Cancer stem cells and tumor-associated macrophages as mates in tumor progression: mechanisms of crosstalk and advanced bioinformatic tools to dissect their phenotypes and interaction

Abstract

Cancer stem cells (CSCs) are a small subset within the tumor mass significantly contributing to cancer progression through dysregulation of various oncogenic pathways, driving tumor growth, chemoresistance and metastasis formation. The aggressive behavior of CSCs is guided by several intracellular signaling pathways such as WNT, NF-kappa-B, NOTCH, Hedgehog, JAK-STAT, PI3K/AKT1/MTOR, TGF/SMAD, PPAR and MAPK kinases, as well as extracellular vesicles such as exosomes, and extracellular signaling molecules such as cytokines, chemokines, pro-angiogenetic and growth factors, which finely regulate CSC phenotype. In this scenario, tumor microenvironment (TME) is a key player in the establishment of a permissive tumor niche, where CSCs engage in intricate communications with diverse immune cells. The “oncogenic” immune cells are mainly represented by B and T lymphocytes, NK cells, and dendritic cells. Among immune cells, macrophages exhibit a more plastic and adaptable phenotype due to their different subpopulations, which are characterized by both immunosuppressive and inflammatory phenotypes. Specifically, tumor-associated macrophages (TAMs) create an immunosuppressive milieu through the production of a plethora of paracrine factors (IL-6, IL-12, TNF-alpha, TGF-beta, CCL1, CCL18) promoting the acquisition by CSCs of a stem-like, invasive and metastatic phenotype. TAMs have demonstrated the ability to communicate with CSCs via direct ligand/receptor (such as CD90/CD11b, LSECtin/BTN3A3, EPHA4/Ephrin) interaction. On the other hand, CSCs exhibited their capacity to influence immune cells, creating a favorable microenvironment for cancer progression. Interestingly, the bidirectional influence of CSCs and TME leads to an epigenetic reprogramming which sustains malignant transformation. Nowadays, the integration of biological and computational data obtained by cutting-edge technologies (single-cell RNA sequencing, spatial transcriptomics, trajectory analysis) has significantly improved the comprehension of the biunivocal multicellular dialogue, providing a comprehensive view of the heterogeneity and dynamics of CSCs, and uncovering alternative mechanisms of immune evasion and therapeutic resistance. Moreover, the combination of biology and computational data will lead to the development of innovative target therapies dampening CSC-TME interaction. Here, we aim to elucidate the most recent insights on CSCs biology and their complex interactions with TME immune cells, specifically TAMs, tracing an exhaustive scenario from the primary tumor to metastasis formation.

Cancer stem cells hallmarks and crosstalk with TAMs: an old story new

In this review we revised the literature period of the last twenty years using as main keywords the following: cancer stem cells, stemness, tumor-associated macrophages, metastasis, metastatic niche, hallmark, proliferation, immune evasion, neo-angiogenesis, epithelial-mesenchymal transition, crosstalk, pathways, chemoresistance, therapy resistance, target therapy, preclinical model, clinical model, clinical trial, immunotherapy, stemness, self-renewal, invasion, tumorigenicity, oncogenic pathways, metastasis-associated macrophages, tumor microenvironment, scRNA-seq, spatial transcriptomic, trajectory analysis, stromal cells, extracellular matrix and immune cells.

Cancer stem cells (CSCs) are a small subpopulation within tumor bulk sharing features of normal stem cells, such as self-renewal and plasticity (1). Accordingly, the CSC model introduced the concept of the capability of CSCs to recapitulate the intertumoral heterogeneity, differentiating into various cancer cell phenotypes and, in parallel, guaranteeing their population maintenance (2). Due to their genetic flexibility, CSCs can be involved in different biological aspects such as tumor initiation, proliferation, invasion, migration, and chemoresistance (1). All these pro-tumoral traits underlined the critical role of CSCs in cancer progression and made CSCs a potential target for innovative therapeutic approaches (3). Tumor microenvironment (TME) provides an essential environmental niche necessary for cancer development (4). Among the immune cells that have a central role in orchestrating TME, tumor-associated macrophages (TAMs) represent a plastic immune cell population that drives multiple interactions within the TME, leading the spatiotemporal evolution from primary tumor to metastasis (5). TAMs can establish with CSCs an intricate complex communication in fueling different aspects of cancer progression: i) direct ligand-receptor interaction: TAMs expressing colony-stimulating factor (CSF1) receptor anchors CSC-derived CSF1, in the promotion of TAM survival and activation (6); ii) indirect interaction: TAMs release chemokines like chemokine (C–C motif) ligand 2 (CCL2), interleukin-6 (IL-6), interleukin-12 (IL-12), tumor necrosis factor-alpha (TNF-alpha), transforming growth factor-beta-1 (TGFB1) (7); TAMs release exosomes containing microRNAs and proteins that regulate CSC behavior by enhancing stemness and chemoresistance; conversely, CSC-derived exosomes can polarize TAM toward a tumor-promoting M2 phenotype (8, 9).

Overall, the interaction between CSCs and the surrounding environmental cells is a complex and ever-evolving process. CSCs arise in “ecological” niches in the TME. These niches, establishing intense trafficking of factors, promote a stem-like and chemoresistant phenotype in the CSCs (10). In this scenario, emerging bioinformatics technologies, such as trajectory analysis and spatial transcriptomics, shed light on unresolved biological complexities. Particularly, these tools enable a deeper investigation of the crosstalk between CSCs and TAMs dissecting unrevealed aspects of their communication. Comprehending the intricate symbiotic relationships between CSCs and TAMs could provide valuable insights to identify an efficacious innovative therapeutic approach. An overview of CSC hallmarks and how these characteristics critically contribute to the complex interplay between CSCs and TME components is illustrated in Figure 1.

Figure 1

From normal to CSCs endowed with tumor-initiation and metastatic potential

In normal adult tissues stem cells are undifferentiated cells that reside in a proper niche, where they are protected and can exert their functions. Stem cells show the ability of self-renewal and differentiation in adult cell tissue, maintaining tissue homeostasis. Stem cell niche can be identified in several tissues such as the crypts of the intestine, the bone marrow, the liver or lung tissues (11). After tissue injury, the niche transmits activation signals such as adhesion molecules, matrix proteins, oxygen, growth factors or cytokines to the stem cells for tissue regeneration. These signals are factors that allow cell-cell interactions between stem cells and neighboring differentiated cells (12).

In normal tissues stem cells remain in an undifferentiated state throughout adult life. Stem cells reach a first stage by becoming transient amplifying cells and highly proliferative cells then they asymmetrically divide and finally reach the last stage of differentiated cells, that leads them to build up and support tissues (13). In both stem and differentiated cells, the potential accumulation of intracellular pathways mutations can lead to a tumor-type phenotype (14). When a critical mutation threshold is reached, cells become CSCs, changing to a more aggressive behavior (15). CSCs, as normal stem cells, have the ability of self-renewal and differentiation, while they create a niche that proliferates independently of the surrounding tissue. These characteristics contribute to tumor initiation, growth and maintenance (13). Among the most deregulated intracellular pathways, wingless-related integration site (WNT)/beta-catenin, NOTCH and Sonic Hedgehog emerge, as they promote self-renewal and tissue morphogenesis (16). In addition, cellular growth, migration, differentiation and epithelial-mesenchymal transition (EMT) are regulated by phosphatidylinositol 3-kinase/AKT serine/threonine kinase 1/Phosphatase and tensin homolog (PI3K/AKT1/PTEN) axis, one of the majors signaling pathways in CSCs (17). TGF, SMAD, peroxisome proliferator-activated receptor (PPAR), mitogen-activated protein kinases (MAPK) and Janus kinase/signal transducers and activators of transcription (JAK-STAT) are often deregulated in CSCs (18). CSCs are not only involved in the process of tumor initiation, growth and maintenance, but also in metastasis (19). CSCs represent the most aggressive tumor subpopulation able to spread and form metastases even at distant sites. One of the key requisites for successful metastasis formation is stemness. Indeed, depletion of various stemness markers such as cluster of differentiation 44 (CD44) in breast CSCs (20) or octamer-binding transcription factor 4 (OCT4) and SRY-Box Transcription Factor 2 (SOX2) in colon CSCs, prevented tumor metastasis and tumor growth (21). Beyond stemness markers, several studies have been focused on the identification of cell-surface markers specifically expressed in the subpopulation of CSCs endowed with metastatic potential such as CD44v6, a CD44 variant isoform, in colon CSCs (22). A broad and extensive description of CSCs hallmarks and the methodologies used to characterize the CSC state is reported in (23). In the next paragraphs, we will briefly introduce how CSCs evade the immune system and resist conventional therapies.

CSCs and immune evasion

Immunosurveillance is a set of immune-system related processes aimed at controlling the development of normal cells and detecting cancer cells. The innate and adaptive cells of the immune system respond to stress conditions, caused by tumor development, mainly by upregulating natural killer (NK) activator ligands and stimulating a more specific T lymphocyte response against cancer cells (24). NK cells are innate immune cells that recognize cells lacking major histocompatibility class I complex (MHC-I) and exert potent cytolytic activity releasing perforin and granzyme against transformed cells (24). NK cells mediate the tumor killing also triggering apoptotic pathways in tumor cells through the production of TNF-alpha or via direct cell–cell contact through activation of the Tumor necrosis factor (TNF)-related apoptosis-inducing ligand (TRAIL) and FAS ligand (FASL) pathways (24). Otherwise, T cells are the main component of the adaptive immunity that orchestrate a protective effector immune response, indeed, a high level of T cell infiltration in tumors is associated with a favorable prognosis in cancer patients (24). CD8+ T and CD4+ T helper 1 cells are the most prominent anti-tumor T cells, instead, through the exocytosis of perforin and granzyme containing granules, the former, and secretion of high amounts of proinflammatory cytokines, such as interleukin-2 (IL-2), TNF-alpha, and interferon-gamma (IFNG), the latter, promote T cell priming activation, cytotoxic T lymphocytes (CTL) cytotoxicity, but also, the anti-tumoral activity of macrophages and NK cells (24).

T and NK cells destructive effect on cancer cells is regulated even by TAMs, by increasing the number of active NKs, upregulating inhibitory T cell receptors programmed cell death protein 1 (PD-1) and Cytotoxic T lymphocyte associated protein 4 (CTLA-4), releasing factors such as TRAIL and inducing apoptosis in cancer cells (25, 26). However, during inflammation, TAMs can directly inhibit the proliferation of CD8+ T cell lymphocytes by regulating their metabolism or recruiting regulatory T cells (Tregs) (27). TAMs can also inhibit dendritic cell (DC) maturation and the secretion of IL-12 by DCs (28). TAMs and Tregs boost an immune-tolerant TME by secretion of molecules such as interleukin-10 (IL-10), TGFB1, and prostaglandins (28). Indeed, poor prognosis and reduced overall survival in oncological patients is correlated with high-grade TAMs (28). Tumor cells can evade the immune system by using different strategies like losing surface antigens that prevent recognition by cytotoxic T cells or downregulating cell surface NK activators, becoming invisible to detection by NK cells (28). However, the immune system can self-contribute to tumor development and progression, orchestrating an immunosuppressive inflammatory TME (24). This process is called “cancer immunoediting” and proceeds through three phases: elimination, equilibrium and escape (29). During the first phase the cytotoxic immune cells such as NK and CD8+ T cells kill transformed cells, although rare tumor subclones can survive (30, 31). These tumor subclones may enter the second phase where their growth is limited and stalled over time (30, 31). The steady pressure from the adaptive immune system and the genetic instability of cancer cells can make tumor subclones escaping immunosurveillance (30, 31). Cancer cells start proliferating unconditionally and adopt many features to escape from the immune system like downregulation of the antigen presentation machinery or inducting inhibitory immune checkpoint molecules (32). Moreover, cancer cells remodel the vasculature and extracellular matrix and supports cancer progression as well as therapy resistance (30, 31). This process can entail decreased IFN-gamma secretion by T cells, loss of antigen presentation and epigenetic changes (33).

Within the tumor CSCs control the immune system and regulate the composition of TME through the release of cytokines, chemokines, growth factors, metabolites and hormones playing an immunomodulatory role (34). CSCs develop different immunosuppressive strategies that promote tumor maintenance and growth. Downregulation of MHC-I complexes and activation of immune molecules such as cluster of differentiation 80 (CD80), human leukocyte antigen (HLA) and major Histocompatibility Complex Class I chain-related protein A/B (MICA/MICB), renders CSCs more resistant to cytotoxic effects exerted by CTL (35). Of note, the degree of tumor progression in the CSC niche has been attributed to a reduced CD8+ T cell infiltration and to an increase in TAMs (35). Moreover, CSCs interact through human leukocyte antigen G (HLA-G) with killer cell immunoglobulin like receptor, two Ig domains and long cytoplasmic tail 4 (KIR2DL4) and killer cell lectin like receptor C1 (KLRC1) to suppress NK activity (34). CSCs further drive recruitment and polarization Treg cells by secretion of factors like Chemokine (C-C motif) ligand 1 (CCL1), IL-2, interleukin-8 (IL-8), IL-10 and Transforming growth factor-beta-1 (TGFB1) (34). Moreover, Tregs produce TGFB1 and interleukin-17 (IL-17) to promote CSCs properties toward tumor progression and invasion (34). CSCs immune evasion properties are influenced by humoral factors: TGFB1, a cytokine that induces immune suppression, EMT and stemness; IL-6, secreted by TAMs, that induces and maintains CSCs, signal transducer and activator of transcription 3 (STAT3), a transcription factor required for the maintenance of pluripotency in stem cells or Chemokine (C-C motif) ligand 20 (CCL20) and its receptor that recruits Tregs to promote tumor progression enhanced by immune evasion (34). CTLA-4 and PD-1/programmed death-ligand 1 (PD-L1) represent two of the major immune checkpoints (34). Immunosuppressive myeloid cells, including macrophages and monocytic myeloid-derived suppressor cells (MDSCs) represent an additional layer of regulation of T cell activity and partially depend on secretion of factors like CSF1, CCL2, Chemokine (C-C motif) ligand 5 (CCL5), TGFB1 and prostaglandin E2 (PGE2), by CSCs (34). Collectively, all these interactions reshape the tumor microenvironment and create a habitat where immune cells support and are suppressed by CSCs (34).

Therapy resistance in CSCs

Conventional therapies developed for cancer treatment are based on the following approaches such as chemotherapy, radiation therapy and surgical excision (36, 37). Chemotherapy is the most widely used and effective treatment for cancer; however, cancer cells as well as CSCs often elaborate simultaneous resistance to many drugs, even if they are structurally and functionally quite different (36, 37). This phenomenon is called multidrug resistance (MDR) or multifactorial pleiotropic drug resistance (36, 37). Many in vivo and in vitro studies demonstrated that administering chemotherapeutic drugs led to an enrichment in CSCs (36, 37). Drug resistance is caused by regular administration of chemotherapy drugs, that are dose- or time-dependent. The multiple mechanisms underlying MDR can be listed as follows: increased drug efflux and reduced drug uptake, efficient DNA repair mechanisms, reduced presence of reactive oxygen species (ROS), apoptosis evasion, hypoxia, vasculogenic mimicry (VM) activation, increased autophagy and decreased ferroptosis (38).

Mechanisms responsible for therapy resistance in CSCs are summarized in Figure 2.

Figure 2

Multidrug resistance transporters

Several studies demonstrated that many chemotherapeutic agents in clinical use are susceptible to ATP-binding cassette transporters-mediated efflux (ABC), such as microtubule-targeting, alkaloids, taxanes, topoisomerase inhibitors, DNA-damaging anthracyclines and tyrosine kinase inhibitors (38, 39). This subfamily of transporters is mainly localized in human tissues of the brain, lung, breast, kidneys, liver, ovaries, prostate, placenta and pancreas (40).

CSCs express higher levels of MDR transporters than cancer cells or healthy cells (41). ABCB1, ABCG2 and ABCB5 are overexpressed respectively in ovarian CSCs (41), breast CSCs (42) and malignant melanoma initiating cells (MMIC) (43). Inhibitors of the ABC transporters are currently used in clinical settings, although side-effects and high toxicity have been reported in patients (44).

DNA repair mechanisms

Efficient DNA repair mechanisms in CSCs are thought to be a major contributing factor in counteracting treatment-induced DNA damage (45). Efficient DNA damage repair system and the CSC long permanence in a quiescent G0 phase greatly reduce potential exogenous and endogenous DNA damage that could occur during DNA replication (45). Evidence demonstrates that DNA damage response (DDR) sensor proteins are upregulated in CSCs rather than tumor bulk cancer cells in monolayer cultures, thus conferring radio and chemotherapy resistance (46). Enhanced expression of DNA polymerase nu (POLN) contributes to chemoresistance in ovarian stem cells (47). Thus, cytotoxicity by chemotherapeutic drugs or radiotherapy-induced can be attenuated in CSCs based on an efficient DNA damage repair system (26).

ROS levels

CSCs show low intracellular levels of ROS, a group of highly reactive molecules, containing oxygen, that can promote DNA damage and influence the DDR machinery (48). Therefore, CSCs can dampen the entity of exogenous DNA damage induced by conventional therapy by expressing low levels of ROS, which production is mainly determined by the slow division rate of CSCs (48). Lower levels of ROS in CSCs result crucial in maintaining a stem cell-like phenotype, along with conferring resistance to radiation therapy and/or chemotherapy (49).

Anoikis

The ability of CSCs to metastasize and reach other organs should be reduced as cells undergo programmed cell death or apoptosis, where they lose contact with their extracellular matrix or neighboring cells (“anoikis”) (50). However, CSCs were reported to be anoikis resistant (50). Indeed, CSCs endowed with metastatic potential evade anoikis mechanism, therefore surviving and promoting the formation of metastatic lesions at a distant site (51). Notably, co-culturing CSCs with non-CSCs conferred anoikis resistance to non-stem cells in breast cancer (51). CSC-like cells protected non-stem cells from anoikis and promoted tumor growth (51).

Hypoxia

Oxygen is necessary for metabolism and cellular energy production. In many tumors, oxygen levels are usually between 0% and 2% compared to normal physiological levels that can reach up to 9% and therefore the high metabolic demand requires the activation of hypoxia-inducible factors (HIFs) (52, 53). HIFs are heterodimers consisting of two subunits a and b that can translocate in the nucleus and interact with specific sequences leading to activation or repression of gene expression (52, 53). There are three different genes encoding for HIF subunits: hypoxia inducible factor 1 subunit alpha (HIF1A), hypoxia inducible factor 2 subunit alpha (HIF2A), and hypoxia inducible factor 3 subunit alpha (HIF3A) (52, 53). All three heterodimerize with the hypoxia inducible factor 1 subunit beta (HIF1B) subunit and are subject to posttranslational regulation that is dependent on oxygen levels in the environment (52–54). HIF1A and HIF2A through the upregulation of regulators such as SOX2, Nanog homeobox (NANOG), OCT4, KLF Transcription Factor 4 (KLF4), and the transcription factor MYC proto-oncogene protein (MYC), have been shown to promote stemness and CSC phenotype (55). Upregulation of HIF-1 induces the expression of genes involved in angiogenesis, cell survival, and metabolism, conferring a selective advantage to CSCs (56). It has been demonstrated that breast cancer cells lines, MCF-7 and MDA-MB-231, display increased subpopulations of tumor cells with stem-like characteristics (56). Hypoxia is a hallmark of the CSCs environment that is essential for CSCs development, maintenance, tumor growth and resistance to therapy (57). Evidence suggests that the hypoxic niche in colon cancer protects CSCs from chemotherapy (58). Moreover, in ovarian cancer stem cell lines, SK-OV-3 and HO-8910, it has been demonstrated that chemotherapy treatment, under hypoxia conditions, induced CSC-like properties (59). The mechanisms through which hypoxia exerts its function are complex but can be summarized in shifting the metabolism toward aerobic glycolysis, reduced expression of pro-apoptotic factors, dysregulation of ROS and redox mechanisms, increasing genomic instability and aberrant cell cycling (48).

Vasculogenic mimicry

Vascularization plays an important role during carcinogenesis and metastasis. VM can provide sufficient blood supply for tumor growth, independently of endothelial cells (60, 61). VM is a process of blood vessel formation that cancer cells and CSCs employ to increase the blood supply of angiogenesis (60, 61). It is a mimicry process whereby malignant cells mimic the function of endothelial cells to form blood vessels by reshaping the extracellular matrix (60, 61). CSC VM has been observed in many tumors such as breast cancer and melanoma (62). Evidence shows that vasculogenic mimicry is mostly present at the early stages of tumor development when blood supply is most needed, as the tumor grows where the vessels created by endothelial cells are established (63). Studies also show that the early stage of CSC serves as tumor vasculogenic stem/progenitor cells that can differentiate into tumor vasculogenic endothelial cells (64, 65). New vessel formation, and particularly VM, makes the eradication of the tumor even more complex and unsuccessful, giving the tumor the ability to metastasize (64, 65).

Autophagy

Autophagy is a catabolic process that degrades and recycles cellular components and exhibits both protective and destructive roles in the TME under physiological stress conditions such as nutrient deprivation and hypoxia (66). The activation of autophagy may lead to an arrest of tumor development, but at the same time it can support CSC self-renewal and resistance to therapy (66). In CSCs, autophagy contributes to maintain self-renewal and proliferation properties, avoiding senescence (67). Evidence suggests that autophagy is involved in mechanisms that mediate resistance to therapy, in renal carcinoma and breast cancer (68, 69). Experiments carried out to inhibit autophagy have shown increasing sensitivity to radio- and chemotherapy in nasopharyngeal and breast CSCs, respectively (70, 71). In addition, the upregulation of signaling pathways mediating autophagy, such as SOX2- beta-catenin/BECLIN1, determines resistance to chemotherapy (72).

Ferroptosis

Iron is an essential cofactor for several metabolic reactions and contributes to the formation of ROS (73). Ferroptosis can be defined as a form of iron-catalyzed necrosis and occurs through the intracellular accumulation of ROS, induced by lipid peroxidation (74). Current studies demonstrate that during tumor development the levels of iron and its transporters increase in CSCs compared to cancer cells (74). Although iron accumulation promotes ferroptosis, CSCs maintain a balance that prevents toxic lipid peroxidation (75). Chemotherapeutic drugs generate ROS that can induce oxidative damage and apoptosis (75). However, CSC ability to control ferroptosis reduces the harmful effect of ROS species conferring chemotherapeutic resistance (75). Inducing high levels of ferroptosis is indeed currently used as an innovative approach to revert chemotherapy resistance, specifically in the CSC population (76).

TME as a key player in promoting CSC stemness and cancer development

Several studies have shown that various types of cells embedded in the TME contribute to maintain and sustain CSCs stemness properties. These findings prove that a crucial role in tumor progression is played by the specific TME surrounding tumor bulk cells and CSCs, which create the ideal conditions for tumor initiation. A detailed description of the key components present in the TME is reported in Figure 3.

Figure 3

TME includes various host healthy cells which enfold the tumor, and by producing cytokines and hormones they can promote its growth and behavior (77). As the core of the TME, tumor cells exploit cellular and non-cellular components for their own advantage by the installation of a complex signaling network (78). The host healthy cells, like fibroblasts or immune cells, as well as the extracellular matrix, undergo a tumor-mediated reprogramming able to convert the host cells into tumor associated ones such as cancer-associated fibroblasts (CAFs) and TAMs. Following the conversion, the tumor-associated cells start to sustain and promote tumor growth in different ways. Hereinafter, an overview of the main cells which compose TME and their contribution to tumor progression will be provided.

Stromal cells and ECM

CAFs are highly heterogeneous stromal cells which represent the major modifiers of TME by the synthesis of soluble factors that promote tumor progression, stemness and angiogenesis in several cancers including prostate, gastric and non-small cell lung cancer (79–81). CAFs also contribute to tumor immune evasion both directly and indirectly. Different studies prove that CAFs are associated with T cells impairment, preventing their activation by secretion of C-X-C motif chemokine ligand 12 (CXCL12) and TGFB1 (82, 83). The primary role of CAFs is the establishment and apposition of the extracellular matrix (ECM) (84). ECM composes the scaffold for tissues and organs and facilitates cells crosstalk, both in healthy and malignant conditions. Jachetti et al. demonstrated that ECM proteins inhibit T cell proliferation and effector function (85). In addition, ECM can improve drug resistance by acting as a physical barrier. Besides, it has been shown that collagen, one of the most abundant proteins in ECM, can promotes stemness through the activation of an integrin/PI3K/AKT1/SNAIL signaling pathway (86).

Mesenchymal stromal cells (MSCs) are a substantial component of TME, recruited and re-educated by tumor cells in order to sustain tumorigenesis (87). Indeed, tumor associated-MSCs are crucial promoters of cancer hallmarks. It is shown that IL-6 produced by MSCs increases endothelin 1 (ET-1) expression in colorectal cancer (CRC) cells, resulting in the activation of AKT1 and ERK in endothelial cells which lead to tumor neo-angiogenesis enhancement (88). Several studies demonstrated that MSCs contribute also to tumor invasiveness and progression by regulating EMT regulators, like Twist, Snail and Zinc finger E-box binding homeobox 1 (ZEB1) (89–92). Finally, MSCs interact and suppress TME-embedded immune cells, either directly or through the release of factors like TGFB1, IL-2 and IL-10 (93) and, moreover, play a crucial role in enhancing stemness of cancer cells. Indeed, in physiological conditions, MSCs shape and support tissues and promote stemness features of the stem cell niches. Similarly, MSCs interact and promote CSC stemness in tumors via soluble factors (52).

Immune cells

Although immune cells should prevent and resolve tumor progression, they act as promoters of cancer development under the pressure of TME signalosome (94). MDSCs are regulators of immune homeostasis (95). Cancer cells exploit MDSCs activity to escape immune surveillance, indeed MDSCs are commonly present in TME for their capability to facilitate tumor progression by establishing immune-suppressive conditions in different ways (96). ROS, IL-10 and TGFB1 produced by MDSCs negatively regulates CD8+ T cells activity against cancer cells (96). Moreover, MDCSs up-regulate PD-L1 expression, resulting in suppression of the immune response against tumors (97). MDSCs also regulate indirectly the immune response exacerbating TME by factors essential for T lymphocytes functions, such as L-arginine, which is crucial for T cells proliferation and activity (98, 99). MDSCs promote CSC stemness by miRNAs able to trigger CSCs stemness program (100).

Tregs are spontaneously attracted by immunosuppressive cytokines produced by tumor and tumor-associated cells (101). As well as MDSCs, Tregs promote tumors immune evasion by releasing cytokines able to suppress the activation of the immune response effectors (102, 103). Recent evidence suggests that Tregs are important regulators of CSCs stemness. Indeed, in several cancers, Tregs promote stemness-related pathways (104), facilitate EMT (105) and angiogenesis (101).

Tumor cells and TME not only re-educate and exploit MDSCs and Tregs but also induce depletion of tumor killing activity exerted by immune response effectors cells, NK cells and lymphocytic cells (106, 107). Although in the early stages of tumorigenesis NK cells are lethal for tumor cells, they slowly exhausted their killing function under the pressure of TME factors (108). Indeed, TGF-beta produced by CSCs, MDSCs and Tregs, impairs NK cells cytotoxicity, inhibits the release of IFNG and reduces the expression levels of killer cell lectin like receptor K1 (KLRK1) receptor in several tumors (109–111). Also, TME hypoxia conditions inhibit NK cells by downregulating expression of NKp46, NKp30, NKp44, KLRK1, perforin (PRF1), and granzyme B (GZMB) (112). Finally, lactate produced by tumor cells leads to the acidification of TME which induces apoptosis of NK cells (113). The same conditions which inhibit NK cells affect also lymphocytic cells activity, the most potent immune weapons against tumor cells (114, 115). Besides, downregulation of MHC-I, along with the up-regulation of immune checkpoints, (i.e. PD-L1) allows tumor cells to ensure themselves immune evasion (116, 117). Among the immune cells present in the TME, a focus on TAMs and their hallmarks will be provided in the next paragraphs.

Recently, CSCs-TME interplay gained interest in cancer research as a potential therapeutic target against tumors. TME promotes a stem-like state in CSCs supporting their self-renewal, survival, and therapeutic resistance through different molecular mechanisms (118). CAFs, the most represented cells in TME, release cytokines like IL-6, able to sustain the expression of stemness-related genes like SOX2, NANOG and OCT4 in CSCs (119). On the other hand, CSCs drive TME immunosuppressive polarization and persistence (35). CSCs can regulate immune system activity through the release of immunosuppressive secretome (i.e. IL-10, TGFB1) showing a more efficient capability to recruit immune cells with pro-tumoral activity (Tregs, MDSCs and especially TAMs) which sustain CSCs stemness by releasing factors like platelet-derived growth factor (PDGF), IL-8, CXCL12 (120, 121).

The TAMS story

As tissue-resident immune cells, macrophages represent an anti-cancer first line of defense thanks to their capability to recognize and phagocyte malignant cells, but they are also the first allies of tumor initiation and development. After malignant transformation, TAMs are the result of the exploitation of macrophages plasticity (M1-M2 dichotomy), by cancer cells (122, 123). TAMs play a pivotal role in vascularization, inflammation, EMT and intravasation in different cancer models (124–127). This review aims to shed new light on the important role of macrophages in cancer development and the close link with TME modulation, the role of macrophages and monocytes, in relation with CSCs stemness and support.

TAMs Hallmarks

The hallmarks of cancer, initially introduced by Hanahan and Weinberg (128) mirror the complex and fundamental biological mechanisms that drive cancer cells to malignancy. In this context, TAMs have emerged as crucial players, within the TME, in cancer progression showing ability in tumor growth and metastasis processes (129). Particularly, TAMs originate from circulating monocytes, in the bloodstream, that migrate to tumor sites where they become macrophages (130). Macrophages are characterized by a peculiar plastic phenotype and can differentiate in wide spectrum of subclasses finely driven by super-enhancers activity (131, 132). Usually, they are classified as M1 or M2, which display pro-inflammatory and immunosuppressive phenotypes respectively. In cancer contexts, TAMs mainly display an M2-like state, which is correlated to oncogenic features such as cancer cell proliferation, immunosuppression, chemoresistance, angiogenesis and metastasis (133). Overall, the acquisition of an M2-like state is critical to create a microenvironment that supports both the survival and progression of cancer cells (134). Moreover, an enrichment of TAMs infiltration, in the context of TME is linked to a worse prognosis in several cancers (135–137).

The most significant TAMs hallmarks, which promote tumor progression are shown in Figure 4 and detailed below.

Figure 4

Cancer cell proliferation

A key hallmark of cancer is the ability to engage in an intricate communication with tumor cells and by activating proliferative signaling programs (138). TAMs positively support the cancer cell-cycle state by secreting various growth factors and cytokines. Among TAMs released factors, IL-6, IL-10 and IL-8 foster signaling pathways directly involved in stimulating cancer cell proliferation and tumor growth (139–142).

Immunosuppression

TAMs predominantly display an immunosuppressive M2 state in TME (130). M2 TAMs unbalance the immune surveillance role of T cells and favor the promotion of cancer cells escape from the immune system (130). More in detail, TAMs produce a plethora of molecules, such as TGFB1, IL-10, and PGE2 that act on T cells, disrupting the anti-tumoral role both CD4+ and CD8+ subtypes, and increasing the recruitment of Tregs, that enhance the pro-tumoral immune depletion (130, 143).

Chemoresistance

Chemoresistance is another cancer hallmark. TAMs play a crucial role in the acquisition of a cancer chemoresistant phenotype, through the secretion of inflammatory cytokines such as IL-6 and TNF-alpha which activate pro-survival programs (144, 145). Moreover, TAMs can enhance the efflux of chemotherapeutic drugs from cancer cells, reducing their therapeutic efficacy (146).

Neo-angiogenesis

TAMs promote neo-angiogenesis, vital for both tumor growth and metastasis. Accordingly, TAMs release pro-angiogenic factors such as vascular endothelial growth factor (VEGF) and fibroblast growth factor (FGF), which drive the activation of new blood vessels formation signaling pathways (147, 148). The neo-angiogenesis not only is essential to feed cancer cells with nutrients and oxygen, but also it is critical for tumor mass growth and to guide metastatic spreading process (147).

EMT and metastasis

The pro-invasiveness and pro-metastatic role of TAMs is well-documented in literature (5, 149). Accordingly, TAMs can induce EMT in tumor cells toward a more mesenchymal phenotype, enhancing their more malignant invasive phenotype (149). Furthermore, TAMs can secrete matrix metalloproteinases through which they digest the extracellular matrix components, allowing cancer cells to reach the surrounding tissues (150).

In summary, TAMs significantly impact multiple hallmarks of cancer. Through their roles in sustaining proliferative signaling, immunosuppression, chemoresistance angiogenesis and metastasis, TAMs represent a critical player in all stages of cancer progression, from early to late ones.

CSCs and TAMs crosstalk and its impact on metastatic niche

One of the major drawbacks in counteracting cancer spread and resistance consists of the capacity of CSCs to migrate into secondary sites and avoid immune surveillance (2). Given their plastic behavior, their self-renewal capacity and treatment resistance, CSCs can foster metastasis formation from the primary tumor environment by disseminating into further districts, establishing the metastatic niche (151). In this context, a model that explains the architecture of the niche has been proposed by Lyden et al., in which the CSCs by migrating, reach a permissive and suitable microenvironment, the pre-metastatic niche, and by becoming disseminated tumor cells (DTCs), they can colonize and proliferate (152) through direct competition with normal stem cells for the niche occupation and establishment (153).

The metastatic niche characteristics vary depending on the specific components considered: the interacting cell types, ECM proteins, survival and self-renewal signals, but mostly the secondary site locations, that can either sustain and foster the metastatic niche, or set a hostile environment for the DTCs (154). The DTCs have to face several issues when colonizing a secondary site, including the lack of growth and extracellular matrix remodeling factors, that can hamper their survival and proliferation, thus adjusting into the new niche and metastasize (155). The disseminated cells will shape their surroundings to build a supportive metastatic niche and exploit the functions of both CSCs and metastatic stromal cells (2). However, studies analyzing human colorectal cancer samples displayed that metastatic occurrence arises from primary tumor cells, that are resistant to chemotherapy and might stay quiescent for a prolonged time (156).

Notwithstanding, little evidence investigating the genetic profiling of the tissue-derived and metastatic CSCs emerged, in the consideration of the metastasis signature mutations occurrence at the level of the primary tumor. This process can represent the first tool of selection in the CSCs population to direct a pool toward migration and extravasation into secondary sites (151). With this premises, the most accredited option relies on the fact that metastasis-driving alterations are present within the heterogeneous CSCs profile, and their expression selects the DTCs that will acquire a plastic and resistant profile (157).

Nevertheless, further evaluations need to be carried out, especially in the context of EMT pathways, and stem-like features involved in both the primary tumoral site and in the metastatic environment, highlighting similarities and differences among CSCs and DTCs (158).

Historically, the metastatic niche has been described as a cell-enriched environment constituted mostly by immune and stromal cells which secrete proteins and factors that sustain growth and self-renewal of CSCs, that consequentially stimulate the activation of angiogenic pathways aimed to the promotion of tumor invasion and metastatization (154, 159).

CSCs form the primary tumor can favor the diffusion of pro-tumorigenic and proangiogenic factors such as VEGF-A, TGFB1, TNF-alpha and lysyl oxidase (LOX) that induce the expression of S100A (a Ca2+ binding protein involved in endothelial remodeling) in the metastatic area (2). In the metastatic site the vasculature system boosts the recruitment of metastatic cancer stem cells (MetCSCs) by producing fibronectin and vascular endothelial cell adhesion molecule (VCAM). Consistently, it has been observed that the CCL2-CCR2 (C-C chemokine receptor type 2) axis promotes the establishment of inflammatory monocytes to the metastatic site, where they will transition into metastatic-associated macrophages (MAMs) and will enhance the extravasation and survival of metastasis-resident cancer cells (160).

Another important lead of metastasis formation is depicted by TAMs. Principally, TAMs promote tumor cell invasion and dissemination, and through their ability to release cytokines and factors that support growth and ECM-shaping (MMP-2, MMP-9), milk fat globule-EGF factor 8 (MFGE8), IL-6 are correlated with tumor progression and metastasis (161). TAMs derive from circulating Ly6C+CCR2+ inflammatory monocytes that are produced by hematopoietic stem cells (HSCs) in the bone marrow, that, when interacting with tumor tissue, are addressed toward a more cancerous-like profile (161, 162).

TAMs and CSCs crosstalk has been widely described in the last years, investigating whether their interaction may be direct or indirect, and which may be the effects on CSCs in the primary tumor, including chemoresistance, differentiation and proliferation (163). TAMs are essential in supporting metastasis establishment once CSCs migration has occurred. More specifically, studies conducted on lung and liver metastatic murine models, showed how inhibiting TAM recruitment in metastatic niches resulted in a reduced burden, indicating its paramount role in the onset and maintenance of metastasis by supporting both extravasation and intravasation in secondary sites of CSCs (164, 165).

One accredited metastasis hypothesis linking the role of TAMs in facilitating CSCs metastatization relies on the ability of metastatic cells to occupy niches in which are present CSCs (166). More in detail, it is thought that TAMs and CSCs derive from cell hybrids and set metastasis in further sites (166). The theory was proposed by John Pawelek in 2006, and he explained that myeloid and tumoral cells could perform a genomic hybridization (167). TAMs due to their migratory ability and the tissue-repair feature could transport the CSCs spheroids through either bloodstream or lymphatic circulation, and permit a favorable environment for metastatic initiation (168). Within the metastatic microenvironment, TAMs play a crucial role in shaping the behavior of CSCs, especially regarding tumor advancement and the colonization of cancer cells at secondary sites (169). A crucial aspect of TAMs is their role in promoting EMT, which is a vital process in the morphological alterations of cancer cells and contributes to the enhancement of their malignant traits (170). In triple-negative breast cancer, CCL2 secreted by TAMs activates AKT signaling pathways, resulting in heightened beta-catenin activity in CSCs (171). This pathway is essential for facilitating EMT and sustaining the properties of CSCs within the TME (172). In oral squamous cell carcinoma (OSCC), high levels of TAM-derived IL-6, promote EMT and enhance the expression of genes associated with stemness, via the IL-6/STAT3/thrombospondin 1 (THBS1) signaling pathway (173).

To sustain CSCs in pancreatic ductal adenocarcinoma (PDAC), TAMs utilize a critical mechanism involving the interferon-stimulated gene 15 (ISG15) signaling pathway (174). By releasing the ISG15, TAMs enhance the self-renewal, invasive potential and tumorigenic capabilities of CSCs (175). Among the several ways in which TAMs support CSCs behavior, the creation of an immunosuppressive microenvironment exerts a key function. Within the TME, TAMs predominantly display a M2 phenotype, which is known for its role in promoting immunosuppression (176). This phenotype fosters a protective microenvironment that shields CSCs from immune system attacks. By releasing immunosuppressive cytokines like IL-10 and TGFB1, TAMs effectively suppress the function of cytotoxic T cells and other immune cells, allowing CSCs to remain undetected and avoid destruction (177). TAMs influence the growth of CSCs through both direct contact and secretory mechanisms. In highly metastatic breast cancer, CSCs express hyaluronan synthase 2 (HAS2), which is crucial for creating a pro-metastatic microenvironment (177). This expression facilitates interactions between CSCs and TAMs, leading to the secretion of platelet-derived growth factor-B subunits (PDGFB) by TAMs (177).

PDGFB subsequently stimulates bone stromal cells to secrete fibroblast growth factors 7 and 9 (FGF7 and FGF9), which support CSC proliferation and survival (178). Moreover, in breast cancer, the EMT enhances the expression of cluster of differentiation 90 (CD90) and ephrin type-a receptor 4 (EPHA4), facilitating direct physical interactions between CSCs and TAMs through the binding with their respective receptors. When the EPHA4 receptor on carcinoma cells is activated, it triggers the sarcoma SRC proto-oncogene, non-receptor tyrosine kinase (SRC) and nuclear factor- kappa B (NF-kappa-B) signaling pathways. This activation leads to NF-kappa-B in CSCs induction of the secretion of various cytokines that help maintaining the stem cell state (179).

By preserving the stem-like properties of CSCs and boosting their migratory and invasive abilities, TAMs facilitate the detachment of CSCs from the primary tumor, enabling the formation of secondary tumors in distant organs (149).

Once malignant cells escape from the primary tumor, they intravasate and disseminate through the lymphatic and/or circulatory system, eventually establishing secondary tumors at distant sites. Research into lung metastasis reveals that when tumor cells reach their target location, they form micro-clots in conjunction with platelets, resulting in their entrapment within the blood vessels of the target tissue (180). Once arrested, the tumor cells secrete CCL2, which creates a gradient that attracts Ly6C monocytes (181). These recruited monocytes undergo differentiation into MAMs, which play a pivotal role in facilitating the extravasation of tumor cells by releasing VEGF, a factor known to enhance vascular permeability (182). Under the influence of CSF1, the primary lineage regulator for most macrophage populations, MAMs support the survival of tumor cells and contribute to their sustained growth through processes related to angiogenesis (183).

Recent studies conducted on CRC evidenced the interaction of CRC cells and TAM. Of note, a paramount interaction between CRC cells and M2 macrophages in the promotion of colorectal liver metastasis (CRLM) emerged (184). To date, CRLM is mediated by interactions between tumor cells and the TME in the liver and is considered one of the most common secondary liver cancers (185). Nevertheless, the mechanisms involved in the cancer cell-derived activation of M2 macrophages need further investigations in both CRC and CRLM. Notably, exosomes derived from tumors can polarize macrophages toward a M2 cellular profile, which in turn promotes metastasis (186, 187). Zhao et al, demonstrated that exosomes derived from CRC cells displayed a role in inducing M2 polarization through the secretion of microRNA-934 (miR-934) and the downregulation of PTEN expression, and activation of PI3K/AKT signaling cascade. Finally, miR-394 activated polarized M2 macrophages which promoted CRLM through C-X-C motif chemokine ligand 5 and 13 (CXCL5)/(CXCL13)/NF-kappa-B/p65/miR-394 positive feedback mechanism (188).

Another study (189) conducted on glioblastoma investigated the role of glioblastoma stem cells (GSCs) and TAMs in tumor progression and metastatic potential. The authors screened GSCs factors that could polarize macrophages, and they evaluated a potential group of proteins produced by GSCs with the ability of behaving as TAMs chemoattractant (189). Periostin (POSTN) emerged as a valuable factor expressed by the stem cells (189). It plays a role in the PI3K/AKT and WNT signaling pathways, which are involved in tumorigenesis (190, 191). In particular, evidence highlighted that CSCs profited from the POSTN-induced WNT augmented signaling, supporting a favorable metastatic colonization in breast cancer setting (192). Additionally, when silencing POSTN, TAM density was sensibly reduced, thus reinforcing the idea that GSCs can recruit TAMs and foster tumor growth by secreting POSTN. Consistently, GSCs established in the tumoral area, where they exploited the surrounding microenvironment by attracting TAMs from the peripheral circulation to set a more beneficial space for the reciprocal survival and growth of the resident populations and enhancing the metastatic CSCs potential. These observations need further investigation and open new scenarios regarding the involvement of TAMs, the role of CSCs, and their complex interplay in affecting the metastatic niche.

TAMs influence on tumor behavior, oncogenic pathways, immune inhibitory responses and therapy resistance

TAMs represent a cellular immune system subpopulation directly involved in the tumor formation and progression through the activation of several pro-tumoral signaling pathways within the CSCs, thus providing the creation of a tumor niche necessary for CSCs survival and expansion. Cellular matrix elements represent critical components for the tumor niche structure maintenance which help the direct crosstalk between CSCs and the surrounding cells, including TAMs. The intricate bi-directional communication between TAMs and CSCs is increasingly recognized as a critical factor in tumor biology. This interaction is underscored by a growing list of factors, ligands/receptors, shown in Table 1, derived from both TAMs and CSCs that are implicated in the mutual co-dependent maintenance of CSC stemness and the supportive actions of TAMs. The complex network of signaling molecules and pathways involved in this crosstalk not only influences tumor progression but also impacts therapeutic resistance, making it a focal point for cancer research.

In breast cancer stem cells (BCSCs) the overexpression of the HAS2 is implicated in the new synthesis of hyaluronic acid, a major polysaccharide component of the ECM which drives the physical interaction to TAMs, via CD44 receptor expressed on their surface. The hyaluronic acid/CD44 interaction stimulates TAMs to release the growth factor PDGFB, which induces CSC self-renewal (178, 193). In addition, TAMs/CSCs in vitro co-culture confirmed the oncogenic role of hyaluronic acid-expressing CSCs/CD44-TAMs interaction in the activation of different signaling pathways such as PI3K–Eukaryotic Translation Initiation Factor 4E Binding Protein 1 (EIF4EBP1)–SOX2, implicated in CSCs pool maintenance (194, 195). Interestingly, it has been found that BCSCs cooperate directly with TAMs through cluster of differentiation 11b (CD11b) and CD90 binding. This anchoring stimulates EPHA4 receptor-mediated induction of both the NF-kappa-B and SRC signaling pathways ensuring CSCs pool stemness state (196). Similarly, in a triple negative breast cancer (TNBC) model, the butyrophilin subfamily member A3 (BTN3A3) receptor enhances cancer stemness markers (i.e. NANOG, OCT4, SOX2) via juxtacrine interaction with its ligand, liver and lymph node sinusoidal endothelial cell C-type lectin (LSECtin), a transmembrane protein expressed on TAMs surface (197). Furthermore, CSCs engage a juxtacrine signaling pathway with the TAMs via GPI-anchored protein CD90/CD11b. Specifically, CSCs express the membrane GPI-anchored protein CD90 and EPHA4. Mechanistically, CD90 creates a bridge to bind the integrin CD11b on TAM surface, whereas the receptor EPHA4 interacts with its ligand, Ephrin, expressed by TAMs, inducing the expression of both SRC and NF-kappa-B driving tumor progression and metastatic dissemination (196). In pancreatic cancer, immunomodulatory cationic antimicrobial peptide 18/LL-37 (hCAP-18/LL-37) on TAM, anchors the formyl peptide receptor 2 (FPR2) and the P2X purinoceptor 7 receptor (P2X7R) expressed on pancreatic cancer cells, which lead to the activation of stemness genes (i.e. KLF4, SOX2, OCT3/4 and NANOG) driving CSC self-renewal, invasion, tumorigenicity (198).

Different studies showed that an indirect paracrine interaction between TAMs and CSCs, driven by a plethora of inflammatory molecules including cytokines, chemokines, growth factors, was also crucial in the determination of CSCs fate and behavior. Particularly, IL-6 is one of the most representative pro-inflammatory cytokines in the context of TME. It is critically upregulated in many tumors, underlying the strong correlation between inflammatory stimuli and tumor progression by affecting multiple cancer signaling pathways (199). IL-6 derived from TAMs induces the proliferation of CD44+ Human Hepatocellular Carcinoma Stem Cells (HHCSCs) via STAT3 pathway induction (139). In addition, TAMs secrete high levels of IL-6 increasing stemness markers (i.e. SOX2, OCT3/4 and NANOG) and consequently CSCs expansion in breast cancer cells via STAT3 pathway supporting tumor cells migration and angiogenesis (125).

Paracrine communication mechanisms between TAM and CSCs are driven by several molecules. TAMs can enhance the CSC-like phenotype via TGFB1, which induces EMT program activation in a hepatocellular carcinoma (HCC) (120). Similarly, TAMs induce stemness, EMT and chemoresistance in HCC by realizing TNF-alpha via the WNT/β-catenin axis (200). It has been discovered that TAMs can produce Chemokine (C-C motif) ligand 18 (CCL18). In squamous cell carcinoma of the head and neck model (SCCHN), CCL18 produced by TAMs regulates metastasis through the activation of EMT program and cancer stemness (201). TAMs releasing CCL2 is correlated with worse prognosis in breast cancer. Particularly, TAM-produced CCL2 in the context of breast cancer microenvironment activates AKT/beta-catenin signaling resulting in EMT and CSC properties in TNBC (172).

Exosomes derived from TAMs have shown unrevealed aspects about the role of TAMs in the support of cancer progression. Specifically, it has been found that annexin A3 (ANXA3)-loaded exosomes derived from TAMs impaired ferroptosis process in laryngeal cancer cells supporting lymphatic metastasis. More in detail, ANXA3 in exosomes regulates negatively the ubiquitination of activating transcription factor 2 (ATF2), a transcription factor that induces ChaC Glutathione Specific Gamma-Glutamylcyclotransferase 1 (CHAC1) expression, thus blocking ferroptosis in lung squamous cell carcinoma (LSCC) cells (202). Moreover, CD163+ TAMs release exosomes that are absorbed by epithelial ovarian cancer cells (EOCCs) (203, 204).

During the tumor progression, TAMs can create an immunosuppressive TME facilitating the immune escape of CSCs. The creation of an immunosuppressive milieu depends on a fine balance between the inhibition of pro-inflammatory immune cells and the activation of immunosuppressive TAMs-dependent counterparts. Accordingly, TAMs promote the upregulation of cluster of differentiation 47 (CD47) ligand on different cancers stem cells (including pancreatic, HCC and leukemia), which interacts to signal-regulatory protein alpha (SIRPA) on immune cells inhibiting phagocytic process (205–207). Parallelly, TAMs can also inhibit the adaptive immune system. Particularly, TAMs boost both inhibitor immune checkpoints expression PD-1 and its ligand PD-L1 in T cells and CSCs, respectively (208). The concomitant expression of PD-L1 and PD-1 impedes the cytotoxicity in T-cells (208).

Overall, some evidence showed how TAM-derived factors and TAM-CSCs physical interactions drive the activation of a great number of pathways in CSCs that are responsible of the maintenance of stemness in different cancer histotypes. These stemness-related hallmark pathways include Sonic hedgehog (SHH), STAT3, NOTCH, PI3K/AKT, WNT/beta-catenin, and NANOG (18). Particularly, TAMs induce STAT3 pathway regulating the expression of stemness genes, via NF-kappa-B activation, in CSCs in different malignancies including breast cancer, liver cancer, prostate cancer, pancreatic cancer and colon cancer (139, 209–213). TAMs activate WNT/beta-catenin and SHH pathways, in CSCs, by leading transcriptional activation of stemness related genes in liver cancer, prostate cancer and lymphoma after secreting TNF-alpha, CCL5, pleiotrophin respectively (200, 211, 214). Furthermore, TAMs support cancer stemness through the direct activation of SHH pathway or through the induction of stemness-related alternative pathways (196, 213, 215–217). Specifically, TAMs sustain stemness via direct activation of SHH pathway in colon cancer (213), meanwhile SHH alternative signaling pathways are TAM-induced in pancreatic cancer (TGFB1/SMAD2/SMAD3/NANOG pathway) (215), in liver cancer (via the NOTCH pathway) (216), breast cancer (via the SRC Proto-Oncogene, Non-Receptor Tyrosine Kinase (SRC) pathway) (196), and in glioma via extracellular regulated kinase 1/2 (ERK1/2) pathway (217).

Innovative studies indicate that the complex communication between CSCs and TAMs has a critical pivotal role in the acquisition of a chemoresistant phenotype refractory to anticancer therapies. In OSCC TAMs influence positively the formation of CSC-like cells, via the induction of stemness markers of the SOX2, OCT4, and NANOG genes, leading to a strong reduction of the percentage of apoptosis in OSCC, supporting cell migration and chemoresistance to vincristine (218). Similarly, TAMs release Pleiotrophin (PTN), which interacts with the protein tyrosine phosphatase receptor type Z1 (PTPRZ1) receptor on the surface of CSCs, in OSCC model. The ligand/receptor interaction activates the FYN proto-oncogene (FYN)-AKT pathway, sustaining both the expression of stemness characteristics in CSCs and chemoresistance in tumor cells (219). Furthermore, MFGE8 in cooperation with IL-6, from TAMs induces both STAT3 and SHH signaling pathways in non-small cell lung cancer stem cells (NSCLCCSCs) leading to chemoresistance (213). Despite a growing body of research that has elucidated various molecular mechanisms underlying the interactions between TAMs and CSCs, significant gaps in our understanding remain. The intricate crosstalk between these two cellular populations is a complex phenomenon that has not yet been fully characterized.

An overview of the most significant mechanisms of indirect and direct interaction between TAMs and CSCs are shown in Figure 5.

Figure 5

Therapeutic strategies targeting the interactions between CSCS and TAMS to improve cancer treatment outcomes

The innovative targeting of the crosstalk between TAMs and CSCs represents a promising frontier in cancer therapy, although several strategies have been already developed to specifically target the CSC subpopulation including differentiative agents, chimeric antigen receptor T cell (CAR-T) therapy, natural compounds and epigenetic inhibitors (56, 220–225). This interaction is crucial as TAMs can enhance the stemness and survival of CSCs, contributing to tumor progression and resistance to conventional treatments (163). Nowadays the aim is to disrupt this communication, for the development of more effective therapeutic strategies that could potentially improve cancer patient prognosis. A therapeutic strategy could be represented by the disruption of CSC-TAM communication centers by blocking soluble factors that reciprocally support each cell type.

IL-6 is an important regulator in paracrine communication between TAMs and CSCs (125, 139). The IL-6 downstream pathway can be unpaired by both anti-IL-6, interleukin-6 receptor (IL-6R) antibodies and by STAT3 inhibitor pathway. Inhibitors against TGF-beta pathway are crucial to target CSCs (226, 227). Additionally, it has been discovered that IL-6 inhibition can impair MFGE8 functionality, which sustains CSC phenotype and cancer chemoresistance (213). Notably, the anti-IL-6R, tocilizumab, has been approved by the FDA for treating rheumatoid arthritis (228). It is currently in phase II clinical study for the treatment of unresectable late-stage melanoma in combination with the anti PD-1 and anti CTLA-4 immune checkpoint inhibitors nivolumab and ipilimumab (NCT03999749) (229). IL-8 is another important TAM-secreted regulator in cancer stemness (230). Reparixin is an anti-IL-8 receptor (IL-8R), known as CXCR1, that reduces CSC population in breast cancer setting (231). Phase I clinical trial study NCT02001974 showed that Reparixin provides a synergistic effect in combination with paclitaxel (231). The inhibition of the glioblastoma multiforme (GBM) CSC-released POSTN has shown a significant reduction in TAMs recruitment in pre-clinical glioblastoma model xenografts (189). In addition, Huang et al. demonstrated that TAMs-secreted CCL5 inhibition could impair stemness and metastasis formation in in pre-clinical prostate model xenografts (211). An alternative targeting strategy is to re-educate the biological role of TAMs toward an anti-tumor phenotype. Specifically, it has been demonstrated that dasatinib inhibitors, directed against SRC, drive the reprogramming from TAMs to M1 anti-tumor macrophages affecting the SRC/cluster of differentiation 155 (CD155)/macrophage inhibitory factor (MIF) signaling (232). This leads to downregulation of stemness markers, NOTCH1 and beta-catenin in cisplatin-resistant lung cancer cells (232).

The reactivation of phagocytic activity in anti-tumoral macrophages toward dead tumor cells represents a really important resource for obtaining cancer cells antigens to boost T cell-mediated immune responses. Accordingly, macrophage phagocytosis can be restored via anti- CD47 administration in immunodeficient pre-clinical xenograft models (233–236). Particularly, anti-CD47 antibodies are currently being designed in clinical trials (NCT02216409, NCT02367196) to overcome the phagocytosis-driven CD47+ TAMs/SIRPA+ CSCs inhibition with promising results (235, 237).

Interestingly, pre-clinical models showed a strong synergism between anti-CD47 and chemotherapies (i.e. paclitaxel, cyclophosphamide) in triggering T cell responses in immunogenic colon and lymphoma tumors (238). ALX148, a CD47 blocking protein, displayed high efficacy in combination with anti-PD-1, anti-human epidermal growth factor 2 (HER-2), anti-vascular endothelial growth factor receptor 2 (VEGFR-2) and anti-CD20 antibodies (known as pembrolizumab, trastuzumab, ramucirumab, rituximab respectively) and conventional chemotherapy (Paclitaxel, fluorouracil, cisplatin) in patients with malignant solid tumor and Non-Hodgkin Lymphoma (NCT03013218) (239).

Humanized IgG4 antibody (Hu5F9-G4), an anti-CD47 antibody, showed combinatorial effect with chemotherapy azacitidine in leukemia stem cells (NCT03248479) (240).

Zoledronic acid represents a double effects drug affecting both TAMs in liver cancer infiltration and decreasing tumor growth in CSCs-derived cervical cancer (241, 242). Zoledronic acid has been chosen for phase III clinical trials aiming at the prevention of bone metastasis in late-stage lung cancer patients (NCT02622607).

Of note, another innovative target is represented by myeloid-epithelial-reproductive tyrosine kinase (MERTK), a tyrosine kinase receptor discovered both in TAMs and several malignancies. MERTK, on TAMs surface, binds to the “eat-me” signal presented on apoptotic cells, activating a biological process known as “efferocytosis”. It drives the shift of macrophages to the pro-tumoral immunosuppressive M2 phenotype (243). MERTK is also overexpressed in cancer cells and is directly correlated to CSC maintenance in glioblastoma multiforme (244). The block of the MERTK signaling pathway represents a promising therapeutic strategy able to have a bidirectional effect both on TAMs and CSCs. Additionally, the administration of the agonist anti-CD40 regulates the activation of the TAM receptor CD40. Anti-CD40 mimics the homonymous ligand physiologically produced by T cells, and it leads to the reprogram of TAMs into anti-cancer macrophages with the establishment of immune surveillance (179, 245, 246). Accordingly, NG-350A, an adenoviral vector encoding for an anti-CD40 monoclonal antibody directed against tumor cells has been used to remodel the immunosuppressive TME. Interestingly, an ongoing phase I trial is investigating its systemic intravenous infusion alone or as a combinatorial treatment with pembrolizumab (NCT05165433) or chemoradiotherapy/radiotherapy (NCT06459869) in patients with advanced epithelial tumors, in particular locally advanced rectal cancer (LARC) (247). Lastly, TAMs reprogramming involves different specific biological sensors for ectopic nucleic acids such as (stimulator of interferon response cGAMP interactor (STING) and some members of toll-like receptors family (TLRs), such as TLR3, TLR7 and TLR8. The design of several synthetic compounds, which regulate these receptors on TAMs endosomal compartments, induces the activation of NF-kappa-B signaling and the consequent release of several immunostimulatory cytokines, including type I interferon (IFN-1), the master regulator of anti-cancer immunity (248–250).

As discussed above, TAMs can create an immunosuppressive TME to facilitate CSCs spreading and progression. Accordingly, the specific TAMs Inhibitor of DNA Binding 1 (ID1) + subpopulation can interact with STAT1 to localize it in the cytoplasm and inhibiting its nuclear translocation for Plasminogen activator inhibitor 2 (SERPINB2) and CCL4 transcription (251). These two factors are responsible for cancer stemness inhibition and CD8+ T cell recruitment (251). Shang et al. demonstrated that ML323 administration reduced ID1 affecting CSCs and increasing CD8+ T cells infiltration (251). In addiction ML323 treatment showed a synergistic effect with both anti-CTLA-4 antibody and 5-fluorouracil (5-FU) alone and in combination, in a colon cancer preclinical model (251). Despite the efforts made in researching therapeutic treatments to address the complex communication between TAMs and CSCs, much remains unresolved and requires further investigation and studies. The main preclinical models and clinical trials targeting CSCs-TAMs axis are summarized in Tables 2, 3, respectively.

Advanced bioinformatics techniques based on single-cell and spatial transcriptomics focused on dissecting the role of TAMS

In the next paragraphs we summarize recent discoveries, enabled by advanced bioinformatics techniques including single-cell RNA-sequencing (scRNA-seq), spatial transcriptomics and trajectories analyses, to study TAMs in cancer progression (252). These techniques have provided crucial insights into the interactions in the TME, highlighting the pivotal role of TAMs in promoting cancer progression, influencing tumor growth, metastasis, and modulating therapeutic responses (252). TAMs exhibit functional plasticity, adopting pro- or anti-tumorigenic roles depending on environmental cues (252). The integration of scRNA-seq and spatial transcriptomics, has facilitated the dissection of TAM trajectories, signaling pathways, and their interactions with other TME components, including CSCs the dissection of TAM trajectories, signaling pathways, and their interactions with other TME components, including CSCs (252). Recent advances have revealed the dynamic interplay between TAMs and CSCs (253). The plasticity of TAMs, influenced by factors such as cytokines, chemokines, and direct cellular interactions, plays a key role in tumor dynamics (252). This review focuses on the transformative impact of bioinformatics in understanding TAM trajectories and signaling within the TME, with an emphasis on their potential for novel therapeutic interventions (252). These bioinformatics techniques are great tools for analyzing all kinds of cells, but in this review, we will focus on applications and studies for the role of TAMs. An overview regarding the bioinformatic tools to specifically study TAMs is reported in Table 4.

Single cell RNA-seq

scRNA-seq has emerged as a pivotal tool for dissecting the heterogeneity of TAMs within the TME. This technology allows the analysis of gene expression at the resolution of individual cells, providing unprecedented insights into the distinct subpopulations of TAMs and their functional states. Numerous studies have highlighted significant variations in the transcriptional profiles of TAMs across different tumor types, underscoring their role in modulating the immunosuppressive landscape of the TME (133). The application of scRNA-seq in cancer research has revealed the coexistence of TAMs with pro-tumor (M2-like) and anti-tumor (M1-like) phenotypes within tumors. This duality emphasizes the functional plasticity of TAMs in cancer progression. Recent advancements have utilized scRNA-seq to trace the developmental trajectories of TAMs, identifying key signaling pathways that regulate their polarization and function. Valdes-Mora et al. demonstrated the utility of high-throughput scRNA-seq for analyzing thousands of tumor cells, including TAMs, revealing transcriptional programs associated with different TAM states, further elucidating their roles within the TME (254).

To fully leverage scRNA-seq for TAM characterization, several bioinformatics methodologies are employed (252):

Quality Control and Filtering: Tools such as Seurat (255) and Scanpy (https://scanpy.readthedocs.io/en/stable/) are commonly used to filter low-quality cells based on the number of detected genes and mitochondrial content.

Normalization: Normalization of scRNA-seq data is essential for accurate downstream analysis. Techniques like Scran or DESeq2 (256) provide effective approaches for normalization.

Clustering: To identify distinct cell populations, clustering algorithms like Louvain or Leiden are employed, allowing for robust community detection in high-dimensional datasets (257).

Differential Expression Analysis: To uncover differences in gene expression across TAM subpopulations, tools such as edgeR (258), DESeq2 (256), or MAST (259) are frequently used, depending on the analysis framework.

scRNA-seq has provided groundbreaking insights into the transcriptional diversity and functional heterogeneity of TAMs across various cancer types (260). This approach has enabled the identification of distinct TAM subtypes, each contributing differently to tumor immunity and progression. Specifically, studies in breast cancer have delineated M1-like and M2-like TAM populations, revealing their unique roles in promoting or inhibiting tumor growth (261, 262). This emerging knowledge is crucial for the development of targeted therapies aimed at reprogramming TAMs to a more anti-tumor state, offering new avenues for therapeutic intervention in cancer. scRNA-seq represents a transformative approach in TAM research, providing high-resolution profiling of individual TAMs and enabling the identification of diverse subpopulations based on their gene expression profiles. By combining scRNA-seq with advanced bioinformatics tools, researchers can uncover the full spectrum of TAM heterogeneity and its implications for cancer progression and therapy. This method excels at revealing transcriptional heterogeneity and elucidating the cellular and molecular mechanisms underlying TAM function within the TME. However, it requires tissue dissociation, which disrupts the spatial organization of the tumor microenvironment and leads to a loss of spatial information. This limitation prevents a direct understanding of TAM interactions within their native tissue context, which is crucial for fully characterizing TAM functionality in relation to the TME.

Spatial transcriptomics for TAM trajectories

While scRNA-seq has provided significant insights into the heterogeneity of TAMs, it lacks spatial resolution, which is crucial for understanding their interactions within the TME. Spatial transcriptomics bridges this gap by integrating gene expression data with spatial information, allowing for the precise mapping of TAM distribution and organization within tumor tissues. This spatial context is essential for capturing the complexity of TAM interactions with other cell types and their influence on tumor progression. Spatial transcriptomics has been extensively applied to study the spatial dynamics of TAMs across various cancer types. It has been demonstrated that TAMs located within the tumor stroma and at invasive tumor margins exhibit distinct gene expression profiles and functional states, which play a pivotal role in driving tumor progression and metastasis (263). By integrating spatial transcriptomics with scRNA-seq data, researchers can gain a more comprehensive understanding of TAM trajectories and their interactions with other cells in the TME. Recent developments in bioinformatics have facilitated the analysis of spatial transcriptomics data, from preprocessing to functional interpretation.

These advancements include:

Data Preprocessing: The initial steps of spatial transcriptomics analysis involve generating a gene expression matrix along with spatial coordinates. Tools such as Space Ranger (10X Genomics), SAW (Stereo-seq), and starfish (ISS/ISH) are widely used for data preprocessing, depending on the platform and methodology employed (264).

Comprehensive Analysis Tools: Seurat (255) and Scanpy (https://scanpy.readthedocs.io/en/stable/) are versatile tools frequently used for both scRNA-seq and spatial transcriptomics analysis. These platforms offer functionalities for filtering, normalization, and various downstream analyses. For more specialized spatial transcriptomics tasks, Giotto (265), STUtility (266), and Squidpy (267) provide extended capabilities, including advanced spatial analyses (264).

Dimensionality Reduction and Clustering: Techniques such as principal component analysis (PCA), t-distributed stochastic neighbor embedding (t-SNE), and uniform manifold approximation and projection (UMAP) are widely employed for dimensionality reduction (268). In addition, spatial-specific algorithms like BayesSpace, SC-MEB, SpaGCN, and STAGATE leverage spatial information to enhance clustering accuracy and identify spatial features (264).

Deconvolution and Cell Typing: Since sequencing data often represents aggregate signals from multiple cell types, deconvolution techniques are required to resolve individual cell-type contributions. Tools such as RCTD and SPOTlight facilitate accurate cell type identification in spatial datasets by leveraging scRNA-seq data for reference (269).

-Spatial Data Integration: Integrating scRNA-seq data with spatial transcriptomics enables comprehensive spatial characterization of gene expression patterns. Regression-based models and deep learning approaches are commonly used to reconstruct missing spatial features and enhance gene expression data (263).

Functional Analysis and Visualization: Tools like Seurat and Scanpy provide robust visualization capabilities for spatial transcriptomics data, while specialized platforms like Giotto and Squidpy allow for more detailed analyses of cellular interactions, spatial neighborhood graphs, and trajectory inference (264).

The integration of spatial transcriptomics with advanced bioinformatics tools has significantly enhanced our understanding of the spatial organization of TAMs within the TME. These methods enable the comprehensive analysis of spatial gene expression patterns, providing valuable insights into the architecture of tumor tissues and the interactions between TAMs and other cell types. These advancements are critical for identifying novel diagnostic markers and therapeutic targets, furthering our ability to design effective cancer therapies (264).

Spatial transcriptomics complements scRNA-seq by offering spatial context to gene expression data, allowing researchers to visualize TAM localization and their interactions with other cell types in situ. This approach has been utilized to map TAM heterogeneity across lung cancer subtypes, revealing distinct macrophage compositions that correlate with specific tumor characteristics (270). By preserving tissue architecture, spatial transcriptomics facilitates a comprehensive analysis of cellular communication and the organization of the TME, which is essential for elucidating the functional roles of TAMs in cancer progression. However, compared to scRNA-seq, spatial transcriptomics typically offers reduced sensitivity and lower coverage, particularly for detecting genes expressed at low levels, which can limit the depth of transcriptomic insights.

Trajectory analysis

scRNA-seq has become an essential tool for studying cellular heterogeneity within tumors, enabling the characterization of distinct cell populations, including TAMs. By applying trajectory inference methods to scRNA-seq data, researchers can reconstruct the developmental pathways of individual cells based on their gene expression profiles, providing critical insights into cellular differentiation and function within the TME.

Pseudotime analysis is a widely used approach to order cells along a developmental trajectory, providing insights into their differentiation states. In the context of TAMs, pseudotime analysis has been employed to reveal the dynamic transitions of these cells as they interact with tumor cells and other components of the TME. Wang et al. elucidated the TAMs transition from pro-inflammatory to immunosuppressive phenotypes during breast cancer progression, demonstrating the utility of pseudotime analysis in understanding TAM functional changes over time (271).

Trajectory inference tools such as Monocle (272) and Slingshot (273) are commonly used to identify genes that are differentially expressed along inferred cellular trajectories. These techniques have proven valuable in uncovering key molecular pathways involved in TAM function and tumor progression. Yang et al. discovered that TAMs regulate BCSCs through a paracrine signaling pathway involving epidermal growth factor receptor (EGFR), STAT3, and SOX2, highlighting the relevance of trajectory analysis in elucidating cell-cell interactions within the TME (274).

Trajectory analysis aims to reconstruct the differentiation pathways and developmental trajectories of cells over time, offering a temporal perspective on how TAMs transition between distinct functional states. Applying trajectory inference to TAM scRNA-seq data has enabled researchers to discern how these macrophages evolve in response to tumor signals and alterations in the TME. Saelens et al. utilized trajectory analysis to gain insights into the temporal dynamics of TAM polarization during cancer progression, identifying key transitions from pro-inflammatory to immunosuppressive states (275).

The interactions between TAMs and CSCs are critical for driving tumor progression and metastasis. TAMs secrete various factors that enhance CSC properties, promoting tumor growth and resistance to therapy. Valdes-Mora et al. showed that TAM-derived cytokines, such as IL-6 and IL-10, help maintain the stemness of CSCs in breast cancer, underscoring the importance of TAM-CSC crosstalk in the TME (254).

In a glioblastoma study the authors used Monocle to trace the differentiation trajectories of TAMs, revealing changes in their functional states in response to tumor-derived signals (276). Furthermore, the integration of spatial transcriptomics with scRNA-seq allows for a more nuanced understanding of TAM interactions with other immune and tumor cells, providing a spatial and temporal view of TAM dynamics within the TME (277).

Signaling pathways in TAMs

Advanced bioinformatics techniques have significantly enhanced the ability to identify critical signaling pathways that regulate the functions of TAMs and their impact on cancer progression. Jin et al. utilized spatial transcriptomics to uncover spatially regulated biomarkers and signaling pathways within TAM populations, providing valuable insights into their roles and functional states within the TME (264). These approaches have also elucidated specific signaling pathways that govern TAM polarization and their pro- or anti-tumoral activities. The CCL2-CCR2 signaling axis has been shown to play a pivotal role in recruiting and polarizing TAMs toward a pro-tumorigenic phenotype (278). Importantly, the inhibition of this pathway holds therapeutic potential by reprogramming TAMs toward an anti-tumor phenotype, thereby enhancing the effectiveness of cancer treatments (278). This underscores the significance of targeting TAM-specific pathways in therapeutic strategies aimed at modulating the TME.

TAMs influence a wide array of signaling pathways within the TME, directly interacting with tumor cells and other TME components to drive cancer progression. Pathways such as WNT, NOTCH, and TGF-beta, which are crucial for maintaining CSC properties and promoting tumor aggressiveness, are modulated by TAM activity (279). These pathways are critical for the survival and function of CSCs, further supporting the tumor’s growth and metastasis.

Recent studies have leveraged computational models to simulate the effects of TAMs on tumor growth, shedding light on the importance of cell-cell communication in the TME. Zhao et al. demonstrated that TAMs secrete cytokines and chemokines that enhance CSC survival and drive tumor progression (138). These computational insights highlight the complex interactions within the TME that are essential for tumor evolution. Emerging bioinformatics tools such as CellChat (https://github.com/sqjin/CellChat) or CellPhoneDB (280) have proven effective in analyzing intercellular communication networks, providing a deeper understanding of ligand-receptor interactions that regulate TAM and CSC dynamics. Through the application of such tools, researchers have been able to map intricate communication networks between TAMs and other cells in the TME. These findings offer opportunities to identify novel therapeutic targets aimed at reprogramming TAMs toward an anti-tumor phenotype, potentially improving cancer treatment outcomes (281). Overall, integrating bioinformatics approaches with experimental data has been pivotal in uncovering the signaling pathways that govern TAM activity. These insights offer potential strategies for therapeutic interventions aimed at altering TAM function and modulating the TME to halt tumor progression. An overview of the most important discoveries made about the CSCs-TAMs axis by advanced bioinformatic technologies in different cancer histotypes is summarized in Table 5.

Concluding remarks

The direct and indirect mechanisms of interaction between TAMs and CSCs are crucial for cancer development, for the regulation of the metastatic niche, and ultimately for the formation of metastatic lesions. TAMs can establish with CSCs an intricate complex communication in fueling different aspects of cancer progression: i) direct ligand-receptor interaction; ii) indirect: TAMs-secreted chemokines/cytokines/exosomes foster CSC stemness, metastatization and chemoresistance respectively; CSC-derived exosomes reprogram TAM toward M2 immunosuppressive phenotype.

Both scRNA-seq and spatial transcriptomics offer unique advantages and limitations in the study of TAMs, with the choice between these techniques largely dependent on the specific research question. In summary, scRNA-seq is advantageous for detailed molecular profiling and understanding TAM heterogeneity, while spatial transcriptomics is better suited for exploring TAM spatial distribution and interactions within the TME. An integrated approach combining both methods would provide a more comprehensive understanding of TAM biology by capturing both transcriptional diversity and spatial dynamics.

The cutting-edge single cell-based and spatial transcriptomics technologies may shed new lights on the specific role of TAMs in promoting CSCs and cancer development and may help to design innovative therapeutic approaches aimed at disrupting this cross talk.

References

• 1

  Batlle E Clevers H . Cancer stem cells revisited. Nat Med. (2017) 23:1124–34. doi: 10.1038/nm.4409

  • CrossRef
  • Google Scholar

• 2

  Plaks V Kong N Werb Z . The cancer stem cell niche: how essential is the niche in regulating stemness of tumor cells? Cell Stem Cell. (2015) 16:225–38. doi: 10.1016/j.stem.2015.02.015

  • CrossRef
  • Google Scholar

• 3

  Turdo A Veschi V Gaggianesi M Chinnici A Bianca P Todaro M et al . Meeting the challenge of targeting cancer stem cells. Front Cell Dev Biol. (2019) 7:16. doi: 10.3389/fcell.2019.00016

  • CrossRef
  • Google Scholar

• 4

  Jahchan NS Mujal AM Pollack JL Binnewies M Sriram V Reyno L et al . Tuning the tumor myeloid microenvironment to fight cancer. Front Immunol. (2019) 10:1611. doi: 10.3389/fimmu.2019.01611

  • CrossRef
  • Google Scholar

• 5

  Bied M Ho WW Ginhoux F Bleriot C . Roles of macrophages in tumor development: a spatiotemp perspect. Cell Mol Immunol. (2023) 20:983–92. doi: 10.1038/s41423-023-01061-6

  • CrossRef
  • Google Scholar

• 6

  Stanley ER Chitu V . CSF-1 receptor signaling in myeloid cells. Cold Spring Harb Perspect Biol. (2014) 6. doi: 10.1101/cshperspect.a021857

  • CrossRef
  • Google Scholar

• 7

  Wang S Wang J Chen Z Luo J Guo W Sun L et al . Targeting M2-like tumor-associated macrophages is a potential therapeutic approach to overcome antitumor drug resistance. NPJ Precis Oncol. (2024) 8:31. doi: 10.1038/s41698-024-00522-z

  • CrossRef
  • Google Scholar

• 8

  Xu Z Chen Y Ma L Chen Y Liu J Guo Y et al . Role of exosomal non-coding RNAs from tumor cells and tumor-associated macrophages in the tumor microenvironment. Mol Ther. (2022) 30:3133–54. doi: 10.1016/j.ymthe.2022.01.046

  • CrossRef
  • Google Scholar

• 9

  Huang S Liu L Xu Z Liu X Wu A Zhang X et al . Exosomal miR-6733-5p mediates cross-talk between glioblastoma stem cells and macrophages and promotes glioblastoma multiform progression synergistically. CNS Neurosci Ther. (2023) 29:3756–73. doi: 10.1111/cns.14296

  • CrossRef
  • Google Scholar

• 10

  Sica A Porta C Amadori A Pasto A . Tumor-associated myeloid cells as guiding forces of cancer cell stemness. Cancer Immunol Immunother. (2017) 66:1025–36. doi: 10.1007/s00262-017-1997-8

  • CrossRef
  • Google Scholar

• 11

  Brunet A Goodell MA Rando TA . Ageing and rejuvenation of tissue stem cells and their niches. Nat Rev Mol Cell Biol. (2023) 24:45–62. doi: 10.1038/s41580-022-00510-w

  • CrossRef
  • Google Scholar

• 12

  Ceafalan LC Enciu AM Fertig TE Popescu BO Gherghiceanu M Hinescu ME et al . Heterocellular molecular contacts in the mammalian stem cell niche. Eur J Cell Biol. (2018) 97:442–61. doi: 10.1016/j.ejcb.2018.07.001

  • CrossRef
  • Google Scholar

• 13

  Walcher L Kistenmacher AK Suo H Kitte R Dluczek S Strauss A et al . Cancer stem cells-origins and biomarkers: perspectives for targeted personalized therapies. Front Immunol. (2020) 11:1280. doi: 10.3389/fimmu.2020.01280

  • CrossRef
  • Google Scholar

• 14

  Ratajczak MZ Bujko K Mack A Kucia M Ratajczak J . Cancer from the perspective of stem cells and misappropriated tissue regeneration mechanisms. Leukemia. (2018) 32:2519–26. doi: 10.1038/s41375-018-0294-7

  • CrossRef
  • Google Scholar

• 15

  Bisht S Nigam M Kunjwal SS Sergey P Mishra AP Sharifi-Rad J . Cancer stem cells: from an insight into the basics to recent advances and therapeutic targeting. Stem Cells Int. (2022) 2022:9653244. doi: 10.1155/2022/9653244

  • CrossRef
  • Google Scholar

• 16

  Clevers H Loh KM Nusse R . Stem cell signaling. An integral program for tissue renewal and regeneration: Wnt signaling and stem cell control. Science. (2014) 346:1248012. doi: 10.1126/science.1248012

  • CrossRef
  • Google Scholar

• 17

  Murai T Matsuda S . Targeting the PI3K-Akt-mTOR signaling pathway involved in vasculogenic mimicry promoted by cancer stem cells. Am J Cancer Res. (2023) 13:5039–46.

  • Google Scholar

• 18

  Yang L Shi P Zhao G Xu J Peng W Zhang J et al . Targeting cancer stem cell pathways for cancer therapy. Signal Transduct Target Ther. (2020) 5:8. doi: 10.1038/s41392-020-0110-5

  • CrossRef
  • Google Scholar

• 19

  Li F Tiede B Massague J Kang Y . Beyond tumorigenesis: cancer stem cells in metastasis. Cell Res. (2007) 17:3–14. doi: 10.1038/sj.cr.7310118

  • CrossRef
  • Google Scholar

• 20

  Liu X Taftaf R Kawaguchi M Chang YF Chen W Entenberg D et al . Homophilic CD44 interactions mediate tumor cell aggregation and polyclonal metastasis in patient-derived breast cancer models. Cancer Discovery. (2019) 9:96–113. doi: 10.1158/2159-8290.CD-18-0065

  • CrossRef
  • Google Scholar

• 21

  Husain K Coppola D Yang CS Malafa MP . Farnesyl dimethyl chromanol targets colon cancer stem cells and prevents colorectal cancer metastasis. Sci Rep. (2021) 11:2185. doi: 10.1038/s41598-020-80911-z

  • CrossRef
  • Google Scholar

• 22

  Todaro M Gaggianesi M Catalano V Benfante A Iovino F Biffoni M et al . CD44v6 is a marker of constitutive and reprogrammed cancer stem cells driving colon cancer metastasis. Cell Stem Cell. (2014) 14:342–56. doi: 10.1016/j.stem.2014.01.009

  • CrossRef
  • Google Scholar

• 23

  Loh JJ Ma S . Hallmarks of cancer stemness. Cell Stem Cell. (2024) 31:617–39. doi: 10.1016/j.stem.2024.04.004

  • CrossRef
  • Google Scholar

• 24

  Gonzalez H Hagerling C Werb Z . Roles of the immune system in cancer: from tumor initiation to metastatic progression. Genes Dev. (2018) 32:1267–84. doi: 10.1101/gad.314617.118

  • CrossRef
  • Google Scholar

• 25

  Ma PF Gao CC Yi J Zhao JL Liang SǪ Zhao Y et al . Cytotherapy with M1-polarized macrophages ameliorates liver fibrosis by modulating immune microenvironment in mice. J Hepatol. (2017) 67:770–9. doi: 10.1016/j.jhep.2017.05.022

  • CrossRef
  • Google Scholar

• 26

  Wang QE . DNA damage responses in cancer stem cells: Implications for cancer therapeutic strategies. World J Biol Chem. (2015) 6:57–64. doi: 10.4331/wjbc.v6.i3.57

  • CrossRef
  • Google Scholar

• 27

  Zhang J Li R Huang S . The immunoregulation effect of tumor microenvironment in pancreatic ductal adenocarcinoma. Front Oncol. (2022) 12:951019. doi: 10.3389/fonc.2022.951019

  • CrossRef
  • Google Scholar

• 28

  Pan Y Yu Y Wang X Zhang T . Tumor-associated macrophages in tumor immunity. Front Immunol. (2020) 11:583084. doi: 10.3389/fimmu.2020.583084

  • CrossRef
  • Google Scholar

• 29

  Schreiber RD Old LJ Smyth MJ . Cancer immunoediting: integrating immunity's roles in cancer suppression and promotion. Science. (2011) 331:1565–70. doi: 10.1126/science.1203486

  • CrossRef
  • Google Scholar

• 30

  Riaz N Havel JJ Makarov V Desrichard A Urba WJ Sims JS et al . Tumor and microenvironment evolution during immunotherapy with nivolumab. Cell. (2017) 171:934–949 e16. doi: 10.1016/j.cell.2017.09.028

  • CrossRef
  • Google Scholar

• 31

  van Weverwijk A de Visser KE . Mechanisms driving the immunoregulatory function of cancer cells. Nat Rev Cancer. (2023) 23:193–215. doi: 10.1038/s41568-022-00544-4

  • CrossRef
  • Google Scholar

• 32

  Kallingal A Olszewski M Maciejewska N Brankiewicz W Baginski M . Cancer immune escape: the role of antigen presentation machinery. J Cancer Res Clin Oncol. (2023) 149:8131–41. doi: 10.1007/s00432-023-04737-8

  • CrossRef
  • Google Scholar

• 33

  O'Donnell JS Teng MWL Smyth MJ . Cancer immunoediting and resistance to T cell-based immunotherapy. Nat Rev Clin Oncol. (2019) 16:151–67. doi: 10.1038/s41571-018-0142-8

  • CrossRef
  • Google Scholar

• 34

  Wu B Shi X Jiang M Liu H . Cross-talk between cancer stem cells and immune cells: potential therapeutic targets in the tumor immune microenvironment. Mol Cancer. (2023) 22:38. doi: 10.1186/s12943-023-01748-4

  • CrossRef
  • Google Scholar

• 35

  Wu A Wei J Kong LY Wang Y Priebe W Ǫiao W et al . Glioma cancer stem cells induce immunosuppressive macrophages/microglia. Neuro Oncol. (2010) 12:1113–25. doi: 10.1093/neuonc/noq082

  • CrossRef
  • Google Scholar

• 36

  Wang L Liu X Ren Y Zhang J Chen J Zhou W et al . Cisplatin-enriching cancer stem cells confer multidrug resistance in non-small cell lung cancer via enhancing TRIB1/HDAC activity. Cell Death Dis. (2017) 8:e2746. doi: 10.1038/cddis.2016.409

  • CrossRef
  • Google Scholar

• 37

  Shafee N Smith CR Wei S Kim Y Mills GB Hortobagyi GN et al . Cancer stem cells contribute to cisplatin resistance in Brca1/p53-mediated mouse mammary tumors. Cancer Res. (2008) 68:3243–50. doi: 10.1158/0008-5472.CAN-07-5480

  • CrossRef
  • Google Scholar

• 38

  Cho Y Kim YK . Cancer stem cells as a potential target to overcome multidrug resistance. Front Oncol. (2020) 10:764. doi: 10.3389/fonc.2020.00764

  • CrossRef
  • Google Scholar

• 39

  Krishna R Mayer LD . Multidrug resistance (MDR) in cancer. Mechanisms, reversal using modulators of MDR and the role of MDR modulators in influencing the pharmacokinetics of anticancer drugs. Eur J Pharm Sci. (2000) 11:265–83. doi: 10.1016/S0928-0987(00)00114-7

  • CrossRef
  • Google Scholar

• 40

  Gottesman MM Fojo T Bates SE . Multidrug resistance in cancer: role of ATP-dependent transporters. Nat Rev Cancer. (2002) 2:48–58. doi: 10.1038/nrc706

  • CrossRef
  • Google Scholar

• 41

  Eyre R Harvey I Stemke-Hale K Lennard TW Tyson-Capper A Meeson AP . Reversing paclitaxel resistance in ovarian cancer cells via inhibition of the ABCB1 expressing side population. Tumour Biol. (2014) 35:9879–92. doi: 10.1007/s13277-014-2277-2

  • CrossRef
  • Google Scholar

• 42

  Chuthapisith S Eremin J El-Sheemey M Eremin O . Breast cancer chemoresistance: emerging importance of cancer stem cells. Surg Oncol. (2010) 19:27–32. doi: 10.1016/j.suronc.2009.01.004

  • CrossRef
  • Google Scholar

• 43

  Frank NY Schatton T Kim S Zhan Ǫ Wilson BJ Ma J et al . VEGFR-1 expressed by Malignant melanoma-initiating cells is required for tumor growth. Cancer Res. (2011) 71:1474–85. doi: 10.1158/0008-5472.CAN-10-1660

  • CrossRef
  • Google Scholar

• 44

  Pote MS Gacche RN . ATP-binding cassette efflux transporters and MDR in cancer. Drug Discovery Today. (2023) 28:103537. doi: 10.1016/j.drudis.2023.103537

  • CrossRef
  • Google Scholar

• 45

  Gillespie MS Ward CM Davies CC . DNA repair and therapeutic strategies in cancer stem cells. Cancers (Basel). (2023) 15:1897–928. doi: 10.3390/cancers15061897

  • CrossRef
  • Google Scholar

• 46

  Valencia-Gonzalez HA Ruiz G Ortiz-Sanchez E Garcia-Carranca A . Cancer stem cells from tumor cell lines activate the DNA damage response pathway after ionizing radiation more efficiently than noncancer stem cells. Stem Cells Int. (2019) 2019:7038953. doi: 10.1155/2019/7038953

  • CrossRef
  • Google Scholar

• 47

  Srivastava AK Han C Zhao R Cui T Dai Y Mao C et al . Enhanced expression of DNA polymerase eta contributes to cisplatin resistance of ovarian cancer stem cells. Proc Natl Acad Sci U.S.A. (2015) 112:4411–6. doi: 10.1073/pnas.1421365112

  • CrossRef
  • Google Scholar

• 48

  Tuy K Rickenbacker L Hjelmeland AB . Reactive oxygen species produced by altered tumor metabolism impacts cancer stem cell maintenance. Redox Biol. (2021) 44:101953. doi: 10.1016/j.redox.2021.101953

  • CrossRef
  • Google Scholar

• 49

  Lendeckel U Wolke C . Redox-regulation in cancer stem cells. Biomedicines. (2022) 10:2413–30. doi: 10.3390/biomedicines10102413

  • CrossRef
  • Google Scholar

• 50

  Ayla S Karahuseyinogluc S . Cancer stem cells, their microenvironment and anoikis. Crit Rev Oncog. (2019) 24:27–34. doi: 10.1615/CritRevOncog.2018029433

  • CrossRef
  • Google Scholar

• 51

  Kim SY Hong SH Basse PH Wu C Bartlett DL Kwon YT et al . Cancer stem cells protect non-stem cells from anoikis: bystander effects. J Cell Biochem. (2016) 117:2289–301. doi: 10.1002/jcb.v117.10

  • CrossRef
  • Google Scholar

• 52

  Zhang P Yao Ǫ Lu L Li Y Chen PJ Duan C . Hypoxia-inducible factor 3 is an oxygen-dependent transcription activator and regulates a distinct transcriptional response to hypoxia. Cell Rep. (2014) 6:1110–21. doi: 10.1016/j.celrep.2014.02.011

  • CrossRef
  • Google Scholar

• 53

  Smythies JA Sun M Masson N Salama R Simpson PD Murray E et al . Inherent DNA-binding specificities of the HIF-1alpha and HIF-2alpha transcription factors in chromatin. EMBO Rep. (2019) 20. doi: 10.15252/embr.201846401

  • CrossRef
  • Google Scholar

• 54

  Peng G Liu Y . Hypoxia-inducible factors in cancer stem cells and inflammation. Trends Pharmacol Sci. (2015) 36:374–83. doi: 10.1016/j.tips.2015.03.003

  • CrossRef
  • Google Scholar

• 55

  Kwak JH Lee NH Lee HY Hong IS Nam JS . HIF2alpha/EFEMP1 cascade mediates hypoxic effects on breast cancer stem cell hierarchy. Oncotarget. (2016) 7:43518–33. doi: 10.18632/oncotarget.v7i28

  • CrossRef
  • Google Scholar

• 56

  Zhi S Chen C Huang H Zhang Z Zeng F Zhang S . Hypoxia-inducible factor in breast cancer: role and target for breast cancer treatment. Front Immunol. (2024) 15:1370800. doi: 10.3389/fimmu.2024.1370800

  • CrossRef
  • Google Scholar

• 57

  Abd GM Laird MC Ku JC Li Y . Hypoxia-induced cancer cell reprogramming: a review on how cancer stem cells arise. Front Oncol. (2023) 13:1227884. doi: 10.3389/fonc.2023.1227884

  • CrossRef
  • Google Scholar

• 58

  Mao Q Zhang Y Fu X Xue J Guo W Meng M et al . A tumor hypoxic niche protects human colon cancer stem cells from chemotherapy. J Cancer Res Clin Oncol. (2013) 139:211–22. doi: 10.1007/s00432-012-1310-3

  • CrossRef
  • Google Scholar

• 59

  Qin J Liu Y Lu Y Liu M Li M Li J et al . Hypoxia-inducible factor 1 alpha promotes cancer stem cells-like properties in human ovarian cancer cells by upregulating SIRT1 expression. Sci Rep. (2017) 7:10592. doi: 10.1038/s41598-017-09244-8

  • CrossRef
  • Google Scholar

• 60

  Luo Q Wang J Zhao W Peng Z Liu X Li B et al . Vasculogenic mimicry in carcinogenesis and clinical applications. J Hematol Oncol. (2020) 13:19. doi: 10.1186/s13045-020-00858-6

  • CrossRef
  • Google Scholar

• 61

  Lizarraga-Verdugo E Avendano-Felix M Bermudez M Ramos-Payan R Perez-Plasencia C Aguilar-Medina M . Cancer stem cells and its role in angiogenesis and vasculogenic mimicry in gastrointestinal cancers. Front Oncol. (2020) 10:413. doi: 10.3389/fonc.2020.00413

  • CrossRef
  • Google Scholar

• 62

  Kirschmann DA Seftor EA Hardy KM Seftor RE Hendrix MJ . Molecular pathways: vasculogenic mimicry in tumor cells: diagnostic and therapeutic implications. Clin Cancer Res. (2012) 18:2726–32. doi: 10.1158/1078-0432.CCR-11-3237

  • CrossRef
  • Google Scholar

• 63

  Folberg R Hendrix MJ Maniotis AJ . Vasculogenic mimicry and tumor angiogenesis. Am J Pathol. (2000) 156:361–81. doi: 10.1016/S0002-9440(10)64739-6

  • CrossRef
  • Google Scholar

• 64

  Yao XH Ping YF Bian XW . Contribution of cancer stem cells to tumor vasculogenic mimicry. Protein Cell. (2011) 2:266–72. doi: 10.1007/s13238-011-1041-2

  • CrossRef
  • Google Scholar

• 65

  Shen R Ye Y Chen L Yan Ǫ Barsky SH Gao JX . Precancerous stem cells can serve as tumor vasculogenic progenitors. PloS One. (2008) 3:e1652. doi: 10.1371/journal.pone.0001652

  • CrossRef
  • Google Scholar

• 66

  Rahman MA Apu EH Rakib-Uz-Zaman SM Chakraborti S Bhajan SK Taleb SA et al . Exploring importance and regulation of autophagy in cancer stem cells and stem cell-based therapies. Cells. (2024) 13:958–79. doi: 10.3390/cells13110958

  • CrossRef
  • Google Scholar

• 67

  Sharif T Martell E Dai C Kennedy BE Murphy P Clements DR et al . Autophagic homeostasis is required for the pluripotency of cancer stem cells. Autophagy. (2017) 13:264–84. doi: 10.1080/15548627.2016.1260808

  • CrossRef
  • Google Scholar

• 68

  Gao X Jiang P Zhang Ǫ Liu Ǫ Jiang S Liu L et al . Peglated-H1/pHGFK1 nanoparticles enhance anti-tumor effects of sorafenib by inhibition of drug-induced autophagy and stemness in renal cell carcinoma. J Exp Clin Cancer Res. (2019) 38:362. doi: 10.1186/s13046-019-1348-z

  • CrossRef
  • Google Scholar

• 69

  Yang G Lu C Mei Z Sun X Han J Ǫian J et al . Association of cancer stem cell radio-resistance under ultra-high dose rate FLASH irradiation with lysosome-mediated autophagy. Front Cell Dev Biol. (2021) 9:672693. doi: 10.3389/fcell.2021.672693

  • CrossRef
  • Google Scholar

• 70

  Ke Y Wu C Zeng Y Chen M Li Y Xie C et al . Radiosensitization of clioquinol combined with zinc in the nasopharyngeal cancer stem-like cells by inhibiting autophagy in vitro and in vivo. Int J Biol Sci. (2020) 16:777–89. doi: 10.7150/ijbs.40305

  • CrossRef
  • Google Scholar

• 71

  Liao M Wang C Yang B Huang D Zheng Y Wang S et al . Corrigendum: autophagy blockade by Ai Du Qing formula promotes chemosensitivity of breast cancer stem cells via GRP78/beta-catenin/ABCG2 axis. Front Pharmacol. (2022) 13:809565. doi: 10.3389/fphar.2022.809565

  • CrossRef
  • Google Scholar

• 72

  Zhu Y Huang S Chen S Chen J Wang Z Wang Y et al . SOX2 promotes chemoresistance, cancer stem cells properties, and epithelial-mesenchymal transition by beta-catenin and Beclin1/autophagy signaling in colorectal cancer. Cell Death Dis. (2021) 12:449. doi: 10.1038/s41419-021-03733-5

  • CrossRef
  • Google Scholar

• 73

  Wang H Zhang Z Ruan S Yan Ǫ Chen Y Cui J et al . Regulation of iron metabolism and ferroptosis in cancer stem cells. Front Oncol. (2023) 13:1251561. doi: 10.3389/fonc.2023.1251561

  • CrossRef
  • Google Scholar

• 74

  Yu R Hang Y Tsai HI Wang D Zhu H . Iron metabolism: backfire of cancer cell stemness and therapeutic modalities. Cancer Cell Int. (2024) 24:157. doi: 10.1186/s12935-024-03329-x

  • CrossRef
  • Google Scholar

• 75

  Cosialls E El Hage R Dos Santos L Gong C Mehrpour M Hamai A . Ferroptosis: Cancer Stem Cells Rely on Iron until "to Die for" It. Cells. (2021) 10:2981–3003. doi: 10.3390/cells10112981

  • CrossRef
  • Google Scholar

• 76

  Elgendy SM Alyammahi SK Alhamad DW Abdin SM Omar HA . Ferroptosis: An emerging approach for targeting cancer stem cells and drug resistance. Crit Rev Oncol Hematol. (2020) 155:103095. doi: 10.1016/j.critrevonc.2020.103095

  • CrossRef
  • Google Scholar

• 77

  Bagaev A Kotlov N Nomie K Svekolkin V Gafurov A Isaeva O et al . Conserved pan-cancer microenvironment subtypes predict response to immunotherapy. Cancer Cell. (2021) 39:845–865 e7. doi: 10.1016/j.ccell.2021.04.014

  • CrossRef
  • Google Scholar

• 78

  de Visser KE Joyce JA . The evolving tumor microenvironment: From cancer initiation to metastatic outgrowth. Cancer Cell. (2023) 41:374–403. doi: 10.1016/j.ccell.2023.02.016

  • CrossRef
  • Google Scholar

• 79

  Giannoni E Bianchini F Masieri L Serni S Torre E Calorini L et al . Reciprocal activation of prostate cancer cells and cancer-associated fibroblasts stimulates epithelial-mesenchymal transition and cancer stemness. Cancer Res. (2010) 70:6945–56. doi: 10.1158/0008-5472.CAN-10-0785

  • CrossRef
  • Google Scholar

• 80

  Chen WJ Ho CC Chang YL Chen HY Lin CA Ling TY et al . Cancer-associated fibroblasts regulate the plasticity of lung cancer stemness via paracrine signalling. Nat Commun. (2014) 5:3472. doi: 10.1038/ncomms4472

  • CrossRef
  • Google Scholar

• 81

  Hasegawa T Yashiro M Nishii T Matsuoka J Fuyuhiro Y Morisaki T et al . Cancer-associated fibroblasts might sustain the stemness of scirrhous gastric cancer cells via transforming growth factor-beta signaling. Int J Cancer. (2014) 134:1785–95. doi: 10.1002/ijc.28520

  • CrossRef
  • Google Scholar

• 82

  Grout JA Sirven P Leader AM Maskey S Hector E Puisieux I et al . Spatial positioning and matrix programs of cancer-associated fibroblasts promote T-cell exclusion in human lung tumors. Cancer Discovery. (2022) 12:2606–25. doi: 10.1158/2159-8290.CD-21-1714

  • CrossRef
  • Google Scholar

• 83

  Feig C Jones JO Kraman M Wells RJ Deonarine A Chan DS et al . Targeting CXCL12 from FAP-expressing carcinoma-associated fibroblasts synergizes with anti-PD-L1 immunotherapy in pancreatic cancer. Proc Natl Acad Sci U.S.A. (2013) 110:20212–7. doi: 10.1073/pnas.1320318110

  • CrossRef
  • Google Scholar

• 84

  Kay EJ Paterson K Riera-Domingo C Sumpton D Dabritz JHM Tardito S et al . Cancer-associated fibroblasts require proline synthesis by PYCR1 for the deposition of pro-tumorigenic extracellular matrix. Nat Metab. (2022) 4:693–710. doi: 10.1038/s42255-022-00582-0

  • CrossRef
  • Google Scholar

• 85

  Jachetti E Caputo S Mazzoleni S Brambillasca CS Parigi SM Grioni M et al . Tenascin-C protects cancer stem-like cells from immune surveillance by arresting T-cell activation. Cancer Res. (2015) 75:2095–108. doi: 10.1158/0008-5472.CAN-14-2346

  • CrossRef
  • Google Scholar

• 86

  Wu X Cai J Zuo Z Li J . Collagen facilitates the colorectal cancer stemness and metastasis through an integrin/PI3K/AKT/Snail signaling pathway. BioMed Pharmacother. (2019) 114:108708. doi: 10.1016/j.biopha.2019.108708

  • CrossRef
  • Google Scholar

• 87

  Rattigan Y Hsu JM Mishra PJ Glod J Banerjee D . Interleukin 6 mediated recruitment of mesenchymal stem cells to the hypoxic tumor milieu. Exp Cell Res. (2010) 316:3417–24. doi: 10.1016/j.yexcr.2010.07.002

  • CrossRef
  • Google Scholar

• 88

  Huang WH Chang MC Tsai KS Hung MC Chen HL Hung SC . Mesenchymal stem cells promote growth and angiogenesis of tumors in mice. Oncogene. (2013) 32:4343–54. doi: 10.1038/onc.2012.458

  • CrossRef
  • Google Scholar

• 89

  Kabashima-Niibe A Higuchi H Takaishi H Masugi Y Matsuzaki Y Mabuchi Y et al . Mesenchymal stem cells regulate epithelial-mesenchymal transition and tumor progression of pancreatic cancer cells. Cancer Sci. (2013) 104:157–64. doi: 10.1111/cas.12059

  • CrossRef
  • Google Scholar

• 90

  Chen D Liu S Ma H Liang X Ma H Yan X et al . Paracrine factors from adipose-mesenchymal stem cells enhance metastatic capacity through Wnt signaling pathway in a colon cancer cell co-culture model. Cancer Cell Int. (2015) 15:42. doi: 10.1186/s12935-015-0198-9

  • CrossRef
  • Google Scholar

• 91

  Mele V Muraro MG Calabrese D Pfaff D Amatruda N Amicarella F et al . Mesenchymal stromal cells induce epithelial-to-mesenchymal transition in human colorectal cancer cells through the expression of surface-bound TGF-beta. Int J Cancer. (2014) 134:2583–94. doi: 10.1002/ijc.v134.11

  • CrossRef
  • Google Scholar

• 92

  Takigawa H Kitadai Y Shinagawa K Yuge R Higashi Y Tanaka S et al . Mesenchymal stem cells induce epithelial to mesenchymal transition in colon cancer cells through direct cell-to-cell contact. Neoplasia. (2017) 19:429–38. doi: 10.1016/j.neo.2017.02.010

  • CrossRef
  • Google Scholar

• 93

  Rasmusson I Ringden O Sundberg B Le Blanc K . Mesenchymal stem cells inhibit lymphocyte proliferation by mitogens and alloantigens by different mechanisms. Exp Cell Res. (2005) 305:33–41. doi: 10.1016/j.yexcr.2004.12.013

  • CrossRef
  • Google Scholar

• 94

  Zamarron BF Chen W . Dual roles of immune cells and their factors in cancer development and progression. Int J Biol Sci. (2011) 7:651–8. doi: 10.7150/ijbs.7.651

  • CrossRef
  • Google Scholar

• 95

  Gabrilovich DI Nagaraj S . Myeloid-derived suppressor cells as regulators of the immune system. Nat Rev Immunol. (2009) 9:162–74. doi: 10.1038/nri2506

  • CrossRef
  • Google Scholar

• 96

  Nagaraj S Gupta K Pisarev V Kinarsky L Sherman S Kang L et al . Altered recognition of antigen is a mechanism of CD8+ T cell tolerance in cancer. Nat Med. (2007) 13:828–35. doi: 10.1038/nm1609

  • CrossRef
  • Google Scholar

• 97

  Lu C Redd PS Lee JR Savage N Liu K . The expression profiles and regulation of PD-L1 in tumor-induced myeloid-derived suppressor cells. Oncoimmunology. (2016) 5:e1247135. doi: 10.1080/2162402X.2016.1247135

  • CrossRef
  • Google Scholar

• 98

  Rodriguez PC Ǫuiceno DG Zabaleta J Ortiz B Zea AH Piazuelo MB et al . Arginase I production in the tumor microenvironment by mature myeloid cells inhibits T-cell receptor expression and antigen-specific T-cell responses. Cancer Res. (2004) 64:5839–49. doi: 10.1158/0008-5472.CAN-04-0465

  • CrossRef
  • Google Scholar

• 99

  Rodriguez PC Zea AH Culotta KS Zabaleta J Ochoa JB Ochoa AC . Regulation of T cell receptor CD3zeta chain expression by L-arginine. J Biol Chem. (2002) 277:21123–9. doi: 10.1074/jbc.M110675200

  • CrossRef
  • Google Scholar

• 100

  Cui TX Kryczek I Zhao L Zhao E Kuick R Roh MH et al . Myeloid-derived suppressor cells enhance stemness of cancer cells by inducing microRNA101 and suppressing the corepressor CtBP2. Immunity. (2013) 39:611–21. doi: 10.1016/j.immuni.2013.08.025

  • CrossRef
  • Google Scholar

• 101

  Facciabene A Peng X Hagemann IS Balint K Barchetti A Wang LP et al . Tumour hypoxia promotes tolerance and angiogenesis via CCL28 and T(reg) cells. Nature. (2011) 475:226–30. doi: 10.1038/nature10169

  • CrossRef
  • Google Scholar

• 102

  Collison LW Workman CJ Kuo TT Boyd K Wang Y Vignali KM et al . The inhibitory cytokine IL-35 contributes to regulatory T-cell function. Nature. (2007) 450:566–9. doi: 10.1038/nature06306

  • CrossRef
  • Google Scholar

• 103

  Sawant DV Yano H Chikina M Zhang Ǫ Liao M Liu C et al . Adaptive plasticity of IL-10(+) and IL-35(+) T(reg) cells cooperatively promotes tumor T cell exhaustion. Nat Immunol. (2019) 20:724–35. doi: 10.1038/s41590-019-0346-9

  • CrossRef
  • Google Scholar

• 104

  Xu Y Dong X Ǫi P Ye Y Shen W Leng L et al . Sox2 communicates with tregs through CCL1 to promote the stemness property of breast cancer cells. Stem Cells. (2017) 35:2351–65. doi: 10.1002/stem.2720

  • CrossRef
  • Google Scholar

• 105

  Shi C Chen Y Chen Y Yang Y Bing W Ǫi J . CD4(+) CD25(+) regulatory T cells promote hepatocellular carcinoma invasion via TGF-beta1-induced epithelial-mesenchymal transition. Onco Targets Ther. (2019) 12:279–89. doi: 10.2147/OTT.S172417

  • CrossRef
  • Google Scholar

• 106

  Dean I Lee CYC Tuong ZK Li Z Tibbitt CA Willis C et al . Rapid functional impairment of natural killer cells following tumor entry limits anti-tumor immunity. Nat Commun. (2024) 15:683. doi: 10.1038/s41467-024-44789-z

  • CrossRef
  • Google Scholar

• 107

  Xie YJ Tian S Huang M Lu LL Liu ZǪ Chen JH et al . Depletion of regulatory T cells enhancing the anti-tumor effect of in situ vaccination in solid tumors. Pharmacol Res. (2024) 203:107174. doi: 10.1016/j.phrs.2024.107174

  • CrossRef
  • Google Scholar

• 108

  Liu X Li L Si F Huang L Zhao Y Zhang C et al . NK and NKT cells have distinct properties and functions in cancer. Oncogene. (2021) 40:4521–37. doi: 10.1038/s41388-021-01880-9

  • CrossRef
  • Google Scholar

• 109

  Trotta R Dal Col J Yu J Ciarlariello D Thomas B Zhang X et al . TGF-beta utilizes SMAD3 to inhibit CD16-mediated IFN-gamma production and antibody-dependent cellular cytotoxicity in human NK cells. J Immunol. (2008) 181:3784–92. doi: 10.4049/jimmunol.181.6.3784

  • CrossRef
  • Google Scholar

• 110

  Castriconi R Cantoni C Della Chiesa M Vitale M Marcenaro E Conte R et al . Transforming growth factor beta 1 inhibits expression of NKp30 and NKG2D receptors: consequences for the NK-mediated killing of dendritic cells. Proc Natl Acad Sci U.S.A. (2003) 100:4120–5. doi: 10.1073/pnas.0730640100

  • CrossRef
  • Google Scholar

• 111

  Lee JC Lee KM Kim DW Heo DS . Elevated TGF-beta1 secretion and down-modulation of NKG2D underlies impaired NK cytotoxicity in cancer patients. J Immunol. (2004) 172:7335–40. doi: 10.4049/jimmunol.172.12.7335

  • CrossRef
  • Google Scholar

• 112

  Balsamo M Manzini C Pietra G Raggi F Blengio F Mingari MC et al . Hypoxia downregulates the expression of activating receptors involved in NK-cell-mediated target cell killing without affecting ADCC. Eur J Immunol. (2013) 43:2756–64. doi: 10.1002/eji.201343448

  • CrossRef
  • Google Scholar

• 113

  Harmon C Robinson MW Hand F Almuaili D Mentor K Houlihan DD et al . Lactate-mediated acidification of tumor microenvironment induces apoptosis of liver-resident NK cells in colorectal liver metastasis. Cancer Immunol Res. (2019) 7:335–46. doi: 10.1158/2326-6066.CIR-18-0481

  • CrossRef
  • Google Scholar

• 114

  Fischbeck AJ Ruehland S Ettinger A Paetzold K Masouris I Noessner E et al . Tumor lactic acidosis: protecting tumor by inhibiting cytotoxic activity through motility arrest and bioenergetic silencing. Front Oncol. (2020) 10:589434. doi: 10.3389/fonc.2020.589434

  • CrossRef
  • Google Scholar

• 115

  Scharping NE Rivadeneira DB Menk AV Vignali PDA Ford BR Rittenhouse NL et al . Mitochondrial stress induced by continuous stimulation under hypoxia rapidly drives T cell exhaustion. Nat Immunol. (2021) 22:205–15. doi: 10.1038/s41590-020-00834-9

  • CrossRef
  • Google Scholar

• 116

  Lee Y Shin JH Longmire M Wang H Kohrt HE Chang HY et al . CD44+ Cells in head and neck squamous cell carcinoma suppress T-cell-mediated immunity by selective constitutive and inducible expression of PD-L1. Clin Cancer Res. (2016) 22:3571–81. doi: 10.1158/1078-0432.CCR-15-2665

  • CrossRef
  • Google Scholar

• 117

  Hsu JM Xia W Hsu YH Chan LC Yu WH Cha JH et al . STT3-dependent PD-L1 accumulation on cancer stem cells promotes immune evasion. Nat Commun. (2018) 9:1908. doi: 10.1038/s41467-018-04313-6

  • CrossRef
  • Google Scholar

• 118

  Nowicki A Kulus M Wieczorkiewicz M Pienkowski W Stefanska K Skupin-Mrugalska P et al . Ovarian cancer and cancer stem cells-cellular and molecular characteristics, signaling pathways, and usefulness as a diagnostic tool in medicine and oncology. Cancers (Basel). (2021) 13:4178–99. doi: 10.3390/cancers13164178

  • CrossRef
  • Google Scholar

• 119

  Alvarez-Teijeiro S Garcia-Inclan C Villaronga MA Casado P Hermida-Prado F Granda-Diaz R et al . Factors secreted by cancer-associated fibroblasts that sustain cancer stem properties in head and neck squamous carcinoma cells as potential therapeutic targets. Cancers (Basel). (2018) 10:334–51. doi: 10.3390/cancers10090334

  • CrossRef
  • Google Scholar

• 120

  Fan QM Jing YY Yu GF Kou XR Ye F Gao L et al . Tumor-associated macrophages promote cancer stem cell-like properties via transforming growth factor-beta1-induced epithelial-mesenchymal transition in hepatocellular carcinoma. Cancer Lett. (2014) 352:160–8. doi: 10.1016/j.canlet.2014.05.008

  • CrossRef
  • Google Scholar

• 121

  Chikamatsu K Takahashi G Sakakura K Ferrone S Masuyama K . Immunoregulatory properties of CD44+ cancer stem-like cells in squamous cell carcinoma of the head and neck. Head Neck. (2011) 33:208–15. doi: 10.1002/hed.21420

  • CrossRef
  • Google Scholar

• 122

  Kerneur C Cano CE Olive D . Major pathways involved in macrophage polarization in cancer. Front Immunol. (2022) 13:1026954. doi: 10.3389/fimmu.2022.1026954

  • CrossRef
  • Google Scholar

• 123

  Ricketts TD Prieto-Dominguez N Gowda PS Ubil E . Mechanisms of macrophage plasticity in the tumor environment: manipulating activation state to improve outcomes. Front Immunol. (2021) 12:642285. doi: 10.3389/fimmu.2021.642285

  • CrossRef
  • Google Scholar

• 124

  Yeo EJ Cassetta L Qian BZ Lewkowich I Li JF Stefater JA3rd et al . Myeloid WNT7b mediates the angiogenic switch and metastasis in breast cancer. Cancer Res. (2014) 74:2962–73. doi: 10.1158/0008-5472.CAN-13-2421

  • CrossRef
  • Google Scholar

• 125

  Radharani NNV Yadav AS Nimma R Kumar TVS Bulbule A Chanukuppa V et al . Tumor-associated macrophage derived IL-6 enriches cancer stem cell population and promotes breast tumor progression via Stat-3 pathway. Cancer Cell Int. (2022) 22:122. doi: 10.1186/s12935-022-02527-9

  • CrossRef
  • Google Scholar

• 126

  Rhim AD Mirek ET Aiello NM Maitra A Bailey JM McAllister F et al . EMT and dissemination precede pancreatic tumor formation. Cell. (2012) 148:349–61. doi: 10.1016/j.cell.2011.11.025

  • CrossRef
  • Google Scholar

• 127

  Wyckoff JB Wang Y Lin EY Li JF Goswami S Stanley ER et al . Direct visualization of macrophage-assisted tumor cell intravasation in mammary tumors. Cancer Res. (2007) 67:2649–56. doi: 10.1158/0008-5472.CAN-06-1823

  • CrossRef
  • Google Scholar

• 128

  Hanahan D Weinberg RA . Hallmarks of cancer: the next generation. Cell. (2011) 144:646–74. doi: 10.1016/j.cell.2011.02.013

  • CrossRef
  • Google Scholar

• 129

  Christofides A Strauss L Yeo A Cao C Charest A Boussiotis VA . The complex role of tumor-infiltrating macrophages. Nat Immunol. (2022) 23:1148–56. doi: 10.1038/s41590-022-01267-2

  • CrossRef
  • Google Scholar

• 130

  Huang R Kang T Chen S . The role of tumor-associated macrophages in tumor immune evasion. J Cancer Res Clin Oncol. (2024) 150:238. doi: 10.1007/s00432-024-05777-4

  • CrossRef
  • Google Scholar

• 131

  Hu G Su Y Kang BH Fan Z Dong T Brown DR et al . High-throughput phenotypic screen and transcriptional analysis identify new compounds and targets for macrophage reprogramming. Nat Commun. (2021) 12:773. doi: 10.1038/s41467-021-21066-x

  • CrossRef
  • Google Scholar

• 132

  Whyte WA Orlando DA Hnisz D Abraham BJ Lin CY Kagey MH et al . Master transcription factors and mediator establish super-enhancers at key cell identity genes. Cell. (2013) 153:307–19. doi: 10.1016/j.cell.2013.03.035

  • CrossRef
  • Google Scholar

• 133

  Wu K Lin K Li X Yuan X Xu P Ni P et al . Redefining tumor-associated macrophage subpopulations and functions in the tumor microenvironment. Front Immunol. (2020) 11:1731. doi: 10.3389/fimmu.2020.01731

  • CrossRef
  • Google Scholar

• 134

  Chen Y Song Y Du W Gong L Chang H Zou Z . Tumor-associated macrophages: an accomplice in solid tumor progression. J BioMed Sci. (2019) 26:78. doi: 10.1186/s12929-019-0568-z

  • CrossRef
  • Google Scholar

• 135

  Liu Y Wang X . Tumor microenvironment-associated gene C3 can predict the prognosis of colorectal adenocarcinoma: a study based on TCGA. Clin Transl Oncol. (2021) 23:1923–33. doi: 10.1007/s12094-021-02602-z

  • CrossRef
  • Google Scholar

• 136

  Sun D Luo T Dong P Zhang N Chen J Zhang S et al . CD86(+)/CD206(+) tumor-associated macrophages predict prognosis of patients with intrahepatic cholangiocarcinoma. PeerJ. (2020) 8:e8458. doi: 10.7717/peerj.8458

  • CrossRef
  • Google Scholar

• 137

  Chen L Wan SC Mao L Huang CF Bu LL Sun ZJ . NLRP3 in tumor-associated macrophages predicts a poor prognosis and promotes tumor growth in head and neck squamous cell carcinoma. Cancer Immunol Immunother. (2023) 72:1647–60. doi: 10.1007/s00262-022-03357-4

  • CrossRef
  • Google Scholar

• 138

  Chen Y Tan W Wang C . Tumor-associated macrophage-derived cytokines enhance cancer stem-like characteristics through epithelial-mesenchymal transition. Onco Targets Ther. (2018) 11:3817–26. doi: 10.2147/OTT.S168317

  • CrossRef
  • Google Scholar

• 139

  Wan S Zhao E Kryczek I Vatan L Sadovskaya A Ludema G et al . Tumor-associated macrophages produce interleukin 6 and signal via STAT3 to promote expansion of human hepatocellular carcinoma stem cells. Gastroenterology. (2014) 147:1393–404. doi: 10.1053/j.gastro.2014.08.039

  • CrossRef
  • Google Scholar

• 140

  Chen S Wang M Lu T Liu Y Hong W He X et al . JMJD6 in tumor-associated macrophage regulates macrophage polarization and cancer progression via STAT3/IL-10 axis. Oncogene. (2023) 42:2737–50. doi: 10.1038/s41388-023-02781-9

  • CrossRef
  • Google Scholar

• 141

  Cai J Xia L Li J Ni S Song H Wu X . Tumor-associated macrophages derived TGF-beta−Induced epithelial to mesenchymal transition in colorectal cancer cells through smad2,3-4/snail signaling pathway. Cancer Res Treat. (2019) 51:252–66. doi: 10.4143/crt.2017.613

  • CrossRef
  • Google Scholar

• 142

  Tatsuno R Ichikawa J Komohara Y Pan C Kawasaki T Enomoto A et al . Pivotal role of IL-8 derived from the interaction between osteosarcoma and tumor-associated macrophages in osteosarcoma growth and metastasis via the FAK pathway. Cell Death Dis. (2024) 15:108. doi: 10.1038/s41419-024-06487-y

  • CrossRef
  • Google Scholar

• 143

  Saha J Sarkar D Pramanik A Mahanti K Adhikary A Bhattacharyya S . PGE2-HIF1alpha reciprocal induction regulates migration, phenotypic alteration and immunosuppressive capacity of macrophages in tumor microenvironment. Life Sci. (2020) 253:117731. doi: 10.1016/j.lfs.2020.117731

  • CrossRef
  • Google Scholar

• 144

  Yin Y Yao S Hu Y Feng Y Li M Bian Z et al . The immune-microenvironment confers chemoresistance of colorectal cancer through macrophage-derived IL6. Clin Cancer Res. (2017) 23:7375–87. doi: 10.1158/1078-0432.CCR-17-1283

  • CrossRef
  • Google Scholar

• 145

  Zhang Z Lin G Yan Y Li X Hu Y Wang J et al . Transmembrane TNF-alpha promotes chemoresistance in breast cancer cells. Oncogene. (2018) 37:3456–70. doi: 10.1038/s41388-018-0221-4

  • CrossRef
  • Google Scholar

• 146

  Zhang L Lu X Xu Y La X Tian J Li A et al . Tumor-associated macrophages confer colorectal cancer 5-fluorouracil resistance by promoting MRP1 membrane translocation via an intercellular CXCL17/CXCL22-CCR4-ATF6-GRP78 axis. Cell Death Dis. (2023) 14:582. doi: 10.1038/s41419-023-06108-0

  • CrossRef
  • Google Scholar

• 147

  Fu LQ Du WL Cai MH Yao JY Zhao YY Mou XZ . The roles of tumor-associated macrophages in tumor angiogenesis and metastasis. Cell Immunol. (2020) 353:104119. doi: 10.1016/j.cellimm.2020.104119

  • CrossRef
  • Google Scholar

• 148

  Liguori M Solinas G Germano G Mantovani A Allavena P . Tumor-associated macrophages as incessant builders and destroyers of the cancer stroma. Cancers (Basel). (2011) 3:3740–61. doi: 10.3390/cancers3043740

  • CrossRef
  • Google Scholar

• 149

  Lin Y Xu J Lan H . Tumor-associated macrophages in tumor metastasis: biological roles and clinical therapeutic applications. J Hematol Oncol. (2019) 12:76. doi: 10.1186/s13045-019-0760-3

  • CrossRef
  • Google Scholar

• 150

  Liu L Ye Y Zhu X . MMP-9 secreted by tumor associated macrophages promoted gastric cancer metastasis through a PI3K/AKT/Snail pathway. BioMed Pharmacother. (2019) 117:109096. doi: 10.1016/j.biopha.2019.109096

  • CrossRef
  • Google Scholar

• 151

  Dieter SM Ball CR Hoffmann CM Nowrouzi A Herbst F Zavidij O et al . Distinct types of tumor-initiating cells form human colon cancer tumors and metastases. Cell Stem Cell. (2011) 9:357–65. doi: 10.1016/j.stem.2011.08.010

  • CrossRef
  • Google Scholar

• 152

  Psaila B Lyden D . The metastatic niche: adapting the foreign soil. Nat Rev Cancer. (2009) 9:285–93. doi: 10.1038/nrc2621

  • CrossRef
  • Google Scholar

• 153

  Shiozawa Y Pedersen EA Havens AM Jung Y Mishra A Joseph J et al . Human prostate cancer metastases target the hematopoietic stem cell niche to establish footholds in mouse bone marrow. J Clin Invest. (2011) 121:1298–312. doi: 10.1172/JCI43414

  • CrossRef
  • Google Scholar

• 154

  Oskarsson T Batlle E Massague J . Metastatic stem cells: sources, niches, and vital pathways. Cell Stem Cell. (2014) 14:306–21. doi: 10.1016/j.stem.2014.02.002

  • CrossRef
  • Google Scholar

• 155

  Barbato L Bocchetti M Di Biase A Regad T . Cancer stem cells and targeting strategies. Cells. (2019) 8:926–45. doi: 10.3390/cells8080926

  • CrossRef
  • Google Scholar

• 156

  Kreso A O'Brien CA van Galen P Gan OI Notta F Brown AM et al . Variable clonal repopulation dynamics influence chemotherapy response in colorectal cancer. Science. (2013) 339:543–8. doi: 10.1126/science.1227670

  • CrossRef
  • Google Scholar

• 157

  Burrell RA McGranahan N Bartek J Swanton C . The causes and consequences of genetic heterogeneity in cancer evolution. Nature. (2013) 501:338–45. doi: 10.1038/nature12625

  • CrossRef
  • Google Scholar

• 158

  Zhao Y Bao Ǫ Schwarz B Zhao L Mysliwietz J Ellwart J et al . Stem cell-like side populations in esophageal cancer: a source of chemotherapy resistance and metastases. Stem Cells Dev. (2014) 23:180–92. doi: 10.1089/scd.2013.0103

  • CrossRef
  • Google Scholar

• 159

  Ye J Wu D Wu P Chen Z Huang J . The cancer stem cell niche: cross talk between cancer stem cells and their microenvironment. Tumour Biol. (2014) 35:3945–51. doi: 10.1007/s13277-013-1561-x

  • CrossRef
  • Google Scholar

• 160

  Kitamura T Qian BZ Pollard JW . Immune Cell promotion of metastasis. Nat Rev Immunol. (2015) 15:73–86. doi: 10.1038/nri3789

  • CrossRef
  • Google Scholar

• 161

  Murgai M Giles A Kaplan R . Physiological, tumor, and metastatic niches: opportunities and challenges for targeting the tumor microenvironment. Crit Rev Oncog. (2015) 20:301–14. doi: 10.1615/CritRevOncog.2015013668

  • CrossRef
  • Google Scholar

• 162

  Mantovani A Bottazzi B Colotta F Sozzani S Ruco L . The origin and function of tumor-associated macrophages. Immunol Today. (1992) 13:265–70. doi: 10.1016/0167-5699(92)90008-U

  • CrossRef
  • Google Scholar

• 163

  Raggi C Mousa HS Correnti M Sica A Invernizzi P . Cancer stem cells and tumor-associated macrophages: a roadmap for multitargeting strategies. Oncogene. (2016) 35:671–82. doi: 10.1038/onc.2015.132

  • CrossRef
  • Google Scholar

• 164

  Rodriguez-Tirado C Entenberg D Li J Ǫian BZ Condeelis JS Pollard JW . Interleukin 4 controls the pro-tumoral role of macrophages in mammary cancer pulmonary metastasis in mice. Cancers (Basel). (2022) 14:4336–57. doi: 10.3390/cancers14174336

  • CrossRef
  • Google Scholar

• 165

  Auguste P Fallavollita L Wang N Burnier J Bikfalvi A Brodt P . The host inflammatory response promotes liver metastasis by increasing tumor cell arrest and extravasation. Am J Pathol. (2007) 170:1781–92. doi: 10.2353/ajpath.2007.060886

  • CrossRef
  • Google Scholar

• 166

  Ding J Jin W Chen C Shao Z Wu J . Tumor associated macrophage x cancer cell hybrids may acquire cancer stem cell properties in breast cancer. PloS One. (2012) 7:e41942. doi: 10.1371/journal.pone.0041942

  • CrossRef
  • Google Scholar

• 167

  Pawelek J Chakraborty A Lazova R Yilmaz Y Cooper D Brash D et al . Co-opting macrophage traits in cancer progression: a consequence of tumor cell fusion? Contrib Microbiol. (2006) 13:138–55. doi: 10.1159/000092970

  • CrossRef
  • Google Scholar

• 168

  Dittmar T Nagler C Niggemann B Zanker KS . The dark side of stem cells: triggering cancer progression by cell fusion. Curr Mol Med. (2013) 13:735–50. doi: 10.2174/1566524011313050005

  • CrossRef
  • Google Scholar

• 169

  Prager BC Xie Ǫ Bao S Rich JN . Cancer stem cells: the architects of the tumor ecosystem. Cell Stem Cell. (2019) 24:41–53. doi: 10.1016/j.stem.2018.12.009

  • CrossRef
  • Google Scholar

• 170

  Li X Chen L Peng X Zhan X . Progress of tumor-associated macrophages in the epithelial-mesenchymal transition of tumor. Front Oncol. (2022) 12:911410. doi: 10.3389/fonc.2022.911410

  • CrossRef
  • Google Scholar

• 171

  Li D Ji H Niu X Yin L Wang Y Gu Y et al . Tumor-associated macrophages secrete CC-chemokine ligand 2 and induce tamoxifen resistance by activating PI3K/Akt/mTOR in breast cancer. Cancer Sci. (2020) 111:47–58. doi: 10.1111/cas.v111.1

  • CrossRef
  • Google Scholar

• 172

  Chen X Yang M Yin J Li P Zeng S Zheng G et al . Tumor-associated macrophages promote epithelial-mesenchymal transition and the cancer stem cell properties in triple-negative breast cancer through CCL2/AKT/beta-catenin signaling. Cell Commun Signal. (2022) 20:92. doi: 10.1186/s12964-022-00888-2

  • CrossRef
  • Google Scholar

• 173

  You Y Tian Z Du Z Wu K Xu G Dai M et al . M1-like tumor-associated macrophages cascade a mesenchymal/stem-like phenotype of oral squamous cell carcinoma via the IL6/Stat3/THBS1 feedback loop. J Exp Clin Cancer Res. (2022) 41:10. doi: 10.1186/s13046-021-02222-z

  • CrossRef
  • Google Scholar

• 174

  Alcala S Sancho P Martinelli P Navarro D Pedrero C Martin-Hijano L et al . ISG15 and ISGylation is required for pancreatic cancer stem cell mitophagy and metabolic plasticity. Nat Commun. (2020) 11:2682. doi: 10.1038/s41467-020-16395-2

  • CrossRef
  • Google Scholar

• 175

  Sainz B Jr. Martin B Tatari M Heeschen C Guerra S . ISG15 is a critical microenvironmental factor for pancreatic cancer stem cells. Cancer Res. (2014) 74:7309–20. doi: 10.1158/0008-5472.CAN-14-1354

  • CrossRef
  • Google Scholar

• 176

  Boutilier AJ Elsawa SF . Macrophage polarization states in the tumor microenvironment. Int J Mol Sci. (2021) 22:6995–7016. doi: 10.3390/ijms22136995

  • CrossRef
  • Google Scholar

• 177

  Basak U Sarkar T Mukherjee S Chakraborty S Dutta A Dutta S et al . Tumor-associated macrophages: an effective player of the tumor microenvironment. Front Immunol. (2023) 14:1295257. doi: 10.3389/fimmu.2023.1295257

  • CrossRef
  • Google Scholar

• 178

  Okuda H Kobayashi A Xia B Watabe M Pai SK Hirota S et al . Hyaluronan synthase HAS2 promotes tumor progression in bone by stimulating the interaction of breast cancer stem-like cells with macrophages and stromal cells. Cancer Res. (2012) 72:537–47. doi: 10.1158/0008-5472.CAN-11-1678

  • CrossRef
  • Google Scholar

• 179

  Huffman AP Lin JH Kim SI Byrne KT Vonderheide RH . CCL5 mediates CD40-driven CD4+ T cell tumor infiltration and immunity. JCI Insight. (2020) 5. doi: 10.1172/jci.insight.137263

  • CrossRef
  • Google Scholar

• 180

  Gil-Bernabe AM Ferjancic S Tlalka M Zhao L Allen PD Im JH et al . Recruitment of monocytes/macrophages by tissue factor-mediated coagulation is essential for metastatic cell survival and premetastatic niche establishment in mice. Blood. (2012) 119:3164–75. doi: 10.1182/blood-2011-08-376426

  • CrossRef
  • Google Scholar

• 181

  Cortez-Retamozo V Etzrodt M Newton A Rauch PJ Chudnovskiy A Berger C et al . Origins of tumor-associated macrophages and neutrophils. Proc Natl Acad Sci U.S.A. (2012) 109:2491–6. doi: 10.1073/pnas.1113744109

  • CrossRef
  • Google Scholar

• 182

  Qian BZ Li J Zhang H Kitamura T Zhang J Campion LR et al . CCL2 recruits inflammatory monocytes to facilitate breast-tumour metastasis. Nature. (2011) 475:222–5. doi: 10.1038/nature10138

  • CrossRef
  • Google Scholar

• 183

  Noy R Pollard JW . Tumor-associated macrophages: from mechanisms to therapy. Immunity. (2014) 41:49–61. doi: 10.1016/j.immuni.2014.06.010

  • CrossRef
  • Google Scholar

• 184

  Beckers RCJ Lambregts DMJ Lahaye MJ Rao SX Kleinen K Grootscholten C et al . Advanced imaging to predict response to chemotherapy in colorectal liver metastases - a systematic review. HPB (Oxford). (2018) 20:120–7. doi: 10.1016/j.hpb.2017.10.013

  • CrossRef
  • Google Scholar

• 185

  Zhao S Mi Y Zheng B Wei P Gu Y Zhang Z et al . Highly-metastatic colorectal cancer cell released miR-181a-5p-rich extracellular vesicles promote liver metastasis by activating hepatic stellate cells and remodelling the tumour microenvironment. J Extracell Vesicles. (2022) 11:e12186. doi: 10.1002/jev2.12186

  • CrossRef
  • Google Scholar

• 186

  Wang D Wang X Si M Yang J Sun S Wu H et al . Exosome-encapsulated miRNAs contribute to CXCL12/CXCR4-induced liver metastasis of colorectal cancer by enhancing M2 polarization of macrophages. Cancer Lett. (2020) 474:36–52. doi: 10.1016/j.canlet.2020.01.005

  • CrossRef
  • Google Scholar

• 187

  Liang ZX Liu HS Wang FW Xiong L Zhou C Hu T et al . LncRNA RPPH1 promotes colorectal cancer metastasis by interacting with TUBB3 and by promoting exosomes-mediated macrophage M2 polarization. Cell Death Dis. (2019) 10:829. doi: 10.1038/s41419-019-2077-0

  • CrossRef
  • Google Scholar

• 188

  Zhao S Mi Y Guan B Zheng B Wei P Gu Y et al . Tumor-derived exosomal miR-934 induces macrophage M2 polarization to promote liver metastasis of colorectal cancer. J Hematol Oncol. (2020) 13:156. doi: 10.1186/s13045-020-00991-2

  • CrossRef
  • Google Scholar

• 189

  Zhou W Ke SǪ Huang Z Flavahan W Fang X Paul J et al . Periostin secreted by glioblastoma stem cells recruits M2 tumour-associated macrophages and promotes Malignant growth. Nat Cell Biol. (2015) 17:170–82. doi: 10.1038/ncb3090

  • CrossRef
  • Google Scholar

• 190

  Baril P Gangeswaran R Mahon PC Caulee K Kocher HM Harada T et al . Periostin promotes invasiveness and resistance of pancreatic cancer cells to hypoxia-induced cell death: role of the beta4 integrin and the PI3k pathway. Oncogene. (2007) 26:2082–94. doi: 10.1038/sj.onc.1210009

  • CrossRef
  • Google Scholar

• 191

  Bao S Ouyang G Bai X Huang Z Ma C Liu M et al . Periostin potently promotes metastatic growth of colon cancer by augmenting cell survival via the Akt/PKB pathway. Cancer Cell. (2004) 5:329–39. doi: 10.1016/S1535-6108(04)00081-9

  • CrossRef
  • Google Scholar

• 192

  Malanchi I Santamaria-Martinez A Susanto E Peng H Lehr HA Delaloye JF et al . Interactions between cancer stem cells and their niche govern metastatic colonization. Nature. (2011) 481:85–9. doi: 10.1038/nature10694

  • CrossRef
  • Google Scholar

• 193

  Kesh K Gupta VK Durden B Garrido V Mateo- Victoriano B Lavania SP et al . Therapy resistance, cancer stem cells and ECM in cancer: the matrix reloaded. Cancers (Basel). (2020) 12:3067–84. doi: 10.3390/cancers12103067

  • CrossRef
  • Google Scholar

• 194

  Gomez KE Wu F Keysar SB Morton JJ Miller B Chimed TS et al . Cancer cell CD44 mediates macrophage/monocyte-driven regulation of head and neck cancer stem cells. Cancer Res. (2020) 80:4185–98. doi: 10.1158/0008-5472.CAN-20-1079

  • CrossRef
  • Google Scholar

• 195

  Skandalis SS Karalis TT Chatzopoulos A Karamanos NK . Hyaluronan-CD44 axis orchestrates cancer stem cell functions. Cell Signal. (2019) 63:109377. doi: 10.1016/j.cellsig.2019.109377

  • CrossRef
  • Google Scholar

• 196

  Lu H Clauser KR Tam WL Frose J Ye X Eaton EN et al . A breast cancer stem cell niche supported by juxtacrine signalling from monocytes and macrophages. Nat Cell Biol. (2014) 16:1105–17. doi: 10.1038/ncb3041

  • CrossRef
  • Google Scholar

• 197

  Liu D Lu Ǫ Wang X Wang J Lu N Jiang Z et al . LSECtin on tumor-associated macrophages enhances breast cancer stemness via interaction with its receptor BTN3A3. Cell Res. (2019) 29:365–78. doi: 10.1038/s41422-019-0155-6

  • CrossRef
  • Google Scholar

• 198

  Sainz B Jr. Alcala S Garcia E Sanchez-Ripoll Y Azevedo MM Cioffi M et al . Microenvironmental hCAP-18/LL-37 promotes pancreatic ductal adenocarcinoma by activating its cancer stem cell compartment. Gut. (2015) 64:1921–35. doi: 10.1136/gutjnl-2014-308935

  • CrossRef
  • Google Scholar

• 199

  Kumari N Dwarakanath BS Das A Bhatt AN . Role of interleukin-6 in cancer progression and therapeutic resistance. Tumour Biol. (2016) 37:11553–72. doi: 10.1007/s13277-016-5098-7

  • CrossRef
  • Google Scholar

• 200

  Chen Y Wen H Zhou C Su Ǫ Lin Y Xie Y et al . TNF-alpha derived from M2 tumor-associated macrophages promotes epithelial-mesenchymal transition and cancer stemness through the Wnt/beta-catenin pathway in SMMC-7721 hepatocellular carcinoma cells. Exp Cell Res. (2019) 378:41–50. doi: 10.1016/j.yexcr.2019.03.005

  • CrossRef
  • Google Scholar

• 201

  She L Ǫin Y Wang J Liu C Zhu G Li G et al . Tumor-associated macrophages derived CCL18 promotes metastasis in squamous cell carcinoma of the head and neck. Cancer Cell Int. (2018) 18:120. doi: 10.1186/s12935-018-0620-1

  • CrossRef
  • Google Scholar

• 202

  Xu L Li W Liu D Cao J Ge J Liu X et al . ANXA3-rich exosomes derived from tumor-associated macrophages regulate ferroptosis and lymphatic metastasis of laryngeal squamous cell carcinoma. Cancer Immunol Res. (2024) 12:614–30. doi: 10.1158/2326-6066.CIR-23-0595

  • CrossRef
  • Google Scholar

• 203

  Zhang X Wang J Liu N Wu W Li H Chen J et al . Molecular mechanism of CD163(+) tumor-associated macrophage (TAM)-derived exosome-induced cisplatin resistance in ovarian cancer ascites. Ann Transl Med. (2022) 10:1014. doi: 10.21037/atm-22-4267

  • CrossRef
  • Google Scholar

• 204

  Zhu X Shen H Yin X Yang M Wei H Chen Ǫ et al . Macrophages derived exosomes deliver miR-223 to epithelial ovarian cancer cells to elicit a chemoresistant phenotype. J Exp Clin Cancer Res. (2019) 38:81. doi: 10.1186/s13046-019-1095-1

  • CrossRef
  • Google Scholar

• 205

  Cioffi M Trabulo S Hidalgo M Costello E Greenhalf W Erkan M et al . Inhibition of CD47 effectively targets pancreatic cancer stem cells via dual mechanisms. Clin Cancer Res. (2015) 21:2325–37. doi: 10.1158/1078-0432.CCR-14-1399

  • CrossRef
  • Google Scholar

• 206

  Lee TK Cheung VC Lu P Lau EY Ma S Tang KH et al . Blockade of CD47-mediated cathepsin S/protease-activated receptor 2 signaling provides a therapeutic target for hepatocellular carcinoma. Hepatology. (2014) 60:179–91. doi: 10.1002/hep.27070

  • CrossRef
  • Google Scholar

• 207

  Majeti R Chao MP Alizadeh AA Pang WW Jaiswal S Gibbs KD Jr. et al . CD47 is an adverse prognostic factor and therapeutic antibody target on human acute myeloid leukemia stem cells. Cell. (2009) 138:286–99. doi: 10.1016/j.cell.2009.05.045

  • CrossRef
  • Google Scholar

• 208

  Lei MML Lee TKW . Cancer stem cells: emerging key players in immune evasion of cancers. Front Cell Dev Biol. (2021) 9:692940. doi: 10.3389/fcell.2021.692940

  • CrossRef
  • Google Scholar

• 209

  Galoczova M Coates P Vojtesek B . STAT3, stem cells, cancer stem cells and p63. Cell Mol Biol Lett. (2018) 23:12. doi: 10.1186/s11658-018-0078-0

  • CrossRef
  • Google Scholar

• 210

  Valeta-Magara A Gadi A Volta V Walters B Arju R Giashuddin S et al . Inflammatory breast cancer promotes development of M2 tumor-associated macrophages and cancer mesenchymal cells through a complex chemokine network. Cancer Res. (2019) 79:3360–71. doi: 10.1158/0008-5472.CAN-17-2158

  • CrossRef
  • Google Scholar

• 211

  Huang R Wang S Wang N Zheng Y Zhou J Yang B et al . CCL5 derived from tumor-associated macrophages promotes prostate cancer stem cells and metastasis via activating beta-catenin/STAT3 signaling. Cell Death Dis. (2020) 11:234. doi: 10.1038/s41419-020-2435-y

  • CrossRef
  • Google Scholar

• 212

  Nomura A Gupta VK Dauer P Sharma NS Dudeja V Merchant N et al . NFkappaB-mediated invasiveness in CD133(+) pancreatic TICs is regulated by autocrine and paracrine activation of IL1 signaling. Mol Cancer Res. (2018) 16:162–72. doi: 10.1158/1541-7786.MCR-17-0221

  • CrossRef
  • Google Scholar

• 213

  Jinushi M Chiba S Yoshiyama H Masutomi K Kinoshita I Dosaka-Akita H et al . Tumor-associated macrophages regulate tumorigenicity and anticancer drug responses of cancer stem/initiating cells. Proc Natl Acad Sci U.S.A. (2011) 108:12425–30. doi: 10.1073/pnas.1106645108

  • CrossRef
  • Google Scholar

• 214

  Wei X Yang S Pu X He S Yang Z Sheng X et al . Tumor-associated macrophages increase the proportion of cancer stem cells in lymphoma by secreting pleiotrophin. Am J Transl Res. (2019) 11:6393–402.

  • Google Scholar

• 215

  Zhang B Ye H Ren X Zheng S Zhou Ǫ Chen C et al . Macrophage-expressed CD51 promotes cancer stem cell properties via the TGF-beta1/smad2/3 axis in pancreatic cancer. Cancer Lett. (2019) 459:204–15. doi: 10.1016/j.canlet.2019.06.005

  • CrossRef
  • Google Scholar

• 216

  Wang H Yang M Lin L Ren H Lin C Lin S et al . HepG2 cells acquire stem cell-like characteristics after immune cell stimulation. Cell Oncol (Dordr). (2016) 39:35–45. doi: 10.1007/s13402-015-0249-1

  • CrossRef
  • Google Scholar

• 217

  Zhang X Chen L Dang WǪ Cao MF Xiao JF Lv SǪ et al . CCL8 secreted by tumor-associated macrophages promotes invasion and stemness of glioblastoma cells via ERK1/2 signaling. Lab Invest. (2020) 100:619–29. doi: 10.1038/s41374-019-0345-3

  • CrossRef
  • Google Scholar

• 218

  Li X Bu W Meng L Liu X Wang S Jiang L et al . CXCL12/CXCR4 pathway orchestrates CSC-like properties by CAF recruited tumor associated macrophage in OSCC. Exp Cell Res. (2019) 378:131–8. doi: 10.1016/j.yexcr.2019.03.013

  • CrossRef
  • Google Scholar

• 219

  Shi Y Ping YF Zhou W He ZC Chen C Bian BS et al . Tumour-associated macrophages secrete pleiotrophin to promote PTPRZ1 signalling in glioblastoma stem cells for tumour growth. Nat Commun. (2017) 8:15080. doi: 10.1038/ncomms15080

  • CrossRef
  • Google Scholar

• 220

  Veschi V Mangiapane LR Nicotra A Di Franco S Scavo E Apuzzo T et al . Targeting chemoresistant colorectal cancer via systemic administration of a BMP7 variant. Oncogene. (2020) 39:987–1003. doi: 10.1038/s41388-019-1047-4

  • CrossRef
  • Google Scholar

• 221

  Gaggianesi M Mangiapane LR Modica C Pantina VD Porcelli G Di Franco S et al . Dual inhibition of myc transcription and PI3K activity effectively targets colorectal cancer stem cells. Cancers (Basel). (2022) 14:673–91. doi: 10.3390/cancers14030673

  • CrossRef
  • Google Scholar

• 222

  Verona F et al . Targeting epigenetic alterations in cancer stem cells. Front Mol Med. (2022) 2:1011882. doi: 10.3389/fmmed.2022.1011882

  • CrossRef
  • Google Scholar

• 223

  Lo Iacono M Gaggianesi M Bianca P Brancato OR Muratore G Modica C et al . Destroying the shield of cancer stem cells: natural compounds as promising players in cancer therapy. J Clin Med. (2022) 11:6996–7025. doi: 10.3390/jcm11236996

  • CrossRef
  • Google Scholar

• 224

  D'Accardo C Porcelli G Mangiapane LR Modica C Pantina VD Roozafzay N et al . Cancer cell targeting by CAR-T cells: A matter of stemness. Front Mol Med. (2022) 2:1055028. doi: 10.3389/fmmed.2022.1055028

  • CrossRef
  • Google Scholar

• 225

  Veschi V Turdo A Stassi G . Novel insights into cancer stem cells targeting: CAR-T therapy and epigenetic drugs as new pillars in cancer treatment. Front Mol Med. (2023) 3:1120090. doi: 10.3389/fmmed.2023.1120090

  • CrossRef
  • Google Scholar

• 226

  Clara JA Monge C Yang Y Takebe N . Targeting signalling pathways and the immune microenvironment of cancer stem cells - a clinical update. Nat Rev Clin Oncol. (2020) 17:204–32. doi: 10.1038/s41571-019-0293-2

  • CrossRef
  • Google Scholar

• 227

  Kim BG Malek E Choi SH Ignatz-Hoover JJ Driscoll JJ . Novel therapies emerging in oncology to target the TGF-beta pathway. J Hematol Oncol. (2021) 14:55. doi: 10.1186/s13045-021-01053-x

  • CrossRef
  • Google Scholar

• 228

  Mihara M Ohsugi Y Kishimoto T . Tocilizumab, a humanized anti-interleukin-6 receptor antibody, for treatment of rheumatoid arthritis. Open Access Rheumatol. (2011) 3:19–29. doi: 10.2147/OARRR.S17118

  • CrossRef
  • Google Scholar

• 229

  Delyon J Lebbe C . IL-6 blockade in cancer patients treated with immune checkpoint blockade: A win-win strategy. Cancer Cell. (2022) 40:450–1. doi: 10.1016/j.ccell.2022.04.010

  • CrossRef
  • Google Scholar

• 230

  Veschi V Verona F Lo Iacono M D'Accardo C Porcelli G Turdo A et al . Cancer stem cells in thyroid tumors: from the origin to metastasis. Front Endocrinol (Lausanne). (2020) 11:566. doi: 10.3389/fendo.2020.00566

  • CrossRef
  • Google Scholar

• 231

  Schott AF Goldstein LJ Cristofanilli M Ruffini PA McCanna S Reuben JM et al . Phase ib pilot study to evaluate reparixin in combination with weekly paclitaxel in patients with HER-2-negative metastatic breast cancer. Clin Cancer Res. (2017) 23:5358–65. doi: 10.1158/1078-0432.CCR-16-2748

  • CrossRef
  • Google Scholar

• 232

  Huang WC Kuo KT Wang CH Yeh CT Wang Y . Cisplatin resistant lung cancer cells promoted M2 polarization of tumor-associated macrophages via the Src/CD155/MIF functional pathway. J Exp Clin Cancer Res. (2019) 38:180. doi: 10.1186/s13046-019-1166-3

  • CrossRef
  • Google Scholar

• 233

  Willingham SB Volkmer JP Gentles AJ Sahoo D Dalerba P Mitra SS et al . The CD47-signal regulatory protein alpha (SIRPa) interaction is a therapeutic target for human solid tumors. Proc Natl Acad Sci U.S.A. (2012) 109:6662–7. doi: 10.1073/pnas.1121623109

  • CrossRef
  • Google Scholar

• 234

  Theocharides AP Jin L Cheng PY Prasolava TK Malko AV Ho JM et al . Disruption of SIRPalpha signaling in macrophages eliminates human acute myeloid leukemia stem cells in xenografts. J Exp Med. (2012) 209:1883–99. doi: 10.1084/jem.20120502

  • CrossRef
  • Google Scholar

• 235

  Liu L Zhang L Yang L Li H Li R Yu J et al . Anti-CD47 antibody as a targeted therapeutic agent for human lung cancer and cancer stem cells. Front Immunol. (2017) 8:404. doi: 10.3389/fimmu.2017.00404

  • CrossRef
  • Google Scholar

• 236

  Weiskopf K Weissman IL . Macrophages are critical effectors of antibody therapies for cancer. MAbs. (2015) 7:303–10. doi: 10.1080/19420862.2015.1011450

  • CrossRef
  • Google Scholar

• 237

  Luo X Shen Y Huang W Bao Y Mo J Yao L et al . Blocking CD47-SIRPalpha signal axis as promising immunotherapy in ovarian cancer. Cancer Control. (2023) 30:10732748231159706. doi: 10.1177/10732748231159706

  • CrossRef
  • Google Scholar

• 238

  Liu X Pu Y Cron K Deng L Kline J Frazier WA et al . CD47 blockade triggers T cell-mediated destruction of immunogenic tumors. Nat Med. (2015) 21:1209–15. doi: 10.1038/nm.3931

  • CrossRef
  • Google Scholar

• 239

  Kauder SE Kuo TC Harrabi O Chen A Sangalang E Doyle L et al . ALX148 blocks CD47 and enhances innate and adaptive antitumor immunity with a favorable safety profile. PloS One. (2018) 13:e0201832. doi: 10.1371/journal.pone.0201832

  • CrossRef
  • Google Scholar

• 240

  Chao MP Takimoto CH Feng DD McKenna K Gip P Liu J et al . Therapeutic targeting of the macrophage immune checkpoint CD47 in myeloid Malignancies. Front Oncol. (2019) 9:1380. doi: 10.3389/fonc.2019.01380

  • CrossRef
  • Google Scholar

• 241

  Wang L Liu Y Zhou Y Wang J Tu L Sun Z et al . Zoledronic acid inhibits the growth of cancer stem cell derived from cervical cancer cell by attenuating their stemness phenotype and inducing apoptosis and cell cycle arrest through the Erk1/2 and Akt pathways. J Exp Clin Cancer Res. (2019) 38:93. doi: 10.1186/s13046-019-1109-z

  • CrossRef
  • Google Scholar

• 242

  Zhou DY Ǫin J Huang J Wang F Xu GP Lv YT et al . Zoledronic acid inhibits infiltration of tumor-associated macrophages and angiogenesis following transcatheter arterial chemoembolization in rat hepatocellular carcinoma models. Oncol Lett. (2017) 14:4078–84. doi: 10.3892/ol.2017.6717

  • CrossRef
  • Google Scholar

• 243

  Myers KV Amend SR Pienta KJ . Targeting Tyro3, Axl and MerTK (TAM receptors): implications for macrophages in the tumor microenvironment. Mol Cancer. (2019) 18:94. doi: 10.1186/s12943-019-1022-2

  • CrossRef
  • Google Scholar

• 244

  Chen CJ Liu YP . MERTK inhibition: potential as a treatment strategy in EGFR tyrosine kinase inhibitor-resistant non-small cell lung cancer. Pharm (Basel). (2021) 14:130–55. doi: 10.3390/ph14020130

  • CrossRef
  • Google Scholar

• 245

  Allavena P Anfray C Ummarino A Andon FT . Therapeutic manipulation of tumor-associated macrophages: facts and hopes from a clinical and translational perspective. Clin Cancer Res. (2021) 27:3291–7. doi: 10.1158/1078-0432.CCR-20-1679

  • CrossRef
  • Google Scholar

• 246

  Vonderheide RH . CD40 agonist antibodies in cancer immunotherapy. Annu Rev Med. (2020) 71:47–58. doi: 10.1146/annurev-med-062518-045435

  • CrossRef
  • Google Scholar

• 247

  Naing A Khalil D Rosen O Camidge DR Lillie T Ji RR et al . First-in-human clinical outcomes with NG-350A, an anti-CD40 expressing tumor-selective vector designed to remodel immunosuppressive tumor microenvironments. J Immunother Cancer. (2024) 12. doi: 10.1136/jitc-2024-010016

  • CrossRef
  • Google Scholar

• 248

  Fitzgerald KA Kagan JC . Toll-like receptors and the control of immunity. Cell. (2020) 180:1044–66. doi: 10.1016/j.cell.2020.02.041

  • CrossRef
  • Google Scholar

• 249

  McWhirter SM Jefferies CA . Nucleic acid sensors as therapeutic targets for human disease. Immunity. (2020) 53:78–97. doi: 10.1016/j.immuni.2020.04.004

  • CrossRef
  • Google Scholar

• 250

  Ishikawa H Barber GN . STING is an endoplasmic reticulum adaptor that facilitates innate immune signalling. Nature. (2008) 455:674–8. doi: 10.1038/nature07317

  • CrossRef
  • Google Scholar

• 251

  Shang S Yang C Chen F Xiang RS Zhang H Dai SY et al . ID1 expressing macrophages support cancer cell stemness and limit CD8(+) T cell infiltration in colorectal cancer. Nat Commun. (2023) 14:7661. doi: 10.1038/s41467-023-43548-w

  • CrossRef
  • Google Scholar

• 252

  Heumos L Schaar AC Lance C Litinetskaya A Drost F Zappia L et al . Best practices for single-cell analysis across modalities. Nat Rev Genet. (2023) 24:550–72. doi: 10.1038/s41576-023-00586-w

  • CrossRef
  • Google Scholar

• 253

  Mantovani A Marchesi F Malesci A Laghi L Allavena P . Tumour-associated macrophages as treatment targets in oncology. Nat Rev Clin Oncol. (2017) 14:399–416. doi: 10.1038/nrclinonc.2016.217

  • CrossRef
  • Google Scholar

• 254

  Valdes-Mora F Handler K Law AMK Salomon R Oakes SR Ormandy CJ et al . Single-cell transcriptomics in cancer immunobiology: the future of precision oncology. Front Immunol. (2018) 9:2582. doi: 10.3389/fimmu.2018.02582

  • CrossRef
  • Google Scholar

• 255

  Hao Y Stuart T Kowalski MH Choudhary S Hoffman P Hartman A et al . Dictionary learning for integrative, multimodal and scalable single-cell analysis. Nat Biotechnol. (2024) 42:293–304. doi: 10.1038/s41587-023-01767-y

  • CrossRef
  • Google Scholar

• 256

  Love MI Huber W Anders S . Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. (2014) 15:550. doi: 10.1186/s13059-014-0550-8

  • CrossRef
  • Google Scholar

• 257

  Blair AP Hu RK Farah EN Chi NC Pollard KS Przytycki PF et al . Cell Layers: uncovering clustering structure in unsupervised single-cell transcriptomic analysis. Bioinform Adv. (2022) 2:vbac051. doi: 10.1093/bioadv/vbac051

  • CrossRef
  • Google Scholar

• 258

  Geng Y Feng J Huang H Wang Y Yi X Wei S et al . Single-cell transcriptome analysis of tumor immune microenvironment characteristics in colorectal cancer liver metastasis. Ann Transl Med. (2022) 10:1170. doi: 10.21037/atm-22-5270

  • CrossRef
  • Google Scholar

• 259

  Finak G McDavid A Yajima M Deng J Gersuk V Shalek AK et al . MAST: a flexible statistical framework for assessing transcriptional changes and characterizing heterogeneity in single-cell RNA sequencing data. Genome Biol. (2015) 16:278. doi: 10.1186/s13059-015-0844-5

  • CrossRef
  • Google Scholar

• 260

  Cao J Spielmann M Ǫiu X Huang X Ibrahim DM Hill AJ et al . The single-cell transcriptional landscape of mammalian organogenesis. Nature. (2019) 566:496–502. doi: 10.1038/s41586-019-0969-x

  • CrossRef
  • Google Scholar

• 261

  Azizi E Carr AJ Plitas G Cornish AE Konopacki C Prabhakaran S et al . Single-cell map of diverse immune phenotypes in the breast tumor microenvironment. Cell. (2018) 174:1293–1308 e36. doi: 10.1016/j.cell.2018.05.060

  • CrossRef
  • Google Scholar

• 262

  Wang X Chen J Jia G . From dichotomy to diversity: deciphering the multifaceted roles of tumor-associated macrophages in cancer progression and therapy. Cancer Biol Med. (2023) 21:132–8. doi: 10.20892/j.issn.2095-3941.2023.0370

  • CrossRef
  • Google Scholar

• 263

  Toninelli M Rossetti G Pagani M . Charting the tumor microenvironment with spatial profiling technologies. Trends Cancer. (2023) 9:1085–96. doi: 10.1016/j.trecan.2023.08.004

  • CrossRef
  • Google Scholar

• 264

  Jin Y Zuo Y Li G Liu W Pan Y Fan T et al . Advances in spatial transcriptomics and its applications in cancer research. Mol Cancer. (2024) 23:129. doi: 10.1186/s12943-024-02040-9

  • CrossRef
  • Google Scholar

• 265

  Dries R Zhu Ǫ Dong R Eng CL Li H Liu K et al . Giotto: a toolbox for integrative analysis and visualization of spatial expression data. Genome Biol. (2021) 22:78. doi: 10.1186/s13059-021-02286-2

  • CrossRef
  • Google Scholar

• 266

  Bergenstrahle J Larsson L Lundeberg J . Seamless integration of image and molecular analysis for spatial transcriptomics workflows. BMC Genomics. (2020) 21:482. doi: 10.1186/s12864-020-06832-3

  • CrossRef
  • Google Scholar

• 267

  Palla G Spitzer H Klein M Fischer D Schaar AC Kuemmerle LB et al . Squidpy: a scalable framework for spatial omics analysis. Nat Methods. (2022) 19:171–8. doi: 10.1038/s41592-021-01358-2

  • CrossRef
  • Google Scholar

• 268

  Dong X Leary JR Yang C Brusko MA Brusko TM Bacher R . Data-driven selection of analysis decisions in single-cell RNA-seq trajectory inference. Brief Bioinform. (2024) 25. doi: 10.1093/bib/bbae216

  • CrossRef
  • Google Scholar

• 269

  Meylan M Petitprez F Becht E Bougouin A Pupier G Calvez A et al . Tertiary lymphoid structures generate and propagate anti-tumor antibody-producing plasma cells in renal cell cancer. Immunity. (2022) 55:527–541 e5. doi: 10.1016/j.immuni.2022.02.001

  • CrossRef
  • Google Scholar

• 270

  Su P Li O Ke K Jiang Z Wu J Wang Y et al . Targeting tumor−associated macrophages: Critical players in tumor progression and therapeutic strategies (Review). Int J Oncol. (2024) 64. doi: 10.3892/ijo.2024.5648

  • CrossRef
  • Google Scholar

• 271

  Wang L Jin Z Master RP Maharjan CK Carelock ME Reccoppa TBA et al . Breast cancer stem cells: signaling pathways, cellular interactions, and therapeutic implications. Cancers (Basel). (2022) 14:3287–314. doi: 10.3390/cancers14133287

  • CrossRef
  • Google Scholar

• 272

  Trapnell C Cacchiarelli D Grimsby J Pokharel P Li S Morse M et al . The dynamics and regulators of cell fate decisions are revealed by pseudotemporal ordering of single cells. Nat Biotechnol. (2014) 32:381–6. doi: 10.1038/nbt.2859

  • CrossRef
  • Google Scholar

• 273

  Street K Risso D Fletcher RB Das D Ngai J Yosef N et al . Slingshot: cell lineage and pseudotime inference for single-cell transcriptomics. BMC Genomics. (2018) 19:477. doi: 10.1186/s12864-018-4772-0

  • CrossRef
  • Google Scholar

• 274

  Yang J Liao D Chen C Liu Y Chuang TH Xiang R et al . Tumor-associated macrophages regulate murine breast cancer stem cells through a novel paracrine EGFR/Stat3/Sox-2 signaling pathway. Stem Cells. (2013) 31:248–58. doi: 10.1002/stem.1281

  • CrossRef
  • Google Scholar

• 275

  Saelens W Cannoodt R Todorov H Saeys Y . A comparison of single-cell trajectory inference methods. Nat Biotechnol. (2019) 37:547–54. doi: 10.1038/s41587-019-0071-9

  • CrossRef
  • Google Scholar

• 276

  Casanova-Acebes M Dalla E Leader AM LeBerichel J Nikolic J Morales BM et al . Tissue-resident macrophages provide a pro-tumorigenic niche to early NSCLC cells. Nature. (2021) 595:578–84. doi: 10.1038/s41586-021-03651-8

  • CrossRef
  • Google Scholar

• 277

  Liu S Iorgulescu JB Li S Borji M Barrera- Lopez IA Shanmugam V et al . Spatial maps of T cell receptors and transcriptomes reveal distinct immune niches and interactions in the adaptive immune response. Immunity. (2022) 55:1940–1952 e5. doi: 10.1016/j.immuni.2022.09.002

  • CrossRef
  • Google Scholar

• 278

  Hao Q Vadgama JV Wang P . CCL2/CCR2 signaling in cancer pathogenesis. Cell Commun Signal. (2020) 18:82. doi: 10.1186/s12964-020-00589-8

  • CrossRef
  • Google Scholar

• 279

  Manni W Min W . Signaling pathways in the regulation of cancer stem cells and associated targeted therapy. MedComm. (2020) 3:e176. doi: 10.1002/mco2.176

  • CrossRef
  • Google Scholar

• 280

  Efremova M Vento-Tormo M Teichmann SA Vento-Tormo R . CellPhoneDB: inferring cell-cell communication from combined expression of multi-subunit ligand-receptor complexes. Nat Protoc. (2020) 15:1484–506. doi: 10.1038/s41596-020-0292-x

  • CrossRef
  • Google Scholar

• 281

  Li M He L Zhu J Zhang P Liang S . Targeting tumor-associated macrophages for cancer treatment. Cell Biosci. (2022) 12:85. doi: 10.1186/s13578-022-00823-5

  • CrossRef
  • Google Scholar

• 282

  Zhang Y Ding X Zhang X Li Y Xu R Li HJ et al . Unveiling the contribution of tumor-associated macrophages in driving epithelial-mesenchymal transition: a review of mechanisms and therapeutic Strategies. Front Pharmacol. (2024) 15:1404687. doi: 10.3389/fphar.2024.1404687

  • CrossRef
  • Google Scholar

• 283

  Fan G Xie T Li L Tang L Han X Shi Y . Single-cell and spatial analyses revealed the co-location of cancer stem cells and SPP1+ macrophage in hypoxic region that determines the poor prognosis in hepatocellular carcinoma. NPJ Precis Oncol. (2024) 8:75. doi: 10.1038/s41698-024-00564-3

  • CrossRef
  • Google Scholar

• 284

  Cao L Lu X Wang X Wu H Miao X . From single-cell to spatial transcriptomics: decoding the glioma stem cell niche and its clinical implications. Front Immunol. (2024) 15:1475235. doi: 10.3389/fimmu.2024.1475235

  • CrossRef
  • Google Scholar

• 285

  Conde-Lopez C Marripati D Elkabets M Hess J Kurth I . Unravelling the complexity of HNSCC using single-cell transcriptomics. Cancers (Basel). (2024) 16:3265–84. doi: 10.3390/cancers16193265

  • CrossRef
  • Google Scholar

• 286

  Xu J Yu B Wang F Yang J . Single-cell RNA sequencing to map tumor heterogeneity in gastric carcinogenesis paving roads to individualized therapy. Cancer Immunol Immunother. (2024) 73:233. doi: 10.1007/s00262-024-03820-4

  • CrossRef
  • Google Scholar

• 287

  Chen C Guo Ǫ Liu Y Hou Ǫ Liao M Guo Y et al . Single-cell and spatial transcriptomics reveal POSTN(+) cancer-associated fibroblasts correlated with immune suppression and tumour progression in non-small cell lung cancer. Clin Transl Med. (2023) 13:e1515. doi: 10.1002/ctm2.v13.12

  • CrossRef
  • Google Scholar

• 288

  Liang Q Huang Y He S Chen K . Pathway centric analysis for single-cell RNA-seq and spatial transcriptomics data with GSDensity. Nat Commun. (2023) 14:8416. doi: 10.1038/s41467-023-44206-x

  • CrossRef
  • Google Scholar

• 289

  Mo Z Liu D Chen Y Luo J Li W Liu J et al . Single-cell transcriptomics reveals the role of Macrophage-Naive CD4 + T cell interaction in the immunosuppressive microenvironment of primary liver carcinoma. J Transl Med. (2022) 20:466. doi: 10.1186/s12967-022-03675-2

  • CrossRef
  • Google Scholar

• 290

  Ji AL Rubin AJ Thrane K Jiang S Reynolds DL Meyers RM et al . Multimodal analysis of composition and spatial architecture in human squamous cell carcinoma. Cell. (2020) 182:497–514 e22. doi: 10.1016/j.cell.2020.05.039

  • CrossRef
  • Google Scholar