Heterogeneity of the tumor immune microenvironment and clinical interventions

Abstract

The tumor immune microenvironment (TIME) is broadly composed of various immune cells, and its heterogeneity is characterized by both immune cells and stromal cells. During the course of tumor formation and progression and anti-tumor treatment, the composition of the TIME becomes heterogeneous. Such immunological heterogeneity is not only present between populations but also exists on temporal and spatial scales. Owing to the existence of TIME, clinical outcomes can differ when a similar treatment strategy is provided to patients. Therefore, a comprehensive assessment of TIME heterogeneity is essential for developing precise and effective therapies. Facilitated by advanced technologies, it is possible to understand the complexity and diversity of the TIME and its influence on therapy responses. In this review, we discuss the potential reasons for TIME heterogeneity and the current approaches used to explore it. We also summarize clinical intervention strategies based on associated mechanisms or targets to control immunological heterogeneity.

Keywords

tumor immune heterogeneity / clinical intervention / tumor microenvironment

1 Background

Tumor-infiltrating immune cells have been recognized as playing an important role in anti-tumor responses [1,2]. In recent decades, therapeutic strategies for tumors, especially those using immune checkpoint inhibitors (ICIs), have been revolutionized by immunotherapy. First-generation ICIs, such as humanized monoclonal antibodies targeting the immune checkpoint molecules CTLA-4 and PD-1/PD-L1, have shown significant clinical benefits for the treatment of solid tumors by inducing adaptive immune responses against tumors via the modulation of T cell activation and function. However, the response rate to ICIs ranges from 5% to 52%, indicating that only a minority of patients benefit from treatment [3,4]. Several comprehensive studies have revealed that one factor directly associated with clinical outcomes in anti-tumor treatment is heterogeneity of the tumor immune microenvironment (TIME).

Heterogeneity of the TIME is organized based on various immune and stromal cells resulting from tumor progression, metastasis, and/or anti-tumor responses. Several reviews have summarized the TIME composition and characteristics [1,5–7]. Genetic, transcriptomic, epigenetic, and phenotypic changes can lead to heterogeneity [8], and TIME heterogeneity can be classified as inter- or intratumoral. Intertumoral heterogeneity refers to elements that can be uniquely identified in patients with the same cancer type, such as EGFR mutations in NSCLC [9]. Conversely, intratumoral heterogeneity comprises temporal heterogeneity, which is characterized the dynamic composition of immune cells in an individual patient over time, and spatial heterogeneity, that is, the distribution of immune cells within the same tumor sample.

Understanding the TIME is key to improving the immunotherapy response rate and developing new clinical intervention strategies. Unfortunately, past efforts to characterize the TIME have focused only on intertumoral heterogeneity, owing to technological limitations. Recent advances in single-cell omics, spatial transcriptomics, and digital pathology have provided an unprecedented view of the immune composition, function, and location within the TIME. As such, an extensive analysis of TIME heterogeneity can help to understand the tumor immune landscape, leading to the rapid development of clinical strategies focused on overcoming this heterogeneity.

In this review, we summarize the current findings regarding the heterogeneity of the TIME and related technologies that can help further elucidate the underlying mechanisms (Fig.1, Tab.1). Finally, we focus on the clinical intervention strategies, that could be used to overcome TIME heterogeneity and successfully applied in antitumor treatment.

2 Heterogeneity of tumor immune microenvironment

The TIME refers to the intrinsic environment in which a tumor resides and progresses; it not only contributes to the structure, function, and metabolism of the tumor itself but is also associated with the intrinsic environment of the tumor cells, such as the nucleus and cytoplasm. A healthy immune microenvironment in the human body is an important barrier that prevents the tumor cell formation and inhibits their growth and development. However, when damaged, it has an opposite role [10]. Tumor cells present in the surrounding normal tissues play an important role in gathering tumor-related secretory factors and cell subsets and forming new blood vessels, thus forming a TME [11]. As the process comprising tumor cell formation and metastasis progresses, cells gradually change, and malignant tumors become diverse, making them more heterogeneous. The different cancer-related processes affected by TIME heterogeneity include the rate of tumor growth, metastasis, invasion, drug sensitivity, and prognosis. Owing to heterogeneity of the TIME, treatment modalities that can be used to target tumors vary from person to person. Therefore, understanding tumor immune heterogeneity is essential for the development of precise therapies. With the development of modern technologies and the exploration of tumor cells using detection technologies, such as large-scale gene sequencing, existing studies have shown that exploring the mechanisms of tumor immune heterogeneity and associated interventions will help to clinically evaluate TIME heterogeneity and effectively enable personalized treatment.

2.1 Origin of heterogeneity of the TIME

2.1.1 Genetic instability

Tumor cells exhibit heterogeneous morphology and functions [12–15]. High-throughput sequencing is currently used to characterize the mutational spectrum and evolutionary trajectories generated by a tumor cell during its initiation, progression, and metastasis. Single-cell RNA sequencing, a form of high-throughput sequencing, is a new technique in which the transcriptome is sequenced at the single-cell level, allowing for an investigation of gene expression within individual cells while accounting for the challenges of cellular heterogeneity that cannot be resolved using tissue sample sequencing. This makes interpreting individual cell behavior, its mechanisms, and how it relates to the host practical. Hence, this approach is also widely used in clinical studies to explore TIME heterogeneity and the microenvironment. These studies have broadly described the genetic heterogeneity of tumors in a spatiotemporal dimension [16], which can be driven by genomic instability due to the presence of cells within the tumor that generate heterogeneity under endogenous or exogenous selection pressures [17]. Alterations in this genetic background usually include single-nucleotide variants, insertions and deletions, structural variants, and copy number variants [18]. Although changes in the host genetic background of a tumor cell typically occur in non-genic coding regions and their effects can be minimal, when a myriad of genes is altered in an individual, they collectively determine the metastatic potential [19].

Lifsted et al. [20] used a classical experimental animal model to confirm the influence of the genetic background on metastasis when conducting relevant studies. They crossed male polyomavirus middle T mice with inbred strains of mice from different genetic backgrounds and found different metastatic potential in the F1 progeny. In addition, the authors found that tumorigenic events coincided, such that differences in metastatic potential arose from the heterogeneity of the genetic background of the parents. In view of previous animal models and based on epidemiological studies, scientists identified SIPA as a metastasis-associated gene when performing experiments to investigate the presence of different heritability patterns in distinct breast cancer subtypes and their association with prognosis. They found that a single nucleotide polymorphism in this gene influenced patient outcomes [21,22]. Further, Peng et al. [23] explored the intratumoral heterogeneity of pancreatic ductal adenocarcinoma (PDAC) using single-cell sequencing and revealed a significant inverse correlation between the expression levels of genes characteristic of proliferative ductal cell subsets and the activation of tumor-infiltrating T cells in tumor tissues. This suggested that the presence of proliferative ductal cells and the absence of activated T cells contribute to poor prognosis for patients with PDAC. Therefore, we suggest that genetic instability originating from clonal and subclonal tumor cells underlies the generation of spatiotemporal heterogeneity in tumors during their evolution. Simultaneously, this genetic heterogeneity shapes the antigenic profile of the tumor and ultimately TIME heterogeneity [24].

2.1.2 Epigenetic modification

Heterogeneity that develops during tumor evolution is not solely and genetically heterogeneous but is associated with many epigenetic mechanisms, including DNA methylation, histone modifications, non-coding RNA regulation, chromatin remodeling, and changes in nucleosome positioning, which can contribute to intratumor diversity. An analysis of TIME heterogeneity in prostate cancer (PCa) [25], glioma [26], and esophageal squamous carcinoma [27] using methylome sequencing yielded results concordant with the heterogeneity captured via genomic sequencing, suggesting that epigenetic approaches might also be effective for studying tumor evolution. In addition to the methylome, multiple epigenetic and chromatin remodeling mechanisms allow tumor cells to adapt to the immune microenvironment [28]. Since epigenetic gene alterations are reversible and heritable, these modifications in tumor cells can be inherited by their progeny. In addition, the site and timing of regulated gene expression, in which epigenetic modifications are involved, are crucial for determining cell fate and tumor development [29] and thus exhibit significant heterogeneity in spatiotemporal dimensions [30].

2.1.3 Systemic immune perturbations

In addition to the genetic background and epigenetic modifications that contribute to tumor cell heterogeneity, the prolonged exposure of tumor cells to the extracellular microenvironment can also affect them [18]. Cancer cells result from mutations and the abnormal growth of normal cells owing to the innate immune system of the host. For example, natural killer cells and natural killer T cells exert direct cytotoxic effects to destroy tumor cells and are the first line of defense against tumor cells. In addition, M1 macrophages exhibit efficient phagocytosis and secrete pro-inflammatory cytokines that help to clear tumor cells. When tumor cells are stressed, they undergo corresponding changes to resist attacks from the TIME. For example, regulatory T cells [31], bone marrow-derived suppressor cells [32], tumor-derived exosomes, and tumor-associated macrophages (TAMs) all help tumor cells resist TIME-mediated damage and cause immune escape. Simultaneously, tumor cells change during this process. Moreover, when they escape to different parts of the body, they can adapt to the new environment for survival. Therefore, tumor cells show significant heterogeneity in their histopathology and vascular architecture [33].

2.1.4 Response to antitumor treatment

Currently, several therapeutic strategies can be used to treat tumors, including surgical resection, radiotherapy, chemotherapy, and immunotherapy. During the course of treatment, either the tumor focus should not be removed completely, it should be irradiated at the designated site using radiotherapy to relieve pain, or chemotherapy drugs should be used to continuously target the tumor cells and related immune components [8]. Nonetheless, to avoid these stimuli, tumors and related immune cells change their adaptability upon stimulation by this environment and establish new mechanisms to adapt to the new immune microenvironment [34]. Due to the inherent heterogeneity of gene mutations and molecular characteristics, the response of tumor cells to different therapies varies significantly. Furthermore, because of the TIME, tumor and immune cells are subjected to mutations, aging, and even apoptosis during their interaction. In the TME, tumor and T cells compete for methionine. When T cells lose methionine, their histone pattern becomes altered, thus promoting T cell damage and leading to tumor cell immune escape [35]. For immune cells, the T cell phenotype changes significantly in response to ICI treatment, which is accompanied by different T cell subsets and cytokine production [36]. For tumor treatment, the complex and dynamic interactions between tumors and immune cells jointly promote the formation of a spatiotemporally heterogeneous immune microenvironment.

Heterogeneity in tumor cells is not only due to the heterogeneity of TME but also the difference between the primary tumor and metastatic site, as well as temporal heterogeneity, which depends on the analysis time point of the primary tumor and metastatic tumor.

2.2 Temporal heterogeneity

Temporal heterogeneity refers to the state in which cancer cells in a tumor change according to the development of the tumor [37]. Many studies have suggested that tumor metastasis occurs during the early stages of tumor progression. Using a mouse model of spontaneous breast cancer, Humann et al. found that malignant metastatic cells can be found in the bone marrow when breast epithelial lesions occur, and most of these cells exhibit heterogeneity [38]. This study showed that the proliferation of breast cancer cells can occur soon after their malignant transformation. RNA-seq showed that the components of immune cell infiltrates also change significantly during PDAC progression from non-invasive to invasive lesions [39], in line with the research of Wu et al. on medulloblastoma metastasis [40].

However, many researchers believe that tumor metastasis occurs in the late stages of tumor progression [37]. Yachida et al. found that most gene mutations in each metastatic tumor of the same patient already existed in the primary tumor and that metastatic lesions in different parts can be linked to corresponding tumor cell subclones in the primary tumor [41]. This result supports the theory that an early diagnosis of pancreatic cancer might lead to a longer effective treatment time window. Sequencing studies on PCa and breast cancer have obtained similar results [42]. Some studies have also reported the migratory ability of specific immune subsets and the replacement of immune cells infiltrating from adjacent tissues or the peripheral circulation, which are important for understanding temporal immune heterogeneity [43]. Izar et al. [44] performed scRNA-seq on malignant ascites from patients with advanced serous ovarian cancer and found significant differences in the cell status and functions between malignant and non-malignant cells. Diverse cancer-related fibroblasts can also be observed among non-malignant cells, of which inflammatory cancer-related fibroblasts largely express IL-6 and other cytokines and might promote tumor growth and drug resistance. Scheffer et al. [45] compared the scRNA-seq data sets of ovarian cancer before and after chemotherapy and found that the types of cancerous epithelial cells and immune and matrix components detected in the samples after chemotherapy were different from those before chemotherapy.

In addition, during disease progression, for multiple tumor types, cytolytic activity is impaired, cell bank expansion and cloning are limited, and progressive T and B cell failure can be observed. The occurrence of immunocompromised regions or lesions in individual patients appears to be inversely proportional to disease control and survival prognosis. This further strengthens the importance of spatiotemporal heterogeneity in disease outcomes.

2.3 Spatial heterogeneity/multiple-site sampling

The spatial heterogeneity of tumors refers to the differences in morphology and composition of tumor cells in different parts from the same individual. Tumor cells undergo various changes in the microenvironment and genomic alterations during their proliferation and evolution; when they obtain multiple mutations, they form different cell subpopulations, and significant differences exist in gene expression, proliferation rates, morphology, drug resistance, and survival status among various cell subpopulations [46]. Berglund et al. [47] used spatial transcriptome technology to study the heterogeneity of PCa gene expression and found that even the same tumor showed significant differences in the transcriptomes of cancer cells from different parts. Similarly, Moncada et al. [48] combined a multimodal intersection analysis method with scRNA-seq and spatial transcription technology to study cell compositions in different regions of PDAC and found that macrophages, ductal cells, dendritic cells, and cancer cell subsets exhibited spatially restricted enrichment. In addition, inflammatory fibroblasts and stress-responsive cancer cells were found to be co-localized. Brannon et al. [49] used KRAS, BRAF, and NRAS mutation analysis results when studying the gene mutation profiles of primary colorectal and metastatic tumors to prove that the results found for primary tumors and metastatic tissues matched completely. This finding also applies to other driver genes, such as APC, PIK3CA, and TP53. However, because there are many “passenger” mutations, far more than driver genes, heterogeneity often occurs between the primary and metastatic tumors [50]. Ji et al. [51] combined scRNA-seq technology with spatial transcriptome technology and multichannel ion beam imaging to determine the cell composition and structure of skin squamous cell carcinoma. This revealed the spatial heterogeneity of this disease and highlighted potential intercellular signals that control the position and status of related cells. They also used a tumor xenotransplantation model and in vivo CRISPR screening to determine the important role of the rich gene network of specific tumor subgroups in tumorigenesis.

In a study by Zhang et al. [52] on the immune microenvironment of colorectal cancer, the SSGSEA [53] method and TCGA database were used to retrieve relevant expression profile data, and the immunophenotypes were compared between left and right colorectal cancer. They found that in a cohort of 638 patients with colorectal cancer, many immune cells showed significant heterogeneity in terms of invasion. This was consistent with a previous report on tumor-infiltrating lymphocyte subsets in colorectal cancer [54].

In addition, the tumor cells of different tumor species have different evolutionary tree shapes; specifically, a greater number of branches indicates more subclones in the tumor and a lower selective pressure on the tumor cells. For this analysis, some tumors had long trunks but few branches. This evolutionary pattern is common in breast, kidney, and lung cancers [55], suggesting large commonalities, small differences, and improved drug-targeted treatment effects among subclones within tumors. The shape of the tumor evolutionary tree between different cancer types or between different individual tumors of the same cancer type is extremely significant and could be related to the TME and different selective pressures.

2.4 Molecular pathological epidemiology

In addition to endogenous factors, exogenous factors, such as diet, nutrients, alcohol, smoking, obesity, lifestyle, environmental exposures, and the microbiome, can contribute to the heterogeneity of tumors and the tumor immune microenvironment. For cancer, exposure to many materials has been established as either risk or protective factors. These can influence tumor progression by altering the tumor microenvironment [56,57]. Systemic physiologic factors, such as immune, inflammatory, metabolic, and hormonal conditions, are affected by exposure and, in turn, influence local tumor development. For example, a prospective cohort study has shown that regular aspirin use is associated with a lower incidence of the colorectal cancer subtype with lower TIL levels, but not that of the subtype with higher TIL levels [58]. Whereas body mass index (BMI) has been reported to be associated with the risk of colorectal cancer subtypes, classified based on lymphocytic infiltrates, this is not significant [59]. Despite the potential benefits of studying the complex interactions among exosomes, the microbiota, and cancer in human populations, there have been significant technical and practical challenges in conducting such integrative analyses. Consequently, only a few large-scale studies have investigated these interactions. To address this gap, the transdisciplinary field of molecular pathological epidemiology (MPE) provides a framework for integrating tumor immunology into population health sciences and connecting exposure and germline genetics, such as HLA genotypes, to tumor and immune characteristics, which increases our understanding of the complex interactions among these factors [56,60]. The strengths of MPE are most apparent when an association between exposure and the tumor has not been established with certainty, such as tumor occurrence, mortality, and biomarkers that might indicate tumor progression [61]. Once etiological links have been established between putative risk factors and particular molecular pathological features, a stronger association with a particular TIME subtype can be obtained [60–62]. Thus, research on MPE can contribute to the discovery of causality [60–62]. A recent study showed that pro-inflammatory diets might be associated with higher mortality, particularly in patients with colorectal cancer with lower levels of or absent TILs [63]. There has also been a study on plant-based diets showing that those with a high proportion of refined grains and sugar might be associated with a higher CRC incidence, whereas whole grains, fruits, and vegetables in the daily diet are related to a lower incidence of CRC, and particularly non-KRAS-mutated CRC [64]. Microbes, including bacteria, fungi, archaea, and viruses, have been implicated in formation and alteration of the TIME [57]. In a preclinical study on CRC and melanoma, the therapeutic efficacy of TIM-3 blockade was reduced by antibiotics, whereas the oral gavage of fecal bacteria restored this [65]. Accordingly, in patients with melanoma refractory to anti-PD-1 therapy, fecal microbiota transplantation (FMT) is associated with the upregulation of CD8⁺ T cell activation and the downregulation of IL-8 expression in myeloid cells, which are involved in immunosuppression. FMT also results in favorable immune and microbial profiles in the gut and TIME [66,67].

3 Technologies in exploring heterogeneity of the time

3.1 Liquid biopsy

Although tumor tissue biopsy is one of the best choice for exploring tumor immune heterogeneity, sampling is still challenging. With the development of sensitive techniques, liquid biopsy was developed to characterize tumors by circulating tumor cells (CTCs), circulating tumor DNA (ctDNA), circulating free DNA or RNA, exosomes, and other circulating extracellular vesicles [68–72]. CTCs and ctDNA have received more attention in the past decades owing to their real-time features [73]. CTCs are tumor cells that are released from primary and/or metastatic tumors, whereas ctDNAs are DNA fragments derived from primary, metastatic, and/or CTCs. The methodologies for ctDNA detection include real-time PCR (qPCR), digital PCR (dPCR), mass spectrometry, and next-generation sequencing (NGS) [74].

qPCR is a cheap and fast PCR method to identify common mutations of more than 10%–20% allele frequency. The basic principle is based on blocking the amplification of the normal allele at the oligo 3′-end to allow amplification of the mutant allele [75]. dPCR is a robust method for detecting single nucleotide polymorphisms in low allele fractions using two techniques: droplet dPCR and beads, emulsions, amplification, and magnetics dPCR (BEAMing). The former technique divides DNA samples into thousands to millions of water–oil emulsions before amplification and then evaluates the specific DNA strand by flow cytometry using fluorescent TaqMan-based probes [74,76]. The latter analyzed fluorescently labeled terminators to detect differences between DNA strands using flow cytometry. However, the lack of multiplexing ability is a major limitation of PCR in the clinical setting. Mass spectrometry was developed to overcome this limitation by detecting up to 40 targets with low-frequency mutations per reaction [77]. NGS is the most efficient technique for obtaining genetic information, such as SNP and CNVs, with high specificity and sensitivity. The individual DNA molecules tagged with bioinformatic analysis and higher-fidelity DNA polymerases allowed for ultrasensitive detection of ctDNA [78]. Commonly used methods include deep sequencing [79], bias-corrected targeted NGS, multiplex-PCR NGS, FAST-SeqS, Tam-Seq, Safe-SeqS, CAPP-Seq, and Duplex-Seq.

Currently, the application of liquid biopsy mainly focuses on early detection of cancer, prognosis prediction of therapy, and tumor burden monitoring due to its association with heterogeneity detection of the TIME [80,81]. Jia et al. [82] individually designed neoantigen panels for ctDNA sequencing to monitor tumor progression in patients with non-small cell lung cancer (NSCLC) receiving ICI treatment. The tracking analysis of each patient showed that the change in neoantigen load indicated the clinical outcome and tumor burden changes; for example, the decline of ctDNA suggested a favorable clinical outcome [82]. ESR1 mutation as one of the factors that lead to metastases of ER-positive breast cancer after endocrine therapy is ineffective. Chu et al. reported that circulating plasma tumor DNA had a higher mutation detection rate for ESR1 mutations than tissue biopsy in metastatic breast cancer [83]. Recently, Zhang et al. performed parallel sequencing of plasma and white blood cells to obtain ctDNA data from over 10 000 pan-cancer Chinese patients and found that the genomic landscape between ctDNA and tissue biopsies was different. Clinically valuable genomic variants were identified to facilitate the development of therapeutic strategies [84].

3.2 Single-cell transcriptome sequencing

As one of the most important breakthrough in sequencing technology, single-cell sequencing, especially single-cell RNA sequencing (scRNA-seq), plays a crucial role in understanding tumor immune heterogeneity. The basic processes of both platforms include single-cell isolation, RNA extraction, reverse transcription, cDNA amplification, library construction, and sequencing [85]. Low-throughput plate-based and high-throughput droplet-based platforms are commonly applied in scRNA-seq. Plate-based platforms isolate cells into wells on a plate, thus enabling visual inspection of empty wells or doublets [86]. It attempts to sequence the transcripts as full-length, which allows not only for the detection of gene expression but also for the analysis of splicing variants and high-abundance receptor repertoires [87]. The major drawback of plate-based platforms is their high cost and limited throughput—only tens of cells can be sequenced at the same time due to the finite number of wells of plates [85].

In comparison, droplet-based platforms take advantage of a microfluidics-based strategy to capture cells into nanoliter droplets with DNA-barcoded reads for reverse transcription. This technique sequenced thousands of cells in one batch, which enabled us to comprehensively analyze the composition of samples and capture rare cell types [88,89]. Recently, the Chromium X platform developed by 10x Genomics was able to sequence up to 1 million cells in one experiment. However, the defect of droplet-based platforms cannot be ignored, such as higher dropout rates than plate-based platforms [5]. Platform selection to solve this biological question should be carefully considered in this study. Plate-based platforms are suitable for characterization of specific cell types or specific purposes, such as allelic gene expression analysis, while droplet-based platforms are more suitable for TME exploration in one or across multiple cancers or rare cell type identification [90,91].

Findings based on scRNA-seq in various cancer types have been well summarized in a review by Jia et al. [7]. Recently, this technique has been used in a large-scale cohort of single tumors to reveal the undiscovered property of TIME or across multiple cancers to uncover the commonalities and differences of specific cell types in TME. Zheng et al. constructed a pan-cancer T cell atlas that compiled 397 810 T cells from 316 patients with tumors of 21 cancer types, adjacent normal tissues, and peripheral blood [91]. This study revealed that exhausted CD8⁺ T cells are the major tumor-reactive T cell populations for CD8⁺ T cells, and follicular helper T cells and T helper 1 dual-functional T cells for CD4⁺ T cells. Moreover, patients with a high frequency of terminally exhausted CD8⁺ T cells or tissue-resident memory CD8⁺ T cells can be classified into different immune types. The latter was associated with better clinical outcomes than the former in patients with multiple tumors. Liu et al. demonstrated that SPP1⁺ TAMs may play an important role in liver metastasis of colorectal cancer by comparing CD45⁺ cell scRNA-seq data between primary and metastatic sites [92]. Another study from the same group collected 189 samples from 124 patients who drew a landscape of TIME in liver tumors [93]. On the basis of 1.3 million single cell transcriptomic data, five TIME subtypes were identified and their association with the clinical outcome of each subtype was also tested. Notably, the heterogeneity of neutrophils in liver tumors was investigated for the first time, and tumor-associated neutrophils were demonstrated to be an unfavorable prognostic factor.

3.3 Spatial omcis technologies

In addition to scRNA-seq, spatial omics can provide another aspect of tumor immune heterogeneity by combining gene expression patterns and spatial locations in one sample. Recent technological advancements in solid-phase sequencing and multiplex imaging have enabled the multiplexed detection of transcripts and proteins based on a spatial distribution. The initial spatial transcriptomic (ST) technology can be used to detect target genes based on the hybridization of a complementary fluorescent probe, called in situ hybridization (ISH) or image-based ST [94,95]. Several ISH-based methods have been developed including smFISH, seqFISH, MERFISH, and split-FISH. In situ sequencing is another image-based ST technology that uses padlock probes to directly read the sequences of transcripts within a tissue [96]. In brief, the limited number of targeted genes and low throughput are the main problems that restrict their applications in tumor research. To overcome this limitation, next-generation sequencing (NGS)-based approaches have been developed, and these have become mainstream ST technologies. The first NGS-based method, introduced in 2016, captures whole STs with positional barcodes at a resolution of 200 μm [97]. Information on the location of the spots was added as barcodes before reverse transcription to ensure that each transcript could be determined accurately. Subsequently, 10x Genomics implemented a similar strategy and released Visium to improve the resolution from 200 μm to 55 μm for each spot. Slide-seq further increases the resolution to 10 μm using whole slides with DNA-barcoded beads for tissue section loading [98]. Recently, spatiotemporal enhanced-resolution omics sequencing (Stereo-seq) has used random barcode-labeled DNA nanoball arrays to achieve a nanometer resolution. Even the lack of z-axis information or merely a one-cell layer of tissue is a limitation of NGS-based ST technology, and this is the most effective solution to date.

STs have also been widely used to explore tumor immune heterogeneity. Wu et al. revealed distinct gene module enrichment in ER⁺ BC and TNBC, whereas EMT-, IFN-, and MHC-related gene modules, as well as proliferation-related gene modules, were found to be mutually exclusive based on a spatial distribution [99]. Wu et al. combined STs with scRNA-seq to create a landscape of liver metastases in colorectal cancer. A comprehensive analysis demonstrated that immunosuppressive cells were spatially reprogrammed in the metastatic microenvironment, and MRC1⁺CCL18⁺ M2-like macrophages were the most significant enrichment subpopulation at the metastatic site [100].

Imaging mass cytometry (IMC), an extension of cytometry time-of-flight (CyTOF), has been applied to spatially resolved measurements at the protein level [101]. The first step in IMC is similar to that of CyTOF, in which tissue sections are stained with more than 30 metal-labeled antibodies [101]. Subsequently, rather than working on single cells in suspension, tissue sections or cells immobilized on slides are ablated using a laser with a 1 μm spot size to generate particles [101–103]. The particles are then transported into a CyTOF mass cytometer using a mixed argon and helium stream, where they are atomized and ionized in the plasma ion source. A time-of-flight mass analyzer can then be used to estimate epitope abundance and distribution based on the metal isotope ion content [101,104]. The entire slide can be scanned, spot by spot, to obtain an intensity plot of all target proteins throughout the tissue section [101,102]. After data collection and preprocessing, a high-dimensional image of the tissue section is generated through combined analysis of 32/44 single isotope signals [101]. The watershed algorithm is further utilized to segment single cells among spots and generate a corresponding feature expression landscape, facilitating downstream analysis and visualization at the subcellular or single-cell resolution.

Moldoveanu and colleagues used CyTOF IMC to characterize components of the TIME from melanoma patients, including their spatial distribution and cell–cell interactions [105]. The increase in proliferating antigen-experienced cytotoxic T cells and the smaller distance between antigen-experienced cytotoxic T cells and malignant cells was found to correlate with better clinical outcomes of ICI treatment [105]. Using IMC in pre-treatment tissue from patients with metastatic melanoma who received ICIs, Rimm et al. revealed 12 markers that were significantly associated with prognosis, including B2M [106]. IMC has been utilized not only for melanoma but also for other cancers, such as lung cancer. Jang et al. showed that the spatiotemporal evolution of tumor-associated macrophages affects LUAD progression and controls the efficacy of immunotherapy through CyTOF and IMC [107]. Immunoregulatory PD-1-expressing TAMs were also found to contribute to immune invasion, tumor protection, and TIME remodeling [107].

3.4 Digital pathology

Pathology has played a crucial role in both research and clinical applications in the field of cancer (Fig.2, Tab.2). With increasing technological advancements and importance, digital pathology has rapidly developed into a quantitative pathological assessment using computational approaches. One of the cores of digital pathology is the novel imaging systems that scan pathology slides into digital versions, such as whole slide images (WSI) [108]. WSI, commonly referred to as “virtual microscopy,” aims to encompass the digitization of entire histology slides or preselected areas in four steps: image scanning, storage, processing, and display [109,110]. Two scanners have been approved by the Food and Drug Administration (FDA) to review and interpret digital surgical pathology slides prepared from biopsied tissues [111]. In combination with staining techniques, bright-field, fluorescence, and multispectral scanning can be supported by WSI [112]. Bright-field scanning is a simulation of bright-field microscopy with low-cost and broad application scenarios. Fluorescent scanning can be used for fluorescent immunohistochemistry (IHC) and fluorescent ISH slides. Unlike the former two, multispectral scanning can work with both bright-field and fluorescent settings to capture spectral information across various light spectra [113]. In addition, the selection of planes, magnifications, scanner models, and the WSI system is important for the digitization of slides [108]. WSI and matched quantitative imaging tools can be applied to accurately identify and quickly quantify specific cell types, as well as to evaluate histological features, morphological patterns, spatial distribution, and cell–cell interactions in cell populations [114–118].

The technique of comprehensive analysis of whole slides rather than selective regions should be another core of digital pathology. Artificial intelligence (AI) is used as an advanced computational approach in digital pathology to process both low-level tasks, focused on object recognition problems, as well as high-level tasks, such as predicting tumor diagnosis and clinical outcomes [72,115,119–126]. Machine learning (ML) is a subset and an applicable concept of AI that allows algorithms to extract features, train models with large amounts of available data, and finally make predictions in new data sets [127]. Supervised and unsupervised learning methods are the two main types of ML algorithm. Supervised learning methods are commonly used in category prediction, such as in response to therapy. Unsupervised methods can be applied to cluster objects with similar patterns to demonstrate hidden relationships [115]. Dozens of algorithms have been developed to solve various problems; therefore, algorithm selection should be carefully performed to make the prediction credible and accurate. However, features in ML should be handcrafted with background knowledge to promote prediction efficacy. To address this, deep learning has been developed as a subset and a further step in machine learning to create a system for feature detection directly from input data [111,128]. Input data commonly contain a set of digital pathological images with matched labels, such as tumors or normal tissues [129,130]. Subsequently, the new data can be input to predict and classify without pre-existing assumptions based on well-trained models [115]. A multilevel deep neural network is always involved in the process, which uses nonlinear transformations in hidden layers to connect the input and output layers to generate prediction [127].

WSI combined with AI has been successfully applied in various fields of digital pathology, such as in enhancing the understanding of TIME and in evaluating treatment response. Pavillon et al. developed a method based on label-free microscopy and a logistic regression model to identify lipopolysaccharide-induced macrophage activation at the single-cell level [131]. Sun et al. constructed a radiomics-based predictor with 84 selective features based on CT images and RNA-seq data from tumor biopsies, which accurately evaluated the infiltration level of CD8⁺ T cells. Another group has developed an AI-powered WSI analyzer that can be applied to define the level of tumor-infiltrating T cells in each tumor as flamed, immune-excluded, and immune desert and predict the clinical outcome of ICI treatment [121]. This classification has been proven to be associated with the clinical outcome of ICI-based treatment. In addition, the incorporation of a weakly supervised ML-based approach and conventional H&E WSI has been proven to improve the prediction performance of breast cancer molecular subtyping and to assist pathologists in selecting formalin-fixed paraffin embedded slides for IHC [132].

Despite recent studies demonstrating the successful application of digital pathology, some limitations remain. The main limitation is that a successful model requires a large amount of high-quality and well-annotated training data, which could limit its accuracy and clinical significance. Other limitations include advanced AI algorithms, computational resources, and time-consuming development processes [132,133]. Additionally, digital pathology has not yet been introduced in hospitals on a large scale. Multiplex IHC, a technological approach that collects the expression patterns of several proteins and their spatial distributions in a single tissue section, might play an even more important role in AI pathology. Recent advancements in multiplexed IHC have been summarized in several reviews [117,134,135].

4 Clinical intervention strategy to overcome tumor immune heterogeneity

There is no doubt that tumor immune heterogeneity is one of the crucial decisive factors that impact clinical outcomes during antitumor treatment. According to Chen and Mellman’s theory, the tumor immune phenotype can be classified into immune-inflamed, immune-excluded, and immune-desert phenotypes. In immune desert tumors, there is almost no T cell infiltration; thus, the most important thing is to activate T cells by promoting antigen release and presentation. The situation of immune-excluded tumors is better than the former, and the location of the immune cells, especially T cells, is restricted to the surrounding tumor. To solve this problem, T cell trafficking into tumors is necessary. Although immune-inflamed phenotype represents the infiltration of T cells in the tumor, some patients do not respond to treatment for several possible reasons, such as less specific tumor-reactive T cells. Adoptive T cell therapy is a possible therapeutic strategy that has been developed to recognize tumor-specific antigens (TSAs) to stimulate and proliferate specific tumor-associated T cells. In addition to the aforementioned immune phenotypes, modulating other components such as genomic instability can also be used for therapy selection. Therefore, we briefly summarize some of the clinical intervention strategies based on their mechanisms or targets (Fig.3, Tab.3).

4.1 Modulating genetic processes of tumor cells

During tumor progression, genetic processes, such as genomic instability, lead to the random generation of these alterations, resulting in tumor immune heterogeneity. PARP inhibitors (PARPi), one of the most notable agents for genomic instability, have been reported to treat patients with wild-type copy loss of BRCA1 or BRCA2 genes in breast cancers [136,137]. The antitumor activity of PARPi can be explained by the trapping of PARP1 in DNA lesions [137–139]. In BRCA1/2-deficient patients, the repair mechanism of double strand breaks (DSBs) that depends on these two genes is interrupted. Trapped PARPi, which results in the accumulation of DSBs during the S phase of the cell cycle, breaks another repair mechanism, leading to synthetic lethality. In addition, evidence derived from preclinical studies has shown that PARPi increases tumor-infiltrating or peripheral blood CD8⁺ and CD4⁺ T cells with monotherapy or a combination of ICIs [140,141]. Wulf et al. described that PARPi could reprogram TAMs into a higher cytotoxicity and phagocytosis status, resulting in the enhancement of antitumor immune responses both in vitro and in vivo [142]. The therapeutic efficacy of PARPi has been proven in several clinical trials [143–147]. Four PARPi, including olaparib, rucaparib, niraparib, and talazoparib, have been approved by the FDA for use in breast, ovarian, and prostate cancers. However, most patients acquire PARPi resistance after prolonged treatment with these drugs following a good initial response, leading to tumor recurrence. Three mechanisms have been described to explain the occurrence of resistance: drug- or target-related alterations, HR restoration, and restoration of replication fork stability [139]. Combination therapy is a potential strategy to overcome resistance, particularly when combined with ICIs. Published phase I/II trials with the combination of PARPi and ICIs reported approximately 15%–44% objective response rates (ORR) in each cohort [148]. Furthermore, a series of phase III clinical trials on combination strategies are ongoing.

In addition to genomic instability, clonal driver mutations known to be directly implicated in immunosuppressive pathways, such as epidermal growth factor receptor (EGFR), RAS, RAF, MYC, PTEN, and WNT/β-catenin, have become another target for sustaining tumor control [149–154]. Kinase inhibition in tumor cells by tyrosine kinase inhibitors (TKIs) offers promising clinical benefits for cancer patients, especially for those with clonal driver mutations [155,156]. Two mechanisms have been summarized for TKIs in the tumor environment: modulating tumor cell sensitivity and regulating the components of immune cell populations.

For example, EGFR is a common driver gene mutation that occurs in approximately 20% of patients with NSCLC [157]. Since the discovery of activating EGFR mutations in 2004 [154,158,159], EGFR-TKIs have been recommended as the first-line therapy drugs for patients with EGFR mutant NSCLC in the current treatment guidelines. In preclinical studies, EGFR-TKIs have been found to increase immune cell infiltration, induce local proliferation of T cells, and mediate the activation of antigen-specific T cells [160,161]. Isomoto et al. described that EGFR-TKI significantly decreased CD8⁺ and FOXP3⁺ TIL densities, whereas CD8⁺ TIL density was significantly higher in PD-L1 strongly positive tumors (≥ 50%) than in others (< 50%) after treatment in a retrospective study [9]. However, multiple clinical trials have proposed that combination strategies of EGFR-TKI with ICIs are not suitable for patients with EGFR-mutated NSCLC because of the low efficacy rate [162], cytotoxicity [163], and explosive progress [164]. Notably, a recent study reported that ORR and disease control rate (DCR) achieved 32.0% and 70.0%, respectively, in ICI therapy after progression to EGFR-TKI treatment [165]. It also reported that front-line ICI post-TKI progression significantly improved prognosis than later-line ICI (mPFS, 7.2 months vs. 3.4 months; mOS, 15.1 months vs. 8.4 months).

Fusions of ALK and EML4 has been identified as clonal driver mutations in approximately 2%–5% of patients with NSCLC, predominantly in young, never, or former smokers with adenocarcinoma [166,167]. Several ALK inhibitors have been approved by the FDA, including crizotinib, ceritinib, alectinib, brigatinib, and lorlatinib. A mouse clinical trial showed that CD8⁺ T cells decreased after ceritinib treatment, while M2 macrophage and Tregs increased. Using multi-omics analysis, ALK-TKI treatment has been found to reconstruct TIM in treatment-responsive patients [168]. Infiltration and cytotoxicity of antitumor cells were significantly increased in responder patients, whereas the same change was not observed between pre-treatment and non-responder patients. Similar to EGFR-TKIs, combination with ICIs also faces problems, including high toxicity and short-term efficacy. A phase I/II clinical trial showed that 5 of 13 patients developed severe hepatic toxicity, and 2 patients died of treatment-related adverse effects, resulting in the closure of patient enrollment [169].

In addition to EGFR-TKIs and ALK-TKIs, multiple clinical trials have shown the efficacy of the combination of BRAF and MEK inhibitors with anti-PD-L1 or anti-PD-1 in melanoma [170–172]. Translation research has shown that the combination strategy is associated with an increase in proliferating CD4⁺ Th cells, but not with an increase in Treg cells [171]. Moreover, patients who had a complete response had significantly lower baseline levels of immunosuppressive TME signatures and had a consistent increase in T cell-inflamed gene expression profiles and a decrease in MAPK pathway activity score from baseline to biopsy at 2–3 weeks. An ongoing phase III clinical trial reported that compared to patients treated with placebo, cobimetinib, and vemurafenib, patients treated with atezolizumab and the same TKI achieved better PFS [173]. The FDA has approved atezolizumab plus cobimetinib and vemurafenib for the treatment of patients with BRAF V600 mutation-positive advanced melanoma.

4.2 Promoting activation of antigen presenting cells

Antigen-presenting cells (APCs) are crucial immune components involved in antigen recognition and presentation during the cancer-immunity cycle [174]. Clinical intervention strategies that activate APCs, especially DCs, may be sufficient to overcome tumor suppression and stimulate tumor antigen-specific T cell responses against tumors. Ongoing strategies include the use of STING agonists, TLR agonists, and CD40 agonists.

Over the past decades, a body of research has clearly linked DCs, type I IFN, and the STING pathway to antitumor responses [175–179]. Tumor-associated antigens (TAAs) can be captured by DCs, resulting in activation of the cGAS-STING signaling pathway that triggers the production of type I IFN and facilitates recruitment and priming of tumor-specific CD8⁺ T cells. Preclinical models have shown that STING agonists can enhance the activation of cytotoxic CD8⁺ T cells, decrease the number of Tregs, upregulate costimulatory molecule expression on cross-presenting DCs, and convert immunosuppressive macrophages into immunosuppressive states [180]. Notably, preliminary results from two clinical trials in solid tumors showed that a few patients achieved CR/PR (9.4% for NCT03172936 and 24% for NCT03010176), indicating that successful clinical application of STING agonists is still a long way to go.

Toll-like receptors (TLRs) are a class of proteins expressed on cells, such as macrophages and DCs, and play a crucial role in the antitumor immune response by recognizing pathogen-associated molecular patterns (PAMPs) [181]. For example, TLR3 agonists have been reported to upregulate the expression of TNF-α, IFN-γ, IL-6, IL12 and IL-10, and potentially increase tumor-specific CTLs [181,182]. TLR9-activated DCs express surface proteins and upregulate cytokines and chemokines to activate NK cells and expand T cells, especially Th1 cells and CTLs [183]. Koh et al. developed a nanoemulsion loaded with a TLR7/8 agonist, combined with ICIs, to demonstrate robust antitumor immune responses in preclinical models. Combination therapy induces tumor-specific T cell activation and inhibition of T cell exhaustion [184]. Recently, clinical trials of TLR agonists have been extended using combination therapy strategies. In a phase II clinical trial of melanoma with SD-101 and pembrolizumab, more than 50% of anti-PD-1/PD-L1 resistant patients responded to treatment, and the increase in immune cell infiltration was correlated with clinical response [185]. In the same clinical trial, the combination seemed to convert the cold TME into a hot TME and showed a promising response rate for naïve-treatment patients [185].

As a cell surface member of the TNF receptor superfamily, CD40 triggers APC activation to induce adaptive immunity via recognition of its ligand on activated T helper cells [186]. One phenotype of CD40 activation results in the production of MHC molecules, Ig superfamily costimulatory molecules, and TNF superfamily ligands, thus enhancing antigen presentation and CD8⁺ T cell activation. In addition, CD40-activated DCs upregulate T cell-stimulatory cytokines, such as IL-12, to activate CD8⁺ T cells. Treatment with a human CD40 agonist in a bladder tumor model induced significant antitumor effects and long-term tumor-specific immunity [187]. Six CD40 agonist monotherapies have been used in clinical testing. However, the ORR of solid tumors with CD40 agonist monotherapy was not expected [188,189]; only 4 of 15 patients with advanced melanoma achieved partial response with Selicrelumab [190]. The exploratory analysis of a long-term, ongoing, complete remission patient from the trial showed that tumor infiltrating T cells were increased and de novo T cell repertoires emerged both in the tumor and blood after treatment, indicating a continuous antitumor response [191]. The combination of selicrelumab and tremelimumab achieved an ORR of 27% in a phase I clinical trial with 22 metastatic melanoma patients [192]. The overall survival of nine patients was more than 36 months, while eight of them were subsequently treated with other therapeutic strategies. CD8⁺ T cell infiltration, reinvigoration, and clonal expansion have been observed in post-treatment patients as well as in preclinical experiments.

4.3 Priming, trafficking, and engineering T cells

As the most important immune components in TME, T cells are not only used to evaluate the immune phenotypes of the TME but are also an ideal target to overcome heterogeneity of the TIME with several strategies, including T cell priming, trafficking of T cells into tumors, and T cell reprogramming.

Originating from the earliest immunotherapy strategy, oncolytic viruses (OVs), which utilize genetically modified viruses to infect tumor cells, are emerging as important agents in cancer treatment. A general mechanism of OVs in tumor cell elimination is the induction of an antitumor immune response by antigen release and presentation [193]. Subsequent to viral infection, classical antiviral innate and adaptive responses are activated, inhibiting the replication and spread of viruses. However, Kroemer et al. stated that such an antiviral response would make viruses act as T cell primers, leading to an antiviral T cell response directly targeting cancer cells [194,195]. Talimogene laherparepvec (T-VEC) was the first FDA-approved HSV-1-based OV for patients with advanced melanoma. A phase III clinical trial before approval reported that the overall response rate and complete response rate of 436 melanoma patients advanced by 26.4% and 10%, respectively, in the T-VEC monotherapy treatment arm [196]. In a phase 1b trial of T-VEC plus pembrolizumab, responders showed a marked increase in CD8⁺ T cells, PD-L1 protein expression, and IFN-γ gene expression. Moreover, the baseline level of CD8⁺ T cell infiltration and IFN-γ signature were not related to clinical benefit, thus indicating the potential capability of OVs in “cold to hot” cold conversion [197]. In addition to T-VEC, several differential OVs, including adenovirus-based and reovirus-based OVs, are under preclinical development or clinical trials.

Tumor vaccines are a rapidly developing immunotherapy strategy to overcome tumor immune heterogeneity because of their potential capability to promote antitumor immune responses by targeting tumor antigens [198–200]. Unlike the common classification, which has introduced tumor vaccines based on the structure or adjuvants of the antigen, such as peptide, RNA, DNA, and DC-based vaccines, the latest review of tumor vaccines from Brody’s group has classified tumor vaccines into two major types: predefined (known) or anonymous (unknown) antigen vaccines based on the (1) understanding of the antigens, (2) expression of tumor antigens, and (3) mechanism between tumor antigens and APCs [201]. Predefined antigens can be further classified into predefined shared antigens, including TSAs, TAAs, and predefined personalized antigens. The final goal of tumor vaccines is to enhance the antitumor immune response and eliminate tumor cells. Tumor vaccination involves four steps. First, tumor antigens must be directly presented on the surface of tumor cells and presented by APCs, such as DCs. Second, APCs migrate to the lymph nodes to present antigens to naïve T cells. Third is the activation and differentiation of naïve T cells into effector CTLs, which then migrate back to the tumor. Finally, effector CTLs fully utilize their functions to eliminate tumor cells [202–205].

TSAs are uniquely found in tumor cells, such as in viral antigens and neo-epitopes, resulting from non-synonymous somatic mutations. The Epstein–Barr virus encodes multiple antigens, including latent membrane proteins (LMP1 and LMP2), which can be expressed in nasopharyngeal carcinoma [206]. The LMP1 vaccine has proven efficacy in suppressing tumor growth and metastasis in vivo. In addition, it can induce large amounts of activated CTLs and LMP1-specific T lymphocytes [207]. More recently, a recombinant vaccinia virus, MVA-EL (an EBNA-LMP2 fusion protein), was reported to induce the differentiation and functional diversification of responsive CD4⁺ and CD8⁺ T cells, specific for EBNA1 and LMP2 [208]. A phase II clinical trial of ISA101, a12 HPV16 E6/E7 synthetic long peptide vaccine, showed that 15 of 19 patients had a clinical response, with a complete response in 9 of 19 patients at 12 months. Notably, a significantly stronger CTLs response was observed in patients with a complete response at 3 months than in others [209].

TAAs are not only found in tumor tissues but also in some normal host cells, including tissue-specific antigens and development-specific antigens. For example, WT1, as a development-specific antigen, has been shown to perform oncogenic rather than tumor-suppressor functions in hematological malignancies and solid tumors [210]. A clinical trial of a WT1-targeted DC vaccine reported that 8 of 10 patients had a significant increase in the WT1-specific CTL response, indicating a stronger antitumor immune response [211]. A phase III clinical trial of galinpepimut-S in AML is ongoing. MAGE-A3 is a member of the cancer-testis antigen family with apoptotic function that is expressed in 30%–50% of NSCLC, 74% of melanoma, and 35% of sarcoma and bladder carcinoma. In vivo experiments have suggested that capture of MAGE-A3 antigens by DCs may promote the priming of naïve T cells and the induction of CD8⁺ T cell response [212,213]. Notably, two recent large phase III clinical trials of melanoma and NSCLC reported that MAGE-A3 vaccine as monotherapy or adjuvant therapy was not efficacious and did not increase disease-free survival compared to placebo in patients [214,215]. In addition to the MAGE-A3 vaccine, clinical trials of several other vaccines, including the nelipepimut-S vaccine and gp100 vaccine, also failed to show the benefit of the vaccine alone [216,217]. To improve the efficacy, an anticancer vaccine comprising five TAAs (MAGE-A2, MAGE-A3, CEA, HER2, and p53) has been developed and proven to have a longer median overall survival than chemotherapy in 118 evaluable patients with NSCLC. Similarly, seviprotimut-L, a multivalent melanoma vaccine, has also shown better clinical outcomes in patients less than 60 years of age or with ulceration [218].

Compared to shared antigens, personalized antigens are generally neoantigens that are unique to the patient. Neoantigens, which are non-autologous proteins with individual specificity, are generated by somatic non-synonymous mutations in tumor cells [219]. With the development of NGS techniques, especially WGS and WES, neoantigen prediction and potentially immunogenic neoantigen selection have become more feasible and effective. A recent study demonstrated that treatment with NeoVax, a long-peptide vaccine targeting up to 20 personal neoantigens per patient, could induce a T cell response that persists for years and increase T cell diversity in melanoma patients [220]. Another study that used PD-1 blockade reported long-term tumor-specific immune memory by increasing CD8⁺ T_rm in a hepatocellular carcinoma (HCC) model [221]. In a phase I clinical trial of NeoVAC, 8 of 13 melanoma patients had no recurrence in 1 year, and 2 of 5 patients with metastasis had significant tumor regression after vaccination [222]. However, large-scale phase III trials are still necessary to prove the benefits of neoantigen vaccines in a broad range of patients.

Trafficking of tumor-reactive T cells at the tumor site is a critical step for successful treatment. Angiogenesis is an important factor in inefficient trafficking [223]. Dozens of antiangiogenic drugs have been approved by the FDA since 2004 and mainly contain antiangiogenic monoclonal antibodies, such as bevacizumab and ramucirumab, and antiangiogenic TKIs, such as sorafenib, regorafenib, and sunitinib. The former predominantly binds to vascular endothelial growth factor A (VEGFA) or VEGFR2, whereas the latter is generally used as a VEGFR inhibitor [224]. The mechanism of action includes the prevention of aberrant vascular formation and upregulation of ICAM-1 and VCAM-1, which have been reported to promote T cell infiltration [225,226]. Notably, various studies have shown that there are interactions between angiogenesis and various immune components; thus, combination therapy with antiangiogenic drugs may enhance the efficacy of immunotherapy [227]. Combining PD-1 blockade and anti-VEGFR-2 in an induced-murine HCC model has shown that TME can be reprogrammed by enhancing CD8⁺ CTL infiltration and activation, shifting the M1/M2 ratio of TAMs, and reducing Tregs [228]. Another study reported that VEGFA induced the expression of TOX to drive T cells into exhaustion status in a PD-1 inhibitor-resistant MSS CRC model; thus, combination therapy reinvigorated the antitumor function of T cells, leading to better tumor control [229]. In a phase III clinical trial, KEYNOTE-426 updated the median OS to 45.7 months in pembrolizumab plus axitinib treatment in advanced RCC patients versus 40.1 months in the sunitinib arm, indicating the superior efficacy of the combination [230]. In a similarly designed phase III trial of mRCC, ORR in PD-L1 positive patients was 43% with 9% complete response in atezolizumab plus bevacizumab versus 35% in sunitinib arm [231]. The final analysis showed that the median OS in PD-L1 positive patients was 38.7 months versus 31.6, respectively [232].

Adoptive cell transfer is a category of immunotherapy that involves isolating patients’ T cells, engineering and expanding them in vitro, and reinfusing them back into patients to overcome tumor immune heterogeneity. Three major ACT therapeutic strategies have been developed: chimeric antigen receptor (CAR)-T cells, T cell receptor (TCR) T cells, and tumor-infiltrating lymphocytes (TILs).

In CAR-T cell therapy, various combinations of antigen binding domains, hinges, transmembrane domains, and intracellular signaling domains support the recognition of specific antigens to kill tumor cells [233]. It is notable that finding common tumor-specific antigens is the most effective and valuable method of treatment; for example, CD19 is a common antigen for blood cancers [234–237]. However, the efficiency of CAR-T cell therapy in solid tumors is still limited because it is difficult to find common expression-specific cell surface antigens in solid tumors. More than 30 antigens are being studied in solid tumors in ongoing or published trials around the world [238]. One possible way to solve this problem is to design bi-specific or multivalent CAR-T cell antigen [239]. Hedge et al. combined two different antigens, HER2 and IL13Rα2, to a single molecule and enhanced T cell functionality to improve glioblastoma control in a preclinical model [240]. A bicistronic construct to drive expression of an EGFRvIII and a bispecific T cell engager against wild-type EGFR has been proposed by Choi and colleagues [241]. In a preclinical model, CART.BiTE cells redirect CAR-T cells and recruit untransduced bystander T cells against wild-type EGFR by secreting EGFR-specific BiTEs, leading to heterogeneous tumor elimination in glioblastoma.

Unlike CAR-T therapy, TCR-T therapy is free from surface tumor-specific antigens of tumor cells. The key step in TCR-T therapy is to recognize peptide-MHC complexes on the surface of tumor cells through antigen-specific T cell receptors [242]. The first report of TCR-T therapy was for 17 metastatic melanoma patients, with an ORR of 12% [243], indicating its clinical potential. Several clinical trials are ongoing to investigate the safety and efficiency of TCR-T therapy for solid tumors. Additionally, to better understand the dynamic changes in the TIME during TCR-T therapy and further improve its efficiency, with the development of a 3D intrahepatic TME microfluidic device, Lee et al. observed that the cytotoxicity of retroviral TCR-T cells was suppressed by monocytes via PD-L1/PD-1 signaling [244]. Pavesi reported that the dependency of IL-2 in mRNA-TCR-T cells was reduced, which was the most important factor for retroviral TCR-T cells. This indicates that IL-2 as an adjuvant in immunotherapy may be more important in TCR-T therapy [245].

TIL therapy has been developed to overcome drug resistance of PD-1/PD-L1 antibodies in solid tumors. It infuses lymphocytes with high-dose IL-2 as an adjuvant to support lymphocyte proliferation before transferring to patients [246,247]. Compared to CAR-T and TCR-T, TIL therapy is composed of multiple TCR clones that can directly target both shared self-antigens and tumor-specific antigens, making it more effective in response to tumor immune heterogeneity [248,249]. To this end, WES technology and neoantigen prediction algorithms have been suggested for identifying candidate neoantigens [250]. Since the first successful application of TIL therapy in metastatic melanoma patients, its clinical benefits have been proven through several clinical trials. In 2016, a 50-year-old woman with metastatic colorectal cancer received a single infusion of 1.48 × 10¹¹ tumor-infiltrating lymphocytes, which consisted of approximately 75% CD8⁺ T cells and uniquely targeted KRAS G12D mutation [251]. The metastatic lesion showed a partial response until 9 months after therapy. Another phase I clinical trial treated 20 patients with advanced NSCLC with a combination of PD-1 and TILs [252]. Approximately 85% of evaluable patients had a reduction in tumor burden and two patients of them achieved a complete response. Moreover, exploratory analyses showed that TIL therapy improved the potential of T cells in cancer mutation recognition, increased the proportion of tumor-reactive T cells, and enhanced the persistence of tumor-reactive T cells in the peripheral blood.

4.4 Inhibition of tumor immune evasion

The adenosine signaling pathway is known to be a key metabolic pathway in tumor immune evasion, thus becoming an attractive therapeutic target in clinics. In brief, ATP is largely released in the tumor environment, catabolized to AMP, and finally converted to adenosine with the participation of CD39 and CD73 to dampen the antitumor immune response [253]. Moreover, CD39 and CD73 can be upregulated by hypoxia through HIF1 transcription factor and other cytokines [254]. Therefore, two different mechanisms have been focused on: blockade of CD39/CD73 to inhibit the production of adenosines or directly targeting the receptors of adenosine signaling, such as A2a and A2b.

The Sitkovsky group focused on the association between adenosine and distinct immune cells in the TME [255–257] and found that the pharmacologic blockade of the A2aR could enhance T cell mediated tumor regression in LL-LCMV tumor model [258]. The combination of A2aR inhibition with ICIs, such as PD-1, TIM-3, or CTLA-4, significantly limits tumor growth and enhances the antitumor immune response based on NK cells, CD8⁺ T cells, and effector molecules [259,260]. Moreover, the efficacy of CAR-T cell therapy can be increased by combination with A2aR blockade in a murine model [261]. The activation of CD8⁺ and CD4⁺ CAR-T cells has been shown to increase with therapy strategy. Currently, A2aR blockade is used alone or in combination with anti-CTLA-4 or anti-PD-1 in safety and efficacy testing. In a phase I clinical trial of RCC and NSCLC, the DCR reached 45% for CPI-444 (an oral A2aR antagonist) single agent and 80% for combination therapy with atezolizumab [262]. Furthermore, CPI-444 induced the infiltration of CD8⁺ T cells, expression of IFN-γ, and systemic adaptive immune response.

Compared to A2aR, upstream regulators are valuable as therapeutic targets. The expression level of CD73 has been associated with immune evasion because it can promote the infiltration of Breg cells, MDSC, and Treg cells [263,264]. Furthermore, anti-CD73 antibody has been confirmed to suppress metastases and tumor cell migration, an effect that is independent of hematopoietic cell involvement [263,265]. In addition to preclinical models, CD73 has been studied as a prognostic biomarker for clinical outcomes in several solid tumors such as breast and lung cancers [261,266–270]. The association between CD73 expression and clinical outcomes is negatively correlated, consistent with the role of the adenosine signaling pathway, resulting in an immunosuppressive TME. Similar to A2aR blockade, anti-CD73 antibodies are currently in the early stages of clinical trials for solid tumors. Notably, a phase II study of oleclumab (an anti-CD73 antibody) combined with durvalumab in stage II NSCLC reported 30% ORR and 62.6% 12-month PFS rates, which were numerically higher than those of durvalumab monotherapy [271]. CD8⁺ T cells, PD-L1, and GZMB have also been reported to increase in patients receiving combination therapy.

CD39, which participates in the initial step of conversion between extracellular ATP and adenosine, is another important upstream regulator of the adenosine signaling pathway. Similar to CD73, CD39 is broadly expressed on tumor-infiltrating immune cells as well as tumor cells [272,273]. CD39-deficient mice showed damage in Treg-suppressive properties in both in vitro and in vivo experiments [274,275]. Notably, blocking CD39 with an inhibitor has been shown to enhance extracellular ATP and the capability of tumor antigen presentation by favoring the recruitment of DCs and IFN-γ CD4⁺ and CD8⁺ T cells [276]. Meta-analyses have shown a positive correlation between CD39 expression and unfavorable clinical outcomes, similar to CD73 [277–279]. Several anti-CD39 drugs have been developed and are undergoing clinical trials; however, there are still no available data to evaluate the response rates of either CD39 monotherapy or combination therapy strategies.

In addition to the adenosine signaling pathway, tumor growth factor β (TGF-β) plays a bidirectional role in both tumor promotion and suppression [280]. In the early stage of tumors, it is expressed by stromal cells and tumor cells that induce cell cycle arrest or apoptosis, whereas in late or advanced tumors, its expression promotes immune evasion by blocking the recruitment of T cells and exhausting infiltration T cells [281]. Despite the promoting activities that have made it valuable for tumor treatment, no inhibitors have been approved by the FDA. A preclinical model of multiple myeloma showed that vactosertib, a TGFBRI inhibitor alone, could impair TGF-β activation, reduce the fraction of MDSC in the bone marrow, and downregulate splenic FOXP3⁺ Tregs [282]. A phase I clinical trial of vactosertib monotherapy in advanced solid tumors reported that 6 of 17 patients achieved stable disease. A marked increase in CD8⁺ and GZMB⁺ T cells was observed in a phase I trial of mCRC after combination treatment with vactosertib and pembrolizumab [283]. There is a novel bifunctional fusion protein, M7824, which consists of TGFBRII and anti-PD-L1 that suppresses TGF-β signaling-induced tumor growth and promotes CD8⁺ T cell and NK cell activation in preclinical models [284]. A phase I trial of M7824 reported that 83.3% of responders had an immune-excluded phenotype, even though the confirmed ORR was only 20% [285].

Indoleamine 2,3-dioxygenase (IDO1) is a metalloprotein enzyme involved in the degradation of L-tryptophan to kynurenine, leading to the suppression of effector T cells and immune escape in numerous tumors [286–288]. Several inhibitors have been developed for clinical trials, including indoximod, epacadostat, BMS-986205, and navoximod. The Wolchok group demonstrated that indoximod reduced the fraction of infiltrating MDSC and Tregs and reversed the immunosuppressive microenvironment in a murine melanoma model [289]. Unfortunately, the completed or terminated clinical trials did not support this phenomenon in preclinical models, and none of them reported a positive clinical outcome in monotherapy. Recently, in multiple tumor models, inhibition of IDO1 has been reported to enhance the efficacy of immunotherapy and chemotherapy [290–294]. A phase II clinical trial using BMS-986205 plus nivolumab showed a 48% DCR in 29 patients with advanced breast cancer. In another recent phase II trial, the combination of epacadostat and nivolumab reported a 62% response rate. However, the failure of the phase III clinical trial, KEYNOTE-252, which did not demonstrate that epacadostat plus pembrolizumab had a longer survival benefit than pembrolizumab monotherapy, has impaired the enthusiasm for the ongoing test of combination therapy strategy [295]. Dual or pan-inhibitors that comprise other enzymes involved in IDO signaling, such as TDO and IDO2, are becoming a new trend. A temporarily successful phase I clinical trial of SHR9146 plus camrelizumab achieved a 21.4% ORR and 42.9% DCR in advanced solid tumors.

5 Conclusions

In summary, genomic instability and clonal driver mutations create an essential foundation for TIME heterogeneity, subsequently maintained by epigenetic modification, systemic immune perturbations, and/or antitumor treatment. Ideally, the balance between tumor components and the immune system can be disrupted even by a single oncogene-driven mutation. Immunological heterogeneity has long been considered a major factor in tumor progression, metastasis, and drug resistance. With advanced NGS technologies and AI algorithms, the elusive heterogeneity of the TIME is being unraveled at the cellular level.

scRNA-seq technology is currently the best tool to demonstrate the immune composition for temporal heterogeneity, and spatial transcriptome is suitable for the spatial dimension. However, the high costs limit the use of these technologies in a large number of patients. Regarding intertumoral heterogeneity detection, liquid biopsy provides potential and unique benefits, such as sampling from blood, enabling exploration of specific genetic mutations between populations, and following dynamic changes in patients to monitor recurrence. In fact, Stanta et al. suggested that ctDNA should be considered a follow-up methodology in recurrent cancers [296]. Clinical trials evaluating liquid biopsies, especially ctDNA assays, are still necessary to summarize meaningful information for decision-making over precision medicine before broad application in clinical settings.

The primary motivation to fully understand immunological heterogeneity is tumor elimination. As immunotherapy progresses, the landscape of tumor treatment has profoundly changed. Noteworthy, the objective response and clinical benefit rates of a single immunotherapy strategy are still not satisfactory, indicating the complexity of TIME. The accurate assessment of dynamic immunological changes, the exploration of the mechanism behind TIME heterogeneity, and the development of novel clinical strategies will help push the frontiers of immuno-oncology. Clinical evidence from pre-clinical models and trials has shown that combination therapy, such as chemotherapy plus ICIs, is a better choice than monotherapy to overcome TIME heterogeneity. By incorporating innovative and advanced technologies, tumor treatment will most likely transition to personalized medicine by monitoring the patient’s response to therapy in real time. As we work toward a comprehensive understanding of the heterogeneity of the TIME, we contribute to improving the future of clinical outcomes and quality of life for cancer patients.

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