HIFs, angiogenesis, and metabolism: elusive enemies in breast cancer

Hypoxia-inducible factors (HIFs) and the HIF-dependent cancer hallmarks angiogenesis and metabolic rewiring are well-established drivers of breast cancer aggressiveness, therapy resistance, and poor prognosis. Targeting of HIF and its downstream targets in angiogenesis and metabolism has been unsuccessful so far in the breast cancer clinical setting, with major unresolved challenges residing in target selection, development of robust biomarkers for response prediction, and understanding and harnessing of escape mechanisms. This Review discusses the pathophysiological role of HIFs, angiogenesis, and metabolism in breast cancer and the challenges of targeting these features in patients with breast cancer. Rational therapeutic combinations, especially with immunotherapy and endocrine therapy, seem most promising in the clinical exploitation of the intricate interplay of HIFs, angiogenesis, and metabolism in breast cancer cells and the tumor microenvironment.

Active HIF is composed of the constitutively expressed HIF-1β subunit, an O₂-dependent HIFα isoform, and essential cofactors. HIF induces transcription of target genes by binding to hypoxia-responsive elements (HREs) in promoters. As in all mammalian cells, in breast cancer, HIFα stability and corresponding HIF activity are greatly increased in hypoxia (Figure 1). In normoxia, HIF activity is repressed through proteasomal degradation of HIFα by the O₂-dependent prolyl hydroxylase domain (PHD) proteins and the von Hippel-Lindau (VHL) protein, and/or by inhibition of HIFα binding to essential cofactors by factor inhibiting HIF-1 (FIH-1) (6). Downstream targets of the HIFα isoforms (HIF-1α and HIF-2α) only partially overlap, and in breast cancer, HIF-1α is the predominantly (over)expressed isoform (7, 8). Recently, specific roles for HIF-2α in breast cancer progression, mediated upstream by the transcription factor FOXA1, and in angiogenesis have been identified (9, 10). In human breast tumors, HIF-1α is already overexpressed in precursor lesions (ductal carcinoma in situ [DCIS]) and early-stage breast cancer, and these levels strongly correlate with tumor grade and invasion (11). HIF-1α foci are predominantly observed surrounding necrotic areas such as the generally hypoxic tumor core.

Figure 1

Schematic overview of HIFs and HIF-induced angiogenesis in breast cancer. HIF is stimulated by both hypoxia and O2-independent oncogenic, metabolic, and therapeutic factors. HIF drives angiogenesis by inducing secretion of proangiogenic growth factors by tumor cells and stromal cells, such as adipocytes and fibroblasts. The newly formed vasculature is disorganized and leaky, which facilitates tumor cell invasion and metastasis, impairs drug delivery, and further aggravates hypoxia in the tumor and the microenvironment. Angiogenic growth factors also contribute to an immunosuppressive tumor microenvironment, particularly by increasing recruitment of immunosuppressive cells. Compounds targeting angiogenic key players are listed in pink text. The key indicates their furthest stage of development in the breast cancer setting and evaluation in clinical trial(s) as monotherapy or as combination therapy. ANGPT(L), angiopoietin(-like) protein; BRCA, breast cancer gene; ER, estrogen receptor; FGFR, fibroblast growth factor receptor; HER, human epidermal growth factor receptor; MET, hepatocyte growth factor receptor; PARP, poly (ADP-ribose) polymerase; PTEN, phosphatase and tensin homolog; RET, rearranged during transfection; TAM, tumor-associated macrophage.

Common genetic alterations in breast cancer, such as loss of the tumor suppressors PTEN, p53, or BRCA1 and hyperactivation of the PI3K/Akt/mTOR or MAPK pathway, increase HIFα transcription, translation, or stability independently of O₂ levels (refs. 4, 12, 13, and Figure 1). Human epidermal growth factor receptor 2 (HER2; overexpressed in 15%–30% of human breast cancers) and estrogen receptor-α (ERα; positive in approximately 70% of breast cancers) increase HIFα levels through increased PI3K/Akt/mTOR signaling (14, 15). ERα also directly induces HIF-1α, but not HIF-2α, expression through an estrogen response element in the HIF1A promoter (16, 17).

HIF-1α immunohistochemistry in patient breast tumors correlates with ERα expression and HER2 positivity in some, but not all, studies (11, 18–22). High HIF-1α levels are consistently reported in triple-negative breast cancer (TNBC), the poor-prognosis subtype that lacks (over)expression of hormonal and HER2 receptors (23–25). TNBC patients show especially high uptake of the PET tracer ¹⁸F-fluoromisonidazole, which selectively accumulates in hypoxic cells (26), and TNBC cells carry a hypoxia gene signature in normoxic conditions (27). In TNBC, there is a high prevalence of p53 loss, PTEN mutations, and EGFR overexpression, all of which can lead to increased HIF activity (25). The transcription factor X-box binding protein 1 may regulate HIF responses in TNBC (28, 29). The lack of elevated HIFA mRNA levels in TNBC cells implies that important post-transcriptional mechanisms also contribute to the high HIF activity (27). Interestingly in this respect, intracellular depletion of the amino acid cysteine stabilizes HIF-1α in TNBCs in normoxia and was associated with dysfunctional PHDs and paracrine glutamate signaling (23).

Multiple other metabolites and HIF-induced metabolic enzymes are involved in feed-forward loops with HIF activity in normoxia, including ROS, acetyl-CoA synthetase 2 (ACSS2), and mitochondrial proteins such as CHCHD4 (refs. 4, 30–33, and Figure 1). HIFα expression, stability, and effector function at HREs are additionally influenced by other (bidirectional) processes such as epigenetics, the circadian rhythm, noncoding RNAs, and HIF-dependent secretion of microvesicles by tumor cells or cells in the tumor microenvironment (TME) (9, 34–38). For instance, tumor-associated macrophages secrete vesicles containing the long noncoding RNA HISLA, which blocks the PHD/HIF-1α interaction and induces glycolysis in normoxic breast cancer cells (35). HISLA secretion itself is increased by high extracellular lactate, demonstrating the intricate bidirectional pathways regulating HIFα expression (29, 36, 38).

O₂ diffusion from the nearest blood vessel, limited to a distance of 100 to 150 μm, typically supports tumor growth until it reaches a volume of 1–2 mm³. Angiogenesis allows tumors to continue growing beyond sizes at which diffusion-mediated O₂ and nutrient supplies fall short. HIF activity is the major driver of angiogenesis. The sprouting microvasculature in the TME is disorganized and leaky, in contrast to angiogenesis in normal tissue, and amplifies intratumoral hypoxia and favors metastatic spread while diminishing drug delivery and hampering antitumor immune responses (Figure 1 and refs. 39, 40). Breast cancer angiogenesis requires a well-balanced interplay between classical HIF-regulated angiogenic inducers (e.g., VEGF), angiogenic receptors (e.g., VEGFR, angiopoietin [ANGPT] receptors), and components of cell adhesion and extracellular matrix remodeling (41–43). Novel mediators of tumor angiogenesis are rapidly being identified (36). The long noncoding mRNA RAB11B-AS1 was increased in hypoxia in a HIF-2α–dependent manner and increased breast cancer angiogenesis and metastatic potential by recruiting RNA polymerase to VEGFA and angiopoietin-like 4 (ANGPTL4) (10). ANGPTL4 itself is a HIF-1 target that promotes lung metastasis when overexpressed in breast cancer cells (44). A recent breast cancer study in mice pointed toward adipocytes as an additional important source of ANGPTL4, and its secretion was synergistically controlled by hypoxia and IL-1β (45, 46). Similarly, other studies reveal HIF-mediated release of (exosomal) proinflammatory and proangiogenic substances such as TGF-β and prostaglandin E₂ by breast cancer cells, adipocytes, infiltrating CD8⁺ T cells, and other stromal cells (36, 39, 47–49), suggesting an intricate interplay between HIFs, proinflammatory factors derived from tumor and various TME cells, and angiogenesis that has yet to be fully elucidated.

Carbohydrate metabolism. HIF-1 activity induces a shift from respiratory, O₂-dependent mitochondrial metabolism toward glycolytic, O₂-independent metabolism through upregulation of nearly all glycolytic enzymes and redirection of pyruvate from entry into the Krebs cycle toward lactate production (refs. 4, 6, and Figure 2). Pyruvate dehydrogenase kinase (PDK) is a HIF-induced key regulator of lactate production via inhibition of pyruvate dehydrogenase (PDH), which rapidly inhibits the first step of the Krebs cycle during hypoxia (50).

Figure 2

HIFs drive reprogramming of multiple metabolic pathways in breast cancer. In general, HIF activity increases glycolysis and related carbohydrate pathways (e.g. pentose phosphate pathway and glycogen metabolism) as well as lactate export while suppressing mitochondrial O₂-dependent metabolism. Amino acid, acetate, and fatty acid uptake are increased to fuel processes that are essential for formation of ROS scavengers and Krebs cycle intermediates. This metabolic rewiring not only allows rapid proliferation and protects cells from ROS-induced damage but also contributes to formation of breast cancer stem cells and generation of an acidic and nutrient-depleted immunosuppressive microenvironment. Drugs with their respective targets or nonpharmaceutical, patient-centered strategies that target the rewired metabolism in breast cancer are listed in blue text. The key notes their furthest stage of (pre)clinical development in the breast cancer setting and/or evaluation in clinical trial(s) as monotherapy or as combination therapy. 1CM, one-carbon metabolism; 2-DG, 2-deoxyglucose; ACC, acetyl-CoA carboxylase; ACSS, acetyl-CoA synthetase; ALDO, aldolase; BNIP3, BCL2- and adenovirus E1B 19-kDa–interacting protein 3; CA, carbonic anhydrase; ETC, electron transport chain; FABP, fatty acid–binding protein; FAO, fatty acid oxidation; FASN, fatty acid synthase; G6PD, glucose-6-phosphate dehydrogenase; GAA, α-1,4-glucosidase; GBE, glycogen branching enzyme; GLUT, glucose transporter; GSH, glutathione; GYS, glycogen synthase; HK, hexokinase; α-KG, α-ketoglutarate; LDHA, lactate dehydrogenase A; MCT, monocarboxylate transporter; NBC, Na⁺-bicarbonate cotransporter; NHE, Na⁺/H⁺ exchanger; PDK, pyruvate dehydrogenase kinase; PFK, phosphofructokinase; PGK, phosphoglycerate kinase; PHGDH, phosphoglycerate dehydrogenase; PPP, pentose phosphate pathway; PYG, glycogen phosphorylase; SLC, solute carrier; SNAT, sodium-coupled neutral amino acid transporter.

These effects of HIF, which occur in hypoxia, are often confused with the Warburg effect, which is defined as aerobic glycolysis and is essential for formation of sufficient intermediates and reducing equivalents for rapid cell division and survival. Although normoxic HIF can mimic these effects, and HIFα may be upregulated by oncogenes, multiple other mechanisms are relevant, e.g., MYC and RAS (51). HIF not only induces glucose transporter (GLUT) expression for uptake of extracellular glucose but also increases glycogen synthesis and breakdown as an additional glucose source to sustain glycolytic and pentose phosphate flux. Breast cancer glycogen metabolism has been implicated in improved ROS scavenging, survival after reoxygenation, cell migration, and radioresistance (52).

HIF-induced membrane expression of lactate, H⁺, and HCO₃^– transporters is crucial for survival of hypoxic tumor cells by preventing intracellular pH reduction caused by lactate production, thereby allowing continuously high glycolytic rates and contributing to an acidic, immunosuppressive TME (53–55). While normal breast tissue does not express carbonic anhydrase 9 (CA9), it is widely overexpressed from DCIS (56) to invasive ductal carcinoma (57, 58) and lymph node metastases (59, 60). CA9 expression correlates well with tumor HIF-1α activity and is particularly pronounced in perinecrotic tumor regions, high-grade breast cancers, and TNBC (54, 58, 61). Besides the canonical CA function of catalyzing the interconversion of CO₂ and water to HCO₃^– and H⁺ (53, 54), the noncatalytic domain of CA9 interacts with monocarboxylate transporters (MCTs) 1 and 4 in human breast cancer tissue, facilitating MCT-mediated lactate and H⁺ efflux in preclinical models (62–65).

Amino acid metabolism. Amino acids, acetyl-CoA, and Krebs cycle intermediates are indispensable for nucleoside, lipid, and glutathione formation. To compensate for the reduced influx of pyruvate into the Krebs cycle, hypoxic cancer cells rely on uptake of amino acids such as glutamine and cystine to fuel this cycle. Glutamine, especially, has a central role in cancer cell metabolism. The amino acid importers SNAT2 (which imports neutral α-amino acids including glutamine and alanine), solute-linked carrier family A1 member 5 (SLC1A5, also known as alanine, serine, cysteine transporter 2 [ASCT2], importing neutral amino acids, especially glutamine), SLC7A11 (a cystine-glutamate antiporter), and SLC7A5 (which mediates import of large neutral amino acids including leucine and tyrosine) and the enzyme glutaminase (GLS), which catalyzes glutamine-to-glutamate conversion, are all upregulated by HIF (refs. 66–70 and Figure 2). SLC1A5 was recently shown to be a HIF-2 target (68) and is especially overexpressed in TNBC. In vitro and in vivo SLC1A5 knockdown inhibits growth in TNBC, but not ERα⁺ breast cancer, sensitizes TNBC cells to chemotherapy, and is lethal in TNBCs that do not show a flexible compensatory increase in other amino acid transporters (71–73).

Serine, a nonessential amino acid derived from the glycolytic intermediate 3-phosphoglycerate, and cysteine are key for NADPH and glutathione formation in hypoxic breast cancer cells (70, 74, 75). Phosphoglycerate dehydrogenase (PHGDH) and all other downstream enzymes in serine, cysteine, and downstream mitochondrial one-carbon metabolism are upregulated by HIF (70, 75). PHGDH knockdown in breast cancer cell lines reduces NADPH and glutathione levels, increases ROS levels, impairs metastatic potential by reducing breast cancer stem cells (BCSCs), and increases chemotherapy sensitivity. In contrast, breast cancer cell proliferation and growth are only impaired upon PHGDH knockdown in low-serine culture medium or in cell lines with a PHGDH copy number gain (a small subset of TNBC). This implicates that breast cancer cells depend heavily on serine metabolism for ROS scavenging but are only dependent on it for biomass in case of intrinsic baseline PHGDH overexpression or serine-limiting environmental conditions (75, 76).

Lipid metabolism. Elevated levels of lipids and upregulation of fatty acid (FA) synthase (FASN) in breast cancer were the first observations consistent with the now well-established importance of lipid metabolism in cancer cells (77, 78). Cancer cells require FAs and lipids as building blocks for cell membranes, signaling molecules, energy, and reducing capacity during reoxygenation (77). HIF-1 activity represses FA oxidation, thereby reducing ROS generation, and upregulates FASN, lipin 1, acetyl-CoA carboxylase (ACC), and others for lipid and FA synthesis (Figure 2). Nevertheless, hypoxic cells are thought to preferably derive FAs from increased uptake by upregulating FA-binding proteins (FABPs), needed for FA uptake and intracellular trafficking, and predominantly use de novo lipid and FA synthesis from acetyl-CoA in nutrient-deprived conditions (77). Acetyl-CoA can be supplied through import of acetate, which is directly converted to acetyl-CoA in the cytoplasm by the HIF target ACSS2 (6, 71, 77, 79).

The HIF-regulated N-myc downstream regulated gene 1 (NDRG1) is predominantly overexpressed in perinecrotic areas and ERα^– breast cancer and is predictive for bevacizumab response and prognostic for survival (80, 81). Homozygous loss of function of NDRG1 in humans causes a neurological disorder with nerve demyelination, and manipulation of NDRG1 in breast cancer cell lines deregulated lipid droplet storage, although its exact metabolic function and discrepancies in its reported effects on migration and breast cancer progression require further clarification (81, 82).

Mitochondrial and ROS metabolism. ROS are produced due to dysfunction of the mitochondrial electron transport chain under hypoxic or hyperoxic conditions. In fact, in experimental hypoxia and HIF-KO models the prime cause of tumor cell death is ROS, rather than absolute O₂ deficiency (83). HIFs keep intracellular ROS levels in check by increasing BCL2- and adenovirus E1B 19-kDa–interacting protein 3/Nip3-like protein X/FUN14 domain containing 1–mediated (BNIP3/NIX/FUNDC1–mediated) mitophagy, suppressing mitochondrial biogenesis, redirecting metabolic pathways to mitochondria-independent alternatives, and increasing production of the ROS scavenger glutathione and the reducing equivalent NAD(P)H (refs. 83–85 and Figure 2).

HIF-mediated suppression of nuclear respiratory factor 1 (NRF-1) decreases transcription of mitochondrial genes, and inhibition of the NRF-1 degrader SIAH2 (seven in absentia homolog 2; an E3 ubiquitin ligase) is associated with elevated NAD⁺/NADH ratios, succinate dehydrogenase activity, and increased mitochondrial mass (85, 86). Besides favoring breast cancer viability and growth, sublethal ROS levels stimulate HIF activity and induce cellular transformation into a BCSC phenotype, characterized by ongoing self-renewal capacity, stem cell markers such as aldehyde dehydrogenase (ALDH), and involvement in relapse and therapy resistance (83, 87). Moreover, HIF-1–dependent BCSC enrichment is observed upon chemotherapy treatment, and the majority of murine metastatic breast cancer cells exhibit a post-hypoxic, ROS-resistant phenotype even after reoxygenation (87–90).

A biomarker is defined as a characteristic that is objectively measured and evaluated as an indicator of normal biological processes, pathogenic processes, or pharmacological responses to a therapeutic intervention (91). Biomarkers can be prognostic, i.e., providing information on survival outcomes irrespective of the received treatment, and/or predictive, i.e., providing information on likelihood of treatment response. For instance, presence or absence of lymph node metastases is a strong prognostic but not a predictive marker, whereas the established breast cancer biomarkers HER2 overexpression and ERα expression are validated as prognostic as well as predictive biomarkers for response to HER2-targeted and hormonal therapy, respectively.

Multiple HIF-regulated angiogenic and metabolic tissue markers — either alone or in combination — have been implicated as prognostic for overall and progression-free survival and/or predictive for breast cancer chemotherapy, hormonal therapy, and kinase-targeted therapies (Table 1). Nevertheless, repeatability and clinical implementation of immunohistochemical markers are notoriously challenging, and study outcomes have been highly variable. Moreover, biopsy-based biomarkers are limited by sampling bias because they represent only a single part of a single tumor lesion. Imaging techniques can overcome this limitation by providing both static and dynamic whole-body measurements, albeit limited by their resolution. Noninvasive imaging approaches that measure real-time tumor blood flow or hemoglobin oxygen saturation or visualize trapped hypoxia-sensitive radioactive probes using PET could replace microvessel density (MVD) assessment, and whole-body ¹⁸F-fluorodeoxyglucose (¹⁸F-FDG) PET/CT imaging may replace GLUT1 immunohistochemistry (refs. 92, 93, and Figure 3). The sections below discuss the most recent developments and previous studies that have been pioneering and/or included relatively large populations.

Figure 3

Approaches to measure HIF activity, cancer angiogenesis, and metabolism. Depending on the method and the scale of application, various degrees of detail, intratumor and intrapatient heterogeneity, and interpatient heterogeneity can be captured. ANGPTL, angiopoietin-like protein; BOLD, blood oxygenation level–dependent; CA, carbonic anhydrase; Cu-ATSM, copper(II)-diacetyl-bis(N⁴-methylthiosemicarbazone); DCE, dynamic contrast–enhanced; ¹⁸F-FAZA, ¹⁸F-fluoroazomycin arabinoside; ¹⁸F-FDG, ¹⁸F-fluorodeoxyglucose; ¹⁸F-MISO, ¹⁸F-fluoromisonidazole; GEO, Gene Expression Omnibus; MRSI, magnetic resonance spectroscopic imaging; PET/CT, positron emission tomography/computed tomography; TCGA, The Cancer Genome Atlas; Tie2, TEK receptor tyrosine kinase 2.

Table 1

Selected studies reporting prognostic and/or predictive value of HIF and HIF targets in metabolism and angiogenesis in breast cancer patients

Prognostic markers. Tumor hypoxia has been measured mainly by determination of HIF-1α expression and surrogates such as MVD and CA9 that are more stable than HIF-1α itself, which has a half-life of ≤5 minutes upon reoxygenation (3, 94). Presence of a hypoxic phenotype is prognostic for relapse and poor survival across breast cancer subtypes and stages, corroborated by well-powered pan-cancer meta-analyses (95, 96). The relative risks of high expression of HIF-1α, MVD, VEGF, CA9, and other hypoxic markers are only moderate compared with known clinical prognostic factors that already represent the aggressive phenotype associated with HIFs (e.g., receptor status, lymph node status, tumor grade). Contradictory results among studies are likely due to inconsistent multivariate correction, methodological differences in antibodies and targets for visualizing vascular endothelium (e.g., CD31⁺, PDGF, factor VIII), variable scoring methodologies (e.g., manual vs. automated, nuclear vs. diffuse HIFα staining), and different stratification cutoffs (97, 98).

Rather than pinpointing of one marker, breast cancer HIF activity is increasingly captured by large-scale RNA sequencing in prognostic hypoxia-signature gene panels that contain components across multiple pathways downstream of HIF (27, 99, 100). This approach enhances the power to detect biologically relevant processes and guides discovery of new therapeutic targets and markers. Derived signatures can be validated in data sets publicly available online and in future studies. Genome-wide analysis of germline variations in almost 100,000 breast cancer patients in different cohorts revealed no major novel individual prognostic factors, whereas a network analysis identified the module “cell growth and angiogenesis” as prognostic for ERα^– but not ERα⁺ breast cancer (101). One of the four components in this module was CHCHD4, which encodes a mitochondrial protein involved in HIF-1α stability and regulation of mitochondrial respiratory chain in tumor cell adaptation to hypoxia (33, 102).

Predictive markers. It is generally acknowledged that tumor hypoxia and multiple HIF-related markers predict worse response to chemoradiotherapy, and neoadjuvant studies have shown lower pathological complete response (pCR) rates in patients with high baseline HIFα expression (22, 103–105). Several biological mechanisms explaining the negative correlation of HIF activity with chemoradiotherapy response have been described. Cytotoxicity of radiotherapy depends on ROS-induced catastrophic DNA damage, which therefore requires at least some O₂. Additionally, the dysfunctional blood supply in hypoxic tumor regions may reduce delivery of cytotoxic drugs, and moreover, HIF upregulates P-glycoprotein, also called multidrug resistance protein 1 (39, 42, 106). Finally, HIFs and chemotherapy both induce chemotherapy-resistant BCSCs (83, 87, 107). The gene panels Oncotype DX and MammaPrint are prognostic for survival and predictive for benefit from adjuvant chemotherapy in ERα⁺HER2^– breast cancer patients and are used in clinical decision making. Both panels consist of gene sets that include known HIF targets and/or players in tumor metabolism and angiogenesis such as matrix metalloproteinase 9 (MMP9) and egl-9 family hypoxia-inducible factor 1 (EGLN1), encoding PHD2 (23, 108). However, two of the control genes, GAPDH and TFRC (transferrin receptor), are well-validated HIF-1 targets, implying that differences driven by hypoxic tumor biology may be missed in these analyses (109, 110).

High expression of HIF-1α and the HIF-regulated amino acid importers SNAT2, SLC1A5, and SLC7A5 has been associated with shorter survival in the ERα⁺ highly proliferative subtype (luminal B) and resistance to the antiestrogen therapies tamoxifen and aromatase inhibitors (66, 111–114). SNAT2 overexpression in hypoxic breast cancers is HIF-1α– and HIF-2α–dependent and strongly corresponds with HIF1A mRNA expression and wider hypoxia gene signatures. SNAT2 has overlapping binding sites for HIF-1α and ERα, and during tamoxifen treatment, which abolished ERα signaling, HIF-1α could replace this signaling and increase SNAT2 expression under hypoxic conditions. SNAT2 knockdown reversed tamoxifen resistance and dampened signaling through the mTOR pathway, the latter being a known resistance mechanism to antiestrogen therapy (66). Other reports also describe a HIF-2α and/or SLC7A5/mTOR regulatory axis underlying endocrine resistance (9, 67). In addition, contralateral breast cancers developing during adjuvant tamoxifen treatment, i.e., indicating intrinsic antiestrogen resistance, were more often HIF-1α–expressing than treatment-naive contralateral tumors (21).

The backbone of systemic therapy in breast cancer patients overexpressing HER2 are drugs that suppress the downstream oncogenic PI3K/Akt/mTOR and MAPK signaling pathways through HER2 inhibition and, in the case of the antibody-drug conjugate trastuzumab-emtansine (T-DM1), additionally deliver localized chemotherapy. The intensity of HER2 expression as determined by immunohistochemistry or FISH in tumor biopsies is the strongest predictive factor for therapy response, but intrinsic or induced resistance is a major clinical challenge that is not predicted by expression alone. ¹⁸F-FDG uptake on PET/CT is prognostic in the neoadjuvant and the metastatic setting for, respectively, pCR and early treatment failure (after approximately 2 cycles) (115, 116). Other markers of HIF-1/2α expression or downstream metabolic or angiogenic targets have not been reported as predictive for response or resistance to HER2-targeted therapy.

The initial progression-free survival (PFS) gain in breast cancer demonstrated for the VEGF-targeting antibody bevacizumab did not translate into an overall survival (OS) benefit. It was subsequently reasoned that only patients with especially deregulated and widespread tumor microvasculature might benefit from bevacizumab-induced vessel normalization. However, in retrospective analyses, intuitively logical biomarkers correlated with pCR rates and normalization of tumor vasculature in some cases but did not predict final clinical outcomes. Evaluated biomarkers include high baseline MVD, high volume transfer constant on dynamic contrast–enhanced MRI, elevated expression of proangiogenic factors (e.g., VEGF, VEGFR, and Tie2 measured immunohistochemically or in patients’ serum), and, more recently, NDRG1 and panels representing DNA methylation status or hypoxia gene sets in HER2^– breast cancer patients on neoadjuvant bevacizumab plus chemotherapy (5, 80, 117–119). Multiple alternative vascular markers are being evaluated in different cancer types, e.g., the vascular co-option players stromal-derived factor 1α and CXCR4, and ANGPT2 (5, 39).

In breast cancer, hypoxia mediates aggressive, metastatic, and therapy-resistant disease, making it an attractive target for novel (combination) therapies (Table 2). Hypoxic tumor cells can be targeted directly, for example by use of hypoxia-activated prodrugs or by specific targeting of HIFs (reviewed in ref. 120). Strategies to target HIFs include downregulating HIFα protein expression, blocking HIFα-HIFβ dimerization or essential cofactor binding, and preventing binding of HIF to HREs. It has, however, been challenging to develop specific, potent HIF-1α inhibitors with suitable pharmacological properties for clinical evaluation. Review of ClinicalTrials.gov does not show any currently active breast cancer trials testing drugs directly targeting HIFs, although there are ongoing studies on (novel) inhibitors of mTOR (e.g., TAK-228), PI3K (e.g., BKM-120 or BYL-719), and histone deacetylases (vorinostat), which all indirectly target HIF signaling. Instead, therapeutic strategies often focus on consequences of hypoxia, including angiogenesis and reprogrammed metabolism, as discussed below (see also Figure 1 and Figure 2).

Table 2

Specific rationales for exploring synergy between approved breast cancer therapies and (novel) therapies targeting HIF/hypoxia, angiogenesis, and HIF-related metabolic reprogramming, as proposed or tested in the preclinical setting

Therapeutic strategies targeting angiogenesis. The largest body of evidence is available for bevacizumab, a monoclonal antibody that blocks VEGF. As mentioned, in metastatic breast cancer only modest benefits in PFS were achieved, not translating into OS benefit, resulting in FDA withdrawal after initial approval. Targeting VEGF signal transduction with tyrosine kinase inhibitors is another strategy, but results in metastatic breast cancer are also disappointing (121). Although suppressing the VEGF pathway indeed decreases vascular density, rapid revascularization occurs within 2 weeks as shown in a neoadjuvant window-of-opportunity bevacizumab study (5, 39, 119). This is likely mediated through induction of hypoxia by the antiangiogenic therapy, resulting in compensatory upregulation of both VEGF and VEGF-independent angiogenesis pathways (119, 122). Proposed resistance mechanisms include vascular mimicry, enhancement of invasive potential, recruitment of bone marrow–derived precursor endothelial cells, and promotion of alternative proangiogenic pathways (5, 39, 42, 123), which are of interest as potential therapeutic targets in breast cancer.

Hypoxia created by VEGF pathway inhibitors correlates with upregulation of the MET oncogene, which promotes invasive behavior and is an adverse prognostic factor in breast cancer (42, 123, 124). Cabozantinib (XL-184) is a potent oral inhibitor of MET and VEGFR2, and phase II trials showed mixed clinical benefit rates (0%–34%) in metastatic TNBC (125, 126).

In TNBC xenografts, dual FGF/VEGF targeting with or without paclitaxel chemotherapy showed synergistic effects in reducing vessel number and growth (127, 128). In a phase II trial of the dual FGF/VEGF inhibitor brivanib in solid tumors, responses were seen in breast cancer patients; however, this cohort was terminated early (129). Nintedanib, an inhibitor of VEGFR, PDGFR, and FGF receptors (FGFRs) that is approved for non–small cell lung cancer, showed preclinical activity in combination with paclitaxel in breast cancer xenografts and is being tested in breast cancer patients (130, 131). Interestingly, FGFR signaling also appears to mediate resistance to CDK4/6 inhibitors in breast cancer (132).

Trebananib (AMG386) is an ANGPT antagonist peptide-Fc fusion protein that selectively binds ANGPT1 and ANGPT2 (133). However, a phase II clinical trial in metastatic breast cancer patients indicated no evidence of benefit when combining AMG386 and paclitaxel with bevacizumab (133).

Src kinase is required for VEGF-induced proliferation of vascular cells, for vascular permeability, and for tumor cell extravasation in preclinical models (134). In phase II breast cancer studies, circulating VEGFR increased during exposure to the Src inhibitor dasatinib, implying that combination of VEGF and Src inhibitors may also be of interest (134).

Inhibition of angiogenesis may result in selection of cells that can use existing vasculature, known as co-option, a growth pattern observed in breast cancer liver metastases (135). In patients with colorectal cancer liver metastases, co-option was associated with poor response to bevacizumab (136). Inhibitors of key players in co-option such as the actin-related protein 2/3 complex (Arp2/3), also expressed in breast cancer liver metastases, enhanced the efficacy of angiogenesis inhibitors in preclinical models of liver metastases (136).

Pharmaceutical targeting of metabolism in breast cancer. In preclinical breast cancer models, agents that directly interfere with high glucose uptake (e.g., the glucose analog 2-deoxyglucose) or decrease glycolysis (e.g., the PDK inhibitor dichloroacetate) reduced proliferation, inhibited HIF-1α, and sensitized cells to chemotherapy and mitochondrial inhibitors (137–139). Although phase I clinical cancer trials have included some breast cancer patients, toxicity has been a problem and no clear efficacy signals have emerged (140).

Lactate dehydrogenase (LDH) is a key enzyme for the interconversion of pyruvate and lactate. Although its complex biochemistry and multiple isoenzymes have made it hard to “drug” (141), several molecules are of interest for further development in cancer, including the old anticonvulsant stiripentol, which inhibits LDHA in vivo (142). Other ways to target lactate metabolism include blocking its transmembrane transport by inhibiting MCT1 and MCT4 (143–145). Inhibition of MCT1 in breast cancer was effective preclinically; however, the main mechanism appeared to be reduced pyruvate export rather than altered lactate transport or reduced glycolytic flux (146). The major immunosuppressive effect of extracellular lactate (147, 148) makes combinations of inhibitors of lactate transport with immune checkpoint inhibition of interest, especially in TNBC, in which checkpoint inhibition has proven effectiveness when combined with chemotherapy. Indeed, MCT1 blockade with AZD3965 increases immune cell infiltration in tumors, and inhibiting CA9 enhances immune responses to PD-L1 inhibition (149, 150). AZD3965 and the CA9 inhibitor SLC-0111 are currently in phase I cancer trials.

Dependence of breast cancer cells on glutamine is increased not only in hypoxia but also in estrogen-independent and antiestrogen treatment–resistant subtypes (151). Preclinically, pharmacological targeting of HIF-regulated amino acid importers, for instance by the SLC1A5 inhibitors benzylserine or V-9302, blocks breast cancer cell growth and is associated with decreased mTOR signaling and increased ROS levels and autophagy (69, 71, 152, 153). Inhibition of GLS by CB-839 also inhibits growth of TNBC cells but not ERα⁺ breast cancer cells, which rely on GLS2 instead (154). Combining CB-839 with the mTOR inhibitor everolimus, however, does inhibit growth of endocrine-resistant breast cancer xenografts (151, 155). This is of interest since mTOR inhibition is already being used clinically in combination with hormonal therapy in ERα⁺ patients to prevent endocrine resistance. CB-839 is now being evaluated in early clinical (breast) cancer trials.

Regarding cancer cell lipid metabolism, blocking FA synthesis has received the most attention, and, in vitro, inhibiting FASN reduced proliferation and induced apoptosis (77). TVB-2640 is a specific FASN inhibitor that has now proceeded into a phase II breast cancer trial. Interestingly, proton pump inhibitors such as omeprazole also inhibit FASN (156). The proton pump inhibitor omeprazole improved survival in metastatic breast cancer patients receiving chemotherapy, making repurposing of this FDA-approved class of drugs of interest, and further clinical evaluation is ongoing (157).

Targeting of components in the glycolytic pathway and vascular normalization induced by antiangiogenic therapy increase dependence of cancer cells on mitochondrial metabolism. Metformin, an AMPK activator that is a cornerstone in the treatment of type 2 diabetes, inhibits mitochondrial complex 1. More recently, it has also been shown to inhibit growth differentiation factor 15 (GDF15), a HIF-1 target (158). In the preclinical setting, metformin increased internalization of caveolin-1/T-DM1 and sensitivity to T-DM1 treatment through suppression of the HIF-responsive Akt/MAPK pathway (159). Metformin is one of the main metabolically targeted drugs currently under investigation in breast cancer with (combination) trials ongoing in the setting of prevention and maintenance (160). However, so far no benefit of metformin has been demonstrated in randomized trials, which may be related to compensatory increases in glucose uptake and transcription of many genes involved in mitochondrial metabolism that occur already within 1–2 weeks of treatment (161).

In a phase 0/I randomized trial in HER2^–, treatment-naive primary breast cancer patients, single-dose bevacizumab treatment was followed by randomization to treatment with the mitochondrial inhibitor ME-344 or placebo. In paired pre- and post-treatment biopsies, reduced proliferation was demonstrated in ME-344–treated patients, especially in the subgroup that had vascular normalization measured using ¹⁸F-FDG PET (162). This illustrates the type of trial design and smart drug combinations that will be essential for further therapeutic development.

Several agents that target ROS are being studied alone or in combination, including decylubiquinone, an FDA-approved coenzyme Q₁₀ analog that inhibits angiogenesis in breast cancer cells through a ROS-dependent mechanism (163).

Nonpharmaceutical targeting of metabolism in breast cancer. Nonpharmaceutical interventions that take advantage of the metabolic differences between cancer cells and normal cells, many mediated by HIF-dependent pathways, are also of interest. Exercise is of increasing importance in breast cancer care and is associated with decreased tumor growth and improved patient mental well-being and survival. Reduction of ROS is one of the multiple hypothesized underlying mechanisms (164). Of specific dietary interventions that have been proposed to have anticancer effects, ketogenic diets and fasting have received the most attention (165, 166).

Ketogenic diets are based on the premise that cancer cells are more dependent on glucose and have defective mitochondrial metabolism compared with normal cells. These diets are composed of high fat, moderate protein, and low carbohydrate content, resulting in increased fat metabolism. FAs are oxidized in the liver to acetyl-CoA, and any excess is converted into ketone bodies, mainly β-hydroxybutyrate. Normal tissues, in contrast to cancers, have the ability to use ketones as a source of energy, thus making these diets more detrimental to cancer cells. Many cancer trials have been initiated to investigate the ketogenic diet and have shown feasibility and reduced central obesity and insulin levels but no clear anticancer efficacy (167, 168). It is now well recognized that mitochondria continue to be functional in cancers, reducing the likelihood of large effect sizes. Furthermore, effects may be compensated by utilization of extracellular β-hydroxybutyrate by breast cancers for acetyl-CoA production (169).

Fasting decreases glucose, insulin, and IGF-1 levels while increasing FA breakdown and production of ketones, similar to the ketogenic diet (166, 170). Reducing IGF-1 reduces Akt signaling, and lower glucose increases AMPK activity. In 13 breast cancer patients, short-term fasting appeared to reduce hematological toxicity of neoadjuvant chemotherapy, possibly through faster recovery of DNA damage in PBMCs (171). Nevertheless, ketogenic and fasting diets are extremely challenging to adhere to, especially for cancer patients in whom malnutrition is detrimental to quality of life, response to therapy, and survival. Thus, although many behavioral modifications have a promising metabolic rationale exploiting the Warburg effect and ROS, strong and mechanistic proof for direct anticancer efficacy from translational studies is warranted.

HIFs and downstream angiogenic and metabolic alterations play a major role in breast cancer aggressiveness, progression, and therapy resistance but have proven to be notoriously difficult targets in the clinic. Novel druggable targets in HIF upstream regulatory pathways and downstream angiogenic and metabolic pathways are increasingly being identified. Continuous technological developments in (noninvasive) measurement of tumor glucose uptake, hypoxia, and vasculature now enable real-time in vivo monitoring of treatment-induced alterations. Approaches to clinically study the fate of metabolites are important for stratification and for understanding responses and escape mechanisms, and novel metabolic tools such as ¹⁸F-glutamine PET/CT and ¹³C-metabolite flux tracing have been developed for clinical use or are in development, e.g., ¹⁸F-labeled MCT inhibitors (161, 172–174). Smart incorporation of these tools into trials at baseline and interim time points can aid in successful translation of proposed antiangiogenic and metabolically targeted therapies to the clinic. Since the narrow therapeutic window and rapid emergence of escape mechanisms have posed major hurdles to monotherapies targeting these pathways, combination of novel antiangiogenic and metabolic drugs with existing therapies and nonpharmaceutical interventions seems most promising.

ALH has research funding from the Breast Cancer Research Foundation, Cancer Research UK (grant number A18974), and the Kennington Cancer Fund. MJ has research funding from the Dutch Cancer Society (Young Investigator Grant 10913/2017-1).

Address correspondence to: Adrian L. Harris, Molecular Oncology Laboratories, Weatherall Institute of Molecular Medicine, University of Oxford, Headley Way, Oxford, OX3 9DS United Kingdom. Email: adrian.harris@oncology.ox.ac.uk.

Conflict of interest: ALH serves in a paid advisory role for Curve Therapeutics. MJ serves in an advisory role for Bristol Myers Squibb, Merck & Co., Novartis, AstraZeneca, Tesaro, and Pierre Fabre (fees paid to the institution).

Copyright: © 2020, American Society for Clinical Investigation.

Reference information: J Clin Invest. 2020;130(10):5074–5087.https://doi.org/10.1172/JCI137552.

1. DeSantis CE, et al. Breast cancer statistics, 2019. CA Cancer J Clin. 2019;69(6):438–451.
   View this article via: PubMed CrossRef Google Scholar
2. Vaupel P, Schlenger K, Knoop C, Höckel M. Oxygenation of human tumors: evaluation of tissue oxygen distribution in breast cancers by computerized O2 tension measurements. Cancer Res. 1991;51(12):3316–3322.
   View this article via: PubMed Google Scholar
3. Wang GL, Jiang BH, Rue EA, Semenza GL. Hypoxia-inducible factor 1 is a basic-helix-loop-helix-PAS heterodimer regulated by cellular O2 tension. Proc Natl Acad Sci U S A. 1995;92(12):5510–5514.
   View this article via: PubMed CrossRef Google Scholar
4. Semenza GL. HIF-1 mediates metabolic responses to intratumoral hypoxia and oncogenic mutations. J Clin Invest. 2013;123(9):3664–3671.
   View this article via: JCI PubMed CrossRef Google Scholar
5. Harris AL. Clinical strategies to inhibit tumor vascularization. In: Ribatti D, Pezzella F, eds. Tumor Vascularization. Elsevier Science; 2020:147–175.
6. Samanta D, Semenza GL. Metabolic adaptation of cancer and immune cells mediated by hypoxia-inducible factors. Biochim Biophys Acta Rev Cancer. 2018;1870(1):15–22.
   View this article via: PubMed CrossRef Google Scholar
7. Schödel J, Mole DR, Ratcliffe PJ. Pan-genomic binding of hypoxia-inducible transcription factors. Biol Chem. 2013;394(4):507–517.
   View this article via: PubMed CrossRef Google Scholar
8. Smythies JA, et al. Inherent DNA-binding specificities of the HIF-1α and HIF-2α transcription factors in chromatin. EMBO Rep. 2019;20(1):e46401.
   View this article via: PubMed Google Scholar
9. Fu X, et al. FOXA1 upregulation promotes enhancer and transcriptional reprogramming in endocrine-resistant breast cancer. Proc Natl Acad Sci U S A. 2019;116(52):26823–26834.
   View this article via: PubMed CrossRef Google Scholar
10. Niu Y, et al. HIF2-induced long noncoding RNA RAB11B-AS1 promotes hypoxia-mediated angiogenesis and breast cancer metastasis. Cancer Res. 2020;80(5):964–975.
    View this article via: PubMed CrossRef Google Scholar
11. Bos R, et al. Levels of hypoxia-inducible factor-1 alpha during breast carcinogenesis. J Natl Cancer Inst. 2001;93(4):309–314.
    View this article via: PubMed CrossRef Google Scholar
12. Li AG, et al. BRCA1-IRIS promotes human tumor progression through PTEN blockade and HIF-1α activation. Proc Natl Acad Sci U S A. 2018;115(41):E9600–E9609.
    View this article via: PubMed CrossRef Google Scholar
13. van der Groep P, et al. HIF-1α overexpression in ductal carcinoma in situ of the breast in BRCA1 and BRCA2 mutation carriers. PLoS One. 2013;8(2):e56055.
    View this article via: PubMed CrossRef Google Scholar
14. Laughner E, Taghavi P, Chiles K, Mahon PC, Semenza GL. HER2 (neu) signaling increases the rate of hypoxia-inducible factor 1alpha (HIF-1alpha) synthesis: novel mechanism for HIF-1-mediated vascular endothelial growth factor expression. Mol Cell Biol. 2001;21(12):3995–4004.
    View this article via: PubMed CrossRef Google Scholar
15. Jarman EJ, et al. HER2 regulates HIF-2α and drives an increased hypoxic response in breast cancer. Breast Cancer Res. 2019;21(1):10.
    View this article via: PubMed CrossRef Google Scholar
16. Yang J, et al. Estrogen receptor-α directly regulates the hypoxia-inducible factor 1 pathway associated with antiestrogen response in breast cancer. Proc Natl Acad Sci U S A. 2015;112(49):15172–15177.
    View this article via: PubMed CrossRef Google Scholar
17. Fuady JH, Gutsche K, Santambrogio S, Varga Z, Hoogewijs D, Wenger RH. Estrogen-dependent downregulation of hypoxia-inducible factor (HIF)-2α in invasive breast cancer cells. Oncotarget. 2016;7(21):31153–31165.
    View this article via: PubMed CrossRef Google Scholar
18. Schindl M, et al. Overexpression of hypoxia-inducible factor 1alpha is associated with an unfavorable prognosis in lymph node-positive breast cancer. Clin Cancer Res. 2002;8(6):1831–1837.
    View this article via: PubMed Google Scholar
19. Giatromanolaki A, et al. c-erbB-2 related aggressiveness in breast cancer is hypoxia inducible factor-1alpha dependent. Clin Cancer Res. 2004;10(23):7972–7977.
    View this article via: PubMed CrossRef Google Scholar
20. Bos R, et al. Levels of hypoxia-inducible factor-1alpha independently predict prognosis in patients with lymph node negative breast carcinoma. Cancer. 2003;97(6):1573–1581.
    View this article via: PubMed CrossRef Google Scholar
21. Jögi A, Ehinger A, Hartman L, Alkner S. Expression of HIF-1α is related to a poor prognosis and tamoxifen resistance in contralateral breast cancer. PLoS One. 2019;14(12):e0226150.
    View this article via: PubMed CrossRef Google Scholar
22. Nie C, Lv H, Bie L, Hou H, Chen X. Hypoxia-inducible factor 1-alpha expression correlates with response to neoadjuvant chemotherapy in women with breast cancer. Medicine (Baltimore). 2018;97(51):e13551.
    View this article via: PubMed CrossRef Google Scholar
23. Briggs KJ, et al. Paracrine induction of HIF by glutamate in breast cancer: EglN1 senses cysteine. Cell. 2016;166(1):126–139.
    View this article via: PubMed CrossRef Google Scholar
24. Bane AL, et al. Tumor factors predictive of response to hypofractionated radiotherapy in a randomized trial following breast conserving therapy. Ann Oncol. 2014;25(5):992–998.
    View this article via: PubMed CrossRef Google Scholar
25. Cancer Genome Atlas Network . Comprehensive molecular portraits of human breast tumours. Nature. 2012;490(7418):61–70.
    View this article via: PubMed CrossRef Google Scholar
26. Asano A, et al. Intracellular hypoxia measured by ¹⁸F-fluoromisonidazole positron emission tomography has prognostic impact in patients with estrogen receptor-positive breast cancer. Breast Cancer Res. 2018;20(1):78.
    View this article via: PubMed CrossRef Google Scholar
27. Ye IC, Fertig EJ, DiGiacomo JW, Considine M, Godet I, Gilkes DM. Molecular portrait of hypoxia in breast cancer: a prognostic signature and novel HIF-regulated genes. Mol Cancer Res. 2018;16(12):1889–1901.
    View this article via: PubMed CrossRef Google Scholar
28. Chen X, et al. XBP1 promotes triple-negative breast cancer by controlling the HIF1α pathway. Nature. 2014;508(7494):103–107.
    View this article via: PubMed CrossRef Google Scholar
29. Liang H, et al. Hypoxia induces miR-153 through the IRE1α-XBP1 pathway to fine tune the HIF1α/VEGFA axis in breast cancer angiogenesis. Oncogene. 2018;37(15):1961–1975.
    View this article via: PubMed CrossRef Google Scholar
30. Kappler M, et al. Causes and consequences of a glutamine induced normoxic HIF1 activity for the tumor metabolism. Int J Mol Sci. 2019;20(19):E4742.
    View this article via: PubMed Google Scholar
31. Grandjean G, et al. Definition of a novel feed-forward mechanism for glycolysis-HIF1α signaling in hypoxic tumors highlights aldolase A as a therapeutic target. Cancer Res. 2016;76(14):4259–4269.
    View this article via: PubMed CrossRef Google Scholar
32. Xiong G, et al. Collagen prolyl 4-hydroxylase 1 is essential for HIF-1α stabilization and TNBC chemoresistance. Nat Commun. 2018;9(1):4456.
    View this article via: PubMed CrossRef Google Scholar
33. Yang J, et al. Human CHCHD4 mitochondrial proteins regulate cellular oxygen consumption rate and metabolism and provide a critical role in hypoxia signaling and tumor progression. J Clin Invest. 2012;122(2):600–611.
    View this article via: JCI PubMed CrossRef Google Scholar
34. Wang T, et al. Hypoxia-inducible factors and RAB22A mediate formation of microvesicles that stimulate breast cancer invasion and metastasis. Proc Natl Acad Sci U S A. 2014;111(31):E3234–E3242.
    View this article via: PubMed CrossRef Google Scholar
35. Chen F, et al. Extracellular vesicle-packaged HIF-1α-stabilizing lncRNA from tumour-associated macrophages regulates aerobic glycolysis of breast cancer cells. Nat Cell Biol. 2019;21(4):498–510.
    View this article via: PubMed CrossRef Google Scholar
36. Choudhry H, Harris AL. Advances in hypoxia-inducible factor biology. Cell Metab. 2018;27(2):281–298.
    View this article via: PubMed CrossRef Google Scholar
37. Sulli G, Lam MTY, Panda S. Interplay between circadian clock and cancer: new frontiers for cancer treatment. Trends Cancer. 2019;5(8):475–494.
    View this article via: PubMed CrossRef Google Scholar
38. Chen Y, et al. ZMYND8 acetylation mediates HIF-dependent breast cancer progression and metastasis. J Clin Invest. 2018;128(5):1937–1955.
    View this article via: JCI PubMed CrossRef Google Scholar
39. Martin JD, Seano G, Jain RK. Normalizing function of tumor vessels: progress, opportunities, and challenges. Annu Rev Physiol. 2019;81:505–534.
    View this article via: PubMed CrossRef Google Scholar
40. Khan KA, Kerbel RS. Improving immunotherapy outcomes with anti-angiogenic treatments and vice versa. Nat Rev Clin Oncol. 2018;15(5):310–324.
    View this article via: PubMed CrossRef Google Scholar
41. Wang JC, et al. Metformin inhibits metastatic breast cancer progression and improves chemosensitivity by inducing vessel normalization via PDGF-B downregulation. J Exp Clin Cancer Res. 2019;38(1):235.
    View this article via: PubMed CrossRef Google Scholar
42. Jayson GC, Kerbel R, Ellis LM, Harris AL. Antiangiogenic therapy in oncology: current status and future directions. Lancet. 2016;388(10043):518–529.
    View this article via: PubMed CrossRef Google Scholar
43. Carmeliet P, Jain RK. Molecular mechanisms and clinical applications of angiogenesis. Nature. 2011;473(7347):298–307.
    View this article via: PubMed CrossRef Google Scholar
44. Zhang H, et al. HIF-1-dependent expression of angiopoietin-like 4 and L1CAM mediates vascular metastasis of hypoxic breast cancer cells to the lungs. Oncogene. 2012;31(14):1757–1770.
    View this article via: PubMed CrossRef Google Scholar
45. Rausch LK, Netzer NC, Hoegel J, Pramsohler S. The linkage between breast cancer, hypoxia, and adipose tissue. Front Oncol. 2017;7:211.
    View this article via: PubMed Google Scholar
46. Kolb R, et al. Obesity-associated inflammation promotes angiogenesis and breast cancer via angiopoietin-like 4. Oncogene. 2019;38(13):2351–2363.
    View this article via: PubMed CrossRef Google Scholar
47. Semenza GL. The hypoxic tumor microenvironment: A driving force for breast cancer progression. Biochim Biophys Acta. 2016;1863(3):382–391.
    View this article via: PubMed Google Scholar
48. Incio J, et al. Obesity promotes resistance to anti-VEGF therapy in breast cancer by up-regulating IL-6 and potentially FGF-2. Sci Transl Med. 2018;10(432):eaag0945.
    View this article via: PubMed CrossRef Google Scholar
49. Palazon A, et al. An HIF-1α/VEGF-A axis in cytotoxic T cells regulates tumor progression. Cancer Cell. 2017;32(5):669–683.e5.
    View this article via: PubMed CrossRef Google Scholar
50. Kim JW, Tchernyshyov I, Semenza GL, Dang CV. HIF-1-mediated expression of pyruvate dehydrogenase kinase: a metabolic switch required for cellular adaptation to hypoxia. Cell Metab. 2006;3(3):177–185.
    View this article via: PubMed CrossRef Google Scholar
51. Koppenol WH, Bounds PL, Dang CV. Otto Warburg’s contributions to current concepts of cancer metabolism. Nat Rev Cancer. 2011;11(5):325–337.
    View this article via: PubMed CrossRef Google Scholar
52. Altemus MA, et al. Breast cancers utilize hypoxic glycogen stores via PYGB, the brain isoform of glycogen phosphorylase, to promote metastatic phenotypes. PLoS One. 2019;14(9):e0220973.
    View this article via: PubMed CrossRef Google Scholar
53. Pastorek J, et al. Cloning and characterization of MN, a human tumor-associated protein with a domain homologous to carbonic anhydrase and a putative helix-loop-helix DNA binding segment. Oncogene. 1994;9(10):2877–2888.
    View this article via: PubMed Google Scholar
54. Wykoff CC, et al. Hypoxia-inducible expression of tumor-associated carbonic anhydrases. Cancer Res. 2000;60(24):7075–7083.
    View this article via: PubMed Google Scholar
55. Boedtkjer E. Na⁺,HCO₃^- cotransporter NBCn1 accelerates breast carcinogenesis. Cancer Metastasis Rev. 2019;38(1-2):165–178.
    View this article via: PubMed CrossRef Google Scholar
56. Wykoff CC, et al. Expression of the hypoxia-inducible and tumor-associated carbonic anhydrases in ductal carcinoma in situ of the breast. Am J Pathol. 2001;158(3):1011–1019.
    View this article via: PubMed CrossRef Google Scholar
57. Chia SK, et al. Prognostic significance of a novel hypoxia-regulated marker, carbonic anhydrase IX, in invasive breast carcinoma. J Clin Oncol. 2001;19(16):3660–3668.
    View this article via: PubMed CrossRef Google Scholar
58. Adams A, et al. The potential of hypoxia markers as target for breast molecular imaging--a systematic review and meta-analysis of human marker expression. BMC Cancer. 2013;13:538.
59. Van den Eynden GG, et al. Angiogenesis and hypoxia in lymph node metastases is predicted by the angiogenesis and hypoxia in the primary tumour in patients with breast cancer. Br J Cancer. 2005;93(10):1128–1136.
    View this article via: PubMed CrossRef Google Scholar
60. Tafreshi NK, et al. Noninvasive detection of breast cancer lymph node metastasis using carbonic anhydrases IX and XII targeted imaging probes. Clin Cancer Res. 2012;18(1):207–219.
    View this article via: PubMed CrossRef Google Scholar
61. Choi J, Jung WH, Koo JS. Metabolism-related proteins are differentially expressed according to the molecular subtype of invasive breast cancer defined by surrogate immunohistochemistry. Pathobiology. 2013;80(1):41–52.
    View this article via: PubMed CrossRef Google Scholar
62. Ames S, Andring JT, McKenna R, Becker HM. CAIX forms a transport metabolon with monocarboxylate transporters in human breast cancer cells. Oncogene. 2020;39(8):1710–1723.
    View this article via: PubMed CrossRef Google Scholar
63. Ames S, Pastorekova S, Becker HM. The proteoglycan-like domain of carbonic anhydrase IX mediates non-catalytic facilitation of lactate transport in cancer cells. Oncotarget. 2018;9(46):27940–27957.
    View this article via: PubMed CrossRef Google Scholar
64. Jamali S, et al. Hypoxia-induced carbonic anhydrase IX facilitates lactate flux in human breast cancer cells by non-catalytic function. Sci Rep. 2015;5:13605.
    View this article via: PubMed Google Scholar
65. Mboge MY, et al. A non-catalytic function of carbonic anhydrase IX contributes to the glycolytic phenotype and pH regulation in human breast cancer cells. Biochem J. 2019;476(10):1497–1513.
    View this article via: PubMed CrossRef Google Scholar
66. Morotti M, et al. Hypoxia-induced switch in SNAT2/SLC38A2 regulation generates endocrine resistance in breast cancer. Proc Natl Acad Sci U S A. 2019;116(25):12452–12461.
    View this article via: PubMed CrossRef Google Scholar
67. Elorza A, et al. HIF2α acts as an mTORC1 activator through the amino acid carrier SLC7A5. Mol Cell. 2012;48(5):681–691.
    View this article via: PubMed CrossRef Google Scholar
68. Yoo HC, et al. A variant of SLC1A5 is a mitochondrial glutamine transporter for metabolic reprogramming in cancer cells. Cell Metab. 2020;31(2):267–283.e12.
    View this article via: PubMed CrossRef Google Scholar
69. Altman BJ, Stine ZE, Dang CV. From Krebs to clinic: glutamine metabolism to cancer therapy. Nat Rev Cancer. 2016;16(10):619–634.
    View this article via: PubMed CrossRef Google Scholar
70. Lu H, et al. Chemotherapy triggers HIF-1-dependent glutathione synthesis and copper chelation that induces the breast cancer stem cell phenotype. Proc Natl Acad Sci U S A. 2015;112(33):E4600–E4609.
    View this article via: PubMed CrossRef Google Scholar
71. van Geldermalsen M, et al. ASCT2/SLC1A5 controls glutamine uptake and tumour growth in triple-negative basal-like breast cancer. Oncogene. 2016;35(24):3201–3208.
    View this article via: PubMed CrossRef Google Scholar
72. Jeon YJ, et al. Regulation of glutamine carrier proteins by RNF5 determines breast cancer response to ER stress-inducing chemotherapies. Cancer Cell. 2015;27(3):354–369.
    View this article via: PubMed CrossRef Google Scholar
73. Bröer A, et al. Ablation of the ASCT2 (SLC1A5) gene encoding a neutral amino acid transporter reveals transporter plasticity and redundancy in cancer cells. J Biol Chem. 2019;294(11):4012–4026.
    View this article via: PubMed CrossRef Google Scholar
74. Ye J, et al. Serine catabolism regulates mitochondrial redox control during hypoxia. Cancer Discov. 2014;4(12):1406–1417.
    View this article via: PubMed CrossRef Google Scholar
75. Samanta D, Park Y, Andrabi SA, Shelton LM, Gilkes DM, Semenza GL. PHGDH expression is required for mitochondrial redox homeostasis, breast cancer stem cell maintenance, and lung metastasis. Cancer Res. 2016;76(15):4430–4442.
    View this article via: PubMed CrossRef Google Scholar
76. Sullivan MR, et al. Increased serine synthesis provides an advantage for tumors arising in tissues where serine levels are limiting. Cell Metab. 2019;29(6):1410–1421.e4.
    View this article via: PubMed CrossRef Google Scholar
77. Röhrig F, Schulze A. The multifaceted roles of fatty acid synthesis in cancer. Nat Rev Cancer. 2016;16(11):732–749.
    View this article via: PubMed CrossRef Google Scholar
78. Szutowicz A, Kwiatkowski J, Angielski S. Lipogenetic and glycolytic enzyme activities in carcinoma and nonmalignant diseases of the human breast. Br J Cancer. 1979;39(6):681–687.
    View this article via: PubMed CrossRef Google Scholar
79. Bharti SK, et al. Metabolic consequences of HIF silencing in a triple negative human breast cancer xenograft. Oncotarget. 2018;9(20):15326–15339.
    View this article via: PubMed CrossRef Google Scholar
80. Karn T, et al. A small hypoxia signature predicted pCR response to bevacizumab in the neoadjuvant GeparQuinto breast cancer trial. Clin Cancer Res. 2020;26(8):1896–1904.
    View this article via: PubMed CrossRef Google Scholar
81. Sevinsky CJ, Khan F, Kokabee L, Darehshouri A, Maddipati KR, Conklin DS. NDRG1 regulates neutral lipid metabolism in breast cancer cells. Breast Cancer Res. 2018;20(1):55.
    View this article via: PubMed CrossRef Google Scholar
82. Godbole M, et al. Up-regulation of the kinase gene SGK1 by progesterone activates the AP-1-NDRG1 axis in both PR-positive and -negative breast cancer cells. J Biol Chem. 2018;293(50):19263–19276.
    View this article via: PubMed CrossRef Google Scholar
83. Xiang L, Semenza GL. Hypoxia-inducible factors promote breast cancer stem cell specification and maintenance in response to hypoxia or cytotoxic chemotherapy. Adv Cancer Res. 2019;141:175–212.
    View this article via: PubMed Google Scholar
84. Tang K, et al. Hypoxia-reprogrammed tricarboxylic acid cycle promotes the growth of human breast tumorigenic cells. Oncogene. 2019;38(44):6970–6984.
    View this article via: PubMed CrossRef Google Scholar
85. Ma B, et al. The SIAH2-NRF1 axis spatially regulates tumor microenvironment remodeling for tumor progression. Nat Commun. 2019;10(1):1034.
    View this article via: PubMed CrossRef Google Scholar
86. Zhang J, et al. EglN2 associates with the NRF1-PGC1α complex and controls mitochondrial function in breast cancer. EMBO J. 2015;34(23):2953–2970.
    View this article via: PubMed CrossRef Google Scholar
87. Semenza GL. Hypoxia-inducible factors: coupling glucose metabolism and redox regulation with induction of the breast cancer stem cell phenotype. EMBO J. 2017;36(3):252–259.
    View this article via: PubMed CrossRef Google Scholar
88. Godet I, Shin YJ, Ju JA, Ye IC, Wang G, Gilkes DM. Fate-mapping post-hypoxic tumor cells reveals a ROS-resistant phenotype that promotes metastasis. Nat Commun. 2019;10(1):4862.
    View this article via: PubMed CrossRef Google Scholar
89. Lee KM, et al. MYC and MCL1 cooperatively promote chemotherapy-resistant breast cancer stem cells via regulation of mitochondrial oxidative phosphorylation. Cell Metab. 2017;26(4):633–647.e7.
    View this article via: PubMed CrossRef Google Scholar
90. Msaki A, et al. A hypoxic signature marks tumors formed by disseminated tumor cells in the BALB-neuT mammary cancer model. Oncotarget. 2016;7(22):33081–33095.
    View this article via: PubMed CrossRef Google Scholar
91. Biomarkers Definitions Working Group. Biomarkers and surrogate endpoints: preferred definitions and conceptual framework. Clin Pharmacol Ther. 2001;69(3):89–95.
    View this article via: PubMed CrossRef Google Scholar
92. Daimiel I. Insights into hypoxia: non-invasive assessment through imaging modalities and its application in breast cancer. J Breast Cancer. 2019;22(2):155–171.
    View this article via: PubMed CrossRef Google Scholar
93. Walsh JC, Lebedev A, Aten E, Madsen K, Marciano L, Kolb HC. The clinical importance of assessing tumor hypoxia: relationship of tumor hypoxia to prognosis and therapeutic opportunities. Antioxid Redox Signal. 2014;21(10):1516–1554.
    View this article via: PubMed CrossRef Google Scholar
94. Sobhanifar S, Aquino-Parsons C, Stanbridge EJ, Olive P. Reduced expression of hypoxia-inducible factor-1alpha in perinecrotic regions of solid tumors. Cancer Res. 2005;65(16):7259–7266.
    View this article via: PubMed CrossRef Google Scholar
95. Han S, Huang T, Hou F, Yao L, Wang X, Wu X. The prognostic value of hypoxia-inducible factor-1α in advanced cancer survivors: a meta-analysis with trial sequential analysis. Ther Adv Med Oncol. 2019;11:1758835919875851.
    View this article via: PubMed Google Scholar
96. Yu M, et al. Prognostic role of glycolysis for cancer outcome: evidence from 86 studies. J Cancer Res Clin Oncol. 2019;145(4):967–999.
    View this article via: PubMed CrossRef Google Scholar
97. Nowak-Sliwinska P, et al. Consensus guidelines for the use and interpretation of angiogenesis assays. Angiogenesis. 2018;21(3):425–532.
    View this article via: PubMed CrossRef Google Scholar
98. Uzzan B, Nicolas P, Cucherat M, Perret GY. Microvessel density as a prognostic factor in women with breast cancer: a systematic review of the literature and meta-analysis. Cancer Res. 2004;64(9):2941–2955.
    View this article via: PubMed CrossRef Google Scholar
99. Abu-Jamous B, Buffa FM, Harris AL, Nandi AK. In vitro downregulated hypoxia transcriptome is associated with poor prognosis in breast cancer. Mol Cancer. 2017;16(1):105.
    View this article via: PubMed CrossRef Google Scholar
100. Ye Y, et al. Characterization of hypoxia-associated molecular features to aid hypoxia-targeted therapy. Nat Metab. 2019;1(4):431–444.
     View this article via: PubMed CrossRef Google Scholar
101. Escala-Garcia M, et al. A network analysis to identify mediators of germline-driven differences in breast cancer prognosis. Nat Commun. 2020;11(1):312.
     View this article via: PubMed CrossRef Google Scholar
102. Thomas LW, et al. CHCHD4 confers metabolic vulnerabilities to tumour cells through its control of the mitochondrial respiratory chain. Cancer Metab. 2019;7:2.
     View this article via: PubMed Google Scholar
103. Milani M, et al. Hypoxia-related biological markers as predictors of epirubicin-based treatment responsiveness and resistance in locally advanced breast cancer. Oncotarget. 2017;8(45):78870–78881.
     View this article via: PubMed CrossRef Google Scholar
104. Generali D, et al. Hypoxia-inducible factor-1alpha expression predicts a poor response to primary chemoendocrine therapy and disease-free survival in primary human breast cancer. Clin Cancer Res. 2006;12(15):4562–4568.
     View this article via: PubMed CrossRef Google Scholar
105. Koukourakis MI, et al. Prospective neoadjuvant analysis of PET imaging and mechanisms of resistance to Trastuzumab shows role of HIF1 and autophagy. Br J Cancer. 2014;110(9):2209–2216.
     View this article via: PubMed CrossRef Google Scholar
106. Samanta D, Gilkes DM, Chaturvedi P, Xiang L, Semenza GL. Hypoxia-inducible factors are required for chemotherapy resistance of breast cancer stem cells. Proc Natl Acad Sci U S A. 2014;111(50):E5429–E5438.
     View this article via: PubMed CrossRef Google Scholar
107. Brooks DL, et al. ITGA6 is directly regulated by hypoxia-inducible factors and enriches for cancer stem cell activity and invasion in metastatic breast cancer models. Mol Cancer. 2016;15:26.
     View this article via: PubMed Google Scholar
108. Schito L, Rey S. Hypoxic pathobiology of breast cancer metastasis. Biochim Biophys Acta Rev Cancer. 2017;1868(1):239–245.
     View this article via: PubMed CrossRef Google Scholar
109. Rolfs A, Kvietikova I, Gassmann M, Wenger RH. Oxygen-regulated transferrin expression is mediated by hypoxia-inducible factor-1. J Biol Chem. 1997;272(32):20055–20062.
     View this article via: PubMed CrossRef Google Scholar
110. Higashimura Y, et al. Up-regulation of glyceraldehyde-3-phosphate dehydrogenase gene expression by HIF-1 activity depending on Sp1 in hypoxic breast cancer cells. Arch Biochem Biophys. 2011;509(1):1–8.
     View this article via: PubMed CrossRef Google Scholar
111. Mihály Z, et al. A meta-analysis of gene expression-based biomarkers predicting outcome after tamoxifen treatment in breast cancer. Breast Cancer Res Treat. 2013;140(2):219–232.
     View this article via: PubMed CrossRef Google Scholar
112. Bartlett JM, et al. Mammostrat as a tool to stratify breast cancer patients at risk of recurrence during endocrine therapy. Breast Cancer Res. 2010;12(4):R47.
     View this article via: PubMed CrossRef Google Scholar
113. El Ansari R, et al. The amino acid transporter SLC7A5 confers a poor prognosis in the highly proliferative breast cancer subtypes and is a key therapeutic target in luminal B tumours. Breast Cancer Res. 2018;20(1):21.
     View this article via: PubMed CrossRef Google Scholar
114. Chen Z, Wang Y, Warden C, Chen S. Cross-talk between ER and HER2 regulates c-MYC-mediated glutamine metabolism in aromatase inhibitor resistant breast cancer cells. J Steroid Biochem Mol Biol. 2015;149:118–127.
     View this article via: PubMed CrossRef Google Scholar
115. Gebhart G, et al. ¹⁸F-FDG PET/CT for early prediction of response to neoadjuvant lapatinib, trastuzumab, and their combination in HER2-positive breast cancer: results from Neo-ALTTO. J Nucl Med. 2013;54(11):1862–1868.
     View this article via: PubMed CrossRef Google Scholar
116. Gebhart G, et al. Molecular imaging as a tool to investigate heterogeneity of advanced HER2-positive breast cancer and to predict patient outcome under trastuzumab emtansine (T-DM1): the ZEPHIR trial. Ann Oncol. 2016;27(4):619–624.
     View this article via: PubMed CrossRef Google Scholar
117. Miles D, et al. Bevacizumab plus paclitaxel versus placebo plus paclitaxel as first-line therapy for HER2-negative metastatic breast cancer (MERiDiAN): a double-blind placebo-controlled randomised phase III trial with prospective biomarker evaluation. Eur J Cancer. 2017;70:146–155.
     View this article via: PubMed Google Scholar
118. Gampenrieder SP, et al. DNA methylation signatures predicting bevacizumab efficacy in metastatic breast cancer. Theranostics. 2018;8(8):2278–2288.
     View this article via: PubMed CrossRef Google Scholar
119. Mehta S, et al. Radiogenomics monitoring in breast cancer identifies metabolism and immune checkpoints as early actionable mechanisms of resistance to anti-angiogenic treatment. EBioMedicine. 2016;10:109–116.
     View this article via: PubMed CrossRef Google Scholar
120. Wigerup C, Påhlman S, Bexell D. Therapeutic targeting of hypoxia and hypoxia-inducible factors in cancer. Pharmacol Ther. 2016;164:152–169.
     View this article via: PubMed CrossRef Google Scholar
121. Barrios CH, et al. Phase III randomized trial of sunitinib versus capecitabine in patients with previously treated HER2-negative advanced breast cancer. Breast Cancer Res Treat. 2010;121(1):121–131.
     View this article via: PubMed CrossRef Google Scholar
122. Ueda S, Saeki T, Osaki A, Yamane T, Kuji I. Bevacizumab induces acute hypoxia and cancer progression in patients with refractory breast cancer: multimodal functional imaging and multiplex cytokine analysis. Clin Cancer Res. 2017;23(19):5769–5778.
     View this article via: PubMed CrossRef Google Scholar
123. Mahdi A, Darvishi B, Majidzadeh-AK , Salehi M, Farahmand L. Challenges facing antiangiogenesis therapy: the significant role of hypoxia-inducible factor and MET in development of resistance to anti-vascular endothelial growth factor-targeted therapies. J Cell Physiol. 2019;234(5):5655–5663.
     View this article via: PubMed CrossRef Google Scholar
124. Yan S, Jiao X, Zou H, Li K. Prognostic significance of c-Met in breast cancer: a meta-analysis of 6010 cases. Diagn Pathol. 2015;10:62.
     View this article via: PubMed Google Scholar
125. Tolaney SM, et al. Phase II and biomarker study of cabozantinib in metastatic triple-negative breast cancer patients. Oncologist. 2017;22(1):25–32.
     View this article via: PubMed CrossRef Google Scholar
126. Leone JP, et al. A phase II study of cabozantinib alone or in combination with trastuzumab in breast cancer patients with brain metastases. Breast Cancer Res Treat. 2020;179(1):113–123.
     View this article via: PubMed CrossRef Google Scholar
127. Bello E, et al. The tyrosine kinase inhibitor E-3810 combined with paclitaxel inhibits the growth of advanced-stage triple-negative breast cancer xenografts. Mol Cancer Ther. 2013;12(2):131–140.
     View this article via: PubMed CrossRef Google Scholar
128. Golfmann K, et al. Synergistic anti-angiogenic treatment effects by dual FGFR1 and VEGFR1 inhibition in FGFR1-amplified breast cancer. Oncogene. 2018;37(42):5682–5693.
     View this article via: PubMed CrossRef Google Scholar
129. Jones RL, et al. Phase II randomised discontinuation trial of brivanib in patients with advanced solid tumours. Eur J Cancer. 2019;120:132–139.
     View this article via: PubMed CrossRef Google Scholar
130. Quintela-Fandino M, et al. Nintedanib plus letrozole in early breast cancer: a phase 0/I pharmacodynamic, pharmacokinetic, and safety clinical trial of combined FGFR1 and aromatase inhibition. Breast Cancer Res. 2019;21(1):69.
     View this article via: PubMed CrossRef Google Scholar
131. Reguera-Nuñez E, Xu P, Chow A, Man S, Hilberg F, Kerbel RS. Therapeutic impact of Nintedanib with paclitaxel and/or a PD-L1 antibody in preclinical models of orthotopic primary or metastatic triple negative breast cancer. J Exp Clin Cancer Res. 2019;38(1):16.
     View this article via: PubMed CrossRef Google Scholar
132. Formisano L, et al. Aberrant FGFR signaling mediates resistance to CDK4/6 inhibitors in ER+ breast cancer. Nat Commun. 2019;10(1):1373.
     View this article via: PubMed CrossRef Google Scholar
133. Diéras V, et al. Trebananib (AMG 386) plus weekly paclitaxel with or without bevacizumab as first-line therapy for HER2-negative locally recurrent or metastatic breast cancer: a phase 2 randomized study. Breast. 2015;24(3):182–190.
     View this article via: PubMed CrossRef Google Scholar
134. Mayer EL, Krop IE. Advances in targeting SRC in the treatment of breast cancer and other solid malignancies. Clin Cancer Res. 2010;16(14):3526–3532.
     View this article via: PubMed CrossRef Google Scholar
135. Stessels F, et al. Breast adenocarcinoma liver metastases, in contrast to colorectal cancer liver metastases, display a non-angiogenic growth pattern that preserves the stroma and lacks hypoxia. Br J Cancer. 2004;90(7):1429–1436.
     View this article via: PubMed CrossRef Google Scholar
136. Frentzas S, et al. Vessel co-option mediates resistance to anti-angiogenic therapy in liver metastases. Nat Med. 2016;22(11):1294–1302.
     View this article via: PubMed CrossRef Google Scholar
137. Tagg SL, et al. 2-Methoxyoestradiol-3,17-O,O-bis-sulphamate and 2-deoxy-D-glucose in combination: a potential treatment for breast and prostate cancer. Br J Cancer. 2008;99(11):1842–1848.
     View this article via: PubMed CrossRef Google Scholar
138. Gang BP, Dilda PJ, Hogg PJ, Blackburn AC. Targeting of two aspects of metabolism in breast cancer treatment. Cancer Biol Ther. 2014;15(11):1533–1541.
     View this article via: PubMed CrossRef Google Scholar
139. Sutendra G, et al. Mitochondrial activation by inhibition of PDKII suppresses HIF1a signaling and angiogenesis in cancer. Oncogene. 2013;32(13):1638–1650.
     View this article via: PubMed CrossRef Google Scholar
140. Chu QS, et al. A phase I open-labeled, single-arm, dose-escalation, study of dichloroacetate (DCA) in patients with advanced solid tumors. Invest New Drugs. 2015;33(3):603–610.
     View this article via: PubMed CrossRef Google Scholar
141. Valvona CJ, Fillmore HL, Nunn PB, Pilkington GJ. The regulation and function of lactate dehydrogenase A: therapeutic potential in brain tumor. Brain Pathol. 2016;26(1):3–17.
     View this article via: PubMed CrossRef Google Scholar
142. Rai G, et al. Discovery and optimization of potent, cell-active pyrazole-based inhibitors of lactate dehydrogenase (LDH). J Med Chem. 2017;60(22):9184–9204.
     View this article via: PubMed CrossRef Google Scholar
143. Pivovarova AI, MacGregor GG. Glucose-dependent growth arrest of leukemia cells by MCT1 inhibition: feeding Warburg’s sweet tooth and blocking acid export as an anticancer strategy. Biomed Pharmacother. 2018;98:173–179.
     View this article via: PubMed CrossRef Google Scholar
144. Benjamin D, et al. Dual inhibition of the lactate transporters MCT1 and MCT4 is synthetic lethal with metformin due to NAD+ depletion in cancer cells. Cell Rep. 2018;25(11):3047–3058.e4.
     View this article via: PubMed CrossRef Google Scholar
145. Payen VL, Mina E, Van Hée VF, Porporato PE, Sonveaux P. Monocarboxylate transporters in cancer. Mol Metab. 2020;33:48–66.
     View this article via: PubMed CrossRef Google Scholar
146. Hong CS, et al. MCT1 modulates cancer cell pyruvate export and growth of tumors that co-express MCT1 and MCT4. Cell Rep. 2016;14(7):1590–1601.
     View this article via: PubMed CrossRef Google Scholar
147. Lacroix R, Rozeman EA, Kreutz M, Renner K, Blank CU. Targeting tumor-associated acidity in cancer immunotherapy. Cancer Immunol Immunother. 2018;67(9):1331–1348.
     View this article via: PubMed CrossRef Google Scholar
148. Serganova I, et al. LDH-A regulates the tumor microenvironment via HIF-signaling and modulates the immune response. PLoS One. 2018;13(9):e0203965.
     View this article via: PubMed CrossRef Google Scholar
149. Beloueche-Babari M, et al. Monocarboxylate transporter 1 blockade with AZD3965 inhibits lipid biosynthesis and increases tumour immune cell infiltration. Br J Cancer. 2020;122(6):895–903.
     View this article via: PubMed CrossRef Google Scholar
150. Chafe SC, et al. Targeting hypoxia-induced carbonic anhydrase IX enhances immune-checkpoint blockade locally and systemically. Cancer Immunol Res. 2019;7(7):1064–1078.
     View this article via: PubMed CrossRef Google Scholar
151. Demas DM, et al. Glutamine metabolism drives growth in advanced hormone receptor positive breast cancer. Front Oncol. 2019;9:686.
     View this article via: PubMed Google Scholar
152. Schulte ML, et al. Pharmacological blockade of ASCT2-dependent glutamine transport leads to antitumor efficacy in preclinical models. Nat Med. 2018;24(2):194–202.
     View this article via: PubMed CrossRef Google Scholar
153. van Geldermalsen M, et al. Benzylserine inhibits breast cancer cell growth by disrupting intracellular amino acid homeostasis and triggering amino acid response pathways. BMC Cancer. 2018;18(1):689.
     View this article via: PubMed CrossRef Google Scholar
154. Lukey MJ, et al. Liver-type glutaminase GLS2 is a druggable metabolic node in luminal-subtype breast cancer. Cell Rep. 2019;29(1):76–88.e7.
     View this article via: PubMed CrossRef Google Scholar
155. Lampa M, et al. Glutaminase is essential for the growth of triple-negative breast cancer cells with a deregulated glutamine metabolism pathway and its suppression synergizes with mTOR inhibition. PLoS One. 2017;12(9):e0185092.
     View this article via: PubMed CrossRef Google Scholar
156. Fako VE, Wu X, Pflug B, Liu JY, Zhang JT. Repositioning proton pump inhibitors as anticancer drugs by targeting the thioesterase domain of human fatty acid synthase. J Med Chem. 2015;58(2):778–784.
     View this article via: PubMed CrossRef Google Scholar
157. Wang BY, et al. Intermittent high dose proton pump inhibitor enhances the antitumor effects of chemotherapy in metastatic breast cancer. J Exp Clin Cancer Res. 2015;34:85.
     View this article via: PubMed Google Scholar
158. Coll AP, et al. GDF15 mediates the effects of metformin on body weight and energy balance. Nature. 2020;578(7795):444–448.
     View this article via: PubMed CrossRef Google Scholar
159. Chung YC, Chang CM, Wei WC, Chang TW, Chang KJ, Chao WT. Metformin-induced caveolin-1 expression promotes T-DM1 drug efficacy in breast cancer cells. Sci Rep. 2018;8(1):3930.
     View this article via: PubMed CrossRef Google Scholar
160. Samuel SM, Varghese E, Kubatka P, Triggle CR, Büsselberg D. Metformin: the answer to cancer in a flower? Current knowledge and future prospects of metformin as an anti-cancer agent in breast cancer. Biomolecules. 2019;9(12):E846.
     View this article via: PubMed Google Scholar
161. Lord SR, et al. Integrated pharmacodynamic analysis identifies two metabolic adaption pathways to metformin in breast cancer. Cell Metab. 2018;28(5):679–688.e4.
     View this article via: PubMed CrossRef Google Scholar
162. Quintela-Fandino M, et al. Randomized phase 0/I trial of the mitochondrial inhibitor ME-344 or placebo added to bevacizumab in early HER2-negative breast cancer. Clin Cancer Res. 2020;26(1):35–45.
     View this article via: PubMed CrossRef Google Scholar
163. Cao J, et al. Decylubiquinone suppresses breast cancer growth and metastasis by inhibiting angiogenesis via the ROS/p53/ BAI1 signaling pathway. Angiogenesis. 2020;23(3):325–338.
     View this article via: PubMed CrossRef Google Scholar
164. Hojman P, Gehl J, Christensen JF, Pedersen BK. Molecular mechanisms linking exercise to cancer prevention and treatment. Cell Metab. 2018;27(1):10–21.
     View this article via: PubMed CrossRef Google Scholar
165. Klement RJ, Brehm N, Sweeney RA. Ketogenic diets in medical oncology: a systematic review with focus on clinical outcomes. Med Oncol. 2020;37(2):14.
     View this article via: PubMed CrossRef Google Scholar
166. Kanarek N, Petrova B, Sabatini DM. Dietary modifications for enhanced cancer therapy. Nature. 2020;579(7800):507–517.
     View this article via: PubMed CrossRef Google Scholar
167. Rieger J, et al. ERGO: a pilot study of ketogenic diet in recurrent glioblastoma. Int J Oncol. 2014;44(6):1843–1852.
     View this article via: PubMed CrossRef Google Scholar
168. Cohen CW, et al. A ketogenic diet reduces central obesity and serum insulin in women with ovarian or endometrial cancer. J Nutr. 2018;148(8):1253–1260.
     View this article via: PubMed CrossRef Google Scholar
169. Rodrigues LM, Uribe-Lewis S, Madhu B, Honess DJ, Stubbs M, Griffiths JR. The action of β-hydroxybutyrate on the growth, metabolism and global histone H3 acetylation of spontaneous mouse mammary tumours: evidence of a β-hydroxybutyrate paradox. Cancer Metab. 2017;5:4.
     View this article via: PubMed Google Scholar
170. Nencioni A, Caffa I, Cortellino S, Longo VD. Fasting and cancer: molecular mechanisms and clinical application. Nat Rev Cancer. 2018;18(11):707–719.
     View this article via: PubMed CrossRef Google Scholar
171. de Groot S, et al. The effects of short-term fasting on tolerance to (neo) adjuvant chemotherapy in HER2-negative breast cancer patients: a randomized pilot study. BMC Cancer. 2015;15:652.
     View this article via: PubMed Google Scholar
172. Antoniewicz MR. A guide to ¹³C metabolic flux analysis for the cancer biologist. Exp Mol Med. 2018;50(4):19.
     View this article via: PubMed CrossRef Google Scholar
173. Maher EA, et al. Metabolism of [U-13 C]glucose in human brain tumors in vivo. NMR Biomed. 2012;25(11):1234–1244.
     View this article via: PubMed CrossRef Google Scholar
174. Sadeghzadeh M, et al. Development and radiosynthesis of the first ¹⁸F-labeled inhibitor of monocarboxylate transporters (MCTs). J Labelled Comp Radiopharm. 2019;62(8):411–424.
     View this article via: PubMed CrossRef Google Scholar
175. Generali D, et al. Phosphorylated ERalpha, HIF-1alpha, and MAPK signaling as predictors of primary endocrine treatment response and resistance in patients with breast cancer. J Clin Oncol. 2009;27(2):227–234.
     View this article via: PubMed CrossRef Google Scholar
176. Helczynska K, et al. Hypoxia-inducible factor-2alpha correlates to distant recurrence and poor outcome in invasive breast cancer. Cancer Res. 2008;68(22):9212–9220.
     View this article via: PubMed CrossRef Google Scholar
177. Luo D, Liu H, Lin D, Lian K, Ren H. The clinicopathologic and prognostic value of hypoxia-inducible factor-2α in cancer patients: a systematic review and meta-analysis. Cancer Epidemiol Biomarkers Prev. 2019;28(5):857–866.
     View this article via: PubMed CrossRef Google Scholar
178. Liu YH, et al. Diffuse optical spectroscopy for monitoring the responses of patients with breast cancer to neoadjuvant chemotherapy: a meta-analysis. Medicine (Baltimore). 2018;97(41):e12683.
     View this article via: PubMed CrossRef Google Scholar
179. Tromberg BJ, et al. Predicting responses to neoadjuvant chemotherapy in breast cancer: ACRIN 6691 trial of diffuse optical spectroscopic imaging. Cancer Res. 2016;76(20):5933–5944.
     View this article via: PubMed CrossRef Google Scholar
180. Cheng J, et al. 18F-fluoromisonidazole PET/CT: a potential tool for predicting primary endocrine therapy resistance in breast cancer. J Nucl Med. 2013;54(3):333–340.
     View this article via: PubMed CrossRef Google Scholar
181. Ho D, et al. Impact of serum HER2, TIMP-1, and CAIX on outcome for HER2+ metastatic breast cancer patients: CCTG MA.31 (lapatinib vs. trastuzumab). Breast Cancer Res Treat. 2017;164(3):571–580.
     View this article via: PubMed CrossRef Google Scholar
182. van Kuijk SJ, Yaromina A, Houben R, Niemans R, Lambin P, Dubois LJ. Prognostic significance of carbonic anhydrase IX expression in cancer patients: a meta-analysis. Front Oncol. 2016;6:69.
     View this article via: PubMed Google Scholar
183. Alves WEFM, et al. CAIX is a predictor of pathological complete response and is associated with higher survival in locally advanced breast cancer submitted to neoadjuvant chemotherapy. BMC Cancer. 2019;19(1):1173.
     View this article via: PubMed CrossRef Google Scholar
184. Garcia S, et al. Poor prognosis in breast carcinomas correlates with increased expression of targetable CD146 and c-Met and with proteomic basal-like phenotype. Hum Pathol. 2007;38(6):830–841.
     View this article via: PubMed CrossRef Google Scholar
185. Yu M, et al. The prognostic value of GLUT1 in cancers: a systematic review and meta-analysis. Oncotarget. 2017;8(26):43356–43367.
     View this article via: PubMed CrossRef Google Scholar
186. Coudert B, et al. Use of [(18)F]-FDG PET to predict response to neoadjuvant trastuzumab and docetaxel in patients with HER2-positive breast cancer, and addition of bevacizumab to neoadjuvant trastuzumab and docetaxel in [(18)F]-FDG PET-predicted non-responders (AVATAXHER): an open-label, randomised phase 2 trial. Lancet Oncol. 2014;15(13):1493–1502.
     View this article via: PubMed CrossRef Google Scholar
187. Humbert O, et al. Breast cancer blood flow and metabolism on dual-acquisition ¹⁸F-FDG PET: correlation with tumor phenotype and neoadjuvant chemotherapy response. J Nucl Med. 2018;59(7):1035–1041.
     View this article via: PubMed CrossRef Google Scholar
188. Tian F, Shen G, Deng Y, Diao W, Jia Z. The accuracy of ¹⁸F-FDG PET/CT in predicting the pathological response to neoadjuvant chemotherapy in patients with breast cancer: a meta-analysis and systematic review. Eur Radiol. 2017;27(11):4786–4796.
     View this article via: PubMed CrossRef Google Scholar
189. Wang F, et al. Predictive role of the overexpression for CXCR4, C-Met, and VEGF-C among breast cancer patients: a meta-analysis. Breast. 2016;28:45–53.
     View this article via: PubMed CrossRef Google Scholar
190. Nieto Y, et al. Prognostic analysis of tumour angiogenesis, determined by microvessel density and expression of vascular endothelial growth factor, in high-risk primary breast cancer patients treated with high-dose chemotherapy. Br J Cancer. 2007;97(3):391–397.
     View this article via: PubMed CrossRef Google Scholar
191. Goussia A, et al. Associations of angiogenesis-related proteins with specific prognostic factors, breast cancer subtypes and survival outcome in early-stage breast cancer patients. A Hellenic Cooperative Oncology Group (HeCOG) trial. PLoS One. 2018;13(7):e0200302.
     View this article via: PubMed CrossRef Google Scholar
192. Zhang Z, Luo G, Tang H, Cheng C, Wang P. Prognostic significance of high VEGF-C expression for patients with breast cancer: an update meta analysis. PLoS One. 2016;11(11):e0165725.
     View this article via: PubMed CrossRef Google Scholar
193. Li Q, et al. Low-dose anti-angiogenic therapy sensitizes breast cancer to PD-1 blockade. Clin Cancer Res. 2020;26(7):1712–1724.
     View this article via: PubMed Google Scholar
194. Wu FTH, et al. Pre- and post-operative anti-PD-L1 plus anti-angiogenic therapies in mouse breast or renal cancer models of micro- or macro-metastatic disease. Br J Cancer. 2019;120(2):196–206.
     View this article via: PubMed CrossRef Google Scholar
195. Schmittnaegel M, et al. Dual angiopoietin-2 and VEGFA inhibition elicits antitumor immunity that is enhanced by PD-1 checkpoint blockade. Sci Transl Med. 2017;9(385):eaak9670.
     View this article via: PubMed CrossRef Google Scholar
196. Allen E, et al. Combined antiangiogenic and anti-PD-L1 therapy stimulates tumor immunity through HEV formation. Sci Transl Med. 2017;9(385):eaak9679.
     View this article via: PubMed CrossRef Google Scholar
197. Tian L, et al. Mutual regulation of tumour vessel normalization and immunostimulatory reprogramming. Nature. 2017;544(7649):250–254.
     View this article via: PubMed CrossRef Google Scholar
198. Hlouschek J, Ritter V, Wirsdörfer F, Klein D, Jendrossek V, Matschke J. Targeting SLC25A10 alleviates improved antioxidant capacity and associated radioresistance of cancer cells induced by chronic-cycling hypoxia. Cancer Lett. 2018;439:24–38.
     View this article via: PubMed CrossRef Google Scholar
199. Wang H, Jiang H, Van De Gucht M, De Ridder M. Hypoxic radioresistance: can ROS be the key to overcome it? Cancers (Basel). 2019;11(1):E112.
     View this article via: PubMed Google Scholar
200. Indira Chandran V, et al. Hypoxia attenuates trastuzumab uptake and trastuzumab-emtansine (T-DM1) cytotoxicity through redistribution of phosphorylated caveolin-1. Mol Cancer Res. 2020;18(4):644–656.
     View this article via: PubMed CrossRef Google Scholar