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Review Article Understanding Immune Cell Adaptation to Tumor Hypoxia for Maximized Therapeutic Efficacy of Immunotherapy: Biology and Non-invasive Imaging Application
Taerim Oh1orcid, Minwoo Kim1, Gi-Sue Kang1, Sung-Joon Ye2, Changhoon Choi3, Won Park3, Michael Hay4, Hiroshi Hirata5orcid, G-One Ahn1,6orcid
Cancer Research and Treatment : Official Journal of Korean Cancer Association 2026;58(1):26-47.
DOI: https://doi.org/10.4143/crt.2025.200
Published online: April 29, 2025

1College of Veterinary Medicine, Seoul National University, Seoul, Korea

2Graduate School of Convergence Science and Technology, Seoul National University, Suwon, Korea

3Department of Radiation Oncology, Samsung Medical Center, Seoul, Korea

4Auckland Cancer Society Research Centre, University of Auckland, Auckland, New Zealand

5Division of Bioengineering and Bioinformatics, Faculty of Information Science and Technology, Hokkaido University, Sapporo, Japan

6Cancer Research Institute, College of Medicine, Seoul National University, Seoul, Korea

Correspondence: G-One Ahn, College of Veterinary Medicine, Seoul National University, 1 Gwanak-ro, Gwanak-gu, Seoul 08826, Korea
Tel: 82-2-880-1178 E-mail: goneahn@snu.ac.kr
Co-correspondence: Hiroshi Hirata, Division of Bioengineering and Bioinformatics, Faculty of Information Science and Technology, Hokkaido University, North 14, West 9, Kita-ku, Sapporo 060-0814, Japan
Tel: 81-11-706-6762 E-mail: hhirata@ist.hokudai.ac.jp
• Received: February 21, 2025   • Accepted: April 28, 2025

Copyright © 2026 by the Korean Cancer Association

This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/4.0/) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

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  • It is extensively documented that tumor hypoxia contributes to the failure of chemotherapy and radiotherapy. Recent evidence suggests that hypoxia is also closely involved in the resistance to immunotherapy. In this review, we highlight how immune cells that are essential for the maximized immunotherapy efficacy, including cytotoxic T cells, dendritic cells, and natural killer cells, can adapt to tumor hypoxia. We then outline previous attempts targeting tumor hypoxia (for example, modulators of tumor cell oxygen consumption, perfusion modulators, hypoxia-activated prodrugs, hypoxia-inducible factor inhibitors, and hypoxia-responsive chimeric antigen receptor T cells) discussing how these approaches have resulted in an improvement of the antitumor response to immunotherapy in preclinical or clinical settings. Lastly, we review various non-invasive techniques to detect the tumor hypoxia and immune responses. We believe that an integration of the biological knowledge of immune cell adaptation to tumor hypoxia with the cutting edge non-invasive imaging technologies may ultimately allow us not only to select for patients who would benefit the most from the immunotherapy but also to monitor their responses in a real-time manner so that we can offer them an optimal personalized medicine in the clinic.
  • Key words: Tumor hypoxia, Immune cells, Immunotherapy, Non-invasive imaging
Hypoxia is defined as the condition in which oxygen availability at the tissue level is reduced thereby affecting the cellular function and organismal homeostasis [1]. At the cellular level, when approximately 7 to 8 cells cluster together, the local oxygen gradients become insufficient with the diffusion limit of the oxygen in tissues, typically around 100-200 μm [2]. Hypoxic conditions can arise in various settings including embryonic development, wound healing, and pathological conditions such as cancer, ischemia, and chronic obstructive pulmonary disease [3].
Tumor hypoxia arises as a consequence of the abnormal tumor vasculature [4]. Present in all human and animal solid tumors, hypoxia has been recognized as a major contributing factor to failure of all anticancer therapies, including chemotherapy, radiotherapy, surgery, and most importantly the latest immunotherapy [5]. Recent clinical evidence has demonstrated that tumors from the recurrent/metastatic head and neck squamous cell carcinoma (HNSCC) patients treated with anti–programmed death-1 (PD-1) antibodies have increased expression of carbonic anhydrase IX (CAIX), a well-characterized hypoxic marker [6] and decreased infiltration of CD8+ cytotoxic T cells [7]. It is extensively documented that tumor hypoxia provides an immunosuppressive environment. For example, it has been shown that tumor hypoxia decreases major histocompatibility complex (MHC) I expression through unfolded protein response-mediated autophagy in cancer cells [8]. Moreover, major immunosuppressive immune cells including tumor-associated macrophages (TAM), myeloid-derived suppressor cells (MDSC), and CD4 regulatory T cells (Treg) have been shown to migrate towards hypoxic regions in the tumors [9]. Casazza et al. [10] have elegantly demonstrated that semaphorin 3A expression in cancer cells defines TAM positioning within the tumor. Specifically, neuropilin-1, a receptor originally identified to control neuronal guidance and axonal growth, in co-operation with vascular endothelial growth factor receptor-1 on TAM has been shown to be a critical factor for their distribution within hypoxic areas of the tumors [10]. Tumor hypoxia has also been reported to increase the expression of programmed death-ligand 1 (PD-L1) on cancer cells, TAM, and dendritic cells through activation of hypoxia-inducible factor-1 (HIF-1) [11-13] thereby inhibiting activation of effector T cell by binding to PD-1 [14].
Mammalian cells can adapt to hypoxic conditions by activating HIF, the master transcription factor regulating hundreds of genes involved in angiogenesis, glycolysis, cell survival, and apoptosis [15]. HIF are heterodimeric complexes comprising an oxygen-sensitive α subunit and a constitutively expressed β subunit [16]. There are three isoforms of HIF-α, namely HIF-1α, HIF-2α, and HIF-3α, each of which has distinct, yet somewhat overlapping roles in maintaining oxygen sensing and homeostasis in cells. The α form contains a basic helix-loop-helix domain, two Per-Arnt-Sim (PAS) domains, and an oxygen-dependent degradation domain (ODD) containing an NH2-terminal transactivation domain [17]. Only HIF-1α and HIF-2α have a COOH-terminal transactivation domain while HIF-3α has been reported to exist as multiple variants, some of which lacking one or more of these domains [17]. Under normoxic conditions, HIF-α is hydroxylated at the defined prolyl residues by prolyl hydroxylase domain (PHD) enzymes (PHD1, PHD2, and PHD3 encoded by egl nine homolog [EGLN] 2, EGLN1, and EGLN3, respectively), leading to its recognition and ubiquitination by the von Hippel-Lindau tumor suppressor [18]. Hydroxylation by the 2-oxoglutarate-dependent factor inhibiting HIF (FIH, encoded by HIF1AN) makes HIF-α subunits unable to bind to their transcriptional co-activators, the histone acetyl transferases CREB-binding protein and p300, preventing the formation of a functional transcriptional complex [19]. In hypoxic conditions, the activity of HIF hydroxylase is inhibited, allowing HIF-α stabilization and subsequent nuclear translocation, where it dimerizes with HIF-β and binds hypoxia response elements (HRE) in target genes [20]. There are stimuli other than the molecular oxygen that can activate HIF. For example, reactive oxygen species (ROS), inflammatory mediators and bacterial products such as lipopolysaccharide [21], nuclear factor erythroid 2–related factor 2, an antioxidant transcription factor [22], microRNA and long non-coding RNA [19] have been shown to regulate transcription and/or translation of HIF in normoxic cancer cells.
HIF-1α is ubiquitously expressed in most of the mammalian cells and the most studied HIF isoform. In cancer cells, HIF-1 is primarily involved in regulating glycolytic metabolism by activating genes such as glucose transporter-1 (GLUT-1) and lactate dehydrogenase A (LDHA) [23]. HIF-2α is expressed rather in a tissue-specific manner, primarily in endothelial cells, kidney interstitial cells, and neural tissues [24]. Unlike HIF-1, HIF-2 has been reported to be more critical for long-term adaptation to hypoxia [25]. For example, it regulates erythropoiesis by activating erythropoietin, thereby increasing red blood cell formation to enhance oxygen transport [26]. HIF-2 also modulates iron homeostasis through genes such as transferrin and ferroportin, ensuring efficient iron utilization for hemoglobin synthesis [27]. In cancer cells, HIF-2 has been demonstrated to act as a key oncogene, driving tumor progression through the activation of cellular myelocytomatosis (c-Myc) and transforming growth factor-α in renal cell carcinoma [28]. HIF-3α, the least understood isoform, functions predominantly as a modulator of the hypoxic response [17]. It has multiple splice variants, some of which act as dominant-negative regulators by competing with HIF-1α and HIF-2α for HRE binding [29]. For instance, the IPAS (inhibitory PAS domain protein) variant lacks transactivation domains and sequesters HIF-β, attenuating the hypoxic response [30]. While HIF-3 is less involved in direct transcriptional activation [31], it has been reported to play disease-specific contexts. For instance, dysregulation of HIF-3 has been implicated in diseases such as pulmonary hypertension, where it influences vascular remodeling, and fibrotic diseases, where it modulates tissue repair [32].
In this review, we will highlight how immune cells, especially those mediating antitumor immune response can adapt to tumor hypoxia through HIF-dependent and -independent mechanisms. Understanding how these cells respond to hypoxic conditions is particularly important in order to maximize antitumor responses to immunotherapy especially given the fact that they may already reside in hypoxia conditions in unperturbed physiological conditions such as lymphoid organ where the O2 concentrations have been reported to be below 2.5% [33] compared to the physiologic pO2 (partial pressure of oxygen) in healthy tissues that typically spans between 3.3% and 7.9% [34-36].
1. Cytotoxic T cells
Cytotoxic CD8+ T cells are essential in mediating antitumor immune response [37]. Exhausted T cells are a subset of cytotoxic T cells that are exposed to persistent antigen presentation thereby losing polyfunctionality and renewal capacity [38]. These exhausted CD8+ cells in tumors have been shown to experience very low levels of oxygen as demonstrated by being strongly positive for the hypoxic marker pimonidazole [38-40].
Then does hypoxia cause T cell exhaustion? There are some studies suggesting that this is the case. For example, Scharping et al. [38] have reported that murine CD8+ T cells cultured in chronic (5 days) hypoxic conditions (1.5% O2) concurrently with anti-CD3/anti-CD28 stimulation has led to T cell exhaustion via HIF-1α-independent, but Blimp-1–dependent manner suppressing mitochondrial biogenesis resulting in elevated level of ROS production from the mitochondria. Interestingly, the T cell exhaustion induced by such chronic hypoxia has been shown to be an irreversible process due to persistent activation of nuclear factor of activated T cells 1 signaling [38]. Vignali et al. [41] have demonstrated that tumor hypoxia induces CD39 expression on terminally exhausted mouse T cells, where CD39 is known to hydrolyze extracellular ATP to adenosine [42] thereby eliciting immunosuppressive functions similar to Foxp3+ CD4+ Treg.
On the other hand, there are studies suggesting that acute hypoxic exposure can enhance the cytotoxic T cell function. Cunha et al. [43] have reported that acute exposure (2 hours) of low oxygen levels (1 or 5% O2) prior to CD3/CD28 activation of 3 days results in a significant increase in effector differentiation of the mouse CD8+ T cells, upregulating molecules such as the interleukin-2 (IL-2) receptor alpha subunit CD25, granzyme B, and interferon-γ via facilitating HIF-1α protein stabilization. Similarly, another study has demonstrated that peripheral blood-derived human T cells cultured in hypoxia (1% O2) for 72 hours concurrently stimulated with CD3/CD28 antibodies have exhibited an enhanced proliferation through increased HIF-1α expression and glycolytic metabolism [44]. Interestingly, the latter study has also demonstrated that the tumor-antigen expressing chimeric antigen receptor T cells (CAR T cells) delivered to neuroblastoma tumor-bearing mice proliferate more efficiently in pimonidazole-positive hypoxic areas of the tumors [44].
2. Dendritic cells
Dendritic cells provide the most important link between the innate and the acquired immune response where they present the antigenic peptides to cytotoxic CD8+ T cells and proinflammatory T helper cells activating the lymphocytes via direct cell-cell contact and proinflammatory cytokines such as IL-12 or IL-6 [45,46]. Studies have demonstrated that hypoxia exposure (1% O2 for 4 days) of immature dendritic cells derived from the human monocytes incubated with IL-4 and granulocyte macrophage colony-stimulating factor has minimal effects on the dendritic cell differentiation (in the expression of surface markers such as human leukocyte antigen [HLA]-II and CD80) [47,48] but induces more migratory phenotypes via increase in the expression of chemokine receptor genes including CC chemokine receptor (CCR) 3, CCR2, and CXC chemokine receptor 1 (CX3CR1) [47], chemotactic response to CCR2 and CXCR4 agonists [47,48], and production of CC chemokine ligand (CCL) 20, C-X-C chemokine ligand (CXCL) 1, CXCL8, and CXCL10 [48]. Similarly, Kohler et al. [49] have demonstrated that hypoxia differentially regulates chemokine and chemokine expression in the bone marrow-derived dendritic cells by increasing CCR7 expression and CCL17 and CCL22 production through a HIF-1–dependent mechanism. Bosco et al. [50] have identified triggering receptor expressed on myeloid cells-1, a member of the Ig superfamily of immune receptor and a strong amplifier of the immune responses [51] as a novel hypoxia-inducible gene in monocyte-derived dendritic cells exposed to 1% O2 for 4 days, responsible for extracellular signal-regulated kinase 1 (ERK-1), Akt, and nuclear factor of kappa light polypeptide gene enhancer in B-cells inhibitor α (IκBα) phosphorylation and proinflammatory cytokine and chemokine secretion.
A transient hypoxic exposure (48 hours at 0.5% O2) to human monocyte-derived dendritic cells has been shown to dramatically increase the cell surface expression of differentiation markers including CD40, CD80, and HLA-DR isotype, as well as the production of IL-10, and proliferation of co-cultured lymphocytes [52]. Interestingly, 5% O2 exposure for 48 hours to dendritic cell progenitors has been reported to inhibit the differentiation towards plasmacytoid dendritic cells by activating HIF-1 and its downstream effector, inhibitor of DNA binding 2 [53], a critical regulator during the dendritic cell subset [54].
3. Natural killer cells
Natural killer (NK) cells are destined to remove abnormal cancer cells through the detection of major MHC class I-deficient targets expressed on various types of cancers [55]. NK cell cytolytic action towards the cancer cells are mediated by an interaction of the cancer cell surface stress proteins such as MHC class I chain-related molecule A and B or UL16 binding protein 1-6 with the natural killer group 2 member D (NKG2D) receptor on NK cells [55]. Studies have consistently reported that hypoxia reduces NK cell cytotoxicity [56-58]. Sarkar et al. [56] have demonstrated that the cytotoxicity of healthy donor-derived human NK cells gradually decreases by lowering the O2 concentrations from 21%, 1%, 0.2%, to 0%, possibly through the reduction in the surface expression of NKG2D. They have further demonstrated that hypoxia-induced reduction in the NK cytotoxicity can be reversed upon reoxygenation, although the reduced cell surface expression of NKG2D seems to be irreversible [56]. It has been suggested that hypoxia-induced reduction in NK cell killing capacity arises due to signal transducer and activator of transcription 3 activation [57] and that these hypoxia-exposed NK cells with reduced cytotoxicity have altered cellular metabolism towards glycolysis to produce ATP accompanied by dysfunctional mitochondrial morphology and increased amount of ROS (Fig. 1) [58].
4. Other PD-L1-expressing immune cells
PD-L1 (also known as CD274) expressed on tumor cells and/or immune cells in the tumor microenvironment interacts with PD-1 on tumor-infiltrating lymphocytes (TILs), attenuating effector T cell responses thereby allowing tumors to escape from the immune attack [59]. Studies have shown that human monocytes can express constitutive PD-L1 [60] and that TAM are the dominant source of PD-L1 in tumors [61]. Interestingly, Wang et al. [62] have reported that PD-L1 expression is significantly increased when monocytes are being rested ex vivo and further so upon monocyte-to-macrophage differentiation. They have further demonstrated that although TAM are extensively characterized to have a pro-tumoral role, PD-L1–expressing TAM are immunostimulatory with spatial preference to T cells in tumors [62]. Interestingly, Gordon et al. [63] have identified PD-1–expressing TAM and found that they are exclusively an M2 phenotype and impaired for phagocytosing PD-L1–expressing cancer cells. It is currently unknown whether tumor hypoxia regulates PD-1 expression in TAM, although it has been previously reported that it impairs the phagocytosing function of TAM. Hussain et al. [64] have demonstrated that hypoxia therefore HIF activation leads to strong induction of Fc gamma receptor IIb (FcγRIIb) expression thereby reducing the ability of TAM to phagocytose the anti-CD20 antibody–opsonized cancer cells. Importantly, FcγRIIb expression on TAM has recently been reported to be a critical mediator for the resistance of anti–PD-1 therapy. By using intravital microscopy technique, Arlauckas et al. [65] have elegantly demonstrated that anti–PD-1 monoclonal antibodies are being transferred from CD8+ T cells to TAM through FcγR at 24 hour post-treatment impairing the immunotherapy efficacy. They have further shown that the uptake of PD-1 antibodies by TAM could be prevented by using FcγRIIb/III blocking antibodies or deglycosylated anti–PD-1 antibodies, which have allowed to prolong antibody binding to T cells therefore an improved antitumor response [65]. Xun et al. [66] have identified a novel subset of TAM expressing activated leukocyte cell adhesion molecule (ALCAM) under hypoxic conditions and that these ALCAMhigh TAM are closely distributed with the exhausted T cells in the tumor.
A number of clinical studies have reported that tumor hypoxia can significantly interfere with antitumor immune cell infiltration hence immunotherapy efficacy. Brooks et al. [67] have demonstrated by multiplex immunostaining that CD3 T cells are spatially distributed away from CAIX-positive hypoxic areas in the tumors of HNSCC patients. Similar results have also been reported by Mayer et al. [68] reporting a mutually exclusive spatial distribution between CD8 T cells and GLUT-1–positive hypoxic areas in the lymph node metastasis of melanoma patients. They have further reported a strong negative relationship between the extent of GLUT-1 expression and CD8 cytotoxic T cell infiltration into the tumors of various types including the brain, skin of the ear, gastrointestinal track, lung, and skin [68]. Zandberg et al. [7] have reported by immunofluorescent staining that tumors from HNSCC patients who have progressed after anti–PD-1 antibodies had higher CAIX-positive areas with lower CD8+ T cell infiltration. Importantly, this study has demonstrated that the tumor hypoxia can serve as a poor prognosis factor predicting poorer outcome of anti–PD-1 therapy [7]. Similar to these results, Estephan et al. [8] have reported that CD8+ T cells not only correlate inversely with the extent of cells positive for CAIX but also are significantly excluded from the hypoxic regions in tumors of colorectal cancer patients. In melanoma patients, Xiao et al. [69] have identified six distinct tumor microenvironment archetypes based on the multicellular compositions in the tumor tissues using multiplexed imaging mass cytometry and found that responders to anti–PD-1 immunotherapy had more diversified cell type compositions with lower CAIX-positive hypoxic signals than non-responders in the invasive margin regions of the tumors. Vos et al. [70] have demonstrated that tumors from HNSCC patients with a major pathological response to nivolumab (anti–PD-1 antibodies) or nivolumab plus a single dose of ipilimumab (anti–cytotoxic T-lymphocyte antigen 4 [CTLA-4] antibodies) had a significant decrease in hypoxic signal, measured by [18F]-HX4, a positron emission tomography (PET) imaging tracer to image tumor hypoxia with an improved signal-to-background ratio than [18F]-MISO and [18F]-AZA [71], along with reduction in hypoxic RNA signature.
As there are no biomarkers derived from tumor hypoxia available, researchers have analyzed various hypoxia-relevant genes from big database such as The Cancer Genome Atlas to apply whether the gene expression can serve as prognostication and prediction of response to immunotherapy (Table 1). By incorporating various analysis techniques some of which even incorporate machine learning approach (Table 1) [72], many studies have reported that their hypoxia-relevant genes are capable of predicting the immune landscape within the tumor microenvironment as well as serving as a prognostic tool for immunotherapy efficacy in various types of tumors (Table 1) [72-83]. There are a couple of studies, however, reporting somewhat contradicting findings such that patients with high expression in hypoxia-relevant genes have displayed an increased infiltration of antitumor immune cells including the effector T cells (Table 1) [84-86].
Based on the above evidence demonstrating how tumor hypoxia negatively impacts immunotherapy efficacy, we have searched for studies combining hypoxia-targeting strategies with immunotherapy. Although there are some studies that have not attempted initially to inhibit hypoxia in order to bring the synergistic effects with immunotherapy, we found that many, if not all, studies have demonstrated a positive outcome from the combination of the two modalities (Fig. 2).
1. Modulators of tumor cell oxygen consumption
Metformin is a broadly prescribed type II diabetes treatment efficiently lowering the blood glucose and insulin levels by inhibiting the liver gluconeogenesis [87]. The primary mechanism of metformin is considered to be a direct inhibition of complex 1 activity of the mitochondrial electron transport chain [87]. Zannella et al. [87] have reported that metformin significantly decreases tumor hypoxia by inhibiting tumor cell oxygen consumption and this enables a significant improvement of the tumor response to ionizing radiation when given 30 minutes prior to radiotherapy. Scharping et al. [88] have demonstrated that metformin leads to synergistic antitumor activities in B16 and MC38 tumors when combined with anti–PD-1 antibodies by reprogramming the hypoxic tumor microenvironment. They have further demonstrated that although metformin alone can increase in the number of CD44hi activated T cells, metformin itself is inefficient to abrogate the tumor growth in mice [88]. Interestingly, in that study metformin alone has also shown to increase the number of PD-1+ and Tim-3+ exhausted T cells in tumors [88] and this might have counter affected the cytotoxic activities of CD44hi activated T cells in exerting the tumor control.
Respiratory hyperoxia (60% O2) mimicking protocols of supplemental oxygen delivery to humans have been utilized as an anti-adenosinergic treatment and shown that hyperoxia itself (without combination with any immune checkpoint blockades) is sufficient to promote regression of tail-vein injected MCA205 and B16 melanoma as well as spontaneous lung metastasis from orthotopically implanted 4T1 tumors in mice [89]. Mechanistically, it has been shown that respiratory hyperoxia increases CD8+ T cell infiltration and function (interferon-γ production) while decreasing CD4+CD25+Foxp3+ Treg infiltration and their CTLA-4 expression in the lung tumor microenvironment [89]. As expected, respiratory hyperoxia combined with immunotherapy (anti–CTLA-4/PD-1 antibodies) has resulted in a significant improvement in the regression of tumor seeding models [89].
2. Perfusion modulators
Losartan, an angiotensin II receptor inhibitor, has been shown to decrease tumor hypoxia by alleviating the tumor interstitial pressure exerted by collagen I production from cancer-associated fibroblasts [90]. Datta et al. [91] have recently demonstrated that losartan normalizes the tumor vasculature, improves perfusion, and decreases tumor hypoxia and immunosuppression, allowing enhanced effector T cell function and antitumor response to anti–PD-1 antibodies.
Telmisartan is another angiotensin II type 1 receptor blocker with longer plasma half-life, higher lipophilicity, and higher binding affinity for the receptor than losartan [92]. Wadsworth et al. [93] have reported similar findings to losartan by showing that it can increase tumor perfusion by reducing cancer-associated fibroblasts and fibrillary type 1 collagen deposition in WiDr tumor xenograft in mice. Therefore, it is feasible that telmisartan may also offer a synergistic antitumor efficacy when combined with immunotherapy, although not yet reported.
3. Hypoxia-activated prodrugs
Hypoxia-activated prodrugs (HAP) are prodrugs (inactive compounds) that can be converted to active cytotoxins under hypoxic conditions capable of killing hypoxic cancer cells. The hypoxia-selective activation is achieved by one-electron reductases, usually P450 oxidoreductase [94], yielding a one-electron intermediate, which is sensitive to back oxidation in the presence of molecular oxygen [95]. Tirapazamine (TPZ), the first member of the benzotriazine di-N-oxide class is the most studied HAP, and had been evaluated up to phase III clinical trials [95]. Although there are no studies to date investigating the effect of TPZ on immunotherapy antitumor efficacy, Wang et al. [96] have observed immunogenic cell death in tumors evoking the development of tumor-specific cytotoxic T cells when an albumin-based nanoplatform containing TPZ is irradiated at near-infrared frequency. Interestingly, phase II clinical trials (NCT03259867) are currently undertaking to evaluate efficacy of trans-arterial tirapazamine embolization treatment of liver cancer followed by PD-1 checkpoint inhibitor (nivolumab or pembrolizumab) in hepatocellular carcinoma, metastatic colorectal cancer, metastatic gastric cancer, and advanced non-small cell lung cancer patients.
Evofosfamide (TH-302), a 2-nitroimidazole compound with a DNA-crosslinking phosphoramidate mustard [97], is another extensively characterized HAP, which has recently completed phase III clinical trials in pancreatic adenocarcinoma and soft tissue sarcoma (NCT01746979 and NCT01440088) demonstrating no significant benefit in increasing the overall survival of the treated patients [98,99]. Jayaprakash et al. [100] have reported that TH-302 in combination with anti–CTLA-4/PD-1 antibodies lead to a significantly improved antitumor response using the transgenic adenocarcinoma of mouse prostate–clone 2 prostate tumor model in mice. They have demonstrated that the decrease in tumor hypoxia caused by TH-302 leads to increased CD8+ T cell infiltration and decreased MDSC influx [100].
Other HAP such as tarloxotinib (a tyrosine kinase inhibitor prodrug) and CP-506 (the second-generation analogue of nitrogen mustard prodrug PR-104) have also been reported to offer synergistic antitumor activities when combined with immune checkpoint blockades through reduction in tumor hypoxia and subsequent increase in CD8+ T cells activation [97].
4. HIF inhibitors
32-134D (4-(6-bromo-1H-indol-3-yl)-2-(7-bromo-1H-indol-3-yl)thiazole) is a small molecule inhibitor of both HIF-1 and HIF-2 demonstrating potent antitumor activities in hepatocellular carcinoma model leading to alteration in the gene expression, reduced infiltration of TAM and MDSC, and increased CD8+ T cell infiltration in tumors [101]. Combining 32-134D with anti–PD-1 antibodies has resulted in a significant abrogation of tumor growth although 32-134D or anti–PD-1 antibodies themselves also offered very potent antitumor activities in that model [101].
PX-478 (S-2-amino-3-[4’-N,N,-bis(chloroethyl)amino]phenyl propionic acid N-oxide dihydrochloride), a selective HIF-1α inhibitor [102]. Luo et al. [103] have reported that PX-478 combined with anti–PD-1 antibodies leads to a significant tumor growth inhibition in Lewis lung carcinoma by inhibiting lysyl oxidase-like-2 signaling pathway reverting the mesenchymal phenotype of lung cancer cells to more an epithelial phenotype.
Many anticancer agents including imatinib, gefitinib, erlotinib, cetuximab, trastuzumab, everolimus, topotecan, taxotere, 17-AAG, YC-1, echinomycin, bortezomib, and trichostatin A have been shown to inhibit HIF-1 activity through a reduction in HIF-1α protein levels, HIF-1 DNA binding activity, or HIF-1-mediated transactivation of target genes [104]. Indeed, combination of imatinib [105], gefitinib [106], erlotinib [106], and taxotere [107] with immune checkpoint blockades have demonstrated synergistic antitumor activities in preclinical or clinical studies. Cardiac glycosides such as digoxin have also been identified as potent inhibitors of HIF-1 activity through inhibition of HIF-1α translation [108]. A recent study by Wang et al. [109] has shown a synergistic antitumor activity when digoxin is combined with a PD-1 inhibitor in 4T1 breast tumor model, although the proposed mechanism does not seem to involve HIF-1 inhibition.
5. Hypoxia-responsive CAR T cells
To overcome the detrimental effects of hypoxia on T cell activation, hypoxia-sensing or hypoxia-inducible CAR T cells have been developed. In this strategy, the expression of CAR genes can only be activated under hypoxic conditions under the control of hypoxic sensors such as the ODD [110,111] and HRE [110,112]. Examples include hypoxia-inducible CAR T cells (HiCAR-T cells) driven by HRE bearing HIF-1α ODD and the tumor antigen such as CD19, AXL receptor tyrosine kinase (AXL), and human epidermal growth factor receptor 2 (HER2) [113]; HypoxiCAR bearing dual sensors, namely C-terminal 203-amino-acid ODD onto the CAR while modifying the CAR’s promoter in the long terminal repeat enhancer region of the vector containing 9 times HRE with ErbB tumor antigen [110]; hypoxia-inducible transcription amplification CAR T cells where transactivators of the gene of interests can be regulated by both HRE and ODD with HER2 tumor antigen [111]; and hypoxia-responsive CAR T cells (5H1P-CEA CAR) bearing 5 times vascular endothelial growth factor HRE and ODD [112]. These CAR T cells all have consistently demonstrated hypoxia (1% O2)-specific presentation of CAR molecules and cytotoxicity [110-113], bypassing the systemic and lethal toxicity [110], and durable antitumor response [110-113]. Although these hypoxia-responsive CAR T cells are a very attractive strategy to target the tumor hypoxia, it has not yet been tested for their antitumor activities in combination with immune checkpoint blockades.
Based on the promising preclinical results of TH-302 described above, a clinical trial of TH-302 in combination with ipilimumab (anti-CTLA4) has been conducted in 2017 in patients with metastatic or locally advanced pancreatic cancer, human papillomavirus-negative HNSCC, immune checkpoint blockade-refractory melanoma, or castration-resistant prostate cancer (NCT03098160). The results have revealed 16.7% partial response and 66.7% stable disease in 15 patients having measurable disease at baseline out of 18 enrolled patients with no dose-limiting toxicities [114]. Analysis of peripheral blood from these patients before, during, and after the treatment has shown a significant increase in CD4+ and CD8+ T cell proliferation and fewer accumulation of PD-1+LAG-3+ exhausted T cells in responders [114]. In paired tumor biopsies obtained before and at 7 weeks after the treatment have revealed that non-responding patients had increased expression of HIF-1α downstream genes and hyper-metabolic phenotype of T cells [114].
Vorinostat (suberoylanilide hydroxamic acid) is a well-known histone deacetylase inhibitor capable of decreasing HIF-1α protein expression [115] and nuclear translocation [116]. The results of phase II open label trial of vorinostat in combination with pembrolizumab (PD-1 antibodies) in patients with recurrent metastatic HNSCC and salivary gland cancer (NCT02538510) have demonstrated 32% partial response and 20% stable disease in HSNCC patients, and 16% partial response and 56% of stable disease in salivary gland cancer patients [117]. Although the study has suggested that overall survival and progression-free survival in HNSCC patients are superior, the toxicity associated with the combination has also shown to be significantly higher than pembrolizumab alone [117].
1. Non-invasive imaging techniques for tumor hypoxia
In this section, we outline non-invasive imaging technologies for hypoxia in solid tumors in small animals and human patients. Some of the imaging techniques described here are used only for preclinical studies. However, these emerging imaging technologies are helpful in animal experiments for malignant tumors and immunotherapy. Therefore, we do not exclude imaging techniques which are only used in the laboratory. The pros and cons of imaging techniques will be discussed later.

1) Fluorescent and phosphorescent imaging

Fluorescent and phosphorescent imaging techniques are widely used in microscopy. Oxygen-sensitive fluorescent/phosphorescent agents are essential for this imaging modality. Some specially designed oxygen-sensitive imaging agents have been used for mapping the partial pressure of oxygen (pO2) [118-120]. Since this imaging modality is based on optical techniques, high-resolution imaging can be used to obtain biological tissue samples. In contrast, absorption and scattering of light in biological tissues hinder optical-based imaging techniques. These drawbacks are challenges for high-resolution tissue imaging in depth. Therefore, high-resolution three-dimensional (3D) fluorescent/phosphorescent imaging is generally challenging for a whole solid tumor.

2) Blood-oxygen level-dependent magnetic resonance imaging

Blood-oxygen level-dependent magnetic resonance imaging (BOLD-MRI) detects the difference in magnetic properties between oxygenated and deoxygenated hemoglobin (oxyhemoglobin and deoxyhemoglobin) [121]. It is widely applied to functional MRI for brain activity studies involving a shift in cerebral blood flow [122]. BOLD-MRI can also be applied to solid tumors to monitor tissue oxygenation [123,124]. However, BOLD-MRI does not quantitatively provide the pO2.

3) Dynamic contrast-enhanced MRI

Dynamic contrast-enhanced (DCE)–MRI is based on a series of MRI scans after rapid intravenous administration of a contrast agent such as a Gd-based contrast agent. The image acquisition sequence in this MRI technique should be fast enough to obtain the dynamic change of contrast-enhanced images like a movie. The obtained dynamic profile of the MRI scans reflects tumor vascularity and the tumor perfusion status [125]. The perfusion parameters obtained by DCE-MRI help predict the tumor treatment response [126].

4) 18F-fluoromisonidazole PET

PET is widely used for patient diagnosis in a clinical setting [127]. PET imaging with the hypoxia tracer, 18F-fluoromisonidazole (18F-FMISO), has been used to image hypoxia in cancer patients [128]. This tracer, 18F-FMISO, accumulates in hypoxic cells and can be visualized with a PET scan. The images of 18F-FMISO PET have been used to stratify patients into two groups of high and low hypoxia but, 18F-FMISO PET does not provide information on pO2 values and tissue oxygenation above the threshold of cellular uptake of hypoxia tracers.

5) Near-infrared spectroscopy and imaging

Near-infrared light has an advantage for tissue penetration compared to visible light or light with a shorter wavelength [129]. Oxyhemoglobin and deoxyhemoglobin have different absorption characteristics that can be detected by near-infrared spectroscopy (NIRS). Therefore, this spectroscopic technique detects oxygen saturation of hemoglobin in tissues [130].

6) Electron paramagnetic resonance imaging

Electron paramagnetic resonance (EPR) is a magnetic resonance technique for detecting unpaired electrons instead of the nuclei that nuclear magnetic resonance detects. There are two protocols, continuous-wave and pulsed operations, in EPR imaging. The EPR spectrum of oxygen-sensitive imaging agents (stable free radicals) can respond to the pO2 in the environment around the imaging agents [131]. Using pulsed EPR, the relaxation process of unpaired electrons responding to the pO2 can be measured [132]. Therefore, EPR imaging can provide pO2 maps in solid tumors [133]. Fig. 3 illustrates in vivo pO2 mapping of mouse xenograft models of pancreatic ductal adenocarcinoma cell lines (A, Hs766t; B, MIA PaCa-2; C, SU.86.86) and anatomic maps with MRI. Moreover, this figure shows the pO2 response to intravenous injection of pyruvate. This imaging modality is powerful for mapping the solid tumors of small animals. EPR imaging does not apply to a clinical setting because of the barrier of injecting free radical imaging agents into a patient (approval from the national regulatory agency is required). In preclinical studies, EPR pO2 mapping can provide a reference truth for 18F-FMISO PET for solid tumors [134].

7) Photoacoustic imaging

Photoacoustic imaging (PAI) uses an imaging agent to sense specific physical quantities in biological tissues [135]. The principle of PAI is the measurement of acoustic waves in biological tissues after irradiating light (laser pulse) into the tissues. PAI agent generates mechanical pressure due to heating by the reaction to irradiated light, leading to acoustic waves. As mentioned in fluorescent/phosphorescent imaging, light has limited penetration in tissues. While PAI uses light irradiation, the signals are received as acoustic waves, such as ultrasonic echo, that can propagate easily in tissue. Therefore, PAI can detect the information for the tissue in depth. In this context, a PAI agent is essential for detecting oxygenation (blood oxygen saturation and pO2) in solid tumors [136].
2. Imaging studies for hypoxia and immune response
The key mediator of hypoxia signaling, HIF-1α, regulates gene expression that influences tumor immunity under hypoxia [137]. Therefore, imaging hypoxia or oxygen-related parameters in tumors is essential information regarding immune response. As mentioned above, the non-invasive imaging techniques for mapping hypoxia and tissue oxygenation-related parameters are available for cancer studies. We briefly describe the studies of immune response and hypoxia imaging.
Using 18F-FMISO PET, hypoxia severity was monitored during checkpoint blockade [138]. This study identified the maximum tumor 18F-FMISO uptake as a predictor of immunotherapy response to the treatment of evofosfamide, a hypoxia-activated prodrug. The treatment outcome of evofosfamide was limited; however, it was enhanced by evofosfamide combined with immune checkpoint inhibitors [139].
Another study reported a longitudinal correlation between hypoxia and response to immunotherapy using 18F-FMISO PET [140]. In this study, tumor hypoxia was quantified in vivo before or during PD-1 and CTLA-4 checkpoint blockade in mouse models of breast and colon cancer. This work demonstrated that hypoxia imaging is an early predictive biomarker of the response to immune checkpoint therapy.
The studies above used 18F-FMISO PET to monitor tumor hypoxia. However, other imaging techniques, such as EPR imaging and PAI, can also be used in studies on hypoxia and immune response. The field of precision medicine needs to explore the relationship between hypoxia and immunotherapy. Such knowledge can support the effective selection of patients responding well to immunotherapy and the combination of chemotherapy and immune checkpoint blockade.
3. Pros and cons of hypoxia imaging techniques
This section discusses the pros and cons of the imaging techniques mentioned above. The three points below are essential perspectives for cancer researchers. Moreover, Table 2 [121,132,140-144] summarizes the imaging techniques mentioned above to compare their feasibility in clinical use, measurement resolution (or “sensitivity and specificity”), and limitations. Diagnosis of malignant tumors is an essential step in clinical practice. Therefore, the sensitivity and specificity of the imaging techniques are critical for cancer diagnosis. However, reading a quantitative oxygen parameter is essential for tumor hypoxia imaging techniques because quantitative hypoxia measurement such as pO2 can be a companion diagnostic test and has much information for immunotherapy, instead of an on-off image like fluorescent hypoxia cell mapping.

1) Ease of operation and access to the instruments

Fluorescent/phosphorescent and NIRS imaging instruments are relatively easy to access. In contrast, MRI- or PET-based imaging (BOLD-MRI, DCE-MRI, and 18F-FMISO PET) require large and expensive scanners. However, MRI and PET scanners are already working in many hospitals. Access to EPR imaging is limited at present, compared to MRI and PET scanners. PAI is more accessible than EPR imaging, but limited compared to currently available clinical imaging instruments.

2) Quantitative pO2 measurements

Oxygen-related imaging techniques do not necessarily provide quantitative pO2 values. Fluorescent, EPR, and PAI can provide tissue pO2 in solid tumors. BOLD-MRI, DCE-MRI, 18F-FMISO PET, and NIRS provide information about oxygenation using various parameters. This comparison does not criticize these imaging techniques. The effectiveness of the imaging techniques depends on their application and the demands of the studies. Researchers should select an appropriate imaging technique for their studies.

3) Clinical use

The most influential factor for clinical applications of tissue oxygenation imaging is the availability of the instruments and techniques. In this context, MRI and PET scanners are widely accessible in hospitals. Fluorescent/phosphorescent imaging and NIRS are also applicable to patients in a clinical setting. However, fluorescent/phosphorescent imaging agents should be approved for clinical applications. Currently, EPR imaging for pO2 mapping is limited to small animal experiments.
From the viewpoint of routine clinical use, the feasibility of implementing the imaging methods is discussed below. Fluorescent/phosphorescent imaging is feasible for superficial tumor regions in clinical use. Also, one can implement an optical imaging setup within a flexible endoscope. That endoscopic approach partly solves the limitation of optical imaging in depth. The availability of fluorescent/phosphorescent imaging agents for sensing pO2 is essential for routine clinical use from the regulatory viewpoint. BOLD-MRI is feasible for routine clinical use. MRI scanners up to the magnetic field of 3T are commonly used in clinical settings. Many BOLD-MRI studies for humans were performed with 3T MRI scanners. However, high-field MRI at 7T or even higher fields has been used for preclinical BOLD-MRI studies for small rodents or bigger animals such as macaque monkeys, common in neuroscience studies [145]. DCE-MRI and 18F-MISO PET are available in clinical use. Gadolinium-based contrast agents used for DCE-MRI are clinically available because standard routine 1H-MRI scans use it. Since PET scans use radioactive tracers, medical service providers should control radiation exposure to patients under national regulations. NIRS and its imaging are feasible for tumor diagnosis in clinical use [146]. EPR imaging is a powerful modality of pO2 monitoring and mapping in tumors. However, a clinical EPR imager for human patients is currently unavailable. This EPR imaging modality is mainly used for preclinical small animal studies. Although not EPR imaging, EPR spectroscopy is being used in a clinical trial for monitoring pO2 in superficial malignant tumors using implantable oxygen sensors [147]. PAI is an emerging method in biomedical imaging modalities [148]. Since PAI is feasible for clinical use, researchers should explore clinical applications more for tumor oxygenation monitoring.
4. Future perspectives for hypoxia imaging applications

1) Artificial intelligence applications

Artificial intelligence (AI) applications in medical imaging are rapidly growing and become more practical in preclinical and clinical imaging studies. Two examples of hypoxia-related applications are briefly introduced here. These AI applications will be translated into clinical applications in the future.
Due to technology developments in AI, deep learning denoising was demonstrated for 3D pO2 maps for mouse tumors in vivo with pulsed EPR imaging modality, as well as those signal amplitude maps [149]. This deep learning denoising technique provided a better signal-to-noise ratio and leading to precise pO2 analysis for tumors. Such an analysis is essential to identify hypoxic region(s) in solid tumors. AI applications for tumor pO2 mapping are emerging but promising, like other AI applications in imaging modalities.
Another application of AI is to identify tumor hypoxia using multiparametric data of MRI maps (T2, T2*, diffusion-weighted imaging, and DCE imaging) [150]. The hypoxic region predicted by deep learning was validated with pimonidazole-stained histology slice of patient-derived xenograft (PDX) rhabdomyosarcoma models and radiation-induced fibrosarcoma (RIF-1) tumors. The AI-predicted hypoxic fractions using multiparametric MRI maps were correlated with true hypoxic fractions using pimonidazole-stained histology.

2) Personalized hypoxia-targeting strategies

As described above, hypoxia imaging is beneficial to understanding the characteristics and malignancy of solid tumors. It also gives helpful information for a personalized treatment plan with a hypoxia-targeting strategy. First, the selection of patients for HAP is a critical application to enhance the outcomes of treatment [151]. Second, hypoxic mapping information can guide the radiation treatment planning (boosted radiation to the hypoxic region) and demonstrate the improved treatment outcome in preclinical studies since tumor hypoxia shows the resistance to radiation therapy [152]. PAI can also contribute personalized treatment. A study using PAI demonstrated the spatial correlation between pretreatment oxygen distribution and radiation therapy efficacy for triple-negative breast cancer PDX [153]. Like these hypoxia-targeting strategies, we should investigate the correlation between hypoxia mapping and immune response more to improve the outcome of immunotherapy.
Currently, there are considerable efforts to transform immunologically ‘cold’ tumors (defined as those having low levels of TIL) to ‘hot’ tumors (infiltrated with high levels of CD8+ T cells) [154]. For example, immunotherapy has been combined with radiotherapy or chemotherapy to increase immunologic cell death thereby facilitating the release of damage-associated molecular patterns to be able to recruit immune cells at higher efficiency [154]. Other strategies such as oncolytic viruses, cancer vaccines, cytokines, co-stimulatory receptor agonist, cyclic GMP-AMP synthase–stimulator of interferon genes (cGAS–STING) agonists have also been proposed [154]. However, immune dynamics in the ‘cold’ tumors, or those in transition from ‘cold’ to ‘hot’ during immune checkpoint blockade, remain poorly understood. Therefore, it is critical to develop non-invasive technologies that allow close monitoring of the tumor microenvironment (especially TIL) before, during, and after the immune checkpoint blockade and any adjuvant therapies to the immunotherapy. In a recent study, Lu et al. [155] have monitored CD4+ and CD8+ T cells in the course of anti–PD-1 antibodies, anti-CTLA4 antibodies, or combination of anti–PD-1 and CTLA-4 antibodies in 4T1 breast cancer model using repetitive PET imaging. They have observed increase in CD8-specific PET signal within 6 days and CD4-specific PET signal in 2 days in tumors of responders [155], indicating the need of serial measurements of PET imaging. Serial measurement of PET imaging analyses may not be practical in the clinic due to a number of limitations including the safety issue (repetitive exposure to radiation) as well as some technical challenges to measure and the cost of measurement as suggested by Dewhirst et al. [156]. However, immune response is a dynamic process that can be dramatically changed within minutes, hours, and days. Furthermore, it has been shown that tumor hypoxia can drastically change from day to day in some cancer patients [157]. Merging with immune cell dynamics and hypoxia using the technologies that we have outlined in this review may allow us to uncover many exciting therapeutics and their mechanisms overcoming tumor hypoxia and promoting the antitumor responses to immunotherapy.

Author Contributions

Conceived and designed the analysis: Ahn GO, Hirata H, Hay M, Park W, Choi C, Ye SJ.

Collected the data: Ahn GO, Hirata H, Hay M, Oh T, Kim M, Kang GS.

Contributed data or analysis tools: Ahn GO, Hay M, Hirata H, Oh T, Kim M.

Performed the analysis: Ahn GO, Hirata H, Hay M, Oh T, Kim M.

Wrote the paper: Ahn GO, Hirata H, Oh T.

Conflicts of Interest

Conflict of interest relevant to this article was not reported.

Funding

This study was supported by the Research Grant from Seoul National University (Multidisciplinary Research Grant, no. 550-20230098 to G.O.A.), grants from the Ministry of Science and ICT (Information and Communications Technology), Korea (grant no. RS-2024-00391742 and 2023-00218623, and 2025-02316610 to G.O. Ahn), the Ministry of Education, Korea (Brain Korea 21 Four, Future Veterinary Medicine Leading Education and Research Center), Research Institute for Veterinary Science, College of Veterinary Medicine, Seoul National University, and the Japan Society for the Promotion of Science (JSPS) KAKENHI, Japan (grant no. JP21K18165 and JP22H00200 to H.H.).

Fig. 1.
A schematic diagram demonstrating how tumor hypoxia impacts the functions of CD8+ T cells, dendritic cells, and natural killer cells. CCL, CC chemokine ligand; CCR, CC chemokine receptor; IFN, interferon; MICA/B, MHC class I chain-related molecule A and B; ROS, reactive oxygen species; ULBP-1/6, UL16 binding protein 1-6.
crt-2025-200f1.jpg
Fig. 2.
A diagram demonstrating how various approaches inhibiting tumor hypoxia could improve immunotherapy efficacy. CAR T cells, chimeric antigen receptor T cells; CTLA-4, cytotoxic T-lymphocyte antigen 4; HAP, hypoxia-activated prodrugs; HIF, hypoxia-inducible factor; HRE, hypoxia response elements; NK, natural killer; ODD, oxygen-dependent degradation domain; PD-L1, programmed death-ligand 1; PD-1, programmed death 1.
crt-2025-200f2.jpg
Fig. 3.
In vivo pO2 mapping of mouse xenograft models of pancreatic ductal adenocarcinoma cell lines (A, Hs766t; B, MIA PaCa-2; C, SU.86.86) and anatomic maps with magnetic resonance imaging. Also, pO2 maps after intravenous injection of pyruvate are given at the time points until 60 minutes. Reprinted from Wojtkowiak et al. (2015), Pyruvate sensitizes pancreatic tumors to hypoxia-activated prodrug TH-302. Cancer Metab., volume 3, article no. 2, licensed under Creative Commons Attribution (CC BY 4.0) license [133].
crt-2025-200f3.jpg
Table 1.
Studies analyzing hypoxia-relevant genes as a biomarker predicting the tumor response to immunotherapy in the patients with various types of cancers
Cancer type Database Method(s) to analyze the relationship between hypoxia and immune cells Hypoxia-relevant gene Result Reference
Osteosarcoma Transcriptional expression data: TARGET, GEO CIBERSORT, Immune cell abundance identifier (ImmuCellAI), ESTIMATE, LASSO regression MAFF, COL5A2, FAM162A, SQOR, UQCRB, SFXN4, PFKFB2, COX6A2 Patients with high risk scores (high hypoxic gene expression) exhibited poor prognosis, ‘cold’ tumor phenotype, predicted to have poor responses to immunotherapy [73]
Transcriptome data: GTEx
Hypoxia-related genes: Molecular Signatures Database (MSigDB)
Breast cancer RNA-seq: TCGA, the University of California Santa Cruz Xena ImmuCellAI, ESTIMATE, Immunophenoscore (IPS), TIDE algorithm DARS2, ESRP1, SLC2A1, TH, MAFF Higher risk group with poor outcomes had lower stromal and immune score with increased M2 & M0 macrophages, resting NK cells, regulatory T cells, monocytes and neutrophils. Prognosis analysis towards anti–PD-L1 therapy did not meet the statistical differences between low and high risk score patient groups [74]
Microarray dataset: GEO
Immunotherapeutic cohorts: IMvigor210 cohort (advanced urothelial cancer with atezolizumab intervention) & GEO
Hypoxia-related genes: MSigDB
Gastric cancer Single-cell RNA-seq: GEO database (10 normal tissue, 26 gastric cancer samples) CIBERSORT, ESTIMATE, LASSO regression, TIDE POSTN, BMP4, MXRA5, LBH Higher hypoxia score patients had fewer antitumor immune cells (e.g., activated NK cells and CD8+ T cells) while cancer-promoting immune cells (e.g., resting NK cells and M2 macrophages) were increased. Patients with high hypoxia score had a poor prognosis to immunotherapy and lower TMB [75]
Bulk RNA-seq: TCGA (32 normal tissues, 375 gastric cancer tissues)
Pancreatic ductal adenocarcinoma RNA-seq: TCGA LASSO regression, XGBoost, Random Forest, TIDE PKP2, SLC2A1, LDHA, PlGA, PLOD1, PTGS2, GCK, POMT1, TIMM50, FUT8, ENO1, PDK1, CHEK2, MPC1, HSPA5, PLAC8, KDM3A, P4HA1, PPlA, NUP98, ALDH1B1, 6TC1, SLC25A4, TIMMDC1, CENPA, NSUN2, SLC25A42, TIPARP, ERRFI1, PNPT1, AK4 Patients with high-risk scores had more severe immunosuppressive environment (e.g., M0 macrophages and dendritic cells) and poor prognosis to immunotherapy [72]
Transcriptome data: GTEx
Clear cell renal carcinoma RNA-seq: TCGA-KIRC sample LASSO regression, t-SNE, ImmuCellAI PLAUR, UCN, SPABPC1L, LC16A12, NFE2L3, KCNAB1 High hypoxia and low immune status are associated with poor overall survival. A prognostic model based on six hypoxia–immune-related genes was constructed and validated, showing good predictive performance [76]
Lung adenocarcinoma RNA-seq: TCGA-LUAD sample LASSO regression HK1, SLC2A1, STC1, XPNPEP1, PDK3, PFKL Hypoxia-related genes that their expression influenced immune cell infiltration can serve as poor prognostic markers and potential targets for immunotherapy in LUAD [77]
Microarray: GEO (GSE72094)
Hypoxia-related genes: MsigDB database (HALLMARK_HYPOXIA)
Head and neck squamous cell carcinoma (HNSCC) RNA-seq: TCGA-HNSCC sample LASSO regression, CIBERSORT, ESTIMATE, TIDE, Xcell SRPX, PGK1, STG1, HS3ST1, CDKN1B, HK1 The group with high expression of hypoxia-related genes showed lower immune cell infiltration and poor prognosis. This prognostic model also has potential for predicting responses to immune checkpoint inhibitor therapy [78]
Microarray: GEO (GSE65858, GSE41613, GSE85446)
Hypoxia-related genes: MsigDB database (HALLMARK_HYPOXIA)
Cervical cancer RNA-seq: TCGA-CESC sample Cox regression, LASSO regression, ESTIMATE, CIBERSORT, MCPcounter EFNA1, IER3, ISG20, KLF7, LDHC, P4HA2, PGM1, RBPJ, STC1 Hypoxia-related genes are associated with increased immunosuppressive cells and decreased antitumor immune cells in tumor. ccHPS model might give us insights into antitumor immunotherapy and further improve the strategies for treating cervical cancer [79]
Microarray: GEO (GSE44001)
Gene annotation: GENCODE (human)
Chemotherapeutic sensitivity for tumor samples: genomics of drug sensitivity in cancer database (GDSC)
Hypoxia-related genes: MsigDB database (HALLMARK_HYPOXIA)
Glioblastoma scRNA-seq: human glioma tissue (18 patients, 44 tumor regions) Single-cell RNA-seq analysis, UMAP, CellPhone DB VEGFA, LDHA, ENO1, CA9, S100A4, P4HA1, ADM Hypoxia-enriched myeloid clusters (MC3-MC5) were associated with immunosuppressive phenotypes and poor survival. Hypoxia influenced immune cell phenotype and spatial distribution in GBM [80]
- Hypoxia-related genes: MSigDB (HALLMARK_HYPOXIA)
Melanoma (skin cutaneous melanoma) RNA-seq: The Cancer Genome Atlas (TCGA-SKCM) CIBERSORT, LASSO regression FBP1, SDC3, FOXO3 IGFBP1, S100A4, EGFR, ISG20, CP, PPARGC1A, KIF5A, DPYSL4 Patients in the high hypoxia score group showed decreased infiltration of CD8+ T cells and activated memory CD4+ T cells, along with increased Tregs and M2 macrophages, indicating an immunosuppressive tumor microenvironment and poor prognosis [81]
- Hypoxia-related genes: MSigDB database
Colon adenocarcinoma RNA-seq: TCGA-COAD CIBERSORT ALDOB, GPC1, ALDOC, SLC2A3 High hypoxia risk scores were associated with increased M0 macrophages, decreased CD8+ T cell infiltration, and poor prognosis. Prognostic signature based on 4 genes was constructed and validated [82]
Microarray: GEO (GSE39582)
Hypoxia gene sets: MSigDB (HALLMARK_HYPOXIA)
Thyroid cancer RNA-seq: TCGA-THCA CIBERSORT, Weighted Gene Co-expression Network Analysis (WGCNA), LASSO regression, machine learning (XGBoost, Random Forest) P4HA2, TFF3, RPS6KA5, EYA1 High P4HA2 expression was correlated with increased M2 macrophages and Tregs, forming an immunosuppressive microenvironment linked to tumor progression [83]
Microarray: GEO (GSE29265, GSE33630)
Hepatocellular carcinoma RNA-seq: TCGA-liver hepatocellular carcinoma CIBERSORT, LASSO regression ANLN, CBX2, DLGAP5, FBLN2, FTCD, HMOX1, IGLV1-44, IL33, LCAT, LPCAT1, MK167, PFN2, RNF145, S100A9, SPP1 Hypoxia subtype cluster 2 (worse overall survival, disease survival, disease-specific specific survival, progression-free survival) displayed increased TIL (tumor-infiltrating lymphocytes) including CD8 T cells and regulatory T cells. [84]
These patients also exhibited higher PD-L1 expression and higher TMB
Bladder cancer RNA-seq: TCGA-BLCA sample ESTIMATE, ssGSEA, MCPcounter, EPIC, TIMER ANXA6, CYBB, SP11, C5AR1, COL6A1, COL6A2, ITGB2, TIMP2, FCER1G, TLR8 Patients with higher hypoxic scores had shorter overall survival while infiltration density and immune score including CD8 T-effector signature were increased [85]
Hypoxia-related genes: GeneCards database
Immunotherapeutic cohorts: IMvigor210 cohort (advanced urothelial cancer with atezolizumab intervention)
Ovarian cancer RNA-seq: TCGA-OV sample ssGSEA, ESTIMATE, Boruta PGAM1, TPI1, SLC2A1, TUBB6, VEGFA, ENO1, ADM, ALDOA, CDKN3, LDHA, MIF, MRPS17, NDRG1, P4HA1 Patients with high hypoxia scores exhibited increased immune cell infiltration and showed greater sensitivity to immunotherapy, displaying characteristics of ‘hot tumors.' Evaluating hypoxia response states may thus aid in developing more effective immunotherapy strategies [86]
Microarray: GEO (GSE18520)
International cancer genome consortium (ICGC-OV-AU),
Transcriptome data: GTEx (normal ovarian tissue)
Hypoxia-related genes: MsigDB database (HALLMARK_HYPOXIA)
Hypoxia score evaluation: CMAP

Note that the rows colored in gray indicate the studies reporting a positive prognosis between hypoxic gene expression and the outcome of patients (i.e., high hypoxic gene signature predicts a better treatment response). BLCA, bladder cancer; CESC, cervical squamous cell carcinoma and endocervical adenocarcinoma; CIBERSORT, Cell type Identification By Estimating Relative Subsets Of RNA Transcripts; CMAP, connectivity map; COAD, colon adenocarcinoma; EPIC, Estimating the Proportion of Immune and Cancer cells; ESTIMATE, Estimation of STromal and Immune cells in MAlignant Tumor tissues using Expression data; GBM, glioblastoma multiforme; GDSC, Genomics of Drug Sensitivity in Cancer; GEO, Gene Expression Omnibus; GTEx, Genotype-Tissue Expression project; HNSCC, head and neck squamous cell carcinoma; ICGC-OV-AU, International Cancer Genome Consortium Ovarian Cancer–Australia project; KIRC, kidney renal clear cell carcinoma; LASSO, Least Absolute Shrinkage and Selection Operator; LUAD, lung adenocarcinoma; OV, ovarian cancer; PD-L1, programmed death-ligand 1; RNA-seq, RNA sequencing; scRNA-seq, single-cell RNA sequencing; SKCM, skin cutaneous melanoma; ssGSEA, single-sample gene set enrichment analysis; TARGET, Therapeutically Applicable Research to Generate Effective Treatments; TCGA, The Cancer Genome Atlas; THCA, thyroid carcinoma; TIDE, Tumor Immune Dysfunction and Exclusion; TIL, tumor-infiltrating lymphocytes; TIMER, Tumor IMmune Estimation Resource; TMB, tumor mutational burden; t-SNE, t-distributed Stochastic Neighbor Embedding; UMAP, Uniform Manifold Approximation and Projection.

Table 2.
Accuracy, sensitivity, specificity, feasibility in the clinical use, and the limitations of non-invasive imaging techniques for tumor hypoxia
Technique Imaging agent requirements Measured entity Measurement accuracy or sensitivity and specificity Feasibility or availability in clinical use Limitations and further comments Reference
Fluorescent/phosphorescent imaging Fluorescent/phosphorescent dyes Hypoxia in living cells, pO2 Phosphorescent lifetime microscopy: pO2 average error 5 mmHg Feasible Limited penetration depth; requirement of the ergulatory agency’s approval for dyes [141]
Blood-oxygen level-dependent magnetic resonance imaging None Difference in magnetization between oxy- and deoxyhemoglobin Sensitivity 73.8%, specificity 52% Available in clinics Not providing tissue pO2 [142]
Dynamic contrast-enhanced MRI Gadolinium-based contrast agent Tissue vasculature and permeability Indirectly associated with hypoxia Available in clinics Not providing tissue pO2 [121]
18F-fluoromisonidazole positron emission tomography 18F-fluoromisonidazole Hypoxia in living cells Sensitivity 100%, specificity 87.5% Available in clinics Radiation exposure; not providing tissue pO2 [140]
Near-infrared spectroscopy and imaging None Optical absorption of oxy- and deoxyhemoglobin, saturation of percutaneous oxygen SpO2, tissue oxygen saturation StO2 StO2 average error 0.3% Feasible Not quantitative for tissue pO2 [143]
A point measurement like a pulse oximeter (SpO2) is routinely used in clinics
Electron paramagnetic resonance (EPR) imaging Free radicals stable in vivo pO2 pO2 resolution 1 mmHg Feasible A human-size EPR imager is not available for clinical use [132]
EPR spectroscopy has been performed in a clinical trial
Requirement of the regulatory agency’s approval for free radical agents
Photoacoustic imaging (PAI) Methylene blue, other PA agents pO2 pO2 average error 7% Feasible Requirement of the regulatory agency’s approval for PA agents [144]

MRI, magnetic resonance imaging; PA, photoacoustic.

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        Understanding Immune Cell Adaptation to Tumor Hypoxia for Maximized Therapeutic Efficacy of Immunotherapy: Biology and Non-invasive Imaging Application
        Cancer Res Treat. 2026;58(1):26-47.   Published online April 29, 2025
        DOI: https://doi.org/10.4143/crt.2025.200
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      Understanding Immune Cell Adaptation to Tumor Hypoxia for Maximized Therapeutic Efficacy of Immunotherapy: Biology and Non-invasive Imaging Application
      Image Image Image
      Fig. 1. A schematic diagram demonstrating how tumor hypoxia impacts the functions of CD8+ T cells, dendritic cells, and natural killer cells. CCL, CC chemokine ligand; CCR, CC chemokine receptor; IFN, interferon; MICA/B, MHC class I chain-related molecule A and B; ROS, reactive oxygen species; ULBP-1/6, UL16 binding protein 1-6.
      Fig. 2. A diagram demonstrating how various approaches inhibiting tumor hypoxia could improve immunotherapy efficacy. CAR T cells, chimeric antigen receptor T cells; CTLA-4, cytotoxic T-lymphocyte antigen 4; HAP, hypoxia-activated prodrugs; HIF, hypoxia-inducible factor; HRE, hypoxia response elements; NK, natural killer; ODD, oxygen-dependent degradation domain; PD-L1, programmed death-ligand 1; PD-1, programmed death 1.
      Fig. 3. In vivo pO2 mapping of mouse xenograft models of pancreatic ductal adenocarcinoma cell lines (A, Hs766t; B, MIA PaCa-2; C, SU.86.86) and anatomic maps with magnetic resonance imaging. Also, pO2 maps after intravenous injection of pyruvate are given at the time points until 60 minutes. Reprinted from Wojtkowiak et al. (2015), Pyruvate sensitizes pancreatic tumors to hypoxia-activated prodrug TH-302. Cancer Metab., volume 3, article no. 2, licensed under Creative Commons Attribution (CC BY 4.0) license [133].

      Fig. 1.

      Fig. 2.

      Fig. 3.

      Understanding Immune Cell Adaptation to Tumor Hypoxia for Maximized Therapeutic Efficacy of Immunotherapy: Biology and Non-invasive Imaging Application
      Cancer type Database Method(s) to analyze the relationship between hypoxia and immune cells Hypoxia-relevant gene Result Reference
      Osteosarcoma Transcriptional expression data: TARGET, GEO CIBERSORT, Immune cell abundance identifier (ImmuCellAI), ESTIMATE, LASSO regression MAFF, COL5A2, FAM162A, SQOR, UQCRB, SFXN4, PFKFB2, COX6A2 Patients with high risk scores (high hypoxic gene expression) exhibited poor prognosis, ‘cold’ tumor phenotype, predicted to have poor responses to immunotherapy [73]
      Transcriptome data: GTEx
      Hypoxia-related genes: Molecular Signatures Database (MSigDB)
      Breast cancer RNA-seq: TCGA, the University of California Santa Cruz Xena ImmuCellAI, ESTIMATE, Immunophenoscore (IPS), TIDE algorithm DARS2, ESRP1, SLC2A1, TH, MAFF Higher risk group with poor outcomes had lower stromal and immune score with increased M2 & M0 macrophages, resting NK cells, regulatory T cells, monocytes and neutrophils. Prognosis analysis towards anti–PD-L1 therapy did not meet the statistical differences between low and high risk score patient groups [74]
      Microarray dataset: GEO
      Immunotherapeutic cohorts: IMvigor210 cohort (advanced urothelial cancer with atezolizumab intervention) & GEO
      Hypoxia-related genes: MSigDB
      Gastric cancer Single-cell RNA-seq: GEO database (10 normal tissue, 26 gastric cancer samples) CIBERSORT, ESTIMATE, LASSO regression, TIDE POSTN, BMP4, MXRA5, LBH Higher hypoxia score patients had fewer antitumor immune cells (e.g., activated NK cells and CD8+ T cells) while cancer-promoting immune cells (e.g., resting NK cells and M2 macrophages) were increased. Patients with high hypoxia score had a poor prognosis to immunotherapy and lower TMB [75]
      Bulk RNA-seq: TCGA (32 normal tissues, 375 gastric cancer tissues)
      Pancreatic ductal adenocarcinoma RNA-seq: TCGA LASSO regression, XGBoost, Random Forest, TIDE PKP2, SLC2A1, LDHA, PlGA, PLOD1, PTGS2, GCK, POMT1, TIMM50, FUT8, ENO1, PDK1, CHEK2, MPC1, HSPA5, PLAC8, KDM3A, P4HA1, PPlA, NUP98, ALDH1B1, 6TC1, SLC25A4, TIMMDC1, CENPA, NSUN2, SLC25A42, TIPARP, ERRFI1, PNPT1, AK4 Patients with high-risk scores had more severe immunosuppressive environment (e.g., M0 macrophages and dendritic cells) and poor prognosis to immunotherapy [72]
      Transcriptome data: GTEx
      Clear cell renal carcinoma RNA-seq: TCGA-KIRC sample LASSO regression, t-SNE, ImmuCellAI PLAUR, UCN, SPABPC1L, LC16A12, NFE2L3, KCNAB1 High hypoxia and low immune status are associated with poor overall survival. A prognostic model based on six hypoxia–immune-related genes was constructed and validated, showing good predictive performance [76]
      Lung adenocarcinoma RNA-seq: TCGA-LUAD sample LASSO regression HK1, SLC2A1, STC1, XPNPEP1, PDK3, PFKL Hypoxia-related genes that their expression influenced immune cell infiltration can serve as poor prognostic markers and potential targets for immunotherapy in LUAD [77]
      Microarray: GEO (GSE72094)
      Hypoxia-related genes: MsigDB database (HALLMARK_HYPOXIA)
      Head and neck squamous cell carcinoma (HNSCC) RNA-seq: TCGA-HNSCC sample LASSO regression, CIBERSORT, ESTIMATE, TIDE, Xcell SRPX, PGK1, STG1, HS3ST1, CDKN1B, HK1 The group with high expression of hypoxia-related genes showed lower immune cell infiltration and poor prognosis. This prognostic model also has potential for predicting responses to immune checkpoint inhibitor therapy [78]
      Microarray: GEO (GSE65858, GSE41613, GSE85446)
      Hypoxia-related genes: MsigDB database (HALLMARK_HYPOXIA)
      Cervical cancer RNA-seq: TCGA-CESC sample Cox regression, LASSO regression, ESTIMATE, CIBERSORT, MCPcounter EFNA1, IER3, ISG20, KLF7, LDHC, P4HA2, PGM1, RBPJ, STC1 Hypoxia-related genes are associated with increased immunosuppressive cells and decreased antitumor immune cells in tumor. ccHPS model might give us insights into antitumor immunotherapy and further improve the strategies for treating cervical cancer [79]
      Microarray: GEO (GSE44001)
      Gene annotation: GENCODE (human)
      Chemotherapeutic sensitivity for tumor samples: genomics of drug sensitivity in cancer database (GDSC)
      Hypoxia-related genes: MsigDB database (HALLMARK_HYPOXIA)
      Glioblastoma scRNA-seq: human glioma tissue (18 patients, 44 tumor regions) Single-cell RNA-seq analysis, UMAP, CellPhone DB VEGFA, LDHA, ENO1, CA9, S100A4, P4HA1, ADM Hypoxia-enriched myeloid clusters (MC3-MC5) were associated with immunosuppressive phenotypes and poor survival. Hypoxia influenced immune cell phenotype and spatial distribution in GBM [80]
      - Hypoxia-related genes: MSigDB (HALLMARK_HYPOXIA)
      Melanoma (skin cutaneous melanoma) RNA-seq: The Cancer Genome Atlas (TCGA-SKCM) CIBERSORT, LASSO regression FBP1, SDC3, FOXO3 IGFBP1, S100A4, EGFR, ISG20, CP, PPARGC1A, KIF5A, DPYSL4 Patients in the high hypoxia score group showed decreased infiltration of CD8+ T cells and activated memory CD4+ T cells, along with increased Tregs and M2 macrophages, indicating an immunosuppressive tumor microenvironment and poor prognosis [81]
      - Hypoxia-related genes: MSigDB database
      Colon adenocarcinoma RNA-seq: TCGA-COAD CIBERSORT ALDOB, GPC1, ALDOC, SLC2A3 High hypoxia risk scores were associated with increased M0 macrophages, decreased CD8+ T cell infiltration, and poor prognosis. Prognostic signature based on 4 genes was constructed and validated [82]
      Microarray: GEO (GSE39582)
      Hypoxia gene sets: MSigDB (HALLMARK_HYPOXIA)
      Thyroid cancer RNA-seq: TCGA-THCA CIBERSORT, Weighted Gene Co-expression Network Analysis (WGCNA), LASSO regression, machine learning (XGBoost, Random Forest) P4HA2, TFF3, RPS6KA5, EYA1 High P4HA2 expression was correlated with increased M2 macrophages and Tregs, forming an immunosuppressive microenvironment linked to tumor progression [83]
      Microarray: GEO (GSE29265, GSE33630)
      Hepatocellular carcinoma RNA-seq: TCGA-liver hepatocellular carcinoma CIBERSORT, LASSO regression ANLN, CBX2, DLGAP5, FBLN2, FTCD, HMOX1, IGLV1-44, IL33, LCAT, LPCAT1, MK167, PFN2, RNF145, S100A9, SPP1 Hypoxia subtype cluster 2 (worse overall survival, disease survival, disease-specific specific survival, progression-free survival) displayed increased TIL (tumor-infiltrating lymphocytes) including CD8 T cells and regulatory T cells. [84]
      These patients also exhibited higher PD-L1 expression and higher TMB
      Bladder cancer RNA-seq: TCGA-BLCA sample ESTIMATE, ssGSEA, MCPcounter, EPIC, TIMER ANXA6, CYBB, SP11, C5AR1, COL6A1, COL6A2, ITGB2, TIMP2, FCER1G, TLR8 Patients with higher hypoxic scores had shorter overall survival while infiltration density and immune score including CD8 T-effector signature were increased [85]
      Hypoxia-related genes: GeneCards database
      Immunotherapeutic cohorts: IMvigor210 cohort (advanced urothelial cancer with atezolizumab intervention)
      Ovarian cancer RNA-seq: TCGA-OV sample ssGSEA, ESTIMATE, Boruta PGAM1, TPI1, SLC2A1, TUBB6, VEGFA, ENO1, ADM, ALDOA, CDKN3, LDHA, MIF, MRPS17, NDRG1, P4HA1 Patients with high hypoxia scores exhibited increased immune cell infiltration and showed greater sensitivity to immunotherapy, displaying characteristics of ‘hot tumors.' Evaluating hypoxia response states may thus aid in developing more effective immunotherapy strategies [86]
      Microarray: GEO (GSE18520)
      International cancer genome consortium (ICGC-OV-AU),
      Transcriptome data: GTEx (normal ovarian tissue)
      Hypoxia-related genes: MsigDB database (HALLMARK_HYPOXIA)
      Hypoxia score evaluation: CMAP
      Technique Imaging agent requirements Measured entity Measurement accuracy or sensitivity and specificity Feasibility or availability in clinical use Limitations and further comments Reference
      Fluorescent/phosphorescent imaging Fluorescent/phosphorescent dyes Hypoxia in living cells, pO2 Phosphorescent lifetime microscopy: pO2 average error 5 mmHg Feasible Limited penetration depth; requirement of the ergulatory agency’s approval for dyes [141]
      Blood-oxygen level-dependent magnetic resonance imaging None Difference in magnetization between oxy- and deoxyhemoglobin Sensitivity 73.8%, specificity 52% Available in clinics Not providing tissue pO2 [142]
      Dynamic contrast-enhanced MRI Gadolinium-based contrast agent Tissue vasculature and permeability Indirectly associated with hypoxia Available in clinics Not providing tissue pO2 [121]
      18F-fluoromisonidazole positron emission tomography 18F-fluoromisonidazole Hypoxia in living cells Sensitivity 100%, specificity 87.5% Available in clinics Radiation exposure; not providing tissue pO2 [140]
      Near-infrared spectroscopy and imaging None Optical absorption of oxy- and deoxyhemoglobin, saturation of percutaneous oxygen SpO2, tissue oxygen saturation StO2 StO2 average error 0.3% Feasible Not quantitative for tissue pO2 [143]
      A point measurement like a pulse oximeter (SpO2) is routinely used in clinics
      Electron paramagnetic resonance (EPR) imaging Free radicals stable in vivo pO2 pO2 resolution 1 mmHg Feasible A human-size EPR imager is not available for clinical use [132]
      EPR spectroscopy has been performed in a clinical trial
      Requirement of the regulatory agency’s approval for free radical agents
      Photoacoustic imaging (PAI) Methylene blue, other PA agents pO2 pO2 average error 7% Feasible Requirement of the regulatory agency’s approval for PA agents [144]
      Table 1. Studies analyzing hypoxia-relevant genes as a biomarker predicting the tumor response to immunotherapy in the patients with various types of cancers

      Note that the rows colored in gray indicate the studies reporting a positive prognosis between hypoxic gene expression and the outcome of patients (i.e., high hypoxic gene signature predicts a better treatment response). BLCA, bladder cancer; CESC, cervical squamous cell carcinoma and endocervical adenocarcinoma; CIBERSORT, Cell type Identification By Estimating Relative Subsets Of RNA Transcripts; CMAP, connectivity map; COAD, colon adenocarcinoma; EPIC, Estimating the Proportion of Immune and Cancer cells; ESTIMATE, Estimation of STromal and Immune cells in MAlignant Tumor tissues using Expression data; GBM, glioblastoma multiforme; GDSC, Genomics of Drug Sensitivity in Cancer; GEO, Gene Expression Omnibus; GTEx, Genotype-Tissue Expression project; HNSCC, head and neck squamous cell carcinoma; ICGC-OV-AU, International Cancer Genome Consortium Ovarian Cancer–Australia project; KIRC, kidney renal clear cell carcinoma; LASSO, Least Absolute Shrinkage and Selection Operator; LUAD, lung adenocarcinoma; OV, ovarian cancer; PD-L1, programmed death-ligand 1; RNA-seq, RNA sequencing; scRNA-seq, single-cell RNA sequencing; SKCM, skin cutaneous melanoma; ssGSEA, single-sample gene set enrichment analysis; TARGET, Therapeutically Applicable Research to Generate Effective Treatments; TCGA, The Cancer Genome Atlas; THCA, thyroid carcinoma; TIDE, Tumor Immune Dysfunction and Exclusion; TIL, tumor-infiltrating lymphocytes; TIMER, Tumor IMmune Estimation Resource; TMB, tumor mutational burden; t-SNE, t-distributed Stochastic Neighbor Embedding; UMAP, Uniform Manifold Approximation and Projection.

      Table 2. Accuracy, sensitivity, specificity, feasibility in the clinical use, and the limitations of non-invasive imaging techniques for tumor hypoxia

      MRI, magnetic resonance imaging; PA, photoacoustic.

      Table 1.

      Table 2.

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