Insights and Strategies of Melanoma Immunotherapy: Predictive Biomarkers of Response and Resistance and Strategies to Improve Response Rates

Despite the recent successes and durable responses with immune checkpoint inhibitors (ICI), many cancer patients, including those with melanoma, do not derive long-term benefits from ICI therapies. The lack of predictive biomarkers to stratify patients to targeted treatments has been the driver of primary treatment failure and represents an unmet medical need in melanoma and other cancers. Understanding genomic correlations with response and resistance to ICI will enhance cancer patients’ benefits. Building on insights into interplay with the complex tumor microenvironment (TME), the ultimate goal should be assessing how the tumor ’instructs’ the local immune system to create its privileged niche with a focus on genomic reprogramming within the TME. It is hypothesized that this genomic reprogramming determines the response to ICI. Furthermore, emerging genomic signatures of ICI response, including those related to neoantigens, antigen presentation, DNA repair, and oncogenic pathways, are gaining momentum. In addition, emerging data suggest a role for checkpoint regulators, T cell functionality, chromatin modifiers, and copy-number alterations in mediating the selective response to ICI. As such, efforts to contextualize genomic correlations with response into a more insightful understanding of tumor immune biology will help the development of novel biomarkers and therapeutic strategies to overcome ICI resistance.


Introduction
Immune checkpoint inhibitors (ICIs) are revolutionizing the treatment of melanoma and other types of cancers, with 7 agents now approved in the US and a rich pipeline of new agents and mechanisms in development [1][2][3][4]. Notwithstanding the excitement around these developments, there is a significant unmet medical need in the form of patient stratification and therapy resistance. Melanoma affects more than 1 million Americans, and there is an increasing incidence of melanoma worldwide. Approx. 300,000 new cases are diagnosed in the US each year [5], with the average annual cost for treatment estimated at $3.3 billion [6].
Numerous successes have been achieved with anti-PD1, anti-CTLA4, or combination therapies [5,7] in treating melanoma and other types of cancers. The groundbreaking finding by Leach et al. [8] showed that antibodies blocking the T cell co-inhibitory receptor CTLA-4 can augment immune responses against tumor cells in mice. This finding gave rise Int. J. Mol. Sci. 2023, 24, 41 2 of 35 to ipilimumab, the first ICI to increase the survival of patients with metastatic melanoma [9,10] which was granted FDA approval in 2011 for the treatment of metastatic melanoma.
In 2022, relatlimab, which targets lymphocyte-activation gene 3 (LAG-3), was approved by the FDA for adult and pediatric use with metastatic melanoma [15].
Following those developments, high levels of PD-L1 expression in cancer cells and tumor mutational burden (TMB) have been shown in melanoma to correlate with clinical responses to ICI [13].
Despite these data, a substantial number of melanoma patients fail to respond to ICI, leading to premature death [16][17][18]. Considerable effort has been made to identify biomarkers that predict clinical response/resistance to ICI [19]. Despite the successes of ICI, even with the combination of ipilimumab and nivolumab, the five-year survival for the intention-to-treat (ITT) population was only 53%. This means that 47% of patients do not reach long-lasting benefits and succumb to the disease [20], suggesting the need for predictive biomarkers of response to overcome ICI resistance.
Despite the approval of all of these new ICIs and combination therapy, physicians are nowhere near having predictive biomarkers of response to ICI. Presently, physicians have no idea which patients will or will not respond to ICI in the absence of predictive biomarkers of response.
Recently, Carlino et al. [21] demonstrated that combining anti-PD-1 and anti-CTLA-4 ICI in stage IV melanoma resulted in the highest 5-year overall survival rate of all the other therapies. Increased PD-L1 expression and TMB have been shown to correlate well with melanoma response to ICI [13]. Unfortunately, both PD-1 and CTLA4 are unable to predict the outcome [22]. As a result, we run the risk of undertreating some patients who might benefit from ICI.
To date, there are no reliable predictive biomarkers able to stratify responders and non-responders to ICI. Clinical factors, such as volume and disease sites, serum lactate dehydrogenase levels, and BRAF mutation status, used to select the initial therapy for patients with advanced melanoma have been rather unreliable [21].
The clinical trial data of melanoma response to ICI have identified three almost equal populations of patients: (1) those who respond (responders), (2) those who fail to ever respond (innate resistance), and (3) those who initially respond or have a prolonged period of disease stabilization, but eventually develop disease progression (acquired resistance) [7,16,[23][24][25].
The current stratification strategies, including genetic mutations and variations in mutational load, have shown some correlation with response to ICI. However, they are unable in predicting patient response to ICI [26]. It has been shown that tumor cells possess genetic and epigenetic traits that facilitate immune evasion [27]. Tumors can mutate to evade the innate and adaptive immune response [19], rendering ICI therapy ineffective [23,28,29]. ICI resistance can start from different cells and their interactions in the tumor ecosystem [29,30]. Recent studies have revealed insights into the mechanism of ICI resistance [7,29,30]. As a result, ICI therapies elicit significantly limited efficacy in many of the cancer types due to drug resistance and toxicity.
The lack of predictive biomarkers to stratify patients to targeted treatments has been the driver of primary treatment failure and represents an unmet medical need in melanoma and other cancers. Therefore, understanding genomic correlations with response and resistance to ICI will be beneficial to cancer patients.
Here, we review recent studies involving ICI therapies in melanoma and other cancers that have revealed important insights about the tumor immune microenvironment in melanoma and other cancers, the mechanisms supporting these findings, and genomic correlations with response and resistance to ICI.
We further discuss how the insights gained from these studies are guiding more precise analyses of the mechanisms of action of ICI therapy and novel immunotherapy approaches, including novel combination therapies to overcome resistance to ICI therapy and turning "cold tumors" into "hot tumors".
Consequently, these efforts will help us better understand the mechanisms involved in response and resistance to ICI, thus achieving more effective and durable use of immunotherapy.

Immune Checkpoint Inhibitors
As illustrated in Figure 1, immunosuppressive mechanisms involving the expression of checkpoint proteins (e.g., PD-1, CTLA-4, LAG-3, TIM-3, and NKG2A/B), activation of cell death programs, and accumulation of various immunosuppressive cells are instrumental in maintaining immune homeostasis and self-tolerance [31][32][33][34]. Cancer cells exploit those immune homeostasis mechanisms to evade the immune system [35]. Several ICI treatments are successful against a wide variety of cancers, given the durability of response and improved side effect profile compared to chemotherapeutic agents. Nonetheless, only a few ICIs have been approved by the FDA [34].

Melanoma Biomarkers of Response to ICI
Physicians would greatly benefit in therapeutic decision-making by having predictive biomarker(s) of response to stratify melanoma and other types of cancer patients into responders and non-responders. A general representation of predictive biomarkers of response to ICIs in several cancers is shown in Table 1. However, the significant proportion of non-responders and treatment-associated toxicities and drug resistance remain one of the major obstacles to therapeutic success of ICI in melanoma and other types of cancers.

Established Clinical Biomarkers
Different types of biomarkers and their clinical utility in metastatic melanoma, including those approved by the FDA and others still at the experimental stage, are shown in Table 2 along with the clinical setting in which they are used.
As summarized in Table 2, the earliest biomarkers that helped to diagnose the presence of melanoma include human melanoma black-45 (HMB-45), melan-A, tyrosinase, microphthalmia transcription factor (MITF), S100, SM5-1, chondroitin sulfate proteoglycan 4 (CSPG4), loss of p16 protein expression, biomarker panels, and gene arrays. Those biomarkers have been used to screen healthy patients before the diagnosis of melanoma in order to help stratify patients into benign vs. malignant stages of the disease. Several immune cells, such as CD8 + T cells, CD4 + T cells, NK cells, and Tregs, express specific receptors, which are rec ognized and bound by their specific ligands present on the surface of various cancer cells or anti gen-presenting cells. The illustration also depicts examples of different receptors and ligands involved in ICI modulation, along with generalized stimulatory (↑) or inhibitory (↓) effects. ICIs block various inhibitory receptor-ligand interactions leading to the activation of immune cells, which leads to tumor regression. ICI therapies, including anti-PD-1/PD-L1 and anti-CTLA-4, hav shown clinical efficacy for many cancers, which provided opportunities for developing alternativ ICIs (e.g., anti-LAG-3, anti-TIM-3, and anti-NKG2). Adapted from ref. [3,34]. Created with BioRe der.com (accessed on 18-11-2022). Definitions of abbreviations are found in the abbreviations list the end of the text. Several immune cells, such as CD8 + T cells, CD4 + T cells, NK cells, and Tregs, express specific receptors, which are recognized and bound by their specific ligands present on the surface of various cancer cells or antigen-presenting cells. The illustration also depicts examples of different receptors and ligands involved in ICI modulation, along with generalized stimulatory (↑) or inhibitory (↓) effects. ICIs block various inhibitory receptor-ligand interactions leading to the activation of immune cells, which leads to tumor regression. ICI therapies, including anti-PD-1/PD-L1 and anti-CTLA-4, have shown clinical efficacy for many cancers, which provided opportunities for developing alternative ICIs (e.g., anti-LAG-3, anti-TIM-3, and anti-NKG2). Adapted from ref. [3,34]. Created with BioRender.com (accessed on 18 November 2022). Definitions of abbreviations are found in the abbreviations list at the end of the text. Predictive biomarkers of response to ICI therapies include the expression of intermolecular interactions within tumor cells to the expression of various molecules and cells in the TME and also circulating tumor and host factors. Adapted from ref. [36]. Definitions of abbreviations are found in the abbreviations list at the end of the text. Adapted from ref. [37][38][39][40]. Definitions of abbreviations are found in the abbreviations list at the end of the text.

Melanoma Biomarkers of Response to ICI
The earliest approved clinical biomarkers that helped to inform the prognosis of metastatic melanoma relied on baseline clinical characteristics, such as serum levels of lactate dehydrogenase (LDH), a marker of tumor burden [41]. Elevated serum LDH levels have been demonstrated to be a negative prognostic marker, irrespective of the given treatment [42][43][44][45]. Generally, high LDH levels are associated with poor overall survival (OS) compared with normal LDH levels. Elevated levels of LDH have been used as a biomarker to assess patient staging [46]. For example, it has been shown that among ca. 30% of patients with 4-5-year OS following treatment with BRAF and MEK inhibitors, only a few of those patients had high LDH levels before the initiation of therapy [47].
Likewise, elevated serum levels of S100B have demonstrated to have a prognostic value in both metastatic [48,49] and high-risk resected melanoma settings [50].
Gene expression profiling has shown to have a prognostic value that complements existing biomarkers in patients with melanoma [47,51,52].
In metastatic melanoma, a well-characterized predictive biomarker of response guiding the therapeutic decision process is the BRAF V600 mutation, which is somehow predictive of response to BRAF ± MEK inhibition with low rates of primary resistance [53][54][55]. The response rate to BRAF and MEK inhibitors in metastatic melanoma patients with the BRAF V600 mutation is ca. 70% in selected patients, with less than 10% of patients having the highest response to progressive disease [55][56][57][58].
Several oncogenic driver mutations have been identified as predictive biomarkers of response from targeted agents, including NRAS, NF-1, and c-KIT, that provide insight into the probability of therapeutic response to a specific treatment [59][60][61][62][63]. Presently, the presence of a BRAF V600 mutation is the only validated predictive biomarker for melanoma patients.

Emerging Predictive Biomarkers
As shown in Table 2, many emerging biomarkers are currently being evaluated in clinical studies. Du et al. [64] reported that several genomic and transcriptomic-based biomarkers have been explored as potential predictors of ICI response.
A recent study by Carter et al. [74] questioned the validity of the immuno-predictive score (IMPRES), a predictor of ICI response in melanoma consisting of 15 pairwise transcriptomic signatures that analyze the relationship between immune checkpoint genes reported by Auslander et al. [76]. The IMPRES is context-dependent and could not reproducibly predict ICI response in the context of metastatic melanoma [76].
Moreover, Xiao et al. [77] questioned the reproducibility of the Immune Cells.Sig [79] signature in melanoma, demonstrating inconsistencies in the prediction capability of Im-muneCells.Sig across different RNA-seq datasets [77]. The performance of the Immune-Cells.Sig signature in predicting ICI outcomes in four melanoma patient datasets, using the same implementation scheme as Xiong et al. [79], showed that there were inconsistencies across different datasets [77].
A panel of pan-tumor T cell-inflamed GES consisting of 18 IFN-γ-responsive genes was validated and confirmed to predict the response to ICI in pre-treatment tumor specimens from nine types of cancers, including melanoma [83]. MHC-I/II gene signatures have also been explored as predictive biomarkers of ICI response in melanoma [85,87,88].
Carter et al. [74] reported that immuno-predictive score (IMPRES), a predictor of ICI response in melanoma encompassing 15 pairwise transcriptomics relations between immune checkpoint gene [76], did not reproducibly predict the response to ICI in metastatic melanoma.
It was argued that many factors may contribute to the limited successes of those biomarkers, such as: (1) the predictive biomarkers have been derived from pre-clinical studies; (2) evaluation of the biomarkers in clinical specimens only included baseline biopsies and peripheral blood samples; (3) batch effect, lack of reproducibility might have contributed to the failure of the biomarkers for ICI response [74,75,77,78].
To address these issues, numerous researchers have developed predictive biomarkers to reduce batch noise and other technical issues. Expanded predictive biomarker panels have resulted in higher reproducibility as opposed to predictive signatures based on individual biomarkers [89][90][91][92].
Tian et al. [91] reported that the combined BRAF, KRAS, and PI3KCA mutation signature resulted in a favorable predictive response to cetuximab for patients with colorectal cancer [91].

Gene Expression Signatures at Baseline and On-Treatment Tumor Specimens
Genome-wide analysis of transcriptomic and genomic profiles of baseline and ontherapy tumor specimens from patients treated with ICI provides a comprehensive view into the mechanisms underlying tumor response and resistance to ICI [93].
Du et al. [64] reported that pathway-based signatures derived from on-treatment tumor specimens were predictive of the response to anti-PD1 blockade in patients with metastatic melanoma.
Other studies of breast cancer suggested that post-treatment tumor samples were more informative than pre-treatment samples [99][100][101].
Conversely, Wallin et al. developed adaptive immune signatures based on tumor samples obtained during the early course of treatment, showing that the signatures were highly predictive of the response to ICI in patients with metastatic melanoma [102].
Auslander et al. built an immune-predictive score (IMPRES), which encompasses 15 pairwise transcriptomic relations between immune checkpoint genes, to predict the response of metastatic melanoma to ICB therapy [76].
The IMPRES signature produced better predictive scores with post-treatment samples than with pre-treatment samples in two independent datasets [76].
In support of these findings, a recent proteome profiling study of samples from patients with metastatic melanoma undergoing either tumor infiltrating lymphocyte based or anti-PD1 immunotherapy demonstrated that the fatty acid oxidation pathway was significantly enriched in responder patients. These results underlined the critical role of mitochondrial metabolism, including fatty acid metabolism, in conferring response to immunotherapy [103].
It is now widely accepted that post-treatment tumor specimens are generally much more informative than pre-treatment specimens and may provide more valuable insight into dynamic changes at the transcriptional level that correlate with clinical response, resulting in a higher predictive score.
In conclusion, although pathway signatures derived from post-treatment samples are highly predictive of therapeutic response to anti-PD1 in patients with metastatic melanoma, 8 of 35 further studies are warranted to confirm the predictive value of those signatures in larger cohorts of patients with metastatic melanoma.

Pathway Signatures
Du et al. [64] developed pathway-specific signatures in pre-treatment (PASS-PRE) and on-treatment (PASS-ON) tumor specimens based on transcriptomic data and clinical information from a large dataset of metastatic melanoma patients treated with anti-PD1. Both PASS-PRE and PASS-ON signatures were validated in three independent datasets of metastatic melanoma. Compared to existing molecular signatures, it was concluded that the on-treatment (PASS-ON) tumor specimen signature exhibited a robust and better predictive value for metastatic melanoma patients who responded to anti-PD1 across all four datasets.
The pre-treatment pathway signatures included six pathways for predicting the response to anti-PD1 treatment, including: (1) complement cascade; (2) regulation of insulinlike growth factor IGF transport and uptake by insulin-like growth factor binding proteins IGFBPS; (3) binding and uptake of ligands by scavenger receptors; (4) plasma lipoprotein remodeling; (5) IL2 family signaling; and (6) retinoic acid (RA) biosynthesis pathways. Complement cascade, binding, and uptake of ligands by scavenger receptors and IL2 family signaling pathways are related to immune and inflammation, whereas plasma lipoprotein remodeling and the RA biosynthesis pathways are related to metabolism.
In contrast to the pathway-based signature analysis of on-treatment samples, Du et al. [64] identified four pathways, including: (1) peroxisomal lipid metabolism; (2) generation of second messenger molecules; (3) fatty acid metabolism; and (4) PD1 signaling. Of note, peroxisomal lipid metabolism and fatty acid metabolism are related to fatty acid and lipid metabolism [103]. Generation of second messenger molecules is a central signaling pathway in T-cell receptor (TCR) stimulation. Likewise, PD1 signaling plays an important role in immunoregulation as an immunoregulatory signaling pathway.
To further validate the predictive performance of a pathway-based super signature for on-treatment samples (PASS-ON), Du et al. [64] tested three independent datasets with RNA-seq data available for on-treatment samples, including those of Gide et al. and Lee et al. [96,104], and the MGH cohort demonstrated the effectiveness of PASS-ON in predicting patient response to anti-PD1. Patients with high PASS-ON signature scores were associated with significantly improved PFS compared to those with low signature scores in all tested patients.
Furthermore, Du et al. [64] demonstrated that the time-response interaction pathwaybased super signature for pre-and on-treatment samples had reasonable predictive power. The study suggested that pathway-based biomarker signatures derived from on-treatment tumor specimens compared to pretreatment tumor specimens were better predictors of response to anti-PD1 therapies in metastatic melanoma patients.

Tumor Antigens
Huang et al. [22] investigated several melanoma-relevant tumor-specific antigens, cancer germline genes, melanocyte differentiation antigens, overexpressed antigens, neoantigens, neuropeptides, and other sources of immunogenic antigens, such as immunogenic epitopes, have also been explored as novel predictive biomarkers of ICI response to melanoma.
Neoantigens are derived from tumor-specific somatic mutations and are exclusively expressed in cancer cells and absent in normal human tissue. The majority (95%) of somatic mutations are single-nucleotide variants (SNVs), which lead to aberrant protein and peptide expression with single amino acid substitutions [105].
Neopeptides also arise from nucleotide insertions or deletions (indels), leading to the expression of aberrant proteins and peptides with frameshift or non-frameshift sequences depending on the number of nucleotides added and deleted. While the minority of mutations are indels (<5% for melanoma) [106,107], frameshift mutations can generate a number of immunogenic neoepitopes that are highly distinct from the self.
Other sources of immunogenic antigens, including immunogenic epitopes, can also derive from mutations associated with gene fusion, aberrant messenger-RNA splicing with retained introns, or aberrant translation resulting in cryptic antigens, and genomically integrated endogenous retroviral sequences as a result of previous retroviral infections, although they are epigenetically silenced, can be reactivated in tumors [106], as in the case of cancer germline antigens.
Furthermore, tumors often present aberrant patterns of DNA methylation, resulting in the demethylation, ectopic expression, and presentation of cancer germline genes to T cells relevant in immune recognition [106,108].
For example, cancer germline genes such as MAGEA1 and NY-ESO-1 are silenced epigenetically through methylation in human tissue, with the exception of male germ cells and trophoblastic cells, which lack MHC-I molecules.
PRAME (preferentially expressed antigen in melanoma), a member of the cancertestis antigen family, has been reported to be frequently overexpressed in many cancers, including melanoma, which indicates advanced cancer stages and poor clinical prognosis [109,110]. As such, overexpressed PRAME is a potential immunotherapy target. PRAME-specific immunotherapies are currently in development for many cancers, including melanoma. For example, a recent study demonstrated that uterine carcinosarcoma, synovial sarcoma, and leiomyosarcoma patients would potentially benefit from PRAME-specific immunotherapies [109].

Genomic Alterations
Considerable effort has been made to identify genomic alterations and transcriptome profiles as predictive biomarkers of ICI response. Numerous studies have identified distinct stages of CD8+ T cells linked to positive response or failure to ICI treatment [98].
Moreover, tumors from patients responding to ICI showed a higher number of cancerassociated somatic mutations (i.e., mutated antigens or neoantigens) targeted by T cells [65]. The IFN-γ signature has also been shown to predict the response to ICI (e.g., anti-PD-1) in melanoma [111] and in other types of cancers [6,93]. Gene expression signatures obtained from bulk melanoma tumor or single-cell profiling and the TME have been shown to be correlated with sensitivity and resistance to several ICIs [86,88,98,[112][113][114].
Collectively, the gene expression signatures associated with response to ICI in metastatic melanoma represent distinct characteristics and play an important function in different signaling pathways, including the inflammatory response, type I interferon signaling pathway, cytokines, and others.

Who Is Responding to ICI?
Recent data have demonstrated that immunotherapies against immune checkpoints (e.g., CTLA-4 or PD-1) downregulate two main negative regulators of the anti-tumor immune response [93,[115][116][117], resulting in durable anti-tumor responses in a subset of cancer patients, including those with melanoma [2,118].
Another key factor contributing to anti-tumor immune response following ICI treatment [93] is the pre-existing level of T cell infiltration of the tumor [119][120][121], representing the immunogenicity of the cancer cells.
Analysis of tumor biopsies from ICI-treated patients showed that clinical responses associated with ICI were mediated by tumor-infiltrating T cells reactivated following ICI treatment [121,122].
A combination of immunohistochemical (IHC) analysis with RNA-seq performed on cancer biopsies from patients treated with anti-CTLA-4 antibody (ipilimumab) before or after treatment with anti-PD-1 antibody (nivolumab) demonstrated that a major response to anti-CTLA-4 requires cancer cells with high levels of MHC-I expression at baseline, whereas the response to anti-PD-1 was more strongly associated with a pre-existing interferon-γ gene expression signature [88].
Liu et al. demonstrated that the response to anti-PD-1 therapy (with or without prior anti-CTLA-4 treatment) was associated with increased MHC-I and MHC-II expression [85]. This study demonstrated that patients not responding to therapy have occasional genetic alterations in antigen presentation genes [85].
Biopsies from patients with metastatic melanoma treated with anti-PD-1 monotherapy (nivolumab) in part 1 of the CheckMate 038 study showed an increase in immune cell subtypes with elevated immune activation gene signatures seen in responders to therapy [25].
The transcriptome analysis of tumor biopsies from patients treated with anti-PD-1 monotherapy (nivolumab) or in combination anti-PD1 plus anti-CTLA-4 therapy (ipilimumab) correlated well with the in vitro analysis of gene expression signatures of melanoma cell lines following exposure to interferon-γ [93].
It appears that cancer cells become enablers of the immune response via the expression of IFN-γ-response genes, triggering the upregulation of antigen presentation, amplification of the interferon response, and induction of chemokines (i.e., CXCL9 and CXCL10) to entice immune cells to the TME. Thus, T cell-induced IFN-γ correlates with ICI therapy response [93]. Collectively, the degree of the anti-tumor T cell response and downstream IFN-γ signaling are the main drivers of response or resistance to ICI therapy [93].
However, little is known about how tumor-intrinsic loss of IFN-γ signaling impacts TILs. The question remains whether tumor-intrinsic IFN-γ signaling actively regulates the infiltration or function of TILs?
Shen et al. [123] demonstrated that IFN-γR1 knockout melanomas and IFN-γR1KO melanomas in B6 mice had reduced infiltration and function of tumor-infiltrating lymphocytes (TILs). Furthermore, long-distance effects of IFN-γ on tumor cells also play a crucial role in anti-tumor immunity [123]. These recent findings revealed an important role of tumor-intrinsic IFN-γ signaling and IFN-γ-response genes in shaping TILs.

Tumor-Immunity Cycle and Resistance Mechanisms Involving Tumor Immunophenotypes
As discussed in the literature [124] and illustrated in Figures 2 and 3, the anti-tumorimmunity cycle is a gradual process mediated to a large extent by CD8+ T lymphocytes and involves a multi-step process.  Figure 2. Mechanisms of distinct tumor phenotypes, including immune-inflamed, immune-desert, and immune-excluded tumors, are associated with specific inhibitory and stimulatory biological mechanisms. Immune-inflamed tumors are permissive to immune cell infiltration; however, immune cells in the TME can be suppressed due to checkpoint activation. On the other hand, immune-desert tumors may be devoid of T cell priming due to the lack of tumor antigens, defective antigen processing and presentation processes, or impaired interactions between dendritic cells and T cells. The immune-excluded tumors, on the other hand, may display aberrant chemokines, Figure 2. Mechanisms of distinct tumor phenotypes, including immune-inflamed, immune-desert, and immune-excluded tumors, are associated with specific inhibitory and stimulatory biological mechanisms. Immune-inflamed tumors are permissive to immune cell infiltration; however, immune cells in the TME can be suppressed due to checkpoint activation. On the other hand, immune-desert tumors may be devoid of T cell priming due to the lack of tumor antigens, defective antigen processing and presentation processes, or impaired interactions between dendritic cells and T cells. The immune-excluded tumors, on the other hand, may display aberrant chemokines, activated oncogenic pathways, hypoxia, aberrant vasculature, or an immunosuppressive TME (e.g., stromal barriers). Adapted from ref. [124]. ↓ (downward arrow), inhibitors; ↑ (upward arrow), stimulatory factors. Created with BioRender.com (URL accessed on 18 November 2022).
Definitions of abbreviations are found in the abbreviations list at the end of the text.
activated oncogenic pathways, hypoxia, aberrant vasculature, or an immunosuppressive TME (e.g., stromal barriers). Adapted from ref. [124]. ↓ (downward arrow), inhibitors; ↑ (upward arrow), stimulatory factors. Created with BioRender.com (URL accessed on 18-11-2022). Definitions of abbreviations are found in the abbreviations list at the end of the text. In the immune-desert phenotype, immune cells are absent from the tumor and its periphery. In the immune-excluded phenotype, immune cells accumulate but do not In the immune-desert phenotype, immune cells are absent from the tumor and its periphery. In the immune-excluded phenotype, immune cells accumulate but do not efficiently infiltrate. In the immune-inflamed phenotype, immune cells infiltrate but their effects are inhibited. Notably, the three different phenotypes have different response rates to immune checkpoint inhibitors.
Based on the spatial distribution of cytotoxic immune cells in the TME, a tumor is classified as an immune-inflamed, immune-excluded, or immune-desert phenotype (Figures 2 and 3) [126]. Immune-inflamed tumors (i.e., "hot tumors") are characterized by increased T cell infiltration, high interferon-γ (IFN-γ) signaling, elevated expression of PD-L1, and increased TMB [127]. Inflamed tumors are more responsive to ICIs than non-inflamed tumors [119,128]. Immune-deserted tumors (i.e., "cold tumors"), on the other hand, exhibit characteristics where CD8+ T lymphocytes localize only at the tumor margin and do not infiltrate the tumor [129]. In immune-desert tumors, CD8+ T lymphocytes are absent from the actual tumor and its periphery [129]. In addition to poor T cell infiltration, "cold tumors" display low mutational load, decreased MHC class I expression, and reduced PD-L1 expression [127].
Cold tumors also harbor immunosuppressive cells, including T-regulatory cells (Tregs) and myeloid-derived suppressor cells (MDSCs), and tumor-associated macrophages (TAMs) are key sources of many of these inhibitory factors [127]. As a result, cold tumors lack innate immunity or the innate antitumor immune features present in cold tumors' may be ineffective due to the lack of immune cells [126]. The three tumor immune phenotypes have different response rates to ICIs and cold tumors respond poorly to ICI monotherapy [119].

Tumor Cell-Intrinsic and Tumor Cell-Extrinsic Resistance Mechanisms
Many factors play a role in T cells driving tumor resistance, ultimately leading to a noninflamed T cell phenotype and failed antitumor immunity (Figures 3 and 4).
Several other resistance mechanisms involving both tumor cell-intrinsic and tumor cellextrinsic sources have been described (Figures 3 and 4 and Table 3) [34]. In the case of tumor cell-intrinsic mechanisms, a lack of neoantigen development, impaired antigen presentation, and other primary factors contribute to the resistance to immunotherapy [130][131][132][133][134][135][136]. Tumor cell-extrinsic mechanisms encompass increased recruitment and activity of inhibitory immune cells within the TME and upregulation of LAG-3 and TIM-3 [16,47,[137][138][139][140]. Based on the spatial distribution of cytotoxic immune cells in the TME, a tumor is classified as an immune-inflamed, immune-excluded, or immune-desert phenotype (Figures 2 and 3) [126]. Immune-inflamed tumors (i.e., "hot tumors") are characterized by increased T cell infiltration, high interferon-γ (IFN-γ) signaling, elevated expression of PD-L1, and increased TMB [127]. Inflamed tumors are more responsive to ICIs than noninflamed tumors [119,128]. Immune-deserted tumors (i.e., "cold tumors"), on the other hand, exhibit characteristics where CD8+ T lymphocytes localize only at the tumor margin and do not infiltrate the tumor [129]. In immune-desert tumors, CD8+ T lymphocytes are absent from the actual tumor and its periphery [129]. In addition to poor T cell infiltration, "cold tumors" display low mutational load, decreased MHC class I expression, and reduced PD-L1 expression [127].
Cold tumors also harbor immunosuppressive cells, including T-regulatory cells (Tregs) and myeloid-derived suppressor cells (MDSCs), and tumor-associated macrophages (TAMs) are key sources of many of these inhibitory factors [127]. As a result, cold tumors lack innate immunity or the innate antitumor immune features present in cold tumors' may be ineffective due to the lack of immune cells [126]. The three tumor immune phenotypes have different response rates to ICIs and cold tumors respond poorly to ICI monotherapy [119].

Tumor Cell-Intrinsic and Tumor Cell-Extrinsic Resistance Mechanisms
Many factors play a role in T cells driving tumor resistance, ultimately leading to a noninflamed T cell phenotype and failed antitumor immunity (Figures 3 and 4).    Adapted from ref. [113]. Definitions of abbreviations are found in the abbreviations list at the end of the text.

Immune Resistance Mechanisms in Melanoma
As illustrated in Figures 2-5 and Table 3, resistance to ICI therapy is one of the most significant challenges to achieving a durable tumor response in many types of cancers [16,24,34,182,183].
Adapted from ref. [113]. Definitions of abbreviations are found in the abbreviations list at the end of the text.

Immune Resistance Mechanisms in Melanoma
As illustrated in Figures 2-5 and Table 3, resistance to ICI therapy is one of the most significant challenges to achieving a durable tumor response in many types of cancers [16,24,34,182,183]. Figure 5. Genomic correlations with response and resistance to ICI therapy within the immune TME. The panel (A) represents correlations with response, focusing on antigen presentation and recognition. The panel (B) represents resistance pathways that promote tumor immune evasion mechanisms that induce immunosuppressive cells, leading to the inhibition of T cell-mediated anti-tumor response (right side). Adapted from [113]. The panel (B) also illustrates the TME, which consists of cellular and non-cellular components. The cellular component consists of cancer cells, endothelial cells, pericytes, carcinoma-associated fibroblasts, and immune cells. The immune compartment comprises many immune cell populations (e.g., T cells, B cells, natural killer cells, tumor-associated macrophages, myeloid-derived suppressor cells, and dendritic cells). The noncellular component of the TME, on the other hand, is represented by the extracellular matrix and functions as a scaffold. Components of the TME interact via the extracellular matrix, cell-cell contacts, and through the release of cytokines, chemokines, extracellular vesicles, and others. Adapted from ref. [113,184,185]. Definitions of abbreviations are found in the abbreviations list at the end of the text.
Several mechanisms, including primary (lack of tumor response to initial immunotherapy) and secondary/acquired resistance to cancer immunotherapy (initial response to tumor followed by a lack of response) have been described as the major resistance mechanisms to ICI therapy [16] (Figures 2-5 and Table 3). Several mechanisms, including primary (lack of tumor response to initial immunotherapy) and secondary/acquired resistance to cancer immunotherapy (initial response to tumor followed by a lack of response) have been described as the major resistance mechanisms to ICI therapy [16] (Figures 2-5 and Table 3).
As for melanoma, Huang et al. [22] demonstrated that several key mechanisms are involved in the immune resistance to ICI, such as immune tolerance, T cell exhaustion, immune cell-mediated immunosuppression, expression of immune checkpoint ligands, and tumor escape.
Immune tolerance describes the lack of response by the immune system to substances or antigens that have the potential to induce an immune response. Immune tolerance to an individual's own antigens occurs through both central and peripheral tolerance mechanisms. While central tolerance occurs via thymic deletion of high-affinity autoreactive T cells, peripheral tolerance is maintained by other mechanisms (e.g., suppression by Treg cells and anergy) and induced by many mechanisms, including sub-optimal T cell co-stimulation, deletion via apoptosis, or conversion into Treg cells. The dose of antigen and TCR affinity are considered to be the major drivers of these mechanisms [22]. Notably, both the lymph node and tumor environments blunt T-cell effector functions and offer a rationale for the failure of tumor-specific responses to effectively counter tumor progression [186].
T cell exhaustion is a specific T cell differentiation process mediated by chronic antigen stimulation, which leads to increased expression of co-inhibitory immune receptors that are presumed to decrease chronic TCR signaling and regulate activation-induced cell death.
In this state of simultaneous TCR stimulation and co-inhibitory pathway stimulation, exhausted T cells (T EX cells) exhibit reduced effector functions (i.e., cytokine production and proliferative potential), but can survive in the hostile TME. Notably, T cell exhaustion appears to be a dynamic and progressive process that includes intermediate reversible states more permissive to stimulation by ICI.
Cell-mediated immunosuppression involves immunosuppressive cells, including MDSCs, Treg cells, and tolerogenic DCs, instructing effector T and B cells to not respond to positive immune stimuli.
Upregulation of immune checkpoint ligands PD-L1 and PD-L2 is often seen in melanoma and other cancers in response to robust inflammatory signals as a homeostatic mechanism adopted by cancer cells to shield themselves from immune attack. Interaction of PD-L1 and PD-L2 ligands through binding to PD-1 receptors expressed on tumor-specific T cells initiate a negative signaling process downstream of PD-1, which reduces T cell activation and impairs tumor-killing function [11]. As a consequence, expression of PD-L1 or PD-L2 in melanoma cells can neutralize the positive T cell signals mediated by the MHC-I and MHC-II antigen presentation process.
Tumor escape is a mechanism that cancer cells utilize to escape from anti-tumor immunity and immune surveillance. Tumor escape can be mediated by tumor-extrinsic mechanisms in the TME and by the tumor itself, which can evolve to evade immune recognition. Under strong immune selective pressure, heterogeneous tumor cells can result in clonal evolution, selection, and enrichment of specific tumor cells that can evade immune recognition, leading to immunotherapy resistance. The evasion of immune recognition by these treatment-resistant cells can take place via inactivation of antigen-presentation processes (e.g., B2M, HLA, TAP, etc.) and/or IFN-γ-response genes (e.g., JAK1, JAK2).

Therapeutic Strategies to Turn "Cold Tumors" into "Hot Tumors"
Several strategies have been investigated to elucidate the mechanisms underlying how T cells are driven into "hot tumors" in order to improve the efficacy of ICI therapy ( Figure 6 and Table 4) [124]. Several clinical trials have tested these novel therapeutic modalities as interventions in combination with ICI to overcome ICI monotherapy resistance and attempt to turn "cold tumors' into "hot tumors" (Table 5). Table 4. Therapeutic strategies to turn "cold tumors" into "hot tumors".

Progressed from Prior Immune Check Point Inhibitor
Examples of recent clinical trials investigating various novel therapeutic modalities as interventions in combination with ICI to overcome monotherapy resistance and attempt to turn "cold tumors' into "hot tumors". Adapted from ref. [113]. Definitions of abbreviations are found in the abbreviations list at the end of the text. Figure 6. Approaches to turn a "cold tumor" into a "hot tumor". Some representative therapeutic strategies to increase T cell infiltration and improve the efficacy of ICI. T cell priming and activation: Oncolytic viruses, local thermal ablation therapy (e.g., radiofrequency ablation), chemotherapy, and radiotherapy are all capable of inducing immunogenic cell death to promote T cell priming and activation. Local administration of immune adjuvants, such as TLR agonists, promotes the activation of DCs. Epigenetic modification inhibitors can promote T cell priming by increasing the expression of tumor antigens and by restoring antigen processing and presentation mechanisms. T cell expansion: Cancer vaccines and adoptive cellular therapies, such as CAR-T cells, can promote the expansion of tumor-specific T cells. T cell trafficking and infiltration: Intrinsic oncogenic pathway inhibitors, antiangiogenic therapies, TGFβ inhibitors, CXCR4 inhibitors, and epigenetic modification inhibitors can promote T cell trafficking and enable T cells to infiltrate the tumor more effectively. Adapted from ref. [124]. Created with Briorender.com (URL (accessed on 18-11-2022).
Definitions of abbreviations are found in the abbreviations list at the end of the text. Grasso et al. [93] showed that a robust anti-tumor immune response relies on the interplay of key factors that can be modulated with innovative interventions.
Recent preclinical and clinical findings have provided insight into the immunological implications of canonical cancer signaling pathways (e.g., WNT-beta-catenin signaling, cell cycle regulatory signaling, mitogen-activated protein kinase signaling, and pathways activated by loss of the tumor suppressor phosphoinositide phosphatase PTEN), thus providing new opportunities for the development of new treatments for those patients who do not respond to ICI monotherapies [218].
Combined therapeutic strategies from preclinical [115,117,219] and clinical studies [14] have shown that anti-PD-1 plus anti-CTLA-4 (nivolumab and ipilimumab) treatments elicit stronger immune stimulation in stage IV melanoma than monotherapy alone, resulting in a favorable anti-tumor immune response. Response to ICI, either following anti-CTLA-4 monotherapy or in combination with anti-PD-1 therapy, triggers a robust T cell response that generated an appreciable antitumor response [218].
Another approach to turn "cold tumors" into "hot tumors" is through the intra-tumoral delivery of oncolytic viruses or Toll-like receptor agonists capable of inducing intra-tumoral interferon production, which triggers the pattern recognition pathways with consequent boosting of the anti-PD1 immune response rate [187,190,220].
Recently, different approaches have been used to boost the response to ICI. Activation of the STING pathway [221], inhibition of immune suppressive factors (e.g., WNT signaling or the adenosine pathway) [111,209,222,223], as well as the release of other immune checkpoints (e.g., LAG-3, TIM-3, or TIGIT, etc.) in T cells [111] have been explored, but no favorable clinical outcomes have yet been reported in patients.
Altogether, these new therapeutic strategies provide new opportunities for cancer immunotherapy for patients who do not respond to ICI [111].

Lessons Learned: ICI Therapies in Melanoma
Huang et al. [22] reported that several factors, including the immune TME, tumorassociated immune cells, and different host factors, contribute to the ICI resistance. Melanoma resistance has armed us with a handful of information that can be applied to other types of cancers, such as: (1) the ability to present cancer antigens through MHC-I and elevated TMB; (2) tumor-antigen-specific T cells play a crucial role in the response to ICI; (3) reactivation of terminally exhausted T cells could be considered a biomarker for PD-1 blockade, which is detectable as early as one week after ICI dosing; (4) melanoma immunosuppressive mechanisms are complicated and need additional research to remodel their interaction, cooperation, and dynamics during tumor progression and in immunotherapy resistance; and (5) Treg cells are emerging as a key mechanism of resistance to PD-1 blockade, but not necessarily CTLA-4 blockade; (6) emerging neoadjuvant immunotherapy trials are anticipated to provide new insight into pharmacodynamic immune responses and advance the development of rational immunotherapy and neoadjuvant combination regimens while avoiding toxicity and significantly improving patient management; (7) longitudinal assessment of pre-and on-treatment patient specimens is required to determine prognostic vs. predictive use of immune and other parameters, including genomic parameters correlating with patient outcomes, and deduce their biologic role in response to ICI therapy based on their modulation during treatment; and last but not least (8) melanoma-specific oncogenic programs supporting metabolic plasticity and fitness, together with clinical and preclinical evidence of differential activity of ICI therapy depending on the tumor metabolic state, should provide new research opportunities to evaluate these relationships as potential biomarkers for patient stratification and treatment allocation, and formulate novel precision-medicine combinations depending on metabolic and immune therapies.

Conclusions and Future Perspectives
Even with a high level of immunogenicity, metastatic melanoma grows and spreads rapidly via escape mutations and immunosuppression. Although combination ICI therapies targeting CTLA-4 and PD-1 can efficiently target some ICI-resistant mechanisms by improving T cell priming, re-activating PD-1 high CD8 + exhausted T cells, and reversing Treg suppression, many patients still do not derive durable clinical benefit. Therefore, with the availability of novel immunotherapeutic agents, the mechanism of single-agent therapies needs to be better characterized in order to guide effective rational ICI combinations. In addition, since the immunologic effects of ICI therapy occur early, we need to focus on these early events to identify specific biomarkers, mechanisms of resistance, and neoadjuvant therapies.
Furthermore, toxicity from current and new ICI combinations remains a critical step to be addressed [224]. Because of concerns regarding adverse events from current and new ICI combinations, insight gained from molecular mediators of immune toxicity (e.g., antibody-vs. T cell-mediated) resulting from combination ICI therapies would help in avoiding these side effects and the development of novel combination ICI therapies to other cancers.
As a result, new efficacy and toxicity data deriving from different ICI therapies across various tumor types will help to characterize key parameters for predicting ICI response, thus limiting toxicity and enhancing therapeutic decision processes to overcome ICI resistance. In fact, new efficacy and toxicity data from many ICI therapies across various tumor types are helping in the characterization of key parameters for predicting response, limiting toxicity, and informing therapeutic decisions to overcome resistance.
Furthermore, the identification of novel genomic correlations with resistance to ICI requires well-annotated data from diverse patient cohorts and tumor histology to detect rare response-associated variants [159,225]. With a greater focus on detailed genomics as well as epigenomics, proteomics, and metabolomics, microbiome and biomarker studies will better characterize the relationships among host immunity, tumor biology, and mechanisms of resistance and response to ICI. Ultimately, molecular and clinical data from these studies will have to be adequately integrated and blended into preclinical studies.
For this reason, systems biology, computational biology, biostatistics, as well as machine learning and artificial intelligence approaches must be coordinated to integrate and help interpret this large dataset, thus translating the findings into actionable novel biomarkers that capture the complexity of multiple alterations affecting response and therapeutic success.
In addition, several other key challenges face ICI therapy that must be addressed to move the field ahead [226], including: (1) development of preclinical models that translate to human immunity; (2) identification and validation of the dominant drivers involved in cancer immunity; (3) deeper insights into organ-specific immune TME; (4) exploration of differences in molecular and cellular drivers between primary and secondary immune escape; (5) characterization of the benefit of endogenous vs. synthetic immunity; (6) investigation of ICI therapy combinations with other drugs (Figure 6, Tables 4 and 5) in early-phase clinical studies; (7) investigation of steroids and immune suppression on immunotherapy and autoimmune adverse events and toxicities; (8) boosting personalized medicine approaches by using composite biomarkers; (9) developing and implementing refined regulatory endpoints for immunotherapy; and (10) optimizing durable survival with a combination of multi-agent immunotherapy regimens [226].
Consequently, all of these efforts will result in a better understanding of the mechanisms involved in response and resistance to ICI, hence facilitating the development of biomarkers and novel therapies.
In conclusion, these efforts should result in a deeper quantitative and conceptual understanding of the mechanisms involved in ICI response and resistance, thus facilitating the development of rational biomarkers and therapies. Funding: No research funding received from specific grant from any funding agency in the public, commercial, or not-for-profit sectors was used in the preparation of this review article.

Institutional Review Board Statement: Not applicable.
Data Availability Statement: The authors grant the publisher the sole and exclusive license of the full copyright in the Contribution, which license the publisher hereby accepts.

Conflicts of Interest:
The authors declare no conflict of interest.