumor vulnerability to immunotherapy and adaptations of the immune contexture under immunotherapeutic pressure. 41 66 67 ScRNA-seq was also employed to unravel the transcriptional programs associated with CAR-T cell behavior after infusion. 68

Spatial analyses of the TME 69 explored mechanistic questions. Nirmal et al integrated high-plex imaging, three-dimensional high-resolution microscopy, and spatially resolved microregion transcriptomics to study immunoediting in primary melanoma and found that hallmarks of immunosuppression are already detectable near premalignant cells. 70 Hwang et al , described a high-resolution molecular landscape of spatial communities that compose pancreatic ductal adenocarcinoma, observing expression programs across malignant cells and fibroblasts associated with poor prognosis. 71 Larroquette et al observed a correlation between CD163+cell infiltration and survival in patients with non-small cell lung cancer (NSCLC) undergoing ICI. 72 ST technologies have also been used to assess the distribution of immunomodulatory molecules targeted by immunotherapies across a variety of cancer types. 73 , 79

Salient questions, however, remain to understand the determinants of distinct TME phenotypes including immune-infiltrated, immune-excluded, and immune-desert tumors 80 and the role that metabolic alteration of the TME such as hypoxia may play. 81

The TIS was originally developed to predict patient response to single-agent pembrolizumab 82 and has been explored as a predictor of response to pembrolizumab in a variety of tumors. 55 83 84 The TIS shares many genes in common with the ICR, and thus is highly correlated with it and other T-cell based, cytotoxic, or IFN-γ-based signatures (r>0.9) 35 37 55 . Thus, any of these can be used as a proxy to measure the immune activation status of tumors, which in turn correlates with the likelihood of therapeutic response. Most recently, the TIS signature was validated retrospectively in seven indications. 85 In the setting of neoadjuvant immunotherapy for stage III melanoma, Blank et al developed a similar signature for IFN-γ signaling that enriches for event-free survival after neoadjuvant ipilimumab plus pembrolizumab. 86 A similar immune profile of pretreatment B-cell lymphoma samples demonstrated that transcriptional activations patterns similar to the ICR and the TIS are predictive of response to anti-CD19 CAR T-cell therapy. 87

While the association between these signatures and clinical response to immunotherapy is established by independent multiple studies and meta-analyses, 88 they are probably insufficient to identify responsive patients with accuracy (ie, their rate of false negative is considered to high) and therefore unlikely to be implemented in clinical practice as stand-alone biomarkers. Combinatorial signatures that measure orthogonal biology, including both immune-stimulatory and immune-suppressive mechanisms, are now being explored. 38

Another important biomarker of response is the TMB. Evidence from trials on patients with multiple cancer types treated with pembrolizumab indicates that the combined assessment of TMB and TIS, which poorly correlate with each other, might result in a better identification of immune-responsive patients. 55 83 84 However, these studies do not include a control arm (ie, chemotherapy), preventing the assessment of their true predictive versus prognostic value. This aspect was addressed by the PD-1 Response Prediction DREAM Challenge 89 that enabled the assessment of predictive models by using data from two randomized controlled clinical trials (RCTs) of ICIs in first-line metastatic NSCLC, both having a chemotherapy control arm. A blind analysis was performed using algorithms submitted by the participants. The combination of ICR and TMB/proliferation resulted in the top performer in terms of overall survival prediction to ICI, corroborating the importance to assess these parameters simultaneously. 90

In summary, while the phenomenology determining cancer immune responsiveness is increasingly being understood, future challenges remain about how to take advantage of the insights gained to develop better therapeutics. 40

Surrogate markers for transcriptional patterns: artificial intelligence in histopathology

Measuring the abundance of RNA transcripts is the gold standard to assess transcriptional patterns in cancer tissue. However, cost, expertize, and resources primarily limit the broad research use of these methods. Thus, surrogate markers to assess transcriptional patterns have been sought. One surrogate process being explored includes the prediction of transcriptional patterns from routine H&E histopathology slides using artificial intelligence (AI). In routine H&E slides, tumor cell morphology reflects the underlying transcriptomic processes to some degree. Hence, by analyzing the morphology with AI methods, predictions about transcriptional patterns can be made. 91 Since 2020, several studies demonstrated that it is, in principle, possible to infer transcriptional patterns from H&E images alone using AI. 92 93 In a study by Schmauch et al , 94 the resulting spatial predictions were validated using immunohistochemistry of selected markers. Although the respective AI systems were only trained on tissue samples with bulk transcriptomic markers, they were able to yield spatially resolved predictions within a resolution of 0.5 mm. 95 Box 2 summarizes take-home messages and unanswered questions regarding TME contributions to CIR.

Take home messages for tumor microenvironment (TME) contributions to cancer immune responsiveness (CIR)

Unanswered questions for TME contributions to CIR

Can mechanisms of distant cancer immune phenotypes be characterized by transcriptional biomarkers?

Can transcriptional biomarkers predict patient response to second-line immunotherapy after failure of anti-PD-1 monotherapy and/or guide combination therapy?

Can transcriptional biomarkers predict patient response to immunotherapies beyond checkpoint inhibitors, such as cell therapies, bispecific antibodies, or cytokine-based therapies?

Can transcriptional biomarkers be developed to discriminate between multiple immunotherapeutic options?

Can transcriptional signatures be developed to predict immunotherapy toxicities?

Can transcriptional profiling of blood provide information about the immune response within the TME, including when multiple metastases are present?

Can cost and complexity of spatial methods be reduced to support routine use of technologies within clinical laboratories?

Modifiable host factors

The role of modifiable host factors on systemic immunity has been relatively well studied, particularly in the setting of viral diseases and response to vaccines. 96 , 98 “Lifestyle factors” also have an established role in cancer risk but are only beginning to be explored in the context of cancer immunotherapy, spurred by the well-established role of the gut microbiome as both an integrator of host factors and a contributor to immunological responses.

Microbiome

In 2013–2015, several reports highlighted the role played by the gut microbiome in immunotherapy. 99 , 102 These seminal studies performed in mice showed a causal association between specific commensal taxa and response to ICI. Several years later, observational cohort studies in patients treated with anti-PD-1 demonstrated that in humans, specific gut microbiome taxa were also associated with response. 103 , 106 A shortcoming of these studies has been the heterogeneity of reported bacterial response-associated candidates. 107 108 In part, discrepancies may have been caused by inconsistencies in defining clinical outcomes, differences in sequencing methodologies and processing methods, and geographical variability of microbiome-environment interactions.

A unique quality of the gut microbiome compared with other biomarkers is the potential to intervene through fecal microbiome transplant (FMT) or other methods, making it a potential therapeutic target. Building on observational cohorts, several studies were conducted in multiple malignancies combining immunotherapy with microbiome intervention through FMT and probiotics. 109 , 112 These early studies demonstrated mixed results with respect to the change in the microbiome and response-associated taxa but corroborated safety and showed some encouraging clinical responses, even in previously PD-1-refractory patients.

Diet

The recognition of the microbiome role in immunotherapy response has galvanized investigations of modifiable determinants, particularly diet. Many microbes associated with response to immunotherapy have known roles in starch degradation, fiber fermentation, and in the diet-microbiome-immune axis. 113 Bacterial fiber fermentation results in the production of short-chain fatty acids (SCFAs), which are the main energy source for intestinal epithelial cells, helping to maintain the integrity of the gut mucosal barrier and reduce oxidative DNA damage. 114 In addition, SCFAs can modulate both mucosal and systemic immunity by promoting anti-inflammatory molecules production (IL-10, transforming growth factor (TGF)-β) and T-cell differentiation via histone deacetylase inhibition. 115

A study demonstrated that patients with melanoma habitually consuming sufficient dietary fiber had improved odds of response to immunotherapy. Additionally, every 5 g/day increase in dietary fiber intake translated into a 30% reduction in the risk of progression or death. 116 Parallel studies in mice further demonstrated that dietary fiber manipulation can modulate the microbiome and synergize with ICI to promote IFN-γ responsive T-cell activation and the STING-mediated IFN-I-NK cell-DC axis. 116 117 Interventional studies in humans are now being conducted in the setting of immunotherapy to prospectively assess the effects of dietary fiber on the gut microbiome.

Diets aimed at targeting host and cancer metabolism, particularly glucose homeostasis, have also been proposed as adjuncts to cancer therapies and recently, attention has turned to the impact of these diets on antitumor immunity. The antitumor effects of fasting and calorie restriction, which lower circulating insulin, IGF-1, and glucose, have been shown to be T-cell dependent in some models and result in increased cytotoxic T-cell infiltration. 118 More recently, preclinical studies have shown that calorie restriction can subvert IGF-1R-mediated immunotherapy resistance. 119 Other preclinical work suggests that the ketogenic diet can induce T cell-dependent tumor growth and enhance response to ICI in resistant models via the ketone body 3-hydoxybutyrate. 120 However, translation of these preclinical findings into “metabolic” therapies in humans will be challenging and further studies are necessary to elucidate their effects on immunometabolism and the microbiome. 121 A recent study demonstrated the safety and feasibility of a 5-day, cyclic, fasting-mimicking diet, which led to an improvement in intratumoral Th1/cytotoxic immune responses and a reduction of the immunosuppressive myeloid compartment in peripheral blood. 122

Obesity

Obesity is considered an established risk factor for 13 different malignancies and has also been associated with increased cancer mortality. 123 Interestingly, numerous investigators have now described an “obesity paradox” in immunotherapy wherein obesity, as measured by body mass index (BMI), is associated with improved outcomes in patients with multiple malignancies treated with ICI, though questions remain whether this is causal. 124 , 126 The obesity paradox is counterintuitive as obesity promotes a state of chronic inflammation that impairs adaptive immunity including NK cell activity, dendritic cell function, and T-cell activation. 127 However, preclinical data suggests that leptin, a key adipokine, increases T-cell expression of PD-1 and results in functional exhaustion, a state that renders immunocytes more vulnerable to ICI. Others, however, have not observed differences in immune populations and checkpoint expression in human pretreatment TME and have instead implicated the systemic state of overnutrition with a more quiescent tumor metabolic phenotype. 128 Obesity has also been associated with a slight increase in the risk of irAEs. 129 Ultimately, the biology underlying the obesity-immune interaction is complex, and additional characterization of the molecular and immunological correlates of obesity is needed, as are studies using measures of adiposity more accurate than BMI, such as cross-sectional imaging or serological markers of metabolic phenotype.

Exercise

Historically, many oncologists recommended “rest” and avoidance of exercise due to concerns about safety and tolerance in patients undergoing active treatment. Formal studies conducted in the last two decades in patients living with and beyond cancer now provide strong support for exercise-induced improvements in quality-of-life metrics, and exercise during treatment bears a low risk of adverse events. 130 131 To date, there is insufficient evidence that exercise can affect disease outcomes, as sufficiently powered prospective trials have not been conducted. 130 However, growing evidence suggests that exercise may impact the TME through a variety of mechanisms, including improving tumor perfusion by reducing hypoxia. 132 133 Vascular normalization may enhance immune effector cell infiltration into the tumor, as dysfunctional tumor vessels downregulate endothelial adhesion molecules critical for translocation from the circulation to the tumor. 134 Additionally, exercise has direct immunomodulatory properties. Preclinical models indicate that exercise increases NK cell infiltration into tumors, 135 induces macrophage differentiation to an M1, and increases their cytolytic activity phenotype. 136 Effects on adaptive immunity are less characterized. A recent preclinical study of voluntary wheel running in murine models of breast cancer and melanoma suggested that the combination of exercise and anti-PD-1 have differing effects on T-cell infiltration depending on tumor type. 137 In a preclinical pancreas cancer model, treadmill running slowed tumor growth by enhancing infiltration of IL-15Ra + CD8 T cells and decreasing immune-suppressive myeloid-derived suppressor cells. 137 Importantly, the T-cell stimulating effects of exercise were confirmed in clinical samples from patients with pancreatic ductal adenocarcinoma enrolled in an exercise “pre-habilitation” program prior to surgical resection.

In summary, research on potentially modifiable patient-centered factors has introduced a new dimension in immunotherapy research. Although several socioeconomic factors may impact immunotherapy outcomes, a focus on modifiable components offers the potential for interventions to improve outcomes. 138 , 141 Microbiome therapeutics are a promising space, but interventions upstream of the gut microbiome may also be possible. The human gut microbiome dwells in one of the largest immune surface areas in the body and is associated with a long list of host factors in patient without cancer populations including diet, obesity status, and physical activity. 142 This opens the door to test whether these factors separately or jointly can impact immunotherapy efficacy and be exploited therapeutically. Box 3 summarizes take-home messages and unanswered questions regarding modifiable host factor contributions to CIR.

Take home messages for modifiable host factor contribution to cancer immune responsiveness (CIR)

Unanswered questions for modifiable host factor contributions to CIR

How does the microbiome mediate the effects of diet, obesity, and exercise?

What are the relative contributions of host factors upon cancer outcomes, and are certain components more influential than others?

Do all types of exercise impact immunity? Is one modality more beneficial than another?

Does exercise influence the gut microbiome in the context of cancer therapy?

Therapeutic inventions

Immune checkpoint inhibitors: 143 New generation of ICI and stimulatory approaches based on the emerging scientific insights summarized above are now in various phases of clinical development and will likely play a pivotal role in combatting resistance mechanisms to current ICI ( table 2 ). 144 , 146

Primary versus secondary resistance

As the majority of tumors are resistant to ICI 147 there has been considerable effort focused on identifying mechanisms of ICI resistance and developing subsequent therapeutic solutions. In 2019, SITC developed definitions for both primary and secondary (or adaptive) resistance to PD-1 pathway blockade to promote semantic consistency in both clinical and research endeavors. 148 The taskforce defined primary resistance as disease progression after at least 6 weeks but no more than 6 months of anti-PD-(L)1 therapy. 148 Secondary resistance was defined as disease progression following an objective or prolonged (>6 months) response to PD-(L)1 blockade. 148 Primary resistance mechanisms include certain oncogene-driving mutations, both lack of quantity and/or quality of neoantigen presentation, and the metabolically hostile and toxic TME that impedes T-cell function. 149 150 Secondary resistance can be attributed to the upregulation of coinhibitory signaling, selective loss of antigenic tumor populations via immunoediting, and T-cell exhaustion. 149 151 However, given the large degree of overlap between primary and secondary resistance mechanisms, further description could be more clearly grouped into tumor intrinsic versus extrinsic factors.

Review of key mechanisms of resistance to checkpoint blockade

Tumor immunogenicity plays a key role in the response to ICI as it serves as the source of pre-existing adaptive immune response. Several biomarkers have been developed to quantify the neoantigen load of each tumor. TMB is a widely used surrogate for tumor neoantigenicity, though not all cancers exhibit a clear correlation between TMB and ICI efficacy. 152 153 MMR-deficient and MSI-high tumors exhibit increased insertion-deletion (indel) mutations that more strongly correlate with ICI benefit, prompting a tumor agnostic Food and Drug Administration approval of pembrolizumab for this indication. 154 However, these biomarkers only scrape the surface in characterizing an individual tumor’s neoantigen load and immunogenic potential, thus highlighting the chasm in our understanding of ICI resistance. 155 Fortunately, advances in predictive modeling may provide more accurate biomarkers of “true neoantigen load” by using novel major histocompatibility complex (MHC)-binding site mutations, TCR recognition features, and intratumoral heterogeneity. 156 , 158 Regardless, selective pressure will inherently lead to an immunoediting process by which the less immunogenic subpopulations will outcompete and lead to cancer progression. 159 Estimation of the immune-editing combining whole-exome data (ie, lower number of observed vs neoantigens), gene-expression data, or IHC assessment, is an emerging field of research. 160 , 162

Specific oncogene signaling and other mutations are also intrinsic elements of tumor immune escape. For example, mutations in KRAS, EGFR, and MAPK not only promote unchecked cellular growth, but can also lead to poor anti-PD(L)1 response due to repressed IRF2 expression, myeloid-derived suppressor cell (MDSC) recruitment, decreased PD-L1 expression, and decreased T-cell infiltration, respectively. 163 , 165 Additionally, loss of PTEN signaling recruits immunosuppressive cells to the TME while dysregulation of the Wnt/β-catenin cascade has been shown to impair naïve T-cell priming in melanoma. 166 167 Finally, mutations in the JAK1/2 pathway can disrupt the antitumor effects of IFN-γ signaling post-MHC-TCR interaction while mutations in the β2 microglobulin component of MHC-I molecules can prevent neoantigen loading and presentation. 168 169 These data again highlight that the quality of TMB may be more significant than pure quantity as measured by current TMB assays.

Based on these genomic and transcriptomic alterations, several novel agents have now entered clinical investigation. For example, numerous Wnt/β-catenin pathway inhibitors have shown efficacy in preclinical models, one of which (CGX1321) has been combined with pembrolizumab or targeted therapy in a trial of patients with pretreated advanced gastrointestinal tumors. Sotorasib, a KRAS G12C inhibitor, has recently shown encouraging results in the clinic: CodeBreak 100/101 showed durable efficacy and an acceptable safety profile of lead-in sotorasib followed by anti-PD(L)1 therapy in patients with pretreated NSCLC, and other cohorts are under investigation ( NCT04185883 ). 170 Further, the ubiquitous MAPK oncogenic pathway has now been targeted with a p38-specific inhibitor, ARRY614. A phase Ib trial is now assessing ARRY614 in combination with ICI in treatment refractory advanced solid tumors ( NCT04074967 ).

In recent years, the TME has been shown to harbor several immunosuppressive features tied to ICI resistance, including the upregulation of novel checkpoint receptors, recruitment of regulatory and suppressive cell populations, concentration of toxic metabolites, and dysfunction and terminal exhaustion of effector T cells. 149 171 As noted, a cascade of clinical trials is underway to assess the efficacy of novel co-inhibitory receptor antagonists alone or alongside anti-PD-1 therapy—including LAG-3, which was recently approved in melanoma in combination with nivolumab, TIM-3, TIGIT, VISTA, BTLA, and ICOS inhibitors, among others ( figure 2a ). 172 Given preclinical data showing upregulation of both TIM-3 and LAG-3 following anti-PD(L)1 therapy, with dual blockade decreasing tumor-infiltrating lymphocytes exhaustion and tumor progression, combinatorial checkpoint inhibition may be a key route toward improving immune responsiveness. 173 174 Recently, the addition of tiragolumab (anti-TIGIT) to atezolizumab in patients with PD-L1 + metastatic NSCLC did not show significantly improved PFS versus anti-PD(L)1 alone in the front-line setting phase III SKYSCRAPER-01 trial, indicating the need for more nuanced predictive biomarkers for combinatorial checkpoint therapy ( NCT04294810 ). Finally, in addition to co-inhibitory agents, numerous co-stimulatory receptor agonists have also entered clinical trials to overcome resistance mechanisms in the TME. These targets include 4-1BB, OX40, ICOS, GITR, CD40(L), and CD27, many in combination with PD-1 blockade ( figure 2a ). 172 The recently reported first-line phase II PRINCE trial combining sotigalimab (CD40 agonist) and/or nivolumab with chemotherapy in patients with advanced pancreatic adenocarcinoma identified blood and tumor-based biomarkers of survival after sotigalimab plus chemotherapy and nivolumab plus chemotherapy, whereas a patient subset benefitting from sotigalimab plus nivolumab plus chemotherapy was not identified. This study suggests that biomarker-based patient selection for combination treatments to enhance efficacy may be needed in future trials of chemoimmunotherapy combinations. 175

While CD8+T cells are often the focus of ICI response, multiple immunosuppressive cells have been shown to hamper antitumor immunity and promote ICI resistance, including regulatory T cell (Treg) cells, MDSCs, and tumor-associated macrophages (TAMs). 176 , 178 For example, selective targeting of MDSCs via cytokine signaling was shown to improve anti-PD-1 responsiveness in a preclinical CRC study, and inhibition of the TAM-dependent CSF1R protein was shown to improve ICI treatment in a pancreatic cancer model 179 180 ( figure 2b ). Antagonists of the CCR5-CCL5 axis, CSF1R, and CXCR2 have now entered clinical trials with or without checkpoint blockade in treatment refractory advanced solid tumors. 181 Furthermore, several recent preclinical and translational publications have demonstrated the interaction between TAMs, adaptive immunity, and how TAMs suppress adaptive immunity in depth with novel mechanisms. 182 , 185 A recent article demonstrated a spatiotemporal co-dependency between TAMs and CD8+exhausted T cells. 182 On the other hand, several publications demonstrate that distinct populations of TAMs have antitumor function. A subset of TAMs C1qa+macrophages have been shown to promote T effector cells by upregulating m6A methyltransferase Mettl14 and epigenetic modulation. 183 Mettl14-deficient C1q+TAMs show a decreased m6A abundance and a higher level of transcripts of Ebi3, a cytokine subunit that suppresses T-cell function. 183 In patients with breast cancer, a subset of tissue residential macrophage FOLR2+that exists in both healthy and tumor tissue correlates positively with CD8+T cells and antitumor immunity. 184 In contrast, TERM2+macrophages are predominantly in tumor but not healthy tissue. 184 185 In addition, targeting TREM2+macrophages with anti-TREM2 antibody sensitizes to PD-1 treatment. 185 Therefore, it is critical to investigate macrophage heterogeneity in different types of tumors and develop subset-targeted therapeutic strategies for macrophage-based cancer therapies. Strategies to target myeloid cells, including TAMs to enhance immunotherapy, are currently under active clinical investigation. Blockade of CD47-SIRPa axis to enhance phagocytosis of macrophages by antagonizing the “don’t eat me” signal in a tumor is widely studied as a novel means to promote antitumor immunity. 186 187 Rational combination of CD47-blockade with treatments that increase prophagocytic marker have yielded some promising results. For example, magrolimab plus rituximab has shown 50% ORR and 36% CR in relapsed/refractory lymphoma. 188 In addition, magrolimab combined with checkpoint therapy showed promise in advanced solid tumors. 189 cGAS-STING signaling in macrophages, responsible for sensing cytosolic dsDNA, plays a critical role in antitumor immunity by inducing the production of type I IFNs. 190 Therefore, STING agonists are actively being studied as a novel immunotherapeutic strategy, with multiple ongoing trials, including in combination with checkpoint blockade. 191 192 Further, toll-like receptors (TLRs) are key regulators of macrophage function and differentiation. TLR activation can drive macrophage towards a more “M1-like” pro-inflammatory phenotype. 193 In a recent phase II study, intratumoral injection of CMP-001—a virus-like particle containing a TLR9 agonist—in combination with PD-1 blockade, provided local and distant responses in patients with advanced anti-PD-1 resistant melanoma ( figure 2b ). 194 A phase II neoadjuvant trial of CMP-001 with ant-PD-1 in patients with melanoma is currently underway ( NCT04401995 ). Trials of TLR 7/8 receptors, which have the potential to activate a broader range of APCs versus targeting TLR9 within the TME, are also underway. 195 However, certain strategies targeting macrophages, such as pattern recognition receptors, have not demonstrated success in large clinical trials. Moving forward, it will be critical to identify the underlying mechanisms of resistance to agents targeting macrophage/myeloid cell biology using clinically derived samples, which will further inform novel combination strategies.

Reversing metabolic insufficiency in the TME to improve outcomes with ICI is also an area of great interest. Tumor-infiltrating T cells face increased acidity, hypoxia, oxidative stress, dysregulated amino acid and lipid metabolism, all of which inhibit antitumor immunity. 171 Early phase clinical trials have now targeted the lactate shuttle via MCT1 inhibition in advanced solid cancers, and metformin has been combined with PD-1 blockade to reverse hypoxia and T-cell dysfunction in the TME. 171 As dysregulated tumor cell metabolism is associated with hypoxia in the TME, 196 targeting tumorous vasculature to remodel the TME is another area of active investigation, including combinations of anti-PD-1 and anti-angiogenic therapy across multiple indications, 197 such as anti-PD-1 refractory melanoma. 198 199 Primarily through HIF-1-dependent processes, hypoxia also promotes adenosine-mediated T-cell dysfunction and dysregulated neovascularization. Thus, numerous clinical trials targeting the adenosine pathway (eg, CD39, CD73, A2AR) are now underway in combination with anti-PD-1 therapy. 200 201 As previously discussed, obesity and the microbiome have been linked to differential ICI response, prompting investigation into modulators of lipid metabolism and fecal microbiota transplant to augment checkpoint blockade. 124 202 While most resistance mechanisms ultimately foster T-cell exhaustion, the lack of anergy alone is insufficient for ICI response. Multiple studies have shown significantly increased levels of memory CD4 + T cells in anti-PD-1 responding patients, highlighting the role of T-cell differentiation in acquired resistance. 203 204 With advances in single-cell and TCR sequencing, autologous TIL therapy may be further refined for the expansion of potent, highly selective antitumor lymphocytes—a critical development as upcoming trials specifically target the ICI-resistant population. 205 206 Box 4 summarizes take home messages and unanswered questions regarding the impact of therapeutic interventions via checkpoint inhibitors to CIR.

Take home messages for therapeutic interventions—checkpoint inhibitors

Unanswered questions for therapeutic intervention contributions to cancer immune responsiveness

Can predictive biomarkers be characterized towards determining patients who are unlikely to benefit from immunotherapy, and for whom other therapeutic strategies be pursued up-front?

How can we best identify patients that may require dual checkpoint blockade versus anti-PD-(L)1 monotherapy?

Can the field identify predictive biomarkers towards characterizing patients that are likely to have significant toxicity so that treatment may be de-escalated and/or IO sparing strategies may be used?

Given the inherent issues of intra and intertumoral heterogeneity, how can we confidently determine which combination strategy to move forward with in each patient?

Several agents have low to minimal single-agent clinical efficacy but have shown clinically meaningful results in combination. How can the field confidently move towards development in combination of drugs with limited efficacy as monotherapy?

Given the failure of later phase trials following promising phase II results, how can we best determine the most rational combinations to proceed with in later phase trials?

Therapeutic interventions: CAR-T

ACT has become an effective therapeutic option for treating advanced blood cancers. In particular, CAR T-cell therapy has demonstrated efficacy in hematological malignancies leading to complete remission (CR) in 40–80% of patients with B-cell non-Hodgkin’s lymphoma refractory to standard of care, 207 , 210 60–90% in refractory acute lymphoblastic leukemia (ALL), 211 212 and 40–80% in heavily pretreated multiple myeloma. 213 , 218 However, the clinical success of CAR-T cells in hematologic malignancies has not yet translated to solid tumors.

Factors affecting CAR T-cell clinical activity

CAR T cell-mediated durable response is associated with pronounced CAR T-cell expansion, persistence, and preservation of naïve and memory T-cell phenotypes. 219 , 222 In contrast, high baseline tumor burden with proinflammatory signaling, potentially related to myeloid cell activation and a tumor hypoxic microenvironment, are correlated with reduced durable responses. 221 Patient-related factors such as patient age, disease state, and prior chemotherapeutic regimens can also affect CAR T-cell fitness and functionality. 223 Prior to infusion, ACT can present critical issues. Access to ACT may fail due to low blood lymphocyte count, causing failure and disease progression during manufacturing. 224 Furthermore, the cost of cell therapy is problematic in many healthcare systems. 225 226 To circumvent some of these challenges, allogeneic CAR T therapies, as well as rapidly manufactured CAR T cells, are in development to provide off-the-shelf treatment accessibility and are extensively reviewed elsewhere. 227 228

Determinants of CAR T-cell resistance

Despite successes in hematologic malignancies, a considerable proportion of these cancer patients still do not benefit from CAR T therapy. 229 Resistance mechanisms vary from lack of pre-infusion T-cell fitness to tumor characteristics, including an immune suppressive TME and impaired CAR T-cell function post-infusion ( table 3 ). Tumor intrinsic mechanisms of resistance can be divided into Ag-negative and Ag-positive relapse. The loss of target-Ag on tumor cells arises following the strong selective pressure imposed by CAR-T therapy. In patients treated with anti-CD19 CAR T therapy, 30–95% of B-ALL 211 230 and about one-third of NHL relapsed with the loss of CD19. 231

Several mechanisms are associated with Ag-negative relapses, including selection of pre-existing Ag-negative tumor subclones, 232 233 Ag mutations 234 235 or splicing-variation, epitope masking, 236 and lineage switch/transdifferentiating. 237 , 239 Several proposed causes of Ag-positive relapse have been observed, including increased expression of inhibitory ligands (eg. PD-L1 and PD-L2), loss of costimulatory ligands (ex. CD58), and impaired apoptosis machinery in tumor cells. 240 , 242 T-cell exhaustion due to chronic Ag exposure or tonic CAR signaling resulting in elevated expression of inhibitory ligands in T cells like PD-1, TIM-3, and LAG3, may also contribute to poor outcomes. 243 , 246 Moreover, an important variable is the limited trafficking of immune effector cells to the tumors particularly in the case of solid malignancies. 40

The TME plays an essential role in CAR resistance. TAMs, MDSCs, and Tregs can reduce the response to CAR when highly infiltrated into the tumor. 247 , 250 Additionally, they can inhibit the function and persistence of CAR T by producing inhibitory surface molecules and cytokines such as PD-L1, IL-10, and TGF-b. 251 Consistently, clinical response and overall survival for axicabtagene ciloleucel is associated with increased T-cell infiltration and density, whereas M2 polarization and myeloid activation were related to poor response. 252

CD19-directed CAR T cells have demonstrated robust responses across several cell-of-origin and molecular subtypes of lymphoma. In DLBCL, double-hit and triple hit lymphoma (DHL/THL) represent classifications of high-grade B-cell lymphoma harboring cMYC, BCL2, and/or BCL6 chromosomal rearrangements. 253 Gene expression signatures also classify DLBCL in two major biologically distinct subtypes: germinal center B cell (GCB) and activated B cell (ABC) lymphomas. 254 These higher-grade subtypes are more aggressive, bearing the worse prognosis following frontline treatment with chemotherapy. 255 , 258 Axicabtagene ciloleucel achieved a 90% objective response rate in DHL/THL in a small subset of patients coupled with a 64% CR rate at 12 months and improved responses in second line R/R LBCL in the higher risk ABC-like DLBCL (HR 0.18) relative to standard of care. 259 260 Tisagenlecleucel similarly achieved 81% among 16 patients with DHL/THL and a CR rate of 60% without differences in outcome between GCB/non-GCB and DH/TH DLBCL subtypes. 208 Furthermore, in follicular lymphoma, ALL, and multiple myeloma, comparable efficacy mediated by CAR T cells was observed within molecular subtypes. This penetrance of response across diverse genetic subtypes suggests that CAR T cells may frequently provide immune effector function where there is an otherwise immune quiescent TME. 261 262

Strategies to combat CAR T resistance

Improved understanding of therapeutic refractoriness and resistance to CAR T cells encouraged development of novel CAR T-cell designs. Allogeneic CAR T cells can be designed for off-the-shelf availability in aggressive disease and tailored for specific disease contexts, such as AML, where T-cell fitness or exhaustion is a concern among the presence of malignant blasts. 263 To overcome Ag loss and escape, CAR T cells can be engineered with multispecificity targeting. For instance, CAR-T cell expressing both CD19 and CD22 show encouraging results: 88% MRD negative complete remission in ALL 230264 , 266 and up to 64% CR in NHL. 230 267 268 Incorporation of ICI into a CAR T regimen, either in combination or through gene editing to combat exhaustion, is supported by the rationale that PD-1 and PD-L1 expression is detectable and upregulated in the TME following CAR T-cell infusion. 269 The ZUMA-6 study examined the efficacy of axicabtagene ciloleucel in combination with atezolizumab (PD-L1 inhibitor) in R/R LBCL and showed ORR and CR rates of 90% and 60%, respectively. 270 Relative to ZUMA-1, axicabtagene ciloleucel cell peak expansion following combination with either atezolizumab or pembrolizumab was twofold higher. 270 271 Similarly, a combination study evaluating pembrolizumab and tisagenlecleucel in 12 R/R DLBCL patients showed a 50% ORR and secondary expansion of CAR T cells after the first pembrolizumab infusion. 272 Similarly, improved T-cell functionality and preservation of memory or TH1 phenotypes with the introduction of BTKi inhibitors, such as ibrutinib, may afford improved responses in CLL and MCL. 273 , 276 To further overcome immunosuppression, armoring CAR T cells with proinflammatory cytokines such as IL-12, IL-15, IL-18, or IL-21 is an approach that promotes recruitment of M1 macrophages, prevents polarization towards Tregs, and in the case of IL-15, promotes a CAR T memory phenotype and stimulates endogenous NK cells. 277 To temper the immunosuppressive state of the TME, the TGF-β DNR is a truncated TGF-β receptor II developed to inhibit or redirect endogenous TGF signaling and prevent the influx of additional immunosuppressive mediators to the TME. However, a phase I clinical trial in advanced standard therapy refractory prostate cancer targeting PSMA experienced two fatalities associated with massive CAR T-cell proliferation and neurotoxicity ( NCT03089203 ). Targeting of myeloid cells, namely TAMs and MDSC within the TME, could represent another effective strategy to combat immunosuppression. NKG2D CAR-NK cells designed to selectively target MDSCs are in clinical development and have conferred the ability in mice to engage MDSCs in the TME, release cytokine, and promote tumor regression with prolonged survival compared with treatment with CAR T cells alone 278 279 ( NCT04623944 ). Another approach to reprogram an immunosuppressive TME to improve efficacy and reduce toxicity is to inhibit granulocyte macrophage-colony stimulating factor (GM-CSF), as clinical evidence indicates that high levels of GM-CSF are associated with severe neurotoxicity for axicabtagene ciloleucel 280 and enhanced secretion of inflammatory mediators like IL-6 and MCP1. 281 282

CAR T therapy for solid tumors

While CAR T cells have transformed the therapeutic landscape for refractory B-cell malignancies, CAR T cells engineered for solid tumors encounter far more challenges and sustained efficacy has yet to be reported. Key obstacles include: (1) impaired trafficking or access to the tumor site; (2) tumor heterogeneity; (3) T-cell exhaustion or impaired expansion due to an immunosuppressive TME; and, (4) scarcity of compelling targets. 283 There are nearly 200 clinical trials investigating CAR T cells that target a vast array of solid tumor-associated Ags. MSLN, GD2, HER2, GPC3, and EGFRv3 are among the most studied. 284 Recent appreciable successes were attained with locoregional delivery and repeated dosing to achieve tumor site accessibility and overcome T-cell exhaustion. Intrapleural administration of an MSLN CAR in combination with anti-PD-1 has shown 83% 1-year survival and two complete metabolic responses. 285 Most recently, a GD2-directed CAR demonstrated clinical and radiographic activity in three pediatric patients with H3K27M-mutated pediatric gliomas after repeated intracranial delivery. 286 A similar study of the GD2-CART01 in high-risk pediatric neuroblastoma demonstrated a profound 63% overall response (17 of 27 pts) and 60% overall survival rate after 3 years. A Claudin6 CAR T product is being evaluated in ovarian and testicular cancer in combination with a CLDN6 RNA vaccine that, with repeat dosing, induces CAR T expansion. This approach has shown a 43% ORR. 287 288 Box 5 summarizes take home messages and unanswered questions regarding the impact of therapeutic interventions via CAR-T to CIR.

Take home messages for therapeutic interventions: chimeric antigen receptor (CAR)-T

Unanswered questions for therapeutic interventions: CAR-T

Although CAR T-cell immunotherapy is one of the most impactful breakthroughs in medicine in recent years for hematological tumors, most patients still experience relapse following cellular therapy. How can this promising therapy be further improved?

Although the control of the peculiar toxicities of CAR T cells has improved with increasing knowledge of this therapy, there are still life-threatening adverse events. How can we decrease the toxicity linked to this therapy?

The excellent results obtained from CAR T therapy are balanced by high costs, potential life-threatening complications, and the necessity to have a highly specialized medical personnel and center for their administration. What are the predictive factors of response that allow us to identify which patients are the best candidates for this type of therapy?

Although promising progress has been made, CAR T cells used in solid tumors do not achieve the same as hematological tumors. How to improve the efficiency of CAR T in solid tumors?

Conclusion

What distinguishes immunotherapy from other anticancer modalities is the breath of its mechanisms of action. 40 Most modalities aim at killing cancer cells by chasing a single target. This limits their efficacy due to the rapidly evolving genetics of neoplasia that swiftly circumvent the original mechanism of action generating therapy-resistant cancer cell clones. Immunotherapy, when successful, goes beyond the effects on the primary target by inducing a switch from a chronic to an acute inflammatory process, initiating a chain reaction that amplifies the original mechanism of action through the engagement and activation of the multi-cellular network surrounding neoplastic cells in the TME. 48 This decreases the chance of developing treatment-resistance resulting in the durable response pattern peculiar to this modality.

This anticancer immune response is, consequently, a complex, multifactorial, dynamic phenomenon primarily determined by the unmodifiable interactions between germline and cancer genetics with the host’s response to them, the latter being affected by other circumstantial factors whose prevalence varies across patient populations. Following the learnings derived from the ICI and ACT experience, in this review, we discussed the interaction between the most salient of these factors. Future studies, however, should aim at better understanding the weight that each of them has on different patient populations, their prevalence across indications, and their susceptibility to immunological modulation. A better assessment of this balance across indications and patient populations will determine more effective patient-specific approaches and increase the chances of biologically meaningful durable clinical responses.

Acknowledgements

The authors thank past members of the SITC Cancer Immune Responsiveness efforts for their continued input and discussions after the publication of the group’s initial manuscript. The authors also would like to thank SITC staff for their logistical support, including Peter J Intile, PhD, and Flynn DeWalt. Lastly, the authors wish to thank SITC for supporting the committee and its efforts, including this manuscript.

Footnotes

Funding: The authors have not declared a specific grant for this research from any funding agency in the public, commercial or not-for-profit sectors.

Patient consent for publication: Not applicable.

Ethics approval: Not applicable.

Provenance and peer review: Commissioned; externally peer reviewed.

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