Metabolic plasticity in pancreatic ductal adenocarcinoma progression and response to treatment.

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Learn more: PMC Disclaimer | PMC Copyright Notice Mol Cancer . 2026 Mar 28;25:108. doi: 10.1186/s12943-026-02620-x Search in PMC Search in PubMed View in NLM Catalog Add to search Metabolic plasticity in pancreatic ductal adenocarcinoma progression and response to treatment Jiarui Ma Jiarui Ma 1 Institute of Biopharmaceutical and Health Engineering, Tsinghua Shenzhen International Graduate School, Tsinghua University, Shenzhen, P.R. 518055 China Find articles by Jiarui Ma 1, # , Vipul Bhardwaj Vipul Bhardwaj 1 Institute of Biopharmaceutical and Health Engineering, Tsinghua Shenzhen International Graduate School, Tsinghua University, Shenzhen, P.R. 518055 China Find articles by Vipul Bhardwaj 1, # , Peter E Lobie Peter E Lobie 1 Institute of Biopharmaceutical and Health Engineering, Tsinghua Shenzhen International Graduate School, Tsinghua University, Shenzhen, P.R. 518055 China Find articles by Peter E Lobie 1, ✉ , Vijay Pandey Vijay Pandey 1 Institute of Biopharmaceutical and Health Engineering, Tsinghua Shenzhen International Graduate School, Tsinghua University, Shenzhen, P.R. 518055 China Find articles by Vijay Pandey 1, ✉ Author information Article notes Copyright and License information 1 Institute of Biopharmaceutical and Health Engineering, Tsinghua Shenzhen International Graduate School, Tsinghua University, Shenzhen, P.R. 518055 China ✉ Corresponding author. # Contributed equally. Received 2025 Oct 9; Accepted 2026 Feb 14; Collection date 2026. © The Author(s) 2026 Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/ . PMC Copyright notice PMCID: PMC13085771  PMID: 41904584 Abstract Pancreatic ductal adenocarcinoma (PDAC) remains among the most lethal gastrointestinal cancers, characterized by rapid progression and resistance to therapy driven by significant metabolic reprogramming. Although alterations in glycolysis, glutamine, and lipid metabolism are well established, recent studies emphasize a more crucial factor: the emergence of context-specific metabolic dependencies within the nutrient-deprived tumor microenvironment. This review highlights that focusing on these dependencies, particularly those resulting from the distinctive metabolic interactions between cancer cells and the surrounding stroma, offers a promising strategy for overcoming treatment challenges. Evidence demonstrating that stromal-driven metabolic pathways supply energy and building blocks and confer resistance to standard chemo- and immune-therapies is examined. Furthermore, innovative approaches to target these vulnerabilities in PDAC metabolic subtypes, including synthetic-lethal interactions and key transporters in metabolic pathways are explored. How functional precision medicine, which uses patient-derived models to identify metabolic vulnerabilities, may convert these insights into personalized therapies is examined. Moving from broad metabolic inhibition to precise targeting of the tumour-stroma metabolic ecosystem could substantially improve the prognosis for PDAC. Graphical abstract Keywords: Pancreatic ductal adenocarcinoma, Metabolic reprogramming, Tumor microenvironment, Therapeutic resistance, Precision oncology Introduction The pancreas, a vital gland weighing approximately 100 g, is located in the upper abdomen and is anatomically divided into five parts: the head, uncinate process, neck, body, and tail [ 1 ]. The pancreas primarily possesses exocrine functions, producing digestive enzymes such as amylase and lipase, along with a bicarbonate-rich fluid that is secreted through a complex ductal system into the duodenum. It also has a vital endocrine role, with the islets of Langerhans making up 1–2% of the pancreas and regulating blood glucose through hormones such as insulin and glucagon. This combination of digestive and metabolic functions makes the pancreas crucial for nutrient absorption and metabolic equilibrium. These diverse functions are intricately regulated by endocrine signals, autonomic nerves, and metabolic cues [ 2 ]. Pancreatic carcinoma (PC) is one of the most lethal malignancies, ranking as the sixth leading cause of cancer deaths globally and representing a significant healthcare challenge [ 3 ]. The predominant subtype, pancreatic ductal adenocarcinoma (PDAC), accounts for ~ 90% of cases and is the most aggressive form. A key risk factor is advanced age, with incidence rising sharply after age 30, peaking in males over 60, with a median age at diagnosis of ~ 70 years [ 4 ]. Lifestyle factors, such as smoking and alcohol consumption, alongside comorbidities such as obesity and type 2 diabetes, have also been implicated to contribute significantly [ 5 ]. Notably, the development of new-onset diabetes can act as a paraneoplastic indicator of occult PDAC [ 6 ]. Underlying these risk factors are key genetic alterations, including driver mutations in genes such as Kirsten rat sarcoma virus oncogene homologue ( KRAS ) (involved in oncogenic signaling), which occur in over 90% of cases, as well as in cyclin-dependent kinase inhibitor 2 A ( CDKN2A ) (related to cell-cycle regulation) and liver kinase B1/serine–threonine kinase 11 (STK11/LKB1) (linked to metabolic control) [ 7 , 8 ]. Furthermore, mutations in germline or somatic cells within the Fanconi anemia gene family ( BRCA1/2 , PALB2 ) and mismatch repair genes (such as MLH1 , MSH2/6 , PMS2 ) further contribute to pathogenesis [ 3 , 9 – 11 ]. Despite this understanding of the aetiology, the asymptomatic nature of the disease and delayed detection continue to pose substantial challenges, with the majority of diagnoses occurring after metastasis, thereby contributing to the poor mortality rate associated with the disease [ 12 ]. Current clinical management approaches in PDAC are guided by a multidisciplinary assessment of the disease stage categorized as resectable, borderline resectable, locally advanced, or metastatic and the patient’s functional (performance) status [ 13 ]. Only 10–15% of patients present with resectable tumors. For these patients, surgical resection (e.g., pancreatoduodenectomy, distal or total pancreatectomy) remains the cornerstone of curative intent. However, surgery alone is insufficient, and adjuvant chemotherapy is needed, which improves 5-year survival from 20% to 30–40% [ 14 ]. Notably, regimen selection in practice varies considerably depending on geography, institutional policies, patient age, functional status, and drug access. In the adjuvant setting, the recommended regimen for patients with good functional status is mFOLFIRINOX (a modified form of FOLFIRINOX consisting of oxaliplatin, irinotecan, leucovorin, and 5-fluorouracil), whereas for those with suboptimal status, gemcitabine-based therapy (with capecitabine or as monotherapy) is standard [ 15 ]. The evidence for these standards is based on a limited number of pivotal randomized trials. It is important to note that while mFOLFIRINOX has not been directly compared to FOLFIRINOX in prospective randomized trials, it is widely adopted due to a more favorable toxicity profile, with retrospective data suggesting similar efficacy [ 13 ]. For borderline resectable and locally advanced disease, neoadjuvant chemotherapy and/or radiotherapy is often used to reduce tumor size before potential intervention [ 13 , 14 ] ( Fig. 1 ) . It is important to note that the optimal neoadjuvant regimen is not yet standardized. The use of neoadjuvant therapy in resectable PDAC is currently confined to clinical trials, as evidence for a clear survival benefit in this setting remains limited [ 13 ]. The majority (up to 90%) of patients with PDAC present with advanced-stage disease, precluding them from curative surgery. For most of these patients, chemotherapy is the primary treatment modality. The key regimens include gemcitabine, GEMNABP (gemcitabine/nab-paclitaxel), mFOLFIRINOX, and NALIRIFOX (liposomal irinotecan, fluorouracil, leucovorin, and oxaliplatin) [ 16 – 18 ]. Despite these advances, treatment effectiveness remains limited, with median overall survival (mOS) rarely exceeding one year. The application of radiotherapy varies as its use is not consistently standardized, and studies such as ALLIANCE and ESPAC-1 found no clear survival advantage or even inferior outcomes with radiation [ 16 , 19 ]. Additionally, the LAP-07 trial demonstrated that adding chemoradiation to chemotherapy did not improve OS [ 20 ]. Consequently, radiotherapy is not a default component of treatment; its use is typically reserved for specific clinical scenarios following multidisciplinary review and is not recommended in certain contexts, such as with FOLFIRINOX-based neoadjuvant therapy. Fig. 1. Open in a new tab Diagnostic and Therapeutic Algorithm for PDAC: A Diagnostic pathway for suspected pancreatic malignancy, leading to pathological confirmation of PDAC. Initial clinical evaluation prioritizes assessment of familial risk, with genetic counseling recommended for individuals with a family history of pancreatic malignancy. In sporadic cases, cross-sectional imaging (MRI/CT) is utilized for tumor localization, followed by potential histopathological confirmation via biopsy. Tumors are subsequently stratified into localized or advanced disease based on radiological and pathological criteria B Stage-adapted treatment paradigm. Localized PDAC is subclassified as resectable or borderline resectable. For resectable tumors in patients with ECOG performance status 0–2, upfront surgical resection is pursued, followed by adjuvant chemoradiotherapy. Borderline resectable tumors in ECOG 0–2 patients undergo neoadjuvant chemotherapy (e.g., mFOLFIRINOX or gemcitabine/nab-paclitaxel) with or without radiotherapy to facilitate margin-negative resection (currently confined to clinical trials). Advanced PDAC includes unresectable locoregional or metastatic disease. Unresectable cases in ECOG 0–2 patients receive systemic chemotherapy (e.g., mFOLFIRINOX) followed by consolidative radiotherapy, while metastatic disease is managed with palliative chemotherapy (gemcitabine-based regimens) or clinical trial enrolment. Patients with ECOG ≥ 3 are offered best supportive care. This framework emphasizes therapeutic personalization to maximize efficacy while balancing patient functional status and disease biology C Approved pharmacological strategies for PDAC. This diagram illustrates the evolution of first-line and subsequent chemotherapeutic and targeted therapy options for PDAC, from historical benchmarks to contemporary standards of care. Approved monotherapies and combination regimens are presented chronologically from 1991 to the present, highlighting key advances in systemic treatment for localized and metastatic disease. (MRI: magnetic resonance imaging, CT: computed tomography, ECOG: Eastern Cooperative Oncology Group). This figure is generated using BioRender.com In addition to chemotherapy, immunotherapy and targeted therapy have become alternate therapeutic approaches [ 21 ]. Unlike other tumor types, several large clinical trials with targeted agents and immunotherapy have failed to produce positive outcomes in PDAC, highlighting the disease’s resistant and complex nature [ 22 , 23 ]. A notable exception exists for the rare subset of tumors with high microsatellite instability (MSI-H) or mismatch repair deficiency (dMMR), which can respond effectively and sustainably to immune checkpoint blockade [ 24 , 25 ]. However, for the majority of patients with PDAC, the primary reason for widespread treatment failure is drug resistance [ 26 – 29 ]. The resistance mechanisms in PDAC are multifactorial, involving factors such as tumor origin, intratumoral heterogeneity, microvascularity, and the tumor microenvironment (TME) [ 3 , 30 ]. Over the past decade, metabolic plasticity and reprogramming have emerged as major drivers of therapeutic resistance in PDAC and are considered the fundamental basis for early recurrence and a dismal prognosis [ 31 , 32 ]. Notably, PDAC exhibits extensive metabolic reprogramming, including increased expression of glycolytic enzymes and increased lactate production [ 31 , 33 ]. These changes result from a combination of factors, including mitochondrial dysfunction, oncogenic driver genes, specific transcription factors, a hypoxic TME, and interactions with stromal cells [ 34 ]. By altering nutrient intake, energy production, and signaling pathways, this adjusted metabolic state directly supports aggressive tumor growth and creates a self-perpetuating cycle of chemoresistance that renders conventional therapies ineffective. This review outlines current insights into the molecular mechanisms of metabolic reprogramming in PDAC, explaining how its disruption drives oncogenic traits, therapy resistance, and immunosuppression. It highlights the vital role of key metabolic dependencies, metabolic effectors, and their influence on the anti-tumor immune response in the TME. Additionally, the review discusses existing therapeutic strategies, such as chemotherapeutic and anti-metabolic agents, as well as combination therapies targeting metabolic vulnerabilities to curb PDAC growth. Lastly, it addresses the limitations of current approaches and explores emerging strategies to overcome metabolic obstacles and improve patient outcomes. Metabolism in PDAC Genetic drivers of metabolic reprogramming Metabolism is essential for cell growth and differentiation and generates adenosine 5′-triphosphate (ATP) to meet cellular energy needs [ 35 ]. Cancer cells grow rapidly, requiring copious energy and resources. Hence, cancer cells rewire their metabolism, a hallmark of cancer, changing the flux, regulation, and localization of biochemical pathways [ 36 ]. In PDAC, this altered energy metabolism not only provides energy for tumor survival but also actively modifies the surrounding immune microenvironment, forming hypoxic, hypoglycemic, and acidic conditions that promote tumor growth and metastasis [ 37 , 38 ]. Decades of research show that the significant metabolic reprogramming in PDAC is an active process driven by recurring genetic mutations, and not simply a passive alteration [ 39 ]. This reprogramming is driven by key mutations, most notably the near-ubiquitous oncogenic activation of KRAS , and loss of tumor suppressors such as tumor protein p53 ( TP53 ), CDKN2A , SMAD family member 4 ( SMAD4 ), and BReast CAncer gene 2 ( BRCA2 ), in 90%, 75%, 50%, and 10% of tumors, respectively, and often occur together [ 39 ]. Inactivation of CDKN2A is detected in precancerous lesions, whereas mutations in TP53 and SMAD4 are late events in PDAC progression. Notably, TP53 mutations are often missense, resulting in a gain of oncogenic function rather than a simple loss of expression. Studies indicate that these alterations function in combination rather than in isolation, forming a network in which each component possesses specific and combined effects on tumor cell metabolism. For example, mutated KRAS , a small GTPase, is constitutively active, continuously stimulating pathways such as PI3K/AKT and RAF/MAPK [ 40 ]. These pathways increase the production of glycolytic enzymes and nucleotides, while also activating nutrient-uptake processes such as autophagy and macropinocytosis, aiding survival of cancer cells in nutrient-deficient environments (Fig. 2 ) [ 41 ]. TP53 dysfunction intensifies this metabolic shift through various mechanisms. When the tumor-supressing role of TP53 is lost, it promotes glycolytic reliance by downregulating TIGAR (TP53-induced glycolysis and apoptosis regulator) and SCO2 (cytochrome C oxidase assembly protein) [ 42 – 44 ]. Additionally, functional TP53 loss results in higher levels of paraoxonase 2 (PON2), an enzyme that enhances glycolysis by promoting glucose uptake [ 45 ]. The absence of SMAD4 signaling also increases the expression of crucial glycolytic enzymes, such as phosphoglycerate kinase 1 (PGK1), thereby accelerating glycolytic flux [ 46 ]. This metabolic reprogramming is further influenced by signals from the TME, intercellular interactions, and microbial dysbiosis (Fig. 2 ) [ 47 , 48 ]. Thus, PDAC metabolism is not just the result of a single mutation, but a rewired, cooperative network where genetic factors and microenvironmental influences combine to support a particularly aggressive tumor. Fig. 2. Open in a new tab Overview of Metabolic Adaptation in PDAC: The schematic illustrates four key interconnected mechanisms of metabolic reprogramming that support PDAC progression A Genetic drivers: Oncogenic mutations and loss of tumor suppressors cooperatively orchestrate a pro-glycolytic state. Mutant KRAS activates PI3K–AKT–mTOR signaling and promotes the Warburg effect, whereas loss of TP53 , SMAD4 , and CDKN2A removes inhibitory brakes on glycolysis and enhances NADPH production to fuel anabolic growth B Microenvironmental adaptation: The hypoxic and nutrient-poor TME induces HIF-1α, which amplifies glycolytic flux. This process is further driven by altered microbiota, which generate metabolites that directly influence host cell metabolism and enhance glycolytic pathways. The resulting lactate production and export acidifies the TME, fostering an immunosuppressive niche that promotes immune evasion and cancer cell survival C Stromal metabolic crosstalk: Metabolic symbiosis between cancer-associated fibroblasts (CAFs) and PDAC cells is depicted. This cross-talk, involving the exchange of metabolites such as amino acids, lipids, and byproducts, creates a synergistic ecosystem that fuels tumor progression. Key integrated pathways include the serine-glycine-one-carbon (SGOC) cycle and lipid metabolism D Nutrient scavenging pathways such as autophagy (including lipophagy) and macropinocytosis are activated to degrade intracellular components and engulf extracellular proteins, providing essential biomolecules to sustain biosynthesis and energy production. This process is regulated by signals such as IPO8 that link nutrient sensing to transcriptional programs, ensuring metabolic flexibility. (CDKN2A: Cyclin Dependent Kinase Inhibitor 2 A, Rb: Retinoblastoma, KRAS: Kirsten Rat Sarcoma, PI3K: Phosphatidylinositol 3-Kinase, AKT: v-Akt Murine Thymoma Viral Oncogene Homolog (Protein Kinase B), mTOR: Mechanistic (Mammalian) Target of Rapamycin, TP53: Tumor Protein p53,TIGAR: TP53-Induced Glycolysis and Apoptosis Regulator, PGK1: Phosphoglycerate Kinase 1,SMAD4: Mothers Against Decapentaplegic Homolog 4,LDHA: Lactate Dehydrogenase A, NOX4: NADPH Oxidase 4,NADH: Nicotinamide Adenine Dinucleotide, TCA: Tricarboxylic Acid (cycle), IL-6: Interleukin-6,MDSC: Myeloid-Derived Suppressor Cell, Treg: Regulatory T Cell, DC: Dendritic Cell, Th2: T Helper 2,HIF-1α: Hypoxia-Inducible Factor 1-Alpha, CAF: Cancer-Associated Fibroblast, PPP: Pentose Phosphate Pathway, HBP: Hexosamine Biosynthesis Pathway, GS: Glutamine Synthetase, MCT1: Monocarboxylate Transporter 1,FASN: Fatty Acid Synthase, FATP1: Fatty Acid Transport Protein 1,Met: Methionine, THF: Tetrahydrofolate, OXPHOS: Oxidative Phosphorylation, MiT/TFE: Microphthalmia/Transcription Factor E (family), ULK: Unc-51-Like Kinase (ULK complex)). This figure is generated using BioRender.com Key metabolic phenotypes The coordinated disruption of enzymatic pathways and signaling networks (see Fig. 2 ) allows cancer cells to reprogram both internal and external metabolic signaling to fulfil their significant energy and biosynthesis needs. This dependency has various metabolic consequences, including changes in glucose metabolism (e.g., the Warburg effect), increased glutaminolysis, lipid dependence, and scavenging pathways [ 49 ] (Fig. 2 ). The Warburg effect, or aerobic glycolysis, was first identified by Otto Warburg in 1927. He observed that cancer cells mainly rely on aerobic glycolysis rather than mitochondrial oxidative phosphorylation (OXPHOS) to generate ATP, a metabolic feature that is especially prominent in oncogene-driven PDAC [ 50 ]. Whereas PDAC cells have previously been linked to an increased reliance on the Warburg effect, recent findings suggest that this phenomenon involves high glucose uptake and lactate production despite adequate oxygen, and active mitochondrial OXPHOS, rather than a complete shift in mitochondrial function [ 51 – 53 ]. This rewiring of energy production allows cancer cells to produce ATP quickly while redirecting glycolysis toward biosynthetic pathways, such as the pentose phosphate pathway (PPP). This pathway provides ribose-5-phosphate and nicotinamide adenine dinucleotide phosphate (NADPH), essential biosynthetic intermediates that support redox balance, cancer cell growth, and disease progression [ 54 ]. Moreover, lactate accumulation from aerobic glycolysis significantly lowers the extracellular pH to 6.4–6.7 in PDAC [ 55 , 56 ]. This acidic environment enhances cancer cell aggressiveness by activating Matrix metalloproteinases (MMPs) that aid invasion and migration, while also modifying immune cell infiltration to establish an immunosuppressive microenvironment. Moreover, the metabolic reprogramming of PDAC involves not only cancer cells but also complex interactions with the TME, which is characterized by a profuse ECM, poor vascularization, and bidirectional signaling [ 57 ]. A key aspect of this dynamic is the crosstalk between cancer cells and non-malignant cells, particularly cancer-associated fibroblasts (CAFs). (Fig. 2 ) [ 58 ]. The hypothesis is that metabolic programs in CAFs promote tumor growth by providing fuel through a process called the “reverse Warburg effect” [ 59 ]. This metabolic symbiosis transforms the TME into a highly immunosuppressive environment that promotes tumor growth and metastasis. Collectively, these metabolic changes significantly influence the metastatic ability of PDAC cells and increase their resistance to standard treatments [ 60 – 62 ]. Exogenous and systemic drivers of metabolic plasticity Smoking, alcohol, and dietary influences PDAC is a complex disease influenced significantly by lifestyle factors, such as diet, tobacco smoking, and alcohol consumption, as well as environmental exposures and genetic risk, all of which drive cancer development through metabolic reprogramming [ 63 ]. Tobacco smoking is noted as an impactful modifiable risk factor (HR, 1.42 [95% CI 1.16–1.73]), accounting for about 25% of population-attributable cases, implying that smoking cessation could substantially reduce PDAC incidence [ 64 – 66 ]. The carcinogenic effect of smoking involves the direct reprogramming of cellular metabolism, which creates an environment conducive to cancer initiation and progression [ 67 ]. The pathogenesis follows a well-defined morphological sequence: recurrent injury and inflammation lead to acinar-to-ductal metaplasia (ADM), which can progress to pancreatic intraepithelial neoplasia (PanIN), the direct pre-invasive precursor [ 68 , 69 ]. These PanIN lesions, are characterized by the sequential acquisition of key genetic mutations (e.g., KRAS , TP53 , SMAD4 , CDKN2A , BRCA2 , GNAS ), which eventually advance from early, low-grade lesions (PanIN-1) to PanIN-3 (high-grade dysplasia), also known as intraductal carcinoma or carcinoma in situ, and ultimately to overt PDAC. This morphological progression, summarized in Fig. 3 , is paralleled by the stage-specific metabolic adaptations. Critically, early PanIN lesions initiate a shift toward glycolytic metabolism and altered nutrient sensing. As lesions advance to high-grade PanIN and invasive PDAC, this reprogramming intensifies, marked by upregulated glycolysis, increased lipogenesis, and enhanced scavenging pathways to support rapid proliferation and survival in the nutrient-scarce microenvironment. Finally, in established PDAC, the tumor exhibits full metabolic autonomy with dominant features such as robust metabolic reprogramming and pronounced nutrient crosstalk with the microenvironment to fuel progression, survival, metastasis, and therapy resistance (Fig. 3 ). In addition to the mutagenic effects of tobacco carcinogens, smoking promotes the irreversible formation of advanced glycation end-products (AGEs), compounds produced through the Maillard reaction between proteins or lipids and sugars such as glucose, fructose, and glyoxal [ 70 – 72 ]. These AGEs induce persistent low-grade inflammation, which may result in episodes of acute pancreatitis potentially progressing to chronic pancreatitis [ 73 ] (Fig. 4 ). This inflammatory state releases a surge of signaling molecules such as TNF-α, IL-1β, IL-6, TGF-β, and EGF, leading to ADM, an established precursor that progresses to PDAC [ 74 ]. Additionally, these cytokines stimulate intracellular pathways, including NF-κB, JAK/STAT, and HIF-1α, leading to increased expression of glucose transporters (e.g., GLUT1) and glycolytic enzymes (e.g., HK2 and LDHA) [ 35 , 75 ]. This coordinated activity drives pre-cancerous cells into a glycolytic state characteristic of the Warburg effect. Fig. 3. Open in a new tab Etiology and pathogenesis of PDAC: This schematic delineates the multistep progression from normal pancreatic exocrine tissue to metastatic PDAC. Initiated by key etiological factors (e.g., genetic predisposition, smoking, obesity, diabetes, microbial dysbiosis), the transformation begins with acinar-to-ductal metaplasia diabetes, microbial dysbiosis), the transformation begins with acinar-to-ductal metaplasia the accumulation of pivotal genetic alterations (e.g., in KRAS , TP53 , SMAD4 , CDKN2A , BRCA2 , GNAS ) and profound metabolic reprogramming. Early PanIN lesions initiate a shift toward glycolytic metabolism and altered nutrient sensing. As lesions advance to high-grade PanIN and invasive PDAC, the metabolic reprogramming intensifies, marked by enhanced glycolysis, increased lipogenesis, and enhanced nutrient-scavenging pathways to support rapid proliferation and survival within a nutrient-scarce, hypoxic, and desmoplastic microenvironment. In established PDAC, the tumor exhibits full metabolic autonomy, characterized by robust metabolic reprogramming and pronounced metabolic crosstalk with stromal and immune cells. The figure highlights how these synergistic mechanisms and features facilitate uncontrolled growth, angiogenesis, local invasion, stemness, drug resistance, and eventual metastasis, underpinning the disease’s aggressive phenotype. ( KRAS : Kirsten rat sarcoma viral oncogene homolog, TP53 : Tumor protein p53, CDKN2A : Cyclin-dependent kinase inhibitor 2 A, SMAD4 : SMAD family member 4, BRCA2 : Breast cancer 2, DNA repair associated, GNAS : G protein subunit alpha stimulating, PanIN: Pancreatic intraepithelial neoplasia, ACC: Acinar Cell Carcinoma.) This figure is generated using BioRender.com Fig. 4. Open in a new tab Integrated mechanistic model of PDAC initiation and progression driven by risk factors, metabolic disorders, and microbial dysbiosis: This schematic illustrates the convergent pathways through which risk factors promote PDAC. Non-modifiable (age, genetics) and modifiable (tobacco smoking, alcohol consumption, diet) factors contribute to metabolic syndromes (obesity, diabetes) and microbial dysbiosis. These conditions foster a pro-tumorigenic environment through distinct mechanisms: Obesity and diabetes promote pancreatitis, advanced glycation end-product (AGE) formation, and receptor for AGE (RAGE) signaling. Microbial dysbiosis enables the production of oncometabolites and pathogen-associated molecular patterns (PAMPs). Together, these signals activate key downstream pathways, including MAPK/ERK, PI3K/AKT/mTOR, JAK/STAT, and NF-κB, by activating several receptors that drive transcriptional upregulation of HIF-1α and pro-inflammatory cytokines. This leads to metabolic reprogramming, suppression of oxidative stress-induced cell death, and establishment of a hypoxic, immunosuppressive oxidative stress-induced cell death, and establishment of a hypoxic, immunosuppressive adiponectin and elevate leptin, further amplifying reactive nitrogen/oxygen species (RNS/ROS) and inflammation. The resultant interplay between metabolic dysfunction, chronic inflammation, impaired cell death, and drug resistance collectively fuels PDAC development and progression. (AGEs: Advanced Glycation End Products, AKT: Protein Kinase B, EGFR: Epidermal Growth Factor Receptor, ERK1/2:Extracellular Signal-Regulated Kinase 1/2,GLUT1:Glucose Transporter 1,GPCR: G-Protein–Coupled Receptor, GTP: Guanosine Triphosphate, HIF-1α:Hypoxia-Inducible Factor 1 Alpha, HIF-1β: Hypoxia-Inducible Factor 1 Beta, HK2 : Hexokinase 2,IKKβ:Inhibitor of Nuclear Factor Kappa-B Kinase Subunit Beta, IκB: Inhibitor of Nuclear Factor Kappa-B, IL-1β: Interleukin-1 Beta, IL-6:Interleukin-6,JAK: Janus Kinase, JNK: c-Jun N-Terminal Kinase, KRAS: Kirsten Rat Sarcoma Viral Oncogene Homolog, LDHA: Lactate Dehydrogenase A, LPS: Lipopolysaccharide, LTA: Lipoteichoic Acid, MAPK: Mitogen-Activated Protein Kinase, MEK1/2:Mitogen-Activated Protein Kinase Kinase 1/2,mTOR: Mammalian Target of Rapamycin, MyD88 :Myeloid Differentiation Primary Response 88, NADPH: Nicotinamide Adenine Dinucleotide Phosphate, NF-κB: Nuclear Factor Kappa-Light-Chain-Enhancer of Activated B Cells, PAMPs: Pathogen-Associated Molecular Patterns, PI3K: Phosphoinositide 3-Kinase, RAGE: Receptor for Advanced Glycation End Products, RAF: Rapidly Accelerated Fibrosarcoma Kinase, RAS: Rat Sarcoma Viral Oncogene Homolog, RNS: Reactive Nitrogen Species, ROS: Reactive Oxygen Species, SCFAs: Short-Chain Fatty Acids, STAT: Signal Transducer and Activator of Transcription, TLR: Toll-Like Receptor, TNFα:Tumor Necrosis Factor Alpha) This figure is generated using BioRender.com Excessive alcohol consumption (> 3 standard drinks per day) is significantly associated with increased PDAC risk (HR, 1.45 [95% CI 1.03–2.0.03.0]), whereas moderate intake generally shows no significant effect [ 76 , 77 ]. Similar to tobacco smoking, prolonged heavy alcohol consumption produces metabolic reprogramming that promotes PDAC development and progression, though distinct mechanisms [ 78 ]. Alcohol metabolism generates toxic byproducts, including acetaldehyde and fatty acid ethyl esters, which induce oxidative stress, mitochondrial damage, and DNA mutations [ 79 ]. This toxicity and stress trigger episodes of acute pancreatitis, which may evolve into chronic pancreatitis, creating a microenvironment rich in pro-inflammatory cytokines such as TNF-α, IL-6, and TGF-β [ 80 ] (Fig. 4 ). These cytokines, together with alcohol-related metabolic stresses (including NAD+/NADH imbalance and HIF-1α stabilization), activate signaling pathways such as NF-κB and STAT3 [ 75 ]. This leads to a metabolic shift toward aerobic glycolysis by upregulating GLUT1 and glycolytic enzymes (HK2, LDHA) [ 81 ]. Consequently, this reprogrammed metabolism provides the energy and biosynthetic materials necessary for initiated cells to proliferate, survive, and advance within an immunosuppressive stromal environment. Dietary habits and specific foods also significantly influence the risk and progression of PDAC, although not as directly [ 82 ]. Studies show that high intake of red and processed meats cooked at high temperatures can alter metabolic and inflammatory states, increasing PDAC risk and accelerating its development (HR, 1.38 [95% CI 1.05–1.81]) [ 83 – 85 ]. High-temperature cooked meat generates carcinogens such as heterocyclic amines (HCAs), polycyclic aromatic hydrocarbons (PAHs), and advanced glycation end-products (AGEs), which promote oxidative stress, inflammation, and insulin resistance, creating a harmful feedback loop (Fig. 4 ) [ 71 , 86 , 87 ]. Additionally, long-term consumption of calorie-, fat-, and sugar-rich diets contributes to obesity, insulin resistance, and hyperinsulinemia [ 88 ]. Elevated insulin and IGF-1 levels hyperactivate the PI3K/AKT/mTOR pathway, enhancing glycolytic traits by upregulating glucose transporters such as GLUT1 and enzymes such as HK2 and LDHA, thereby driving the Warburg effect [ 89 , 90 ]. A high-fat diet promotes the conversion of precancerous lesions to PDAC by activating KRAS-mediated COX2 pathways [ 91 ]. In obesity, adipose tissue functions as an endocrine organ, releasing pro-inflammatory cytokines such as TNF-α, IL-6, and leptin, which drive chronic low-grade inflammation that activates oncogenic pathways such as NF-κB and JAK/STAT. This inflammatory environment, together with a nutrient-rich diet providing lipids, is characterized by dyslipidaemia, with excess of very-low-density lipoprotein (VLDL), low-density lipoprotein (LDL), and triglycerides (TAGs). It supports energy production through fatty acid oxidation (FAO), nurturing the metabolic rewiring essential for PDAC progression and immune evasion [ 92 , 93 ]. Conversely, specific dietary interventions may counteract these metabolic changes. For example, ketogenic diets, high in fat and low in carbs, induce ketosis and elevate ketone bodies such as β-hydroxybutyrate and acetoacetate, which modulate tumor metabolism by reversing the Warburg effect, reducing glucose uptake (via GLUT1), glycolysis (via LDHA), and glutaminolysis, partly through downregulating c-MYC, a key oncogenic transcription factor [ 94 ]. This shift may induce cancer cell apoptosis and suppress tumor growth, while also improving cancer-related cachexia by reducing tumor-driven muscle and adipose tissue breakdown. Thus, targeting PDAC metabolism through dietary intervention holds promise as an adjunct therapy. Additionally, plant-based diets high in fiber, as well as fruits and vegetables are associated with a lower risk of PDAC, likely as they improve insulin sensitivity, provide antioxidants such as vitamins C and E and flavonoids to combat oxidative stress, and contain anti-inflammatory phytochemicals such as curcumin and resveratrol that inhibit pathways such as NF-κB [ 95 , 96 ]. Altogether, tobacco smoking, alcohol consumption, and dietary patterns act as exogenous insults that inflict genotoxic stress, acinar cell damage, and sustained pancreatic inflammation. This inflamed, injury-repair microenvironment selects for and promotes the clonal expansion of initiated cells harboring driver mutations such as KRAS . These factors catalyze the transition from normal pancreas to metabolically reprogrammed PanINs and fuel the subsequent evolution of these lesions into invasive PDAC. Therefore, lifestyle exposures are best understood as active catalysts that ignite and accelerate the multistep pathogenesis of PDAC. Comorbidities and microbial dysbiosis Comorbidities, including new-onset type 2 diabetes mellitus (T2DM), obesity, and metabolic syndrome, significantly contribute to PDAC development and progression, a link strongly supported by extensive clinical data and meta-analyses [ 5 , 97 – 100 ]. The rising global incidence of PDAC is closely linked to the epidemics of obesity and T2DM, which act as systemic metabolic disorders that create a permissive ‘soil’ for tumorigenesis [ 90 ]. These conditions do not simply co-occur with PDAC; they actively remodel the pancreatic microenvironment from its earliest pre-cancerous stages [ 90 ]. By establishing a state of chronic hyperinsulinemia, adipose inflammation, and nutrient excess, they lower the barrier for oncogenic transformation, support the metabolic dependencies of emerging PanIN lesions, and later fuel the aggressive autonomy of invasive PDAC. Notably, new-onset diabetes in older adults significantly increases PDAC risk, with a 7–8-fold rise, and up to 80% of patients exhibit hyperglycemia or diabetes at diagnosis [ 101 – 103 ]. Additionally, even prediabetes, characterized by blood glucose levels above 7 mmol/L that do not fulfil T2DM criteria, is associated with a higher PDAC risk [ 104 , 105 ]. Previously, obesity-related PDAC was thought to involve imbalances in adipokine levels with lower adiponectin and higher leptin, which activate pro-oncogenic pathways [ 106 , 107 ]. Recent evidence indicates obesity also promotes PDAC through mechanisms such as immunosuppression and tumor progression driven by inflammatory signaling and ferroptosis defense, especially in the presence of oncogenic KRAS [ 108 , 109 ]. Critically, beyond elevating risk, obesity is associated with worse PDAC-specific survival and higher mortality, underscoring its role in aggressive disease biology [ 110 ]. Preclinical studies provide direct causal evidence: diet-induced obesity (DIO) markedly accelerates the progression of low-grade to high-grade PanIN and overt PDAC in KC mice ( LSL- K ras G12D ; p48- C re mice), driven by enhanced cell proliferation and sustained inflammation [ 111 , 112 ]. Conversely, interventions that mitigate obesity such as the antidiabetic drug metformin or increased physical activity significantly reduce PDAC development in this model [ 113 , 114 ]. These comorbidities not only occur alongside PDAC but also contribute to other systemic metabolic conditions (e.g., hyperinsulinemia and chronic inflammation) that promote PDAC progression. This process is primarily driven by endocrine imbalances in the pancreas, leading to the accumulation of nutrients and metabolites, as seen in obesity and T2DM [ 115 ]. This results in AGE/RAGE (Receptor of AGE) crosstalk, which reprogrammes metabolism and modifies the pancreatic microenvironment, triggering oncogenic pathways, inflammation, and angiogenesis [ 72 , 90 ] (Fig. 4 ). RAGEs inhibit cell death and autophagy, aiding cancer cell survival, while increasing HIF-1α promotes hypoxia and further angiogenesis and inflammation. Hence, nutrients and growth factors create a highly inflammatory and immunosuppressive microenvironment that favors PDAC progression [ 90 , 116 , 117 ]. Obesity and dyslipidemia further accelerate progression through mechanisms like abnormal cholecystokinin (CCK) expression and high cholesterol increasing metastatic potential via AKT phosphorylation [ 115 ]. Microbial dysbiosis in the oral and gut microbiomes is emerging as a factor in pancreatic diseases, including PDAC; altered microbial diversity and community structure are linked to disease development, progression, and reduced chemotherapy efficacy ( Table 1 ) [ 130 – 135 ]. Population studies associate certain oral microbes with higher PDAC risk, and mechanistic insights reveal that microbiota imbalances promote cancer progression by disrupting metabolism, evading immune responses, and sustaining inflammation [ 136 ]. Dysbiosis damages the gut barrier, allowing pathogen-associated molecular patterns (PAMPs), such as LPS, flagellin, and bacterial DNA, to enter the circulation, thereby activating immune receptors, such as TLR4, and triggering inflammatory pathways and cytokine release [ 137 ]. This inflammatory milieu influences metabolic rewiring, stabilizing HIF-1α and activating PI3K/AKT/mTOR, which enhances glycolysis. Microbiota also directly affects therapeutic responses, with bacteria such as Gammaproteobacteria capable of degrading gemcitabine via bacterial enzymes [ 138 ], while some microbial metabolites may enhance chemotherapy effectiveness and improve prognosis in PDAC [ 139 ]. Overall, comorbidities such as obesity and diabetes establish a systemic permissive state characterized by chronic hyperinsulinemia, adipose inflammation, and altered adipokine signaling. This state does not simply increase background risk; it actively remodels the pancreatic microenvironment, fostering low-grade inflammation and providing a continuous supply of lipids and growth signals. This metabolically primed ‘soil’ dramatically lowers the barrier for oncogenic transformation, supports the early metabolic dependencies of emerging PanINs, and later contributes to the tumor’s aggressive metabolic autonomy and therapy resistance. Thus, these conditions are integral to the metabolic foundation upon which PDAC progresses (Fig. 3 ). Table 1. Overview of effects of microbiota in PDAC Bacteria Number Origin Role Function Reference Pseudomonas ↑ Pancreatic tissue Correlated with the host’s antibacterial immune response and tumor signaling pathways (Kras, MAPK, Wnt/β-catenin). Metabolic reprogramming (+); Immunosuppression (+); Drug resistance (+) [ 118 , 119 ] Elizabethkingia ↑ Pancreatic tissue Tumor promoting microbial dysbiosis. Microbial dysbiosis (+) [ 118 ] F usobacterium ↑ Pancreatic tissue Induce pro-inflammatory cytokines (GM-CSF, CXCL1, IL-8); Activate autocrine and paracrine signals. Proliferation (+); Migration (+); Invasion (+) [ 120 ] Alpha diversity; Pseudoxanthomonas; Streptomyces; Saccharopolyspora; Bacillus clausii ↓ Pancreatic tissue The microbiota is dominated by a limited number of bacteria, including both tolerant and potentially pathogenic species. Inflammation (+); Immunosuppression (+) [ 121 ] Porphyromonas gingivalis ↑ Oral Directly infects tumor cells, enhancing their proliferation and survival; Drives pancreatic epithelial cells toward PanIN transformation༛Modulates immunity (e.g., neutrophil elastase). Oxidative stress (-); EMT (+); Immunosuppression (+) [ 122 – 124 ] Aggregatibacter actinomycetemcomitans ↑ Oral The degradation of gemcitabine by cellular tryptophan deaminase (CDD). Drug resistance (+) [ 125 ] Neisseria elongata; Streptococcus mitis ↓ Saliva Impossible to maintain the balance of the oral microecology. Inflammation (+); Immunosuppression (+) [ 126 ] Granulicatella adiacens ↑ Saliva Triggers chronic inflammatory responses and promotes the formation of TME. Inflammation (+); Immunosuppression (+) [ 127 ] Leptotrichia ↑ Saliva Triggers chronic inflammatory responses and promotes the formation of TME. Inflammation (+); Immunosuppression (+) [ 128 ] Porphyromonas; Neisseria ↓ Saliva Triggers chronic inflammatory responses and promotes the formation of TME. Inflammation (+); Immunosuppression (+) [ 128 ] Firmicutes ↑ Saliva Triggers chronic inflammatory responses and promotes the formation of TME. Inflammation (+); Immunosuppression (+) [ 129 ] Haemophilus; Porphyromonas; Leptotrichia; Fusobacterium ↑ Saliva Triggers chronic inflammatory responses and promotes the formation of TME. Inflammation (+); Immunosuppression (+). [ 120 ] Bacteroidetes ↑ Faeces Affects the host’s immune system through its metabolic products. Inflammation (+); Immunosuppression (+) [ 129 ] Firmicutes; Proteobacteria ↓ Faeces Triggers chronic inflammatory responses and promotes the formation of TME. Inflammation (+); Immunosuppression (+) [ 129 ] Proteobacteria ; Synergistetes ; Euryarchaeota ↑ Faeces Triggers chronic inflammatory responses and promotes the formation of TME. Inflammation (+); Immunosuppression (+) [ 118 ] Open in a new tab † NCT: GM-CSF: Granulocyte-Macrophage Colony-Stimulating Factor; CXCL1: C-X-C motif chemokine ligand 1; EMT: Epithelial–Mesenchymal Transition Dysregulation of core metabolic pathways in PDAC Altered glucose metabolism Metabolic reprogramming is a hallmark of cancer with most tumors, including PDAC, exhibit altered glucose, amino acid (AA), and lipid metabolism. Glucose metabolism, in particular has been extensively studied as part of the rewired metabolic network [ 140 , 141 ]. The shift in glucose metabolism, known as aerobic glycolysis or the Warburg effect, is apparently the predominant metabolic change in PDAC cells, providing energy necessary for progression [ 142 – 144 ]. This form of glycolysis gives PDAC cells a notable advantage: it produces ATP more rapidly than (OXPHOS) by increasing glycolytic flux [ 145 , 146 ]. This rapid ATP generation is crucial for meeting the high energy (ATP/ADP) ratio demands of unrestrained cell growth. In addition to energy production, aerobic glycolysis supports rapid biosynthesis by diverting glycolytic intermediates into anabolic pathways such as the hexosamine biosynthesis pathway (HBP) and the nonoxidative PPP, supplying components for nucleotides, proteins, and lipids [ 34 , 47 ] (Fig. 5 ). It also reduces electron flow through the mitochondrial electron transport chain (ETC), thereby limiting excessive production of reactive oxygen species (ROS) and helping maintain redox balance, thereby preventing oxidative damage [ 147 ]. Central to this reprogramming is the oncogenic KRAS mutation, which persistently activates downstream effectors such as PI3K and RAF, promoting glucose uptake, aerobic glycolysis, and rerouting of intermediates into the HBP and PPP [ 40 , 148 ]. This increased glucose uptake is mainly due to higher expression of glucose transporters, such as GLUT1, which is increased in expression in PDAC cells [ 40 , 47 ] (Fig. 5 ). The enhanced glycolytic flux is controlled by key rate-limiting enzymes, including hexokinase 1/2 (HK1/2), phosphofructokinase 1 (PFK1), and lactate dehydrogenase A (LDHA) [ 40 , 149 ]. HK1/2 initiates glycolysis by converting glucose to glucose-6-phosphate (G-6-P), with the HK2 isoform being particularly overexpressed in PDAC cells; its mitochondrial binding enhances glycolysis and supports PDAC cell survival [ 150 ]. High HK2 levels also correlate with shorter survival in PDAC patient [ 151 ]. PFK1, which converts fructose-6-phosphate (F6P) into fructose-1,6-bisphosphate, is also elevated in PDAC [ 152 – 154 ]. Pyruvate kinase (PK) completes the third rate-limiting step, producing pyruvate and ATP from phosphoenolpyruvate (PEP) and ADP [ 34 , 155 , 156 ]. Among its four isoforms (M1, M2, L, R), the PKM2 isoform is the most prevalent and plays a significant role in progression of PDAC. However, the precise role of PKM2 in PDAC pathogenesis remains complex and is sometimes debated. For example, studies show that oxidative activation of PKM2 by methionine metabolism can enhance metastatic behavior in PC [ 157 ]. Conversely, some experimental models suggest that PKM2 expression might not be essential for PDAC tumor formation or progression [ 158 ]. Nevertheless, PKM2 is frequently overexpressed in PDAC tissues, and suppressing its gene expression can decrease tumor growth. This anti-tumor activity results from interfering with cell-cycle regulators, particularly those controlling the G1-S phase transition, and altering metabolic pathways [ 159 ]. In addition to these rate-limiting enzymes, others such as aldolase, glyceraldehyde 3-phosphate dehydrogenase (GAPDH), phosphoglycerate kinase (PGK), phosphoglycerate mutase (PGAM), and alpha enolase (ENO-1) are also upregulated in PDAC cells, ensuring high glycolytic throughput [ 160 – 164 ]. The final step converts pyruvate to lactate, and is driven by increased LDHA expression in PDAC cells [ 165 ]. Collectively, increased expression of these enzymes in PDAC cells intensifies the Warburg effect, pushing glucose toward lactate production. To prevent intracellular acidification from lactate production, PDAC cells express CD147, which promotes the expression of monocarboxylate transporters (MCTs), mainly MCT4, to actively export lactate out of the cell [ 166 – 169 ] (Fig. 5 ). This lactate removal contributes to extracellular acidosis, remodeling the microenvironment to favor PDAC cell invasion, immune suppression, and chemoresistance [ 149 , 170 ]. Fig. 5. Open in a new tab Overview of Metabolic Pathways Involved in Glucose, Lipid, and Glutamine Metabolism in PDAC Cells: This schematic illustrates the key anabolic and catabolic pathways that PDAC cells co-opt to fuel proliferation and survival in a nutrient-scarce tumor microenvironment. Oncogenic drivers (e.g., KRAS , HIF-1α, c-MYC , PI3K/AKT/mTOR) promote a metabolic shift characterized by enhanced glucose uptake (via GLUT1) and glycolytic flux, glutaminolysis (via SLC1A5 and GLS), and macropinocytosis for nutrient acquisition. These pathways converge to supply carbon and nitrogen precursors for biosynthesis. Key nodes include: Glycolysis: Generates ATP, pyruvate, and precursors. Pyruvate can be converted to acetyl-CoA to feed the TCA cycle. Lipid Metabolism: Features fatty acid (FA) uptake (via CD36/FATP), oxidation (FAO) for energy, and de novo synthesis (via FASN and ACC) for membrane production. Lipid droplets and prostaglandin E2 (PGE2) synthesis are also critical. Glutaminolysis: Provides α-ketoglutarate (α-KG) to anaplerotically replenish the TCA cycle. Redox Homeostasis: Malic enzyme 1 (ME1) and malate dehydrogenase 1 (MDH1) generate NADPH to maintain glutathione (GSH) levels and mitigate oxidative stress. The integration of these pathways enables PDAC cells to adapt to metabolic stress, support rapid growth, and resist therapy. (GLUT1: Glucose transporter 1, HK: Hexokinase, GPI: Glucose-6-phosphate isomerase, PFK1: Phosphofructokinase 1, TPI: Triosephosphate isomerase, GAPDH: Glyceraldehyde-3-phosphate dehydrogenase, PGK: Phosphoglycerate kinase, PGAM: Phosphoglycerate mutase, PK: Pyruvate kinase, FA: Fatty acid, FATP: Fatty acid transport protein, CD36: Cluster of differentiation 36 (fatty acid translocase), FAO: Fatty acid oxidation, FASN: Fatty acid synthase, ACC: Acetyl-CoA carboxylase, COX: Cyclooxygenase, PGE2: Prostaglandin E2, BCAAs: Branched-chain amino acids, SLC1A5: Solute carrier family 1 member 5 (glutamine transporter), GLS: Glutaminase, Asp: Aspartate, OAA: Oxaloacetate, LDL: Low-density lipoprotein, LDLR: Low-density lipoprotein receptor, ACAT: Acyl-CoA cholesterol acyltransferase, MDH1: Malate dehydrogenase 1 (cytoplasmic), ME1: Malic enzyme 1 (cytoplasmic), GSSG: Oxidized glutathione, GSH: Glutathione.) This figure is generated using BioRender.com The altered glycolytic program in PDAC cells is coordinated by the oncogenic KRAS mutation, which cooperates with other genetic alterations to optimize the metabolic phenotype [ 171 ]. For instance, mutant TP53 (loss-of-function) increases PON2 expression, promoting glucose uptake and inhibiting AMPK, thereby enhancing glycolytic flux [ 45 ]. Concurrently, mutant TP53 represses TIGAR, leading to the accumulation of the potent glycolytic activator fructose-2,6-bisphosphate in PDAC cells [ 43 , 145 ]. Furthermore, specific TP53 mutants (e.g., R273H) disrupt AA and fatty acid (FA) metabolism and prevent cytoplasmic degradation of GAPDH, increasing cellular sensitivity to glycolysis inhibitors such as 2-deoxyglucose (2-DG) [ 160 , 172 ]. SMAD4 loss also introduces another layer of complexity by reprogramming central carbon metabolism. A pivotal mediator in this context is PGK1, which is upregulated upon SMAD4 deletion [ 46 ] (Fig. 2 ). In SMAD4 -deficient cells, PGK1 exhibits a dichotomous, compartment-specific role: its cytoplasmic activity drives glycolysis to fuel proliferation, whereas its nuclear translocation promotes mitochondrial OXPHOS and activates pro-metastatic transcriptional programmes [ 46 ]. Notably, this subcellular localization is regulated upstream by mutant KRAS signaling, which stimulates the mitochondrial translocation of PGK1. This leads to phosphorylation of PDHK1 and subsequent restriction of OXPHOS, illustrating a precise, multi-driver control node that fine-tunes metabolic phenotype in PDAC cells [ 173 ]. This genetic network is further modulated by transcription factors such as FOXM1, which regulates LDHA, and is profoundly influenced by the TME [ 174 ]. Epigenetic regulation also plays a key role; expression of the ubiquitin-like protein UHRF1 (ubiquitin 1 similar to plant homologous domain and ring finger domain) is increased in PDAC cells and promotes aerobic glycolysis by both elevating HIF1α levels and directly inhibiting the mitochondrial tumor suppressor SIRT4 [ 175 ]. This UHRF1/SIRT4 axis represents a distinct layer of control over glycolytic flux and cell proliferation, highlighting its potential as a therapeutic target. Beyond cell-intrinsic genetic drivers, the enhanced aerobic glycolysis in PDAC cells is further driven by a hypoxic and fibrotic microenvironment [ 34 , 176 ]. Hypoxia, common in PDAC due to desmoplastic fibrosis and poor vascularization, stabilizes HIF-1α, which then translocates to the nucleus and upregulates glycolytic genes. HIF-1α synergizes with mutant KRAS to enhance glycolytic flux, maintain cytosolic ATP levels, and increase the expression of HBP enzymes, such as GFPT2 [ 177 ]. Simultaneously, HIF-1α suppresses mitochondrial oxidation by downregulating pyruvate dehydrogenase, thereby limiting carbon flow into the TCA cycle [ 178 ]. This hypoxic adaptation creates a cycle where increased glycolysis and lactate export acidify the TME, further stabilizing HIF-1α and promoting invasive, therapy-resistant cell states. In addition to hypoxia, inflammatory signals from the TME also regulate glycolysis. The cytokine MIF, highly expressed in PDAC, links inflammation to metabolism by activating the MAPK/ERK pathway, thereby upregulating glycolytic enzymes such as HK1, HK2, and LDHA, which promote glucose uptake and lactate production. A negative feedback loop involving MIF-induced NR3C2, which interacts with AP-1 (c-JUN/c-FOS), can repress these same glycolytic genes [ 148 ]. This demonstrates that PDAC’s glycolytic reliance results not only from oncogenic mutations but also from the interaction of core pathways, transcriptional regulators, and adaptive inflammatory-metabolic circuits like MIF/NR3C2, especially within a hypoxic, nutrient-limited environmental niche. Dysregulated lipid metabolism Reprogramming lipid metabolism is a key aspect of PDAC progression, providing essential components for rapid membrane growth, signaling molecules, and substrates for protein modifications [ 179 , 180 ]. PDAC cells are metabolically flexible, acquiring lipids both from external sources and through internal ( de novo ) synthesis [ 181 , 182 ] (Fig. 5 ). Unlike normal cells that mainly depend on dietary lipids, PDAC tumors generate about 93% of their fatty acids (FAs) and cholesterol through de novo synthesis from mitochondrial citrate, indicating a shift towards autonomous production. This lipogenesis occurs even when external lipids are available, driven by the upregulation of several enzymes such as citrate synthase (CS), ATP-citrate lyase (ACLY), acetyl-CoA carboxylase (ACC), fatty acid synthase (FASN), and HMG-CoA reductase (HMGCR) that convert citrate into palmitate [ 183 ]. Palmitate is then used to produce more complex FAs, build plasma membranes, and modify proteins through palmitoylation. The pathway is influenced by enzymes such as stearoyl-CoA desaturase (SCD1) and elongases, which regulate FA saturation and chain length. High SCD1 levels are linked to worse outcomes in PDAC and aid cancer cell survival against ferroptosis [ 184 , 185 ]. FASN, essential for palmitate synthesis, is often increased in expression and associated with poor prognosis in patients with PDAC [ 186 ]. Besides enzyme-driven lipogenesis, glucose and glutamine (Gln) contribute to lipid synthesis as well [ 187 ]. Glucose-derived pyruvate is oxidized in mitochondria to produce citrate, a key lipogenic precursor [ 188 ]. Gln also supports this process via reductive carboxylation, providing an alternative route to generate citrate for lipogenesis [ 188 ]. Recent studies show that inhibiting lysosomal function by targeting the lipid kinase PIKfyve forces PDAC cells to compensate by increasing de novo lipid synthesis [ 189 ]. This metabolic state, driven by the KRAS-MAPK pathway, creates a vulnerability wherein PDAC cells become highly sensitive to combined PIKfyve and KRAS-MAPK inhibition. Additionally, PDAC cells increase the uptake of external lipids, enhancing their metabolic flexibility. This uptake involves transporters such as fatty acid transport proteins (FATPs), CD36, and fatty acid-binding proteins (FABPs) [ 185 , 190 ]. Notably, CD36 expression varies during PDAC progression; lower levels are linked to advanced stages and chemoresistance [ 191 ]. Excess external FAs are primarily stored in lipid droplets (LDs), which store FAs as triacylglycerides (TAGs) and sterol esters for energy or phospholipid synthesis [ 192 ]. Higher LD levels are associated with tumor aggressiveness, whereas reducing LDs can decrease the invasiveness of KRAS-mutant PDAC [ 193 ]. PDAC cells selectively utilize different FA types: saturated (SFAs) and monounsaturated FAs (MUFAs) mainly support membrane formation and signaling. In contrast, polyunsaturated FAs (PUFAs) are potent signaling modulators with dichotomous roles. Specifically, ω−6 PUFAs promote PDAC proliferation in experimental models, an effect mechanistically linked to activation of the AKT pathway. Conversely, ω−3 PUFAs inhibit the proliferation of PDAC cells and suppress tumor progression by antagonizing AKT activation [ 194 ]. FAs are also vital energy sources through mitochondrial FAO, known as β-oxidation [ 195 ]. PDAC cells heavily rely on FAO, which may become their primary energy source, especially under nutrient limitation [ 196 ]. This reliance begins early, as seen in precursor lesions like intraductal papillary mucinous neoplasms (IPMNs), wherein oncogenic GNAS reprograms lipid metabolism to enhance FAO via PKA-driven suppression of salt-inducible kinases (Sik1, Sik2, and Sik3) [ 197 ]. FAO plays central role in advanced PDAC. To support this metabolic reliance, the enzyme NADPH quinone dehydrogenase 1 (NQO1) is often increased in PDAC cells, and higher levels of NQO1 are associated with worse prognosis in patients with PDAC [ 198 , 199 ]. Studies using PDAC cell lines and xenograft models show that NQO1 facilitates PDAC progression by stabilizing CPT1A, which is the key enzyme controlling FAO [ 200 ]. As a result, the NQO1/CPT1A/FAO axis is recognized as a promising therapeutic target, highlighting the important role of FAO in PDAC metabolism. Cholesterol metabolism is vital for PDAC development and progression, contributing not just to lipogenesis, but also maintaining membrane integrity, fluidity, and regulating oncogenic signaling [ 181 ]. PDAC cells rely heavily on cholesterol, acquiring it through increased uptake and de novo synthesis. Low -density lipoprotein receptor (LDLR) primarily facilitates this uptake; LDL particles are endocytosed via LDLR and hydrolyzed in lysosomes to release free cholesterol (Fig. 5 ). High LDLR levels are associated with recurrence and poor prognosis in patients with PDAC, highlighting the pathological significance [ 201 ]. Simultaneously, cholesterol de novo biosynthesis is achieved through the mevalonate pathway, involving enzymes such as acetyl-CoA acetyltransferase (ACAT), HMGCR, squalene monooxygenase (SM), and sterol-O-acyltransferase (SOAT1) [ 202 ]. This pathway is enhanced by increased expression of aldo-keto reductase 1B10 (AKR1B10) in PDAC cells, which converts lipid substrates into farnesyl and geranylgeranyl pyrophosphates- key intermediates for cholesterol synthesis and oncoprotein activation [ 203 ]. Cholesterol also serves as a precursor for steroid hormones and can be converted into bioactive oxysterols [ 90 ]. These metabolites regulate transcription factors including sterol regulatory element-binding proteins (SREBPs), which increase cholesterol production and uptake genes, and liver X receptors (LXR), which promote cholesterol removal [ 204 , 205 ]. Disruption of this SREBP/LXR pathway, common in PDAC, impacts tumor growth, DNA repair, and inflammation. PDAC cells store excess free cholesterol as cholesteryl esters via ACAT-1 (SOAT1), maintaining cholesterol balance and preventing toxicity [ 206 ] (Fig. 5 ). The reliance on cholesterol metabolism in PDAC is driven by specific genetic events. In preclinical models, TP53-mutant PDAC with loss of heterozygosity (LOH) evades cholesterol feedback inhibition by upregulating SOAT1 [ 207 ]. Selective inhibition of SOAT1 impairs the growth of these PDAC organoids and tumors in vivo, revealing a genotype-specific vulnerability and a therapeutic target distinct from general cholesterol synthesis inhibition. However, depleting cholesterol systemically can surprisingly increase the aggressiveness of PDAC. Preclinical studies indicate that blocking cholesterol synthesis (e.g., with statins) activates the SREBP1-TGFβ pathway, leading to a basal-like/EMT (Epithelial-to-mesenchymal) phenotype. This may clarify why statin trials have not been successful [ 208 ]. Targeted SOAT1 inhibition could be a potential therapy, whereas broadly blocking synthesis may lead to metastasis. Overall, PDAC’s reliance on lipids is complex, involving multiple interconnected and altered pathways including de novo lipogenesis, external lipid uptake, cholesterol reprogramming, and FAO. This metabolic network presents multiple vulnerabilities. Nonetheless, therapeutic interventions must be precise, as shown by the unintended effects of widespread cholesterol inhibition. Therefore, effective treatment will depend on mapping specific lipid dependencies to the genetic and metabolic subtypes of PDAC (discussed in Sect. 6) to exploit metabolic vulnerabilities accurately without provoking adaptive resistance. Reprogrammed amino acid metabolism Emerging evidence indicates that AA metabolism is significantly altered in PDAC to support rapid cell proliferation, maintain redox balance, and suppress the immune response [ 209 – 211 ]. Gln, the most abundant nonessential AA in humans serves as the main source of carbon and nitrogen for anaplerosis of the TCA cycle [ 212 ] (Fig. 5 ). Gln is essential and plays a key role in PDAC progression through redox homeostasis. PDAC utilizes a unique, KRAS-driven non-canonical pathway to process Gln [ 213 ]. In this pathway, KRAS activation suppresses glutamate dehydrogenase (GDH) and promotes a transaminase-based route. The process starts with mitochondrial glutaminase (GLS1) converting Gln into glutamate (Glu). Instead of being deaminated by GDH, this Glu combines with oxaloacetate (OAA) via mitochondrial aspartate transaminase (GOT2) to produce aspartate (ASP) and α-ketoglutarate (α-KG) through glutaminolysis. ASP is then transported to the cytoplasm, where it is converted back to OAA by upregulated cytoplasmic aspartate transaminase (GOT1) [ 214 , 215 ]. This OAA is subsequently reduced to malate-by-malate dehydrogenase 1 (MDH1) and then decarboxylated to pyruvate by malic enzyme (ME1) [ 213 ]. This series of reactions is crucial, as it generates NADPH, which is essential for redox balance by reducing ROS, a vital survival mechanism in the stressful, acidic PDAC TME [ 213 ]. The regulation of the pathway is tightly controlled by enzymes such as coactivator-associated arginine methyltransferase 1 (CARM1), which methylate MDH1, thereby inhibiting its activity and suppressing Gln metabolism [ 216 , 217 ]. Gln uptake is facilitated by specific transporters, notably a variant of SLC1A5 whose expression is induced under hypoxia via HIF-2α activation. Whereas PDAC cells may not heavily rely on Gln for ATP under normoxic, glucose-rich conditions, this transporter variant becomes critical under hypoxia or glucose deprivation, aiding Gln uptake for NADPH production and glutathione (GSH) synthesis [ 218 ]. This mechanism significantly contributes to gemcitabine resistance, as GSH neutralizes oxidative stress caused by the drug [ 219 ]. Recent studies indicate that in PDAC, the tetraspanin CD9, which marks tumor-initiating cells (TICs), promotes the plasma membrane localization of the Gln transporter ASCT2. This increased Gln uptake supports the metabolic needs of TICs, thereby contributing to tumor initiation, progression, and resistance to GLS inhibitors [ 220 ]. Beyond Gln, other AAs including arginine, serine, tryptophan, and methionine, also contribute to PDAC progression, stemness, chemoresistance, and immunosuppression [ 149 , 221 , 222 ]. For instance, arginine is metabolized by nitric oxide synthases (NOS1-3) into nitric oxide (NO), which promotes PDAC cell growth in KRAS-mutant contexts; this effect can be diminished by inhibiting NOS3 [ 223 ]. Arginine deprivation reduces migration, invasion, and EMT of PDAC cells [ 221 ]. Tryptophan breakdown, driven by indoleamine 2,3-dioxygenase 1 (IDO1), decreases local tryptophan levels and produces kynurenine, an AhR ligand that fosters immunosuppressive Tregs and M2 macrophages [ 224 ]. Additionally, the RNA-binding protein BICC1 supports a stem-like, therapy-resistant PDAC phenotype by upregulating IDO1, diverting tryptophan catabolism towards NAD+ synthesis to promote OXPHOS [ 32 ]. Methionine, required for S-adenosylmethionine (SAM) production, influences epigenetic modifications that reinforce an immunosuppressive TME. The enzyme methionine sulfoxide reductase A (MSRA) suppresses metastasis; its loss oxidizes methionine residue M239 on PKM2, thereby increasing PDAC metastasis [ 225 ]. Proline metabolism also aids adaptation under nutrient stress, with proline dehydrogenase (PRODH1) upregulated in PDAC cells to support survival under low-glucose or Gln-limited conditions [ 226 , 227 ]. PDAC exhibits liver-like urea cycle activity, breaking down arginine via enzymes such as ARG2, which is upregulated by obesity or persistent AKT signaling, thereby reducing extracellular arginine and serine, impairing T cell function, and promoting immunosuppressive iNOS+/Arg1 + MDSCs and TAMs, that inhibit cytotoxic T cells [ 228 ]. Research indicates that branched-chain amino acids (BCAAs), leucine, isoleucine, and valine, are essential for PDAC progression [ 229 ]. These AAs are imported via transporters such as SLC7A5, SLC7A8, and SLC43A1/2, with plasma levels elevated early in the disease [ 230 ]. The catabolism of these AAs, driven by the enzyme BCAT2, is vital for PDAC progression, particularly in KRAS-driven lesions. Mechanistically, BCAT2 catalyses the first step in BCAA breakdown, transferring nitrogen to α-KG to regenerate Glu. This Glu pool is crucial for de novo nucleotide synthesis, providing the building blocks (purines and pyrimidines) required for the rapid proliferation of PDAC cells [ 229 , 231 , 232 ]. PDAC cells also acquire AAs via micropinocytosis, a scavenging process, by degrading extracellular albumin to release free AAs, highlighting the role of nutrient scavenging in PDAC progression. This metabolic reprogramming is supported by stromal cells, which provide metabolites such as acetate to fuel AA-derived pathways like polyamine metabolism, highlighting tumor-stroma metabolic cooperation [ 233 ]. Overall, altered AA metabolism in PDAC promotes tumor stress resilience and creates an immunosuppressive environment. Targeting these pathways, particularly the non-canonical Gln route, offers a promising approach to both impairing PDAC progression and also addressing therapeutic resistance. Metabolic plasticity, crosstalk, and adaptation Intratumoral metabolic crosstalk and heterogeneity PDAC cells demonstrate remarkable metabolic adaptability to fulfil their significant energy and biosynthetic demands. Rather than simply upregulating individual pathways such as glycolysis, glutathione synthesis, and lipid biosynthesis, these cells coordinate these processes through extensive metabolic crosstalk. This layered regulatory system not only meets bioenergetic requirements but also actively supports tumor growth by enabling rapid proliferation, resistance to oxidative stress, increased motility, invasion, and immune evasion [ 234 ]. A key example of this flexibility is the strategic diversion of glucose-derived carbon, following enhanced glycolysis, into branching anabolic pathways like the PPP and HBP, which facilitate macromolecular synthesis and cell growth [ 217 ]. The PPP, a vital pathway branching from glycolysis, provides essential intermediates for nucleotide production and redox balance [ 47 ]. It operates through two interconnected arms: the oxidative arm, which oxidizes glucose-6-phosphate to generate ribose-5-phosphate and NADPH, crucial for redox homeostasis and FA synthesis, and the non-oxidative arm, which involves reversible reactions that produce ribose-5-phosphate for nucleotides and can feed intermediates back into glycolysis. These arms function independently, as the non- oxidative branch does not produce NADPH. In PDAC, oncogenic reprogramming causes a preferential and vital dependence on the non- oxidative arm. Studies show that oncogenic KRAS increases glucose flux through this branch by upregulating key enzymes such as ribulose-5-phosphate isomerase (RPIA) and ribulose-5-phosphate-3-epimerase (RPE) [ 41 ]. The Ronald group demonstrated that inhibiting these enzymes impairs clonogenicity and tumor progression, highlighting the critical role of the PPP [ 47 ]. In advanced, metastatic PDAC, this metabolic rewiring serves two key purposes that have been selected through evolution. First, it efficiently provides ribose-5-phosphate for nucleotide production, supporting rapid cell growth. Second, it directs metabolic flux specifically through the oxidative pentose phosphate pathway (oxPPP), which is epigenetically reprogrammed in metastatic subclones. This oxPPP activity is essential not only for producing NADPH to maintain redox homeostasis but also for preserving the malignant epigenetic state that promotes tumor growth in distant metastases [ 235 ]. Beyond oncogene-driven regulation, the PPP also responds dynamically to the TME. For instance, extracellular acidosis prompts glucose rerouting into the PPP, decreasing glycolytic flux and increasing cellular ATP levels. Elevated ATP inhibits the AMPK/HIPPO signaling pathway, which, in turn, activates YAP/TAZ transcription factors, leading to increased expression of MMPs (e.g., MMP1) that promote invasion and metastasis in PDAC. This complex crosstalk, where glycolysis affects the environment (causing acidosis), reprogrammes central carbon metabolism (including the PPP shift), and modulates cell signaling pathways (such as AMPK/HIPPO/YAP), demonstrates the intricate, layered network driving PDAC progression [ 236 ]. Another key glucose-responsive pathway in PDAC is the HBP, a central hub for metabolic crosstalk [ 149 , 217 , 237 ]. When KRAS is activated, the HBP integrates primary metabolic products from glycolysis, AA metabolism, FA metabolism, and nucleotide synthesis- specifically using F6P, Gln, acetyl-CoA, and UTP to produce UDP-GlcNAc. This molecule performs multiple roles, including intracellular signaling and post-translational modifications (PTMs) [ 238 ]. The rate-limiting step is catalyzed by GFPT1, which is upregulated in PDAC in a KRAS -dependent manner. UDP-GlcNAc is widely used for protein modification via O-GlcNAcylation, a reversible process regulated by OGT and OGA [ 239 ]. In PDAC, excessive O-GlcNAcylation alters cellular signalling by modifying key oncogenic proteins, including Notch1, SOX2, SIRT7, and NF-κB, thereby promoting PDAC cell stemness, survival, and inflammation [ 240 – 243 ]. Moreover, this modification creates feedback loops with core metabolism; for example, O-GlcNAcylation of PFK1 can reduce glycolytic flux [ 244 , 245 ]. Overall, the HBP exemplifies extensive metabolic crosstalk, translating nutrient signals into broad cellular changes that support tumor progression. The mevalonate pathway is also central to the metabolic network of PDAC, acting as a key hub for crosstalk between lipid metabolism, oncogenic signaling, and the TME [ 207 ]. Beyond cholesterol production, it generates vital isoprenoids such as farnesyl pyrophosphate (FPP) and geranylgeranyl pyrophosphate (GGPP), which prenylate and activate important oncoproteins including KRAS, YAP/TAZ, and Hedgehog pathway components. This forms a feed-forward loop wherein mutant KRAS enhances the mevalonate pathway activity, which then supports KRAS -driven signaling. The pathway also intersects with other metabolic branches, supplying dolichol for protein N-glycosylation, thus linking lipid flux to the HBP and post-translational modifications (PTMS) [ 246 ]. This multifaceted crosstalk makes the mevalonate pathway a promising therapeutic target- its inhibition can disrupt lipid synthesis, downstream signaling, and immune adaptations that contribute to PDAC progression and resistance to chemotherapy. The complex interactions and regulatory flexibility among glucose partitioning into the PPP and HBP, the reciprocal regulation of glycolysis and O-GlcNAcylation, and the role of the mevalonate pathway role as a signaling hub do not function uniformly. Instead, they underpin the development of distinct metabolic phenotypes or subtypes within PDAC. These interconnected networks tend to favor glycolytic and lipogenic programmess (discussed below in Sect. 6.1). Crucially, these subtypes are not fixed categories but rather dynamic, plastic metabolic states that PDAC cells may adopt and modulate over time. Metabolic symbiosis in the tumor microenvironment A key feature of the PDAC TME is its extensive metabolic reprogramming, which creates an immunosuppressive environment that supports PDAC progression and therapy resistance [ 48 ]. The TME comprises stromal cells (such as CAFs), vascular endothelial cells, and a variety of immune cells, predominantly immunosuppressive populations including MDSCs, Tregs, and TAMs, all within a dense, collagen-rich ECM replete with cytokines and growth factors [ 247 – 249 ]. Lactate, a primary byproduct of aerobic glycolysis, is now recognized as a vital oncometabolite. Beyond its role in tumor cell metabolism, it orchestrates immunosuppression and stromal activation in the TME, a mechanism conserved across multiple malignancies including PDAC [ 33 , 250 , 251 ]. Lactate attracts and activates immunosuppressive myeloid cells, significantly enhancing MDSC activity by increasing expression of Arg1, NOX2, IDO1, and iNOS via HIF-1α signaling [ 250 , 252 ]. This process depletes arginine, generates nitric oxide, and suppresses T cell responses [ 253 , 254 ]. Lactate also polarizes macrophages toward a pro-tumor M2-like phenotype and supports TAM function [ 255 , 256 ]. TAMs, which often exhibit an M2-like phenotype, are pivotal regulators of inflammation and disease progression. TAMs can enhance glycolysis in PDAC cells through multiple paracrine signals, including the secretion of cytokines such as IL-8 [ 257 ]. A key mechanism is their ability to drive paracrine metabolic rewiring in PDAC cells, inducing a glycolytic (Warburg) phenotype via the CCL18/VCAM-1 axis, thereby promoting metastasis [ 256 ]. Lactate influences TAMs through mechanisms involving HIF-1α, STAT3, and histone lactylation, leading to increased production of pro-angiogenic factors (e.g., VEGF) and immunosuppressive enzymes (e.g., Arg1), while also promoting anti-inflammatory cytokines such as TGF-β, IL-6, and IL-10, further inhibiting anti-tumor immunity [ 33 , 258 ]. The acidic, lactate-rich environment directly affects effector immune cells, including T cells, NK cells, CTLs, and dendritic cells (DCs), by impairing their glycolytic metabolism and cytokine production [ 33 ]. This immunosuppression is mediated in part by specific lactate-sensing receptors. Although direct evidence in PDAC is evolving, studies in other cancers demonstrate that lactate binding to receptors such as GPR81 (HCAR1) and GPR132 transduces potent immunosuppressive signals [ 259 , 260 ]. For example, GPR81 activation in lung cancer cells inhibits cAMP/PKA signaling, leading to TAZ-mediated upregulation of the PD-L1 expression and impaired T-cell function. Elevated lactate is a hallmark of the immunosuppressive TME in PDAC. Within this environment, metastatic PDAC cells actively target NK cells by shedding soluble natural killer cell receptor (NKG2D) ligands, which downregulates the NKG2D receptor and reduces tumor recognition [ 33 , 261 ]. Evidence from various malignancies shows that lactate enhances Treg differentiation and increases their suppressive ability by upregulating FOXP3 via HIF-1α signaling, a mechanism likely conserved in the PDAC TME [ 262 ]. Additionally, lactate’s immunosuppressive effects extend to antigen-presenting cells. Multiple studies across different cancers suggest that lactate inhibits DC differentiation and function, leading to lower IL-12 production and fostering a tolerogenic state that supports Treg proliferation. This mechanism is likely active in PDAC as well [ 250 ]. Recent research indicates that metabolic imbalance may have a wider effect on mucosal immunity. Elevated lactate levels and reduced microbially-produced SCFAs, such as propionate, both markers of a dysbiotic TME, might impair the mucosal barrier and decrease the abundance of mucosa-associated invariant T cells (MAITs). Since MAIT cells are involved in anti-tumor responses in other cancers, this could represent an unconfirmed yet significant pathway of immune suppression in PDAC [ 263 – 266 ] (Fig. 6 ). Fig. 6. Open in a new tab Immunometabolic crosstalk within the PDAC tumor microenvironment. This schematic illustrates how metabolic competition and crosstalk between tumor cells, stromal cells, and immune cells shape an immunosuppressive PDAC TME Metabolic Reprogramming and Byproduct Accumulation: Tumor and stromal cell metabolism driven by elevated glycolysis, amino acid consumption, and altered lipid handling results in nutrient depletion (e.g., glucose, arginine, tryptophan) and accumulation of oncometabolites (e.g., lactate, kynurenine, cholesterol). The production and accumulation of oncometabolites, specifically lactate, directly impairs effector immune cell function: it dampens the antigen-presenting capacity of dendritic cells (DCs) and B cells, inhibits the cytotoxicity of CD8⁺ T cells and natural killer (NK) cells, and promotes the expansion and immunosuppressive activity of regulatory T cells (Tregs), regulatory B cells (Bregs), myeloid-derived suppressor cells (MDSCs), and M2-like macrophages. These shifts increase concentrations of immunosuppressive cytokines (e.g., IL-10, TGF-β) Stromal Amplification Loop: Cancer-associated fibroblasts (CAFs) are activated by tumor-derived growth factors and engage in metabolic symbiosis. Through a “Reverse Warburg effect,” CAFs export metabolites (e.g., amino acids, lipids, fatty acids, deoxycytidine) to fuel tumor growth, further exacerbating nutrient scarcity and immunosuppression. These changes foster angiogenesis, drug resistance, and eventually metastatic progression, highlighting the critical role of immunometabolic interactions in PDAC pathobiology. (CAF: Cancer-associated fibroblast, MCT1:Monocarboxylate transporter 1,PD-1: Programmed cell death protein 1,MDSC: Myeloid-derived suppressor cell, Treg: Regulatory T cell, TAM: Tumor-associated macrophage, NK cell: Natural killer cell, CTL: Cytotoxic T lymphocyte, DC: Dendritic cell, ARG1:Arginase 1,iNOS: Inducible nitric oxide synthase, NOX2: NADPH oxidase 2,IDO1:Indoleamine 2,3-dioxygenase 1,TGF-β:Transforming growth factor beta, PGE2:Prostaglandin E2,VEGF: Vascular endothelial growth factor, IFN-γ:Interferon gamma, HIF-1α:Hypoxia-inducible factor 1-alpha, PI3K: Phosphoinositide 3-kinase, AKT: Protein kinase B, mTOR: Mechanistic target of rapamycin, Foxp3:Forkhead box P3,SHH: Sonic hedgehog, SMO: Smoothened, miR21:MicroRNA-21,TLR8:Toll-like receptor 8,NF-κB: Nuclear factor kappa-light-chain-enhancer of activated B cells, GPR81:G-protein-coupled receptor 81,ERK1/2:Extracellular signal-regulated kinase 1/2,JAK2:Janus kinase 2,STAT3:Signal transducer and activator of transcription 3,MAPK: Mitogen-activated protein kinase, JNK: c-Jun N-terminal kinase, ATP: Adenosine triphosphate,α-KG: Alpha-ketoglutarate, ROS: Reactive oxygen species, PDGF: Platelet-derived growth factor, GLUT1:Glucose transporter 1,SRC: Proto-oncogene tyrosine-protein kinase Src, PHD: Prolyl hydroxylase domain protein, LPA: Lysophosphatidic acid.)This figure is generated using BioRender.com Beyond immune modulation, lactate serves as a currency of metabolic crosstalk between cancer cells and stromal cells, such as CAFs and endothelial cells (ECs), thereby supporting metabolic symbiosis [ 48 , 267 ]. CAFs undergo aerobic glycolysis and produce high levels of energy-rich mitochondrial ‘fuels’ (such as pyruvate, ketone bodies, FAs, and lactate) [ 268 ]. These high-energy metabolites produced by CAFs are exported via MCT4 and then taken up by cancer cells via MCT1, a process known as the "Reverse Warburg effect" which has attracted considerable attention [ 269 , 270 ]. Once taken up, lactate in particular is metabolized through mitochondrial OXPHOS. This metabolic symbiosis generates abundant ATP for PDAC progression [ 149 , 271 ]. Beyond lactate, CAFs supply essential metabolic substrates that influence both PDAC cell metabolism and epigenetics. Under acidic conditions, CAFs release acetate, which PDAC cells take up through the enzyme ACSS2. This acetate promotes histone acetylation and stabilizes the transcription factor SP1, activating a pro-tumorigenic pathway that enhances polyamine synthesis to support tumor growth in the challenging TME [ 233 ]. Metabolic interactions between PDAC cells and CAFs involve the uptake of alanine through specific transporters, notably the neutral AA transporter SLC38A2 [ 271 ]. Additionally, CAFs facilitate PDAC progression by secreting lysophosphatidylcholine (LPC), which activates the AKT pathway via the autotaxin-lysophosphatidic acid (LPA) axis [ 272 ]. They also secrete pyrimidines and deoxycytidine that antagonize gemcitabine efficacy, while releasing cytokines and chemokines that promote tumor growth [ 273 , 274 ]. Recent research shows that CAFs enhance ferroptosis resistance in PDAC by supplying bioavailable iron to counteract autophagy-dependent cell death and secreting cysteine to support glutathione synthesis [ 275 , 276 ]. Dysregulation of Caveolin-1 (CAV-1) in CAFs is a key mechanism driving metabolic reprogramming and tumor-stroma interaction in PDAC [ 59 ]. CAV-1 acts as a negative regulator of pro-oxidant and pro-glycolytic enzymes, directly binding to and inhibiting nitric oxide synthase (NOS). Its loss results in nitric oxide (NO) accumulation, leading to mitochondrial dysfunction and a shift toward glycolytic metabolism. This metabolic change is intensified by CAV-1 knockdown-induced upregulation of the glycolytic enzyme PKM2, which triggers aerobic glycolysis, inhibits mitochondrial OXPHOS, and causes lactate accumulation [ 277 ]. These interactions create a self-reinforcing ecosystem in TME of PDAC. Tumor cell-derived lactate suppresses immunity and activates stromal support, driving aggressive disease. This metabolic symbiosis contributes to therapy resistance but also reveals vulnerabilities, such as lactate transport and nutrient exchange, that could be targeted to disrupt stromal support and restore anti-tumorimmunity. Nutrient scavenging and recycling pathways PDAC growth demands a significant increase in biosynthetic nutrients, yet the dense, desmoplastic stroma produces severe nutrient scarcity, as discussed in previous sections. To survive and proliferate in this challenging environment, PDAC cells not only alter their core metabolic pathways but also rely heavily on scavenging and recycling processes to obtain essential building blocks [ 217 , 278 ]. Among these, autophagy, a conserved lysosomal degradation pathway, serves as a vital, non-redundant source of metabolic substrates. Although not a direct metabolic pathway, autophagy enables nutrient supply by degrading intracellular components (such as proteins, lipids, and organelles) into basic metabolites, including AAs and free FAs, thereby fueling central carbon metabolism, energy production, and biosynthesis under stress [ 279 , 280 ]. Autophagy has a dual, context-dependent role in cancer, acting as a tumor suppressor in normal cells by removing damaged components and maintaining homeostasis, yet is exploited by established cancers such as PDAC as a crucial metabolic lifeline [ 279 , 281 ]. This dependence is driven by PDAC’s oncogenotype, with oncogenic KRAS enhancing autophagy gene expression and initiating the autophagic process, integrating self-recycling into tumor metabolism [ 282 ]. Interestingly, inhibiting downstream KRAS pathways, such as MEK/ERK, paradoxically increases autophagic flux, suggesting that autophagy compensates for compromised glycolysis and mitochondrial function in PDAC [ 283 ]. Rebecca et al. also reported that direct inhibition of autophagy decreases oxygen consumption in PDAC cell lines, indicating reduced mitochondrial OXPHOS [ 284 ]. Additionally, the frequent loss of TP53 removes a key regulatory checkpoint, leading to uncontrolled autophagy and an enhanced scavenging capacity [ 285 , 286 ]. Notably, in vivo studies show that autophagy is essential for early P53-normal lesions but that inhibiting autophagy in advanced, P53-deficient PDAC can paradoxically accelerate tumor growth through compensatory metabolic reprogramming, including increased glucose uptake and anabolic pathways. Thus, the role of autophagy shifts from pro-tumor to tumor-suppressive depending on TP53 status, highlighting the importance of patient stratification for autophagy-targeted therapies [ 286 ]. Autophagy regulation also involves MiT/TFE transcription factors, which promote autophagy-lysosome gene expression and maintain AA pools in PDAC [ 287 ]. This process involves the nuclear import proteins IPO7 and IPO8, which enable constant nuclear localization of MiT/TFE factors, though the exact mechanism by which increased IPO7/8 levels bypass mTORC1 regulation remains unknown [ 288 ]. Unlike normal cells, PDAC cells possess constitutively active MiT/TFE factors that sustain autophagic and lysosomal functions even under nutrient-rich conditions. Silencing these transcription factors disrupts autophagy, thereby impairing tumor growth. Autophagy initiation is also regulated by specific phosphatases, such as PP2A-B55α, which activates ULK1 (the mammalian ATG1 kinase), and sustains high basal autophagy flux in PDAC [ 289 ]. Beyond nutrient supply, autophagy also helps PDAC progress by reducing oxidative stress; it controls ROS production and supports OXPHOS, with high ROS levels further promoting autophagy and nutrient acquisition in PDAC cells [ 278 ]. The dependence on autophagy extends beyond cancer cells to shape the TME. Autophagy in CAFs is activated under nutrient stress, wherein CAFs degrade their own rRNA via NUFIP1-dependent autophagy to release nucleosides that the tumor cells consume to support glycolysis, the TCA cycle, and MYC activation under nutrient deprivation [ 290 – 292 ]. Pancreatic stellate cells (PSCs) also use autophagy to produce and secrete alanine in response to signals from cancer cells [ 211 ]. PDAC cells then import this alanine to fuel mitochondrial respiration and biosynthesis. Additionally, autophagy helps regulate nutrient uptake by controlling the cystine/glutamate antiporter SLC7A11, thereby maintaining redox balance [ 180 ]. Beyond general macromolecular recycling, autophagy also includes specialized forms, such as lipophagy, which degrades lipid droplets to sustain FA homeostasis, especially during metabolic stress [ 293 ]. When glycolysis is suppressed, PDAC cells enhance mitochondrial FAO, a process supported by lipophagy that releases stored FAs to fuel β-oxidation, highlighting the role of autophagy as a key metabolic regulator ensuring PDAC survival amid a hostile niche. PDAC cells rely on autophagy for intracellular nutrient recycling and also utilize macropinocytosis, a potent mechanism for extracting nutrients from the extracellular space [ 294 , 295 ]. Interestingly, this nonspecific intake of extracellular fluid is vital for maintaining AA levels through subsequent digestion and breakdown [ 296 ]. Initially, macropinocytosis was thought to be common in oncogenic KRAS -driven tumors [ 297 ], but only a subset of KRAS-mutant PDAC cell lines exhibit constitutive activity, whereas others activate it only under Gln scarcity [ 298 ]. The decision between autophagy and macropinocytosis is tightly controlled by mTORC1, a key nutrient sensor. SLC38A9, an arginine-sensitive lysosomal lysosomal transporter, releases AAs from lysosomes and activates mTORC1 [ 299 ]. When nutrients are abundant, mTORC1 suppresses both processes; under scarcity, it promotes autophagy and macropinocytosis to sustain tumor growth [ 274 , 300 ]. These scavenging pathways significantly influence immune cells and the TME. For instance, fibroblasts transfer alanine and other non-essential AAs to PDAC cells via autophagy and micropinocytosis to support tumor metabolism [ 211 , 301 ]. Additionally, Gln deficiency prompts both PDAC cells and CAFs to enhance micropinocytosis [ 302 ]. In CAFs, this pathway helps maintain a pro-fibrotic, tumor-supportive phenotype, such as that of myofibroblastic CAFs (myCAFs). Zhang et al. demonstrated that inhibiting macropinocytosis can promote the transition from myCAFs to inflammatory CAFs (iCAFs), remodel the tumor stroma, and enhance the response of PDAC to immunotherapy and chemotherapy [ 302 ]. Overall, these insights reveal significant metabolic heterogeneity in PDAC, involving adaptive nutrient scavenging beyond simple metabolic reprogramming, which allows PDAC cells to survive under nutrient stress and provides the flexibility needed for PDAC progression and metastasis. Metabolic dependencies of PDAC and therapeutic challenges, especially the issue of plasticity Vulnerabilities specific to PDAC metabolic subtypes PDAC exhibits profound metabolic heterogeneity, reflecting extensive metabolic reprogramming that varies both across tumors and within individual lesions. Integrated metabolomic, transcriptomic, and epigenomic profiling has revealed that PDAC tumors segregate into distinct metabolic subtypes, each characterized by specific nutrient dependencies, bioenergetic strategies, and redox requirements [ 303 ] (Fig. 7 ). These metabolic programs partially overlap with established molecular and clinical classifications and strongly influence prognosis, therapeutic response, and the evolution of resistance. Importantly, this stratification explains why broad, one-size-fits-all metabolic targeting has largely failed in PDAC and highlights the need to transition toward subtype-informed, precision metabolic therapies. Fig. 7. Open in a new tab Schematic overview of the four primary metabolic subtypes in pancreatic ductal adenocarcinoma (PDAC). Each panel depicts the dominant nutrient fluxes, pathway activities, and storage organelle dynamics that define the subtypes. Arrow thickness represents the relative flux rate of a pathway. Upward (↑) or downward (↓) arrows next to metabolite pools indicate relative levels. Specific visual cues denote active upregulation, downregulation, or suppression. This figure is generated using BioRender.com A foundational study by Daemen et al. performed comprehensive metabolomic profiling across 38 PDAC cell lines and identified three principal metabolic phenotypes: a slow-proliferating subtype, a glycolytic subtype, and a lipogenic subtype [ 304 ]. The slow-proliferating subtype is characterized by globally reduced metabolite pools, including AAs and carbohydrates, consistent with limited biosynthetic demand and reduced proliferative capacity. In contrast, the glycolytic subtype displays elevated levels of glycolytic intermediates, PPP metabolites, and serine biosynthesis components, reflecting heavy reliance on glucose-derived carbon for rapid proliferation and nucleotide production. Notably, this subtype exhibits depletion of redox-related metabolites such as NADPH, reduced glutathione (GSH), and oxidized glutathione (GSSG), suggesting a precarious redox balance that may create context-dependent vulnerabilities. The lipogenic subtype, by contrast, demonstrates enhanced mitochondrial OXPHOS and robust de novo lipid and cholesterol biosynthesis, supported by active FA synthesis and sterol regulatory networks [ 305 ]. These metabolically defined subtypes show strikingly different sensitivities to pathway-specific inhibitors. Glycolytic tumors are more sensitive to inhibitors targeting glycolysis, nucleotide biosynthesis, and redox homeostasis, whereas lipogenic tumors display increased vulnerability to perturbations of lipid metabolism, mitochondrial respiration, and cholesterol synthesis. Subsequent transcriptomic analyses in patient tumors have directly linked these metabolic programs to transcriptional cell states [ 305 ]. Specifically, the lipogenic subtype aligns strongly with the classical epithelial phenotype, whereas the glycolytic subtype corresponds to basal-like, squamous, or quasi-mesenchymal states, as defined by Collisson et al. [ 305 ]. Clinically, the glycolytic/basal-like subtype is more aggressive, exhibits accelerated growth and metastatic dissemination, and responds poorly to standard chemotherapies, whereas the lipogenic/classical subtype is comparatively indolent and more treatment-responsive. Clinical validation of these concepts was provided by Karasinska et al., who analyzed PDAC transcriptomes and identified glycolytic, cholesterogenic (lipogenic), quiescent, and mixed metabolic subgroups [ 179 ]. The glycolytic subtype was associated with significantly worse overall survival in patients with PDAC, whereas the cholesterogenic subtype exhibited more favorable outcomes [ 179 , 306 ]. Glycolytic tumors were enriched for KRAS and Myc amplification, consistent with transcriptional activation of glycolytic and nucleotide biosynthetic programs. These tumors were overrepresented within basal-like/squamous PDAC, whereas quiescent and cholesterogenic tumors were predominantly classical or progenitor-like. Subtype-specific regulatory nodes further reinforce these metabolic identities: HIF-1α drives glycolytic reprogramming, whereas SREBP2 sustains cholesterol and lipid metabolism in cholesterogenic PDAC. Stabilization of HIF-1α not only enhances glycolysis but also increases pyrimidine biosynthesis, leading to dCTP accumulation and competitive resistance to gemcitabine incorporation [ 307 ]. Crucially, emerging evidence demonstrates that these metabolic states are not fixed but instead exhibit remarkable flexibility and bidirectional influence on transcriptional identity [ 308 ]. Disruption of cholesterol biosynthesis, for example, through statin treatment, paradoxically induces a shift from the classical to the basal-like phenotype through SREBP1-dependent activation of TGFβ–SMAD signaling and EMT. Conversely, sustained FA and cholesterol metabolism mediated by SREBP2 reinforces the classical epithelial state. These findings reveal a reciprocal relationship in which metabolism both shapes and is shaped by PDAC cell identity [ 208 ]. Organoid-based studies have further refined this metabolic framework. Li et al. classified PDAC organoids into glucomet-PDAC, characterized by elevated glucose uptake, enhanced nucleotide metabolism, chemoresistance, and poor prognosis, and lipomet-PDAC, which exhibits dominant lipid metabolic activity and improved therapeutic response. Mechanistically, glucomet-PDAC exploits a GLUT1–ALDOB–G6PD metabolic axis that reroutes glucose into the non-oxidative PPP, fueling pyrimidine biosynthesis and antagonizing nucleoside analogue chemotherapy. Importantly, pharmacologic inhibition of G6PD or upstream glucose transport restores chemosensitivity, highlighting a subtype-specific vulnerability that is therapeutically actionable [ 309 ]. Epigenetic analyses add an additional regulatory layer to this metabolic-molecular axis. Super-enhancer mapping has identified lineage-specific transcriptional dependencies that stabilize metabolic programs, including PPAR-associated lipid metabolism in classical PDAC and MYC-driven glycolysis in basal-like tumors [ 310 ]. These epigenetic architectures reinforce metabolic identity and contribute to the persistence of subtype-specific vulnerabilities. Beyond metabolic flux, recent studies have identified active molecular gatekeepers that stabilize metabolic and phenotypic states. ELAPOR1/KIAA1324 promotes the classical/lipogenic program by enhancing lipogenesis, suppressing AA metabolism, and limiting invasive behavior; its loss shifts tumors toward glycolysis and basal-like identity [ 311 ]. Similarly, LMO3 sustains classical differentiation by promoting glycerol-3-phosphate-dependent lipid metabolism while repressing AA utilization, correlating with improved patient survival [ 182 ]. Conversely, ADRA2A suppresses basal-like glycolytic programs through MYC inhibition and reduced AA metabolism, favoring re-differentiation toward a classical state [ 312 ]. These findings highlight that metabolic subtypes represent actively maintained cell fates, not merely passive metabolic consequences. Collectively, evidence from cell lines, patient tumors, organoids, and epigenomic analyses converges on a unifying concept: PDAC harbors a tightly coupled metabolic–molecular axis in which highly glycolytic, basal-like tumors are aggressive and therapy-resistant, whereas lipid-dependent classical tumors are more treatment-responsive. However, two significant challenges persist: first, the intrinsic plasticity of these metabolic states enables rapid adaptation to therapy, and second, the overlap between tumor and normal metabolic pathways constrains therapeutic windows. Future strategies must therefore combine subtype-matched metabolic targeting with approaches that suppress adaptive reprogramming and exploit cancer-specific synthetic lethal interactions. Adaptive resistance: how PDAC plasticity challenges therapy Despite advances in understanding PDAC metabolic vulnerabilities, therapeutic efficacy remains severely limited by rapid and adaptive resistance. Systemic chemotherapy, particularly gemcitabine, remains a cornerstone of PDAC treatment, yet its clinical benefit is modest owing to the extraordinary capacity of PDAC for metabolic and phenotypic reprogramming. Accumulating evidence indicates that resistance is not driven by static genetic alterations alone, but rather emerges from dynamic remodeling of core metabolic pathways that support PDAC cell survival under therapeutic stress [ 308 ]. Metabolomic profiling of gemcitabine-sensitive versus resistant PDAC cells reveals extensive rewiring of glucose metabolism as a central mechanism of resistance. Chemoresistant cells exhibit increased aerobic glycolysis, frequently driven by stabilization of HIF-1α under hypoxic conditions or through oncogenic drivers such as MUC1 [ 307 ]. This glycolytic shift supports resistance by maintaining low intracellular ROS levels, thereby activating pro-survival signaling pathways including NF-κB and STAT3 and promoting cancer stem-like and EMT phenotypes [ 313 , 314 ]. Under nutrient-limited conditions, moderate ROS levels activate the RNA-binding protein HuR, which upregulates IDH1 to regenerate NADPH and preserve redox balance, further reinforcing resistance. Clinically, this may explain why patients with lower serum glucose, reflecting a nutrient-restricted TME, often exhibit intrinsic resistance to gemcitabine [ 315 ]. Enhanced glycolysis also feeds directly into nucleotide metabolism. Increased flux through the non-oxidative PPP increases pyrimidine biosynthesis, generating endogenous nucleotides that compete with gemcitabine metabolites for incorporation into DNA [ 316 ]. Simultaneously, activation of the HBP increases protein glycosylation, stabilizing signaling molecules that promote survival and drug resistance [ 317 ]. Parallel metabolic circuits further sustain this glycolytic state: LAT2-mediated mTOR activation enhances anabolic metabolism, whereas increased NAMPT expression maintains NAD + pools necessary for sustained glycolytic flux and lactate production [ 318 ]. Beyond glucose metabolism, altered AA and lipid pathways contribute substantially to therapy escape. Increased Gln utilization, often driven by increased MUC5AC expression, fuels mTOR signaling, maintains redox homeostasis, and provides biosynthetic intermediates [ 317 ]. Enhanced lipogenesis, marked by increased FASN expression, correlates with poor clinical prognosis and resistance to both chemotherapy and radiotherapy [ 145 , 186 ]. Lipid composition within the TME also modulates therapeutic response; for e.g. lipid peroxidation induced by elevated tumor temperature can partially mediate gemcitabine-induced ferroptosis, whereas upregulation of antioxidant defenses or inhibition of p38 MAPK blunts this effect, revealing a temperature-dependent ferroptosis resistance axis [ 319 ]. Autophagy further supports resistance by recycling intracellular components to sustain metabolism under therapeutic stress. Importantly, these metabolic adaptations are not terminal states but rather precursors to more profound phenotypic reprogramming. Chemotherapeutic pressure can drive a transition from the classical/lipogenic subtype toward the basal-like/glycolytic state, fundamentally altering tumor identity. This shift represents a strategic metabolic escape, allowing tumors to abandon lipid-dependent programs in favor of glucose-driven biosynthesis. Patient-derived organoid models demonstrate that therapy induces a shift from classical to basal-like states, which are linked to multi-drug resistance [ 308 ]. Additionally, studies of neoadjuvant-treated patient samples indicate an increase in basal-like transcriptional signatures following chemotherapy [ 320 , 321 ]. Single-cell transcriptomic studies further illuminate this process by identifying intermediate hybrid states that act as evolutionary hubs during subtype switching. These persister populations exhibit unique metabolic and molecular features, such as CYP3A-mediated drug detoxification, QKI-driven splicing reprogramming in basal-like cells, and increased vulnerabilities to replication stress in squamous subtypes [ 322 – 324 ]. Resistance thus unfolds in two temporally distinct phases: an initial rapid metabolic rewiring that neutralizes drug efficacy, followed by a slower but more stable reprogramming of cellular identity toward a basal-like, therapy-refractory state. Adaptive resistance in PDAC is a consequence of extraordinary metabolic and phenotypic plasticity, producing constantly shifting vulnerabilities that weaken both traditional and metabolic treatment strategies alike. Addressing this challenge requires approaches that either stabilize tumors in a therapy-sensitive state, simultaneously target multiple metabolic identities, or directly inhibit the molecular drivers of plasticity (discussed in Sect. 8). Promising strategies include blocking subtype maintenance pathways (like ERK inhibition to suppress basal-like programs), leveraging synthetic lethal interactions in replication-stressed tumors (such as ATR or WEE1 inhibition), and combining agents that target different subtypes effectively- such as KRAS inhibitors for basal-like cells alongside chemotherapy or lipid-targeting agents for classical populations [ 308 ]. Therapeutic strategies targeting metabolic adaptation Targeting glucose metabolism Although targeting metabolism is not yet standard for many cancers, targeting glucose metabolism, particularly aerobic glycolysis, has shown promise in PDAC. Inhibiting glycolysis proves especially effective in PDAC because it exploits a key tumor dependency. This reliance is most evident in the glycolytic/basal-like subtype, wherein tumors heavily depend on high glucose flux, suggesting these patients might benefit most from glycolytic inhibition. Warburg-addicted PDAC cells rely solely on glycolysis to generate ATP for the cellular calcium pump (PMCA) [ 325 ]. Blocking glycolysis causes the pump to fail, leading to dangerous calcium accumulation inside the cell. Several drugs are designed to target this metabolic weakness. HK inhibitors, such as 2-DG, disrupt the first step of glycolysis by competing with glucose for HK phosphorylation. Studies show 2-DG reduces glycolysis and inhibits PDAC cell proliferation [ 326 – 329 ]. Yan et al. found that combining 2-DG with the MAPK inhibitor Trametinib induces apoptosis in KRAS -driven PDAC [ 330 ]. Targeting GAPDH with covalent agents such as spirocyclic BDHI derivatives (e.g., compound 11) effectively inactivates it, exhibiting strong anti-proliferative effects across PDAC lines [ 331 ]. Downstream, inhibiting PKM2 with agents such as shikonin disrupts a key metabolic complex that supplies ATP to PKMα, leading to toxic calcium overload and killing PDAC [ 332 ]. LDHA, which turns pyruvate into lactate, is another direct target [ 33 ]. Inhibitors such as GNE-140 and FX11, as well as lactate transporter inhibitors like AZD3965, aim to disrupt lactate metabolism, which is vital for PDAC flexibility and rapid progression. Other compounds, such as BAY-8002, 3-OBA, and AZ1729, exhibit preclinical promise by targeting lactate pathways, though clinical use faces issues such as bioavailability and off-target effects [ 33 , 333 ]. Targeting GLUTs, essential for glucose entry, offers another approach. PDAC characterized by high glycolytic activity exhibits elevated GLUT1 and low ALDOB levels. Modulating these pathways by suppressing GLUT1 or upregulating ALDOB can shift these features. The GLUT1/ALDOB/G6PD profile defines the glycolytic subtype, making G6PD inhibitors a targeted therapy for these patients [ 309 ]. Inhibiting GLUT1 or G6PD pharmacologically can also enhance the sensitivity of PDAC cells to chemotherapy [ 309 ]. WZB117, a GLUT1 inhibitor tested in vitro and in mice, shows potential, thereby targeting PDAC stem cells [ 334 ]. In addition to enzymes and transporters, upstream regulators like PFKFB3 are promising targets. PFKFB3 produces fructose-2,6-bisphosphate, which activates PFK1 and thereby controls glycolysis. Virtual screening has identified FDA-approved drugs such as lomitapide and cabozantinib as PFKFB3 inhibitors. Lomitapide, combined with gemcitabine in models, enhances efficacy, highlighting repurposing potential [ 335 ]. LDHA, crucial for lactate production, is also a target for drug repurposing; the diabetes drug canagliflozin inhibits glycolysis by reducing glucose uptake and lactate, downregulating GLUT1 and LDHA, and inducing apoptosis through the PI3K/AKT/mTOR/HIF-1α pathway. It works well with gemcitabine, suggesting rapid clinical application [ 336 ]. Another emerging strategy targets the transcription machinery driving glycolytic dependency. Inhibiting deubiquitinase USP25 destabilizes HIF-1α under hypoxia, thereby collapsing the glycolytic program by reducing GLUT1 and enzyme expression, which impairs serine/glycine synthesis and reduces xenograft growth in preclinical studies [ 337 ]. Additionally, a stromal-metabolic co-targeting approach involves disrupting the TME role in fueling glycolysis. TAMs release IL-8, which activates STAT3 in PDAC cells, increasing GLUT3 and glycolytic flux. The study shows that reparixin, an IL-8 receptor inhibitor, blocks the STAT3 pathway, reduces glycolytic activity, and exhibits anti-tumor effects in vivo, thereby disrupting this essential stromal support [ 257 ]. Beyond the studies discussed, numerous other agents targeting aerobic glycolysis are under clinical investigation (Table 2 ). Furthermore, promising preclinical candidates demonstrating efficacy in xenograft models are also under development (Table 3 ). Table 2. Overview of Clinical Trials on Metabolic Targeting in PDAC NCT number Phase Status Agents Target Target Molecule Related Toxicities and side effects Reference NCT00096707 I Completed 2-DG Glycolysis HK2 Hyperglycaemia, Gastrointestinal bleeding [ 326 ] NCT03504423 III Completed CPI-613 TCA cycle PDH Anaemia, Febrile neutropenia, Leukocytosis, Abdominal pain, Diarrhoea [ 338 ] NCT01835041 I Completed CPI-613 TCA cycle PDH Hyperglycaemia, hypokalaemia, diarrhea, neutropenia, lymphopenia [ 339 ] NCT02223247 I Completed TVB-2640 Lipid synthesis FASN Alopecia, PPE syndrome, fatigue, decreased appetite, and dry skin [ 340 ] NCT03829436 I Completed TPST-1120 Lipid synthesis PPARα NA [ 341 ] NCT05733000 II Recruiting Devimistat + HCQ and 5-FU/gem Mitochondrial function PDHA1, PDHB, DLAT, PDHX, OGDH, DLST, DLD, ATG5, ATG7, LC3B, SQSTM1, TYMS, RRM1, RRM2 NA ClinicalTrials.gov NCT02650804 II Completed Ubidecarenone + gem Mitochondrial function NDUFS1, SDHA, UQCRC1, CYC1, MT-CYB, SOD2, GPX1, CAT, NFE2L2, RRM1, RRM2, TYMS Anaemia, Thrombocytopenia, Abdominal distension, Abdominal pain, Fatigue ClinicalTrials.gov NCT03965845 I Completed CB-839 Glutamine GLS NA [ 271 ] NCT04634539 I Completed L-glutamine Glutamine SLC1A5, SLC7A5 NA [ 342 ] NCT03665441 III Completed Eryaspase Asparagine ASNS Gastrointestinal obstruction, Pyrexia, Cholangitis, Sepsis, Pulmonary embolism [ 343 ] NCT01523808 I Completed GRASPA Asparagine ASNS Abdominal Pain, Constipation, Nausea, Pyrexia, Back pain [ 344 ] NCT02195180 II Completed GRASPA Asparagine ASNS NA [ 345 ] NCT05034627 I Recruiting Calaspargase pegol-mknl Asparagine/aspartate ASNS NA [ 346 ] NCT02077881 I/II Completed Indoximod Tryptophan IDO1 NA [ 347 ] NCT00739609 I Terminated 1-methyl-D-tryptophan Tryptophan IDO1 NA [ 348 ] NCT03006302 II Completed Epacadostat and CRS-207 Tryptophan IDO1, MSLN Pancreatitis, Sinus tachycardia, Abdominal pain, Nausea, Chills [ 349 ] NCT03085914 I/II Completed Epacadostat Tryptophan IDO1 Anaemia, Abnormal sensation in eye, Constipation, Diarrhoea, Stomatitis [ 350 ] NCT02101580 I Completed ADI-PEG 20 Arginine ASS1 NA [ 351 ] NCT03435250 I Terminated AG-270 Methionine MAT2A NA [ 352 ] NCT03450018 I/II Terminated SLC-0111 Ferroptosis CA IX NA [ 353 ] NCT02353026 I Completed Artesunate Redox balance TP53 NA [ 354 ] NCT01049880 I Completed Ascorbic Acid Redox balance SOD1, GPX1, CAT NA [ 355 ] NCT02514031 I Terminated ARQ-761 Redox balance NQO1 NA [ 355 ] NCT03825289 II Active, not recruiting Choloroquine Autophagy LC3, p62/SQSTM1 NA [ 356 ] NCT01777477 I Completed Choloroquine Autophagy LC3, p62/SQSTM1 NA [ 271 ] NCT04132505 I Active, not recruiting Choloroquine Autophagy LC3, p62/SQSTM1 NA [ 271 ] NCT01978184 II Completed HCQ + gem and nab-pacl Autophagy LC3, p62/SQSTM1, TFEB, RRM1/2, TK1, p53, BAX, BCL2, TUBB, MAPs, Cyclin B1/CDK1 Anemia, Febrile neutropenia, Fatigue, Infections and infestations, Skin infection [ 357 ] NCT04386057 II Completed HCQ+ temuterkib Autophagy LC3, p62/SQSTM1, TFEB, CHK1, CDC25s, CDKs, ATR, p53 Abdominal pain, Dehydration, Anemia, Constipation, Diarrhea ClinicalTrials.gov NCT03825289 I Active, not recruiting HCQ + trametinib Autophagy LC3, p62/SQSTM1, TFEB, MEK1/2, ERK1/2, DUSP6, ELK1, FOS, MYC NA ClinicalTrials.gov NCT04132505 I Active, not recruiting HCQ + binimetinib Autophagy LC3, p62/SQSTM1, TFEB, MEK1/2, ERK1/2, DUSP6, ELK1, FOS, MYC NA ClinicalTrials.gov NCT01019382 II Completed Omega-3 fatty acid Dietary intervention PPARs NA [ 271 ] NCT01419483 I Terminated Ketogenic diet Dietary intervention PPARs NA [ 271 ] NCT02336087 I Active, not recruiting Dietary supplements+ Metformin Dietary intervention PRKAA1/2 (AMPK α1/α2), MTOR, AKT1, PGC1α (PPARGC1A) NA [ 271 ] NCT04930991 I Recruiting Omeprazole Metabolic reprogramming ATP4A/ATP4B NA [ 271 ] NCT04141995 II Terminated Digoxin Metabolic reprogramming ATP1A1 NA [ 271 ] NCT02048384 I Completed Metformin Metabolic reprogramming AMPK NA [ 358 ] NCT04542291 I Completed Dapagliflozin Metabolic reprogramming SGLT2 Atrial fibrillation, Diarrhea, Fever, Sepsis, Hypotension [ 359 ] NCT00944463 II Completed Simvastatin Metabolic reprogramming HMGCR NA [ 271 ] NCT05634525 I Withdrawn Adagrasib Metabolic reprogramming KRAS G12C NA [ 360 ] NCT06008288 II Recruiting JAB-21,822 Metabolic reprogramming KRAS G12C NA [ 361 ] NCT06625320 III Recruiting RMC-6236 Metabolic reprogramming KRAS G12C、G12D、G12V NA [ 362 ] Open in a new tab † NCT: National clinical trial number,2-DG:2-Deoxy-D-glucose, FOLFIRINOX : (FOL–Leucovorin; F–5-Fluorouracil; IRIN–Irinotecan; OX–Oxaliplatin), HCQ: Hydroxychloroquine,5-FU : 5-Fluorouracil, gem : Gemcitabine, ADI-PEG 20 : Arginine Deiminase Pegylated 20,nab-pacl : Nab-Paclitaxel (Nanoparticle albumin-bound paclitaxel), TCA: Tricarboxylic Acid Cycle, FASN : Fatty Acid Synthase, HK2 : Hexokinase 2, PDH : Pyruvate Dehydrogenase, FASN : Fatty Acid Synthase, PPARα : Peroxisome Proliferator-Activated Receptor Alpha, PDHA1 : Pyruvate Dehydrogenase E1 Subunit Alpha 1,PDHB : Pyruvate Dehydrogenase E1 Subunit Beta, DLAT : Dihydrolipoamide S-Acetyltransferase, PDHX : Pyruvate Dehydrogenase Complex Component X, OGDH : Oxoglutarate Dehydrogenase, DLST : Dihydrolipoamide S-Succinyltransferase, DLD : Dihydrolipoamide Dehydrogenase, ATG5 : Autophagy Related 5,ATG7 : Autophagy Related 7,LC3B : Microtubule-Associated Proteins 1 A/1B Light Chain 3B, SQSTM1 : Sequestosome 1,TYMS : Thymidylate Synthase, RRM1 : Ribonucleotide Reductase Regulatory Subunit M1,RRM2 : Ribonucleotide Reductase Regulatory Subunit M2,NDUFS1 : NADH: Ubiquinone Oxidoreductase Core Subunit S1,SDHA : Succinate Dehydrogenase Complex Flavoprotein Subunit A, UQCRC1 : Ubiquinol-Cytochrome C Reductase Core Protein 1,CYC1 : Cytochrome C1,MT-CYB : Mitochondrial Cytochrome B, SOD2 : Superoxide Dismutase 2,GPX1 : Glutathione Peroxidase 1,CAT : Catalase, NFE2L2 : Nuclear Factor, Erythroid 2 Like 2, GLS : Glutaminase, SLC1A5 : Solute Carrier Family 1 Member 5,SLC7A5 : Solute Carrier Family 7 Member 5,ASNS : Asparagine Synthetase, IDO1 : Indoleamine 2,3-Dioxygenase 1,MSLN : Mesothelin, ASS1 : Argininosuccinate Synthase 1,MAT2A : Methionine Adenosyltransferase 2 A, CA IX : Carbonic Anhydrase IX, TP53 : Tumor Protein P53,SOD1 : Superoxide Dismutase 1,NQO1 : NAD(P)H Quinone Dehydrogenase 1,TFEB : Transcription Factor EB, BAX : BCL2 Associated X, Apoptosis Regulator, TUBB : Tubulin Beta Class I, MAPs : Microtubule-Associated Proteins, CDK1 : Cyclin Dependent Kinase 1,CHK1 : Checkpoint Kinase 1,CDC25s : Cell Division Cycle 25 Phosphatases, CDKs : Cyclin Dependent Kinases, ATR : ATM And Rad3 Related, MEK1/2 : Mitogen-Activated Protein Kinase Kinase 1/2,ERK1/2 : Extracellular Signal-Regulated Kinases 1/2,DUSP6 : Dual Specificity Phosphatase 6,ELK1 : ETS Transcription Factor ELK1,FOS : Fos Proto-Oncogene, AP-1 Transcription Factor Subunit, MYC : MYC Proto-Oncogene, BHLH Transcription Factor, PPARs : Peroxisome Proliferator-Activated Receptors, PRKAA1 : Protein Kinase AMP-Activated Catalytic Subunit Alpha 1,PRKAA2 : Protein Kinase AMP-Activated Catalytic Subunit Alpha 2,AKT1 : AKT Serine/Threonine Kinase 1,PGC1α : Peroxisome Proliferator-Activated Receptor Gamma Coactivator 1-Alpha, ATP4A : ATPase H+/K+ Transporting Alpha Subunit, ATP4B : ATPase H+/K+ Transporting Beta Subunit, ATP1A1 : Na+/K+ ATPase Alpha 1 Subunit, AMPK : AMP-Activated Protein Kinase, SGLT2 : Sodium-Glucose Cotransporter 2,HMGCR : 3-Hydroxy-3-Methylglutaryl-CoA Reductase Table 3. Emerging Preclinical Drugs for Metabolic Targeting in PDAC Agents Target Reference 3-BP Glycolysis inhibitor [ 363 ] STF-31 GLUT1 inhibitor, reduce stemness [ 364 ] Phloretin GLU1 and GLUT2 inhibitor suppresses progression [ 365 ] WZB117 GLUT1 inhibitor reducing glucose uptake and glycolysis [ 366 ] 3PO PFKFB3 inhibitor reduces levels of fructose-2,6-bisphosphate [ 367 ] Oxamic acid Inhibits lactate dehydrogenase (LDH), disrupting the conversion of pyruvate to lactate in glycolysis. [ 368 ] Galloflavin Inhibits LDH [ 369 ] Oxamate Inhibits LDH [ 370 ] FX11 Inhibits LDHA(lactate dehydrogenase A) [ 43 ] GNE-140 Inhibits LDHA [ 371 ] BAY-876 Inhibits GLUT1 [ 309 ] Iodoacetate Inhibits glyceraldehyde-3-phosphate dehydrogenase (GAPDH) [ 372 ] Koningic acid (KA) Inhibits GAPDH [ 361 ] Syrosingopine MCT1/MCT4 inhibitor [ 373 ] Shikonin Targets PKM2 [ 374 ] DON Inhibits glutaminase. [ 375 ] DRP-104 Glutamine antagonist [ 375 ] V-9302 Glutamine transporter antagonist [ 376 ] CB-839 Glutaminase inhibitor [ 377 ] BPTES Inhibitor of GLS1 [ 378 ] V-9302 Targets ASCT2 [ 379 ] Arsenite Inhibits TCA [ 380 ] Alpha-cyano-4-hydroxycinnamate (CHC) Inhibits the mitochondrial pyruvate carrier [ 168 ] Oligomycin Inhibits ATP synthase [ 381 ] Etomoxir Inhibits carnitine palmitoyltransferase I (CPT-1) [ 382 ] Meldonium Inhibits gamma-butyrobetaine dioxygenase [ 383 ] Cerulenin Inhibits fatty acid synthase (FAS) [ 304 ] Bempedoic Acid Inhibits ATP-citrate lyase [ 187 ] Myricetin Inhibits GLUT1 and GLUT4 [ 384 ] Apigenin Inhibits GLUT1 [ 385 ] 7ACC2 Inhibitor of MCT1 [ 386 ] BAY-8002 MCT4 inhibitor [ 33 ] Lonidamine Inhibits MCTs [ 317 ] Syrosingopine Inhibits MCT1 and MCT4 [ 373 ] BCH Inhibitor of LAT1 [ 387 ] IDH-C35 Inhibitor of the IDH1 R132H mutant [ 388 ] BCH Inhibitor of LAT1 [ 389 ] Soraphen A Inhibits ACC [ 390 ] Rotenone Inhibits Complex I [ 391 ] Antimycin A Blocks the transfer of electrons from ubiquinol to cytochrome c [ 392 ] Napthoquinones Inhibits Complex III [ 393 ] IACS-010759 Mitochondrial complex I inhibitor [ 394 ] BX-795 HK2 allosteric inhibitor [ 395 ] SP-2509 LSD1 inhibitor [ 293 ] FT-1101 FASN-PROTAC Degrader [ 396 ] Perhexiline FAO inhibitor [ 397 ] AICAR AMPK activator [ 397 ] 6-AN PPP inhibitor [ 398 ] Bafilomycin A1 Autophagy [ 399 ] PX-12 Disruption of redox homeostasis [ 400 ] Brucein D Enhance chemosensitivity via inhibiting Nrf2 pathway [ 401 , 402 ] GCN2iB Amino Acid Sensing Pathway Intervention [ 403 ] BAY 2,402,234 Nucleotide metabolism intervention [ 404 ] Metformin + Gemcitabine Inhibit OXPHOS and activate AMPK [ 398 ] 2DG+Trametinib Induce apoptosis [ 330 ] Galloflavin+Metaformin Inhibits LDH and induce cancer cell death [ 405 ] Open in a new tab †3-BP:3-Bromopyruvate, BX-795:4-(5-Amino-1 H-pyrazol-4-yl)−6-(4-methylphenyl)pyrimidin-2-amine, Perhexiline:2-(2,2-Dicyclohexylethyl)piperidine, AICAR:5-Aminoimidazole-4-carboxamide-1-β-D-ribofuranoside,6-AN:6-Aminonicotinamide, Bafilomycin A1:A Macrolide Antibiotic from Streptomyces griseus, PX-12:1-Methylpropyl 2-imidazolyl disulfide Despite these promising strategies, the effectiveness of glucose metabolism-focused monotherapy is limited by the inherent metabolic flexibility of PDAC. Cancers, especially under treatment pressure, can activate alternative and scavenging pathways to adapt their metabolism. For example, blocking glycolysis may lead to increased reliance on Gln for anaplerosis or trigger protective autophagy, as discussed in Sect. 5.3 and 6.2. Additionally, the lipogenic or classical metabolic subtype may be naturally less responsive to glycolytic inhibitors. Thus, future approaches should involve combination therapies tailored to metabolic subtyping, such as combining glycolytic inhibitors with agents targeting adaptive mechanisms, such as GLS or autophagy inhibitors, or selecting inhibitors based on the patient’s dominant metabolic profile (Fig. 8 ). Fig. 8. Open in a new tab Targeting PDAC Metabolism Adaptation: This schematic illustrates key nodes of metabolic reprogramming in PDAC and promising pharmacological strategies (indicated in red) designed to disrupt them. Clinical trial status is denoted by a colored star (★) superscript next to each trial identifier. A green star indicates a completed trial; a red star, a terminated trial; a blue star, an active trial that is not currently recruiting; a yellow star, a trial that is actively recruiting participants; and a black star, a withdrawn trial. Glycolysis & Energy Metabolism: 2-Deoxy-D-glucose (2-DG) inhibits glucose metabolism; CPI-613 disrupts the TCA cycle. Lipid Metabolism: Fatty acid synthesis and signaling are targeted by TVB-2640 (FASN inhibitor) and TPST-1120 (antagonist of arachidonic acid metabolism) Amino Acid Metabolism: Glutamine: CB-839 inhibits glutaminase (GLS), reducing glutamine availability. Arginine: ADI-PEG 20 degrades circulating arginine. Asparagine: Asparaginases (e.g., calaspargase pegol-mknl, eryaspase) deplete asparagine. Methionine: AG-270 inhibits methionine adenosyltransferase 2 A (MAT2A). Tryptophan: IDO1/TDO inhibitors (e.g., epacadostat, indoximod) block kynurenine production. Redox & One-Carbon Metabolism: Agents like artesunate, ascorbic acid, and ARQ-761 target the folate cycle and oxidative stress pathways to disrupt nucleotide synthesis and redox balance Signaling & Nutrient Scavenging: The PI3K/AKT/mTOR axis, a master regulator of cell survival and proliferation and metabolism, is targeted by various inhibitors. Autophagy, a key nutrient scavenging pathway, is inhibited by chloroquine. Collectively, these interventions aim to exploit the metabolic dependencies of PDAC cells to inhibit PDAC progression and overcome therapeutic resistance. This figure is generated using BioRender.com Targeting lipid metabolism Dysregulated lipid metabolism plays a central role in PDAC progression, making it an attractive therapeutic target. This metabolic reprogramming is driven by key transcriptional regulators, such as SREBPs, which are potential targets for cancer therapy [ 181 ]. Inhibitors like betulin and fatostatin have been proposed to block SREBP activity. For instance, combining betulinic acid with mithramycin A effectively suppressed PDAC xenograft growth in mice [ 406 ]. Additionally, pharmacological inhibition of SREBP1 with fatostatin reduced PDAC cell viability and proliferation [ 205 ]. Natural compounds such as Yarrow CO₂ supercritical extract (Yarrow SFE) have also shown SREBP-inhibitory effects, decreasing tumor growth in xenograft models and suggesting potential as a supplementary treatment in PDAC [ 407 ]. Similarly, liver X receptors (LXRs), which regulate gene transcription in lipid metabolism, can be targeted with agents such as GAC0001E5, a novel LXR inverse agonist and degrader that inhibits PDAC cell proliferation, highlighting their therapeutic potential [ 408 ]. Downstream of these regulators, inhibiting FA synthesis offers a promising approach, as FA supply is crucial for energy and building blocks in proliferating tumor cells [ 187 ]. For example, ACLY, a key enzyme in FA biosynthesis, can be targeted with SB-204,990, which reduces tumor xenograft growth [ 183 ]. A significant enzymatic target is FASN, which facilitates the final steps in FA synthesis. Inhibitors such as epigallocatechin-3-gallate (EGCG) target the β-ketoacyl synthase domain of FASN to prevent PDAC progression; notably, proton pump inhibitors including lansoprazole, rabeprazole, omeprazole, and pantoprazole also suppress FASN activity, leading to PDAC cell death [ 409 , 410 ]. Targeting cholesterol metabolism is another critical aspect of lipid-based therapy in PDAC. Inhibiting HMG-CoA reductase (HMGCR), the rate-limiting enzyme of the mevalonate pathway, with statins shows promising effects. Remarkably, statins are effective against gemcitabine-resistant PDAC cells in vitro, suggesting that disrupting cholesterol synthesis may bypass resistance mechanisms [ 411 ]. This highlights the potential of metabolic reprogramming as a strategy to enhance PDAC treatment outcomes. Clinical evidence further supports this approach; meta-analyses have observed that statin use correlates with improved survival in PDAC patients, positioning statins as promising candidates for repurposing in PDAC therapy [ 412 , 413 ]. Additionally, inhibiting SCD, an enzyme critical to monounsaturated FA production, offers another therapeutic strategy. The SCD inhibitor A939572 effectively induces cell death in precancerous PanIN lesions and early pancreatic tumor organoids, suggesting that targeting lipid desaturation is a promising therapeutic tactic for early-stage disease [ 414 ]. FAO also serves as a vital energy source for PDAC survival and progression. Inhibiting CPT1, the rate-limiting enzyme in FAO, disrupts this pathway. The CPT1 inhibitor etomoxir suppresses acid-induced invasion, and genetic knockdown of CPT1C suppresses tumor growth and progression, validating the potential of this target [ 415 , 416 ]. Beyond oxidation, interfering with lipid storage is another key strategy. Lipid droplets (LDs), which store cholesterol esters, can be destabilized by inhibiting SOAT1/ACAT1. The SOAT1 inhibitor avasimin induces apoptosis and halts proliferation of PDAC cells [ 417 ]. Although early-phase clinical trials for other cancers have shown that SOAT1 inhibitors such as ATR-101 are safe, their efficacy in PDAC remains to be evaluated through dedicated clinical studies. Exposure to FAs such as palmitic, oleic, and linolenic acids notably enhances the self-renewal and chemoresistance of CD133 + PDAC cancer stem cells (CSCs). Conversely, blocking FA uptake, storage, and metabolism reduces the PDAC CSC population in both in vitro and in vivo models. Mechanistically, inhibiting FA metabolism disrupts OXPHOS, depletes cellular energy, and leads to cell death in PDAC CSCs [ 418 ]. Critically, combining the FAO inhibitor Ranolazine with standard chemotherapy (gemcitabine) improves therapeutic outcomes in PDAC patient-derived xenograft (PDX) models. This combination therapy sensitizes tumors to gemcitabine, reducing both the number and functional capacity (self-renewal and tumorigenicity) of PDAC CSCs in vitro and in vivo [ 418 ]. Overall, focusing on lipid metabolic reprogramming presents promising opportunities for developing new treatments against PDAC. However, while most of these findings remain at the preclinical stage, the transition to clinical investigation is underway, as reflected by ongoing and completed trials (See Table 2 ). A deeper understanding of how lipid metabolism changes during disease progression and how it interacts with the TME is essential for developing more targeted and effective therapies or combination treatment strategies. Targeting AA metabolism Targeting AA metabolism is a promising therapeutic strategy in PDAC. Gln is particularly vital because PDAC cells rely heavily on it; consequently disrupting Gln metabolism has thus become a key focus for treatment (Fig. 8 ). Various approaches to interfere with Gln utilization have been explored. The broad-spectrum Gln antagonist 6-diazo-5-oxo-L-norleucine (DON) inhibits multiple Gln-utilizing enzymes, including GLS, glutamine aminotransferases, and transglutaminases [ 419 ]. Beyond its metabolic effects, DON also induces ferroptosis in PDAC cells [ 420 ]. Furthermore, DON has been shown to remodel the immunosuppressive ECM and increase CD8 + T-cell infiltration by inhibiting GFPT1 [ 421 , 422 ]. However, clinical use of DON is limited by systemic toxicity and poor pharmacokinetics. To overcome these limitations, a tumor-targeted prodrug, DRP-104 (sirpiglenastat), was developed. DRP-104 shows strong antitumor and anti-metastatic effects in syngeneic and immunocompromised PDAC mouse models by blocking multiple Gln-dependent pathways involved in central carbon, nucleotide, and lipid metabolism [ 375 ]. This broad targeting prevents PDAC cells from rewiring their metabolism, a common resistance mechanism. Nonetheless, evidence suggests that DRP-104 may lead to resistance via ERK pathway activation and increased expression of receptor tyrosine kinases, such as AXL and HER (ErbB) family members (observed in human PDAC cell lines), indicating adaptive survival mechanisms, including macropinocytosis. Combining DRP-104 with the MEK inhibitor trametinib enhances tumor suppression and prolongs survival in syngeneic PDAC mouse models. Further targeted treatments aim to directly inhibit GLS1, with small molecules such as BPTES, CB-839, and compound 968 under development [ 423 ]. While effective in vitro, these inhibitors often show limited efficacy in vivo due to the metabolic flexibility of PDAC [ 424 , 425 ]. This flexibility allows PDAC cells in nutrient-poor areas to stabilize GS via mTORC1, enabling de novo Gln synthesis and thereby bypassing Gln deprivation or GLS1 inhibition. Therefore, GS represents a key, context-dependent target, emphasizing the need for therapies that block both Gln utilization (e.g., via GLS1 inhibition) and adaptive Gln synthesis (via GS inhibition). This requirement has led to advances in combination therapies and in drug delivery technologies. For example, the enzyme GOT1, part of a non-canonical Gln pathway, depends on mitochondrial export of Gln-derived aspartate. Recent studies identified mitochondrial uncoupling protein 2 (UCP2) as the transporter mediating this export, which is vital for cytosolic NADPH production required for KRAS -driven PDAC growth. Silencing UCP2 disrupts glutaminolysis, impairs redox balance, and inhibits KRAS-mutant PDAC growth, establishing it as a key metabolic target [ 426 ]. Additionally, GOT1 inhibition can suppress the growth of PDAC cell lines, primary cancer models, and xenograft tumors and can sensitize PDAC cells to ferroptosis, especially when combined with inhibitors of glutathione synthesis (e.g., BSO) or cystine import [ 214 ]. Efforts to improve drug delivery include nanoparticle-encapsulated BPTES (BPTES-NPs), which enhances tumor targeting and safety. When combined with the glycolytic inhibitor metformin, this dual-metabolic targeting approach significantly suppresses tumor growth in patient-derived orthotopic PDAC xenograft models by simultaneously inhibiting GLS in cycling cells and glycolysis in the surviving, hypoxic cell population [ 378 ]. Similarly, combining GLS1 inhibitor CB-839 with L-buthionine-sulfoximine (BSO), an inhibitor of γ-glutamylcysteine, synergistically inhibits PDAC proliferation in vitro and PDAC xenograft growth in vivo [ 425 ]. The translational potential of this approach is demonstrated by ongoing clinical trials evaluating CB-839 with other therapies in solid tumors ( NCT02861300 , NCT03965845 , and NCT03875313 ), warranting further assessment in PDAC. Targeting Gln import also presents a complementary strategy. PDAC cells utilize AA transporters such as SLC1A5, SLC38A1/A2, and SLC6A14 for Gln uptake. Although not yet tested in PDAC, inhibitors such as compound 57E (targeting SLC38A2) have been shown in other cancers to synergize with glucose transport inhibitors such as BAY-876, inhibiting proliferation by disrupting the balance between glutaminolysis and glycolysis, suggesting a potential combinatorial approach for PDAC [ 427 ]. The synthetic triterpenoid CDDO-Me downregulates SLC1A5, disrupting oxidative metabolism and glycolysis, which suppresses PDAC progression [ 428 ]. Alloferon, which inhibits SLC6A14, reduces Gln uptake and increases the sensitivity of gemcitabine-resistant PDAC cells to chemotherapy [ 429 ]. Beyond Gln, arginine metabolism is also a promising target. Arginine is essential for protein synthesis, polyamine production, and NO synthesis in PDAC. Some tumors lack argininosuccinate synthetase (ASS1), rendering them dependent on external arginine and thus vulnerable to deprivation therapies. The enzyme pegylated arginine deiminase (ADI-PEG20) exploits this vulnerability, inhibiting growth in ASS1-deficient PDAC both in vitro (cell lines) and in vivo (xenograft models) [ 430 ]. Preclinical data show that ADI-PEG20 can enhance the effectiveness of HDAC inhibitors, chemotherapies, and radiotherapy, partly by suppressing ribonucleoside-diphosphate reductase subunit M2 (RRM2) and increasing gemcitabine uptake through c-MYC–mediated hENT1 upregulation [ 431 – 433 ]. It also sensitizes ASS1-deficient PDAC to radiation via endoplasmic recticulum (ER) stress activation [ 434 ]. A phase 1/1B trial of ADI-PEG20 with gemcitabine and nab-paclitaxel has shown tolerability in advanced PDAC, prompting further studies ( NCT02101580 ) [ 435 ]. In addition to Gln and arginine, cysteine (Cys) dependency offers another target. Cys import through SLC7A11 (system xC⁻) is key for glutathione and coenzyme A synthesis. Depleting Cys with agents like cyst(e)inase induces ferroptosis selectively in tumors and inhibits PDAC growth in xenografts, revealing a targetable weakness [ 180 ]. Targeting immunomodulatory AA metabolism is also promising. The enzyme IDO, expressed in PDAC and other carcinomas, degrades tryptophan within the TME to drive immunosuppression [ 436 , 437 ]. A compelling preclinical strategy combines IDO knockdown with stromal disruption. Specifically, a Salmonella-based therapy delivering shIDO (shIDO-ST) combined with the hyaluronan-depleting enzyme PEGPH20 achieved frequent total tumor regression and significantly extended survival in autochthonous and orthotopic PDAC mouse models, highlighting the efficacy of this dual metabolic-stromal targeting approach [ 438 ]. This rationale has advanced to clinical testing in PDAC. Several small-molecule IDO inhibitors, such as indoximod, epacadostat, and navoximod, have been evaluated. A phase I/II trial combining indoximod with chemotherapy in patients with metastatic pancreatic cancer (PC) has been completed ( NCT02077881 ). Furthermore, phase II trials evaluating epacadostat in combination with either immunotherapy or chemotherapy in PC patients have also been completed ( NCT03006302 , NCT03085914 ) [ 439 ]. Additionally, inhibiting arginine-depleting MDSCs can restore T-cell function, a strategy directly relevant to the arginase-rich PDAC TME. While the ARG1 inhibitor INCB001158 has been clinically evaluated in advanced or metastatic solid tumors (non-pancreatic) in combination with chemotherapy or pembrolizumab ( NCT03314935 , NCT02903914 ), its specific potential to counteract immunosuppression in PDAC warrants further investigation [ 440 , 441 ]. In addition to the specific agents and strategies discussed above, numerous other compounds targeting AA metabolism in several malignancies including PDAC are under preclinical investigation, many demonstrating promising efficacy in xenograft models (Table 3 ). Furthermore, several candidates have advanced to clinical trials (Table 2 ), reflecting the translational momentum in this area. Collectively, PDAC’s reliance on AAs is not a single vulnerability but a network of interconnected, adaptable pathways. The progression from broad glutamine antagonists (DON) to targeted prodrugs (DRP-104), transporter inhibitors, and immunometabolic agents reflects an evolving understanding: durable efficacy will require combination regimens that simultaneously block nutrient acquisition, utilization, and adaptive synthesis while leveraging the resulting immune modulation. Targeting scavenging mechanisms It is now clear that scavenging processes such as autophagy and macropinocytosis are vital to PDAC metabolism, providing essential substrates for core pathways. Targeting these mechanisms has therefore become an attractive and promising strategy. The ULK1 kinase has been strongly validated as a therapeutic target in PDAC through genetic studies; deletion of Ulk1 in spontaneous (KPC) and orthotopic PDAC mouse models significantly delays tumor onset, reduces tumor burden, extends survival, and reprograms the immunosuppressive TME [ 442 ]. Pharmacological inhibitors of ULK1 (e.g., MRT68921, SBI-0206965), along with inhibitors of other key initiation nodes such as lipid kinase VPS34 (e.g., 3-methyladenine, wortmannin, LY294002) and the PI3KC3-C1 complex (e.g., Spautin-1, SAR405) have primarily demonstrated efficacy in vitro and in mouse models of various other cancers. Their specific activity and therapeutic window in PDAC models require further preclinical investigation [ 443 – 448 ]. Conversely, hydroxychloroquine (HCQ), which disrupts autophagic flux, has been extensively tested in clinical studies. Early research indicated HCQ monotherapy is ineffective in advanced, chemotherapy-resistant PDAC, highlighting that autophagy inhibition alone is insufficient in late-stage disease [ 449 ]. However, the high dependence on autophagy in PDAC suggests potential for combination therapy. For example, a pH-sensitive nano-formulation co-delivering chloroquine (CQ) and gemcitabine improved stromal penetration and synergistically reduced tumor growth and metastasis in both subcutaneous xenograft and orthotopic PDAC mouse models growth and metastasis via combined autophagy blockade, cytotoxicity, and ECM modulation [ 450 ]. Clinically, combination strategies targeting autophagy have shown more promising results. Phase I/II trials of HCQ with gemcitabine reported decreases in CA19-9, increased autophagic markers (LC3-II), and improved patient progression-free survival, though the small cohort limited definitive conclusions [ 451 , 452 ]. Additionally, a phase II trial combining HCQ with gemcitabine/nab-paclitaxel in advanced PDAC demonstrated significant inhibition of autophagy, increased immune cell infiltration, and a higher rate of pathological response compared with chemotherapy alone [ 453 ]. Beyond lysosomotropic agents like HCQ, new small molecules that indirectly inhibit autophagy are emerging. For example, the clinical-stage compound ONC212 induces mitochondrial proteotoxic stress via ClpP activation, thereby activating the integrated stress response and concurrently inhibiting autophagy in PDAC. Importantly, ONC212 synergizes with MEK inhibitors in preclinical models of PDAC (both in vitro and in xenograft mouse models), partly due to its autophagy-inhibitory effect [ 454 , 455 ]. This underlines an alternative approach to impair autophagic survival pathways in PDAC. The immunomodulatory effects of autophagy inhibition support the combination of such agents with immunotherapy. Preclinical studies show that autophagy blockade upregulates MHC-I expression on PDAC cells and enhances immune cell infiltration, suggesting synergy with immune checkpoint inhibitors [ 456 ]. However, clinical translation remains challenging. Whereas autophagy inhibitors have been tested with chemotherapy and are under investigation with targeted therapies ( Table 2 ) , their combination with metabolic-targeting agents remains largely unexplored in PDAC. Considering the pivotal role of autophagy in supporting metabolism under stress, pairing drugs like HCQ with inhibitors of pathways such as glutaminolysis or FAO offers a logical, yet untested, strategy to induce synthetic lethality and overcome metabolic plasticity [ 457 ]. Therefore, although autophagy inhibition holds promise as part of combination treatments, its full potential against core metabolic dependencies of PDAC has yet to be realized. Future perspective The remarkable metabolic flexibility of PDAC, driven by altered pathways, nutrient scavenging, stromal interactions, and rapid evolution, remains a key factor in treatment failure. Adding to this complexity, PDAC exhibits significant heterogeneity, with interconvertible metabolic subtypes such as the glycolytic/basal-like and lipogenic/classical states, each possessing unique dependencies and characteristics, and the ability to switch phenotypes under therapy driving resistance [ 308 ]. This plasticity extends beyond cancer cells to generate a system-wide metabolic rewiring within the TME. Future PDAC treatments need a multilayered approach that includes dynamic subtype identification, targeted pathway intervention, prevention of phenotype switching, and disruption of metabolic cooperation, all within a patient-specific feedback system that can predict and respond to adaptation in real time. Achieving this vision require the integration of functional precision oncology, advanced physiological models, AI-driven analytics, and nanoscale precision targeting. From classifying subtypes to targeting functional subtypes Current metabolomic and transcriptomic classifications provide a static snapshot, but metabolic plasticity represents a dynamic functional state. Future research should focus on real-time functional validation of subtype-specific metabolic dependencies. Techniques such as pharmacoscopy, a high-content imaging drug screening enable rapid, single-cell testing of primary cancer cells against hundreds of drug combinations, helping determine, for example, whether a “glycolytic subtype” tumor is sensitive to GLUT1 or LDHA inhibitors, or whether a “lipogenic subtype” responds to statins or FASN inhibitors [ 458 ]. This method is particularly valuable for prioritizing the numerous preclinical candidates (Table 3 ) that show promise as single agents or in rational combinations, thereby increasing the translational relevance of preclinical findings by directly linking metabolic targeting to functional response. Additionally, BH3 profiling assesses mitochondrial apoptotic priming, providing functional insight into the reliance of a cancer cell on specific anti-apoptotic proteins and their modulation by metabolic states [ 459 ]. For instance, a glycolytic subtype with high oxidative PPP activity may exhibit a BH3 profile indicating dependence on specific pro-survival proteins such as MCL-1 or BCL-2, which could be targeted with BH3 mimetics like venetoclax or navitoclax. This functional approach is essential for identifying synthetic lethal combinations with metabolism inhibitors. A prime example is the targeting of dihydroorotate dehydrogenase (DHODH) in the de novo pyrimidine pathway. Although DHODH inhibition alone shows limited efficacy, integrated multiomic screens revealed that it reprograms the apoptotic proteome, creating a profound dependency on BCL-XL. This functional insight directly enabled a highly effective combination strategy: co-targeting DHODH and BCL-XL synergistically induces apoptosis in PDAC models [ 460 ]. Future efforts should also focus on molecular regulators that maintain metabolic states. Promising targets include oncogenic survival kinases like PIM kinases, and key secreted factors that integrate cancer cell function and plasticity such as trefoil family proteins (TFF1/3), neurotrophic factors like GDNF and ARTN, TGF-β, fibroblast growth factor 4 (FGF4), sonic hedgehog (Shh), secreted protein acidic and rich in cysteine (SPARC), and Complement factor B (CFB) [ 461 – 468 ]. The more effective approaches depend on identifying and pharmacologically targeting pro-survival dependencies, whether they are genetic (e.g., BRCA ) or functional/metabolic (e.g., secreted growth effectors driven pro-survival activities) [ 469 – 471 ]. This approach mirrors the successful paradigm of targeting BRCA mutations with PARP inhibitors, demonstrating how defining a specific molecular dependency can translate into effective, subtype-specific therapy [ 472 ]. Ultimately, the integration of functional assays such as pharmacoscopy and BH3 profiling with multi-omics data will not merely create static maps but dynamic, actionable blueprints. These blueprints will guide the rational design of combination therapies that proactively target the survival mechanisms of each unique metabolic PDAC subtype. Developing dynamic patient avatars: transitioning from static organoids to perfusable metabolic ecosystems Validating these functionally guided combinations requires experimental models that accurately reflect the adaptive pressures of the PDAC TME. Current preclinical models are insufficient, as they cannot replicate the dynamic plasticity among metabolic subtypes or the complex metabolic symbiosis within this ecosystem. Future research should therefore focus on organ-on-a-chip (OoC) perfusable organoid platforms potentially combined with 3D bioprinting to develop dynamic patient avatars. These platforms can mimic nutrient and oxygen gradients, stromal architecture, and multicellular interactions that facilitate adaptation in vivo [ 473 ]. Crucially, they can model specific symbiotic exchanges, such as the lactate shuttle from glycolytic CAFs to oxidative cancer cells, allowing researchers to test whether promising drugs (e.g., a G6PD inhibitor) are countered by stromal rescue via metabolite transfer. Such avatars will be essential for testing advanced “disrupt-and-lock” strategies, which target a key metabolic pathway (e.g., glycolysis) while employing stabilizing therapies (such as an ELAPOR1 mimetic, DRP-104, a Gln antagonist) to lock in a beneficial subtype and monitor symbiosis-driven escape mechanisms. AI-driven ecosystem surveillance and adaptive therapy The complex, multimodal data from functional screens, dynamic avatars, and clinical samples present a significant analytical challenge, requiring novel integrative approaches. Artificial intelligence (AI) and machine learning (ML) hold promise for interpreting these datasets in a unified manner. In principle, powerful ML algorithms could be trained to connect functional signatures, such as BH3 profiling curves, and pharmacoscopic imaging features, with molecular profiles (metabolomic, proteomic) and clinical outcomes [ 474 , 475 ]. For instance, one might envision an AI model that identifies a specific pharmacoscopic “death signature” coupled with a low mitochondrial priming score, thereby predicting resistance to certain monotherapies while indicating sensitivity to a specific nanoscale combination therapy. However, realizing this potential faces substantial hurdles. These models require vast, high-quality, longitudinally curated datasets that currently do not exist at the necessary scale for PDAC. Moreover, the “black-box” nature of many advanced AI systems presents a major limitation for clinical translation, as biological interpretability and physician trust are paramount [ 476 ]. While the concept of real-time AI monitoring remains exploratory, integrating baseline multi-omics data with dynamic liquid biopsy data tracking circulating metabolites (e.g., lactate, acetate), cytokines, and cell-free DNA could allow AI systems to detect early signs of metabolic adaptation. This includes identifying rising immunosuppressive metabolites signaling subtype shifts and increased metabolic cooperation. Future liquid biopsies might involve “functional liquid biopsies,” wherein circulating tumor cells (CTCs) are used for serial, miniaturized BH3 profiling or pharmacoscopy during treatment. An AI platform analyzing this continuous functional and molecular data could identify emerging adaptive mechanisms, such as rising serum pyrimidine precursors indicating G6PD pathway escape, and recommend pre-emptive therapy adjustments weeks before clinical symptoms of progression appear. While AI/ML offers a powerful conceptual framework for addressing metabolic plasticity, its application remains in the early stages of development. Future progress will depend on concerted efforts in data generation, creating interpretable models, and rigorous validation in preclinical and clinical settings. An integrated clinical perspective: the adaptive metabolic feedback loop One could envision a future clinical workflow that moves beyond static, genomics-driven precision oncology toward a fully integrated, functional precision oncology platform. Patient's tumor samples from distinct clinical cohorts are comprehensively analyzed using multi-omics and functional precision assays (e.g., pharmacoscopy and BH3 profiling). An AI system integrates these data to develop a personalized, multi-target treatment plan, for example, a nanocarrier delivering a subtype-specific drug in combination with an agent that disrupts adaptive resistance. A patient-specific OoC avatar then evaluates effectiveness of the treatment and its ability to prevent resistance. During treatment, serial liquid biopsies continuously update the AI, creating a closed-loop system that enables preemptive therapy adjustments. This approach has the potential to transform PDAC management from a trial-and-error process into a proactive, adaptive strategy to counter tumor evolution in real time (Fig. 9 ). Fig. 9. Open in a new tab From static precision oncology to functional precision oncology in PDAC:The figure contrasts conventional, genomics-driven approaches with emerging dynamic, functional platforms for identifying and targeting metabolic vulnerabilities Upper panel (precision oncology) Patient-derived samples from distinct clinical cohorts (e.g., primary, metastatic, recurrent, treatment responders, and non-responders) undergo multi-omics profiling (genomics, epigenomics, transcriptomics, proteomics, metabolomics, and spatial omics). Integrated bioinformatics and artificial intelligence/machine learning (AI/ML) analyses decode these data to identify cohort-specific molecular and cellular signatures. This static, data-centric approach informs target discovery but may not fully capture dynamic tumor behavior or functional therapy responses Lower panel (functional precision oncology) To overcome the limitations of static profiling, dynamic functional assays and advanced models are integrated: Pharmacoscopy & BH3 Profiling: High-content imaging and mitochondrial priming assays to measure real-time, single-cell drug response and apoptotic readiness Nanoparticle Drug Delivery: Engineered carriers for targeted delivery of therapeutics to enhance tumor-specific exposure and reduce systemic toxicity Patient-Derived Organoids (PDOs): Ex vivo models that preserve patient-specific tumor biology for high-fidelity therapy response prediction Organ-on-a-Chip (OoC) Platforms: Microphysiological systems that model the tumor’s metabolic activity, proliferation, and immune microenvironment Circulating Tumor DNA (ctDNA) Analysis: Liquid biopsy for dynamic monitoring of minimal residual disease (MRD) and early detection of recurrence Collectively, this integrative pipeline aims to overcome therapeutic resistance and achieve a durable clinical response in PDAC. This figure is generated using BioRender.com. Conclusion Overcoming PDAC requires understanding its metabolic flexibility as both a cellular trait (subtype plasticity) and an ecosystem characteristic (metabolic symbiosis). By integrating advanced functional-precision techniques, physiologically accurate models, and AI-driven analysis, a therapeutic strategy can be developed that enables dynamic system-wide intervention. This approach represents a shift from merely targeting individual metabolic pathways to modulating the TME, thereby rendering PDAC more susceptible, disrupting cooperative networks, and preventing escape mechanisms. Such an integrated methodology holds promise in rendering PDAC, a highly adaptable and lethal disease, more manageable. Collectively, this comprehensive approach aims to overcome therapeutic resistance and achieve a durable clinical response in PDAC. Acknowledgements We thank the Fall 2024 students enrolled in the course “Molecular Basis of Cancer” (MBC, ID: 76000123 Tsinghua SIGS) for engaging in the discussion on the topic covered in this review article. This work utilized Grammarly (www.grammarly.com) for language editing to ensure grammatical precision and improve overall readability. We utilize Biorender.com to produce the illustrations presented in the review article. Abbreviations ACSS2 Acyl-CoA Synthetase Short-Chain Family Member 2 ADRA2A Adrenoceptor alpha 2 A AhR Aryl Hydrocarbon Receptor ALDOB Aldolase B AMPK AMP-Activated Protein Kinase AP-1 Activator Protein 1 Arg1 Arginase 1 Arg2 Arginase 2 ARTN Artemin ASCT2 Alanine-Serine-Cysteine Transporter 2 ATG1 Autophagy-Related Gene 1 BCAT2 Branched-Chain Amino Acid Transaminase 2 BDHI 3-Hydroxybutyrate Dehydrogenase Type 1 BICC1 BicC Family RNA Binding Protein 1 BCL-2 B-Cell Lymphoma 2 BCL-XL B-Cell Lymphoma Extra Large BH3 BCL-2 Homology 3 BPTES Bis-2-(5-phenylacetamido-1,2,4-thiadiazol-2-yl)ethyl sulfide BRCA1 Breast Cancer Gene 1 BRCA2 Breast Cancer Gene 2 c-JUN Jun Proto-Oncogene c-FOS Fos Proto-Oncogene c-MYC MYC Proto-Oncogene CAMP/PKA Cyclic AMP/Protein Kinase A CD36 Cluster of Differentiation 36 CDKN2A Cyclin-Dependent Kinase Inhibitor 2 A COX2 Cyclooxygenase-2 CPT1 Carnitine Palmitoyltransferase 1 CPT1A Carnitine Palmitoyltransferase 1 A CTLs Cytotoxic T Lymphocytes CYP3A Cytochrome P450 Family 3 Subfamily A dCTP Deoxycytidine Triphosphate EGF Epidermal Growth Factor ELAPOR1/KIAA1324 Endoplasmic Reticulum Stress-Associated Apoptosis Regulator 1 FOXM1 Forkhead Box M1 G6PD Glucose-6-Phosphate Dehydrogenase GDNF Glial Cell Line-Derived Neurotrophic Factor GFPT2 Glutamine-Fructose-6-Phosphate Transaminase 2 GLS1 Glutaminase 1 GLUT1 Glucose Transporter 1 GOT1 Glutamic-Oxaloacetic Transaminase 1 GPR81 G protein-coupled receptor 81 GPR132 G Protein-Coupled Receptor 132 HER (ErbB) Human Epidermal Growth Factor Receptor Family HIF-1α Hypoxia-Inducible Factor 1 Alpha HIF-2α Hypoxia-Inducible Factor 2 Alpha HK2 Hexokinase 2 HuR Human Antigen R IDH1 Isocitrate Dehydrogenase 1 IGF-1 Insulin-Like Growth Factor 1 IL-1β Interleukin-1 Beta IL-6 Interleukin-6 IL-8 Interleukin-8 IL-10 Interleukin-10 IL-12 Interleukin-12 IPO7/8 Importin 7/Importin 8 JAK/STAT Janus Kinase/Signal Transducer and Activator of Transcription KRAS Kirsten Rat Sarcoma Viral Oncogene Homolog LAT2 L-Type Amino Acid Transporter 2 LDHA Lactate Dehydrogenase A LKB1 Liver Kinase B1 LMO3 LIM Domain Only Protein 3 MAPK/ERK Mitogen-Activated Protein Kinase/Extracellular Signal-Regulated Kinase MCL-1 Myeloid Cell Leukemia 1 MCT1 Monocarboxylate Transporter 1 MCT4 Monocarboxylate Transporter 4 MIF Macrophage Migration Inhibitory Factor MiT/TFE Microphthalmia/TFE Transcription Factor Family MLH1 MutL Homolog 1 MSH2/6 MutS Homolog 2/6 mTORC1 Mechanistic Target of Rapamycin Complex 1 MUC1 Mucin 1 MUC5AC Mucin 5AC NAMPT Nicotinamide Phosphoribosyltransferase NF-κB Nuclear Factor Kappa-Light-Chain-Enhancer of Activated B Cells NOS3 Nitric Oxide Synthase 3 NOX2 NADPH Oxidase 2 NR3C2 Nuclear Receptor Subfamily 3 Group C Member 2 NUFIP1 Nuclear Fragile X Mental Retardation Protein Interacting Protein 1 OGA O-GlcNAcase OGT O-GlcNAc Transferase PALB2 Partner and Localizer of BRCA2 PARP Poly(ADP-Ribose) Polymerase PD-L1 Programmed Death-Ligand 1 PDHK1 Pyruvate Dehydrogenase Kinase 1 PFK1 Phosphofructokinase-1 PFKFB3 6-Phosphofructo-2-Kinase/Fructose-2,6-Bisphosphatase 3 PI3K/AKT Phosphoinositide 3-Kinase/Protein Kinase B PIKfyve Phosphoinositide Kinase FYVE-Type Zinc Finger Containing PKM2 Pyruvate Kinase M2 PMS2 Postmeiotic Segregation Increased 2 PON2 Paraoxonase 2 PP2A-B55α Protein Phosphatase 2 A Regulatory Subunit B55 Alpha QKI Quaking Homolog SCD Stearoyl-CoA Desaturase SIRT7 Sirtuin 7 SLC1A5 Solute Carrier Family 1 Member 5 SLC6A14 Solute Carrier Family 6 Member 14 SLC7A5 Solute Carrier Family 7 Member 5 SLC7A8 Solute Carrier Family 7 Member 8 SLC38A1 Solute Carrier Family 38 Member 1 SLC38A2 Solute Carrier Family 38 Member 2 SLC38A9 Solute Carrier Family 38 Member 9 SLC43A1/2 Solute Carrier Family 43 Member 1/2 SMAD4 SMAD Family Member 4 SOAT1 Sterol O-Acyltransferase 1 SOX2 SRY-Box Transcription Factor 2 SP1 Specificity Protein 1 SREBP Sterol Regulatory Element-Binding Protein STAT3 Signal Transducer and Activator of Transcription 3 TNF-α Tumor Necrosis Factor Alpha TGF-β Transforming Growth Factor Beta TIGAR TP53-Induced Glycolysis and Apoptosis Regulator TP53 Tumor Protein p53 UDP-GlcNAc Uridine Diphosphate N-Acetylglucosamine ULK1 Unc-51 Like Autophagy Activating Kinase 1 USP25 Ubiquitin-Specific Peptidase 25 YAP/TAZ Yes-Associated Protein/Transcriptional Coactivator with PDZ-Binding Motif Authors’ contributions J.M. V.B., and V.P. contributed to the conceptualization and writing, original draft preparation. J.M., and V.B. were responsible for writing, editing, figure preparation, and visualization. P.E.L. and V.P. reviewed and edited the manuscript. P.E.L. and V.P. provided supervision, project administration, and resources, and secured funding for the study. All the authors have read and approved the final version of the manuscript. Funding This work was supported by the National Natural Science Foundation of China (82172618); National Key R&D Program of China (2023YFA0913602; 2024YFE0102700 & 2024YFA1700064); Guangdong Basic and Applied Basic Research Foundation (2025A1515012767); Shenzhen Science and Technology Program (ZDSYS20200820165400003 & JCYJ20241202123909013 & WDZC20200821150704001); Tsinghua Shenzhen International Graduate School (JC2024010 & JC2022005 & Department of Chemical Engineering-iBHE Special Collaboration Joint Fund (010201000012021)); Shenzhen Bay Laborator (Oncotherapeutics, 21310031).TBSI Faculty Start-up Funds, China. Data availability No datasets were generated or analysed during the current study. Declarations Ethics approval and consent to participate Not applicable. Competing interests The authors declare no competing interests. Footnotes Publisher’s Note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. 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