Drug repurposing, the identification of new therapeutic applications for existing drugs, has emerged as a pragmatic and cost-efficient strategy to accelerate oncology drug discovery. Faced with rising development costs, protracted timelines, and high attrition rates associated with traditional de novo drug development, repurposing leverages known pharmacokinetics, safety profiles, and manufacturing processes to expedite clinical translation. This review synthesizes current advances in computational and experimental methodologies and mechanistic insights that drive drug repurposing for cancer therapy. In silico strategies, including molecular docking, machine learning, transcriptomic-proteomic signature reversal, and network-based modeling, have enabled rapid identification of repurposable agents by mining multi-omics and historical pharmacological data. Experimental pipelines spanning high-throughput screening, phenotypic assays, biochemical validations, and functional animal models remain essential to establish efficacy and delineate mechanisms of action. Notably, repurposed drugs exhibit anticancer activity by modulating key pathways, including phosphatidylinositol 3-kinase/Ak strain transforming/mechanistic target of rapamycin, mitogen-activated protein kinase/extracellular signal-regulated kinase, wingless-related integration site/β-catenin signaling, as well as redox homeostasis and DNA response. Despite their promise, repurposed candidates face barriers including limited intellectual property protections, dose optimization challenges, and regulatory uncertainty. Moreover, clinical translation is often hindered by insufficient mechanistic understanding and a lack of predictive biomarkers. Integration of multi-omics datasets, explainable artificial intelligence, patient-derived organoids, and clustered regularly interspaced short palindromic repeats-based genetic screens now offers unprecedented precision in identifying context-specific drug effects and synthetic lethal interactions. With cancer causing one in six global deaths and marked by therapeutic resistance and molecular heterogeneity, drug repurposing provides a scalable solution. This approach bridged preclinical insight with clinical application, potentially transforming cancer therapeutics through rational, data-driven innovation.

The mechanism by which membrane-expressed programmed death-ligand 1 (PD-L1), a key immune checkpoint molecule, mediates tumor immune evasion is well established. Recent studies, however, have revealed that PD-L1 can undergo nuclear translocation (nuclear PD-L1, nPD-L1) and contribute to tumor progression through nonimmune-dependent mechanisms. Nuclear translocation of PD-L1 involves p300-mediated acetylation, Huntingtin-interacting protein 1-related protein-dependent endocytosis, and Vimentin-importin α/β pathway-mediated nuclear transport, and is dynamically regulated by histone deacetylase 2-mediated deacetylation. Once localized in the nucleus, PD-L1 promotes angiogenesis and immune evasion by regulating target genes such as early growth response 1. It also participates in nonimmune functions, including DNA damage repair, sister chromatid cohesion, and pyroptosis, exhibiting both pro-tumorigenic and potential tumor-suppressive roles. Moreover, regulation of PD-L1 subcellular localization via MIB2-mediated ubiquitination and small-molecule inhibitors such as BMS1166 offers new insights for targeted therapy. This review systematically summarizes the molecular mechanisms underlying PD-L1 nuclear translocation, its biological functions, roles within the tumor microenvironment, and potential as a biomarker and therapeutic target. Emphasis is placed on addressing functional heterogeneity and mitigating experimental artifacts, which is critical for translational research.

We read with interest the prospective longitudinal study by van Liere et al. on urinary volatile organic compounds (VOCs) for detecting colorectal neoplasia in Lynch syndrome (LS).1 This investigation addresses a critical unmet need, that is, optimizing surveillance in a population with up to 70% lifetime colorectal cancer (CRC) risk, for whom biannual colonoscopy imposes both clinical and psychological burdens.2 The study's methodological rigor, that is, multi-platform VOC profiling [gas chromatography-ion mobility spectrometry (GC-IMS), field asymmetric ion mobility spectrometry (FAIMS), gas chromatography–time-of-flight mass spectrometry (GC-TOF-MS)], blinded analyses, and machine learning models (MLM), sets an important benchmark. The observation that urinary VOC signatures normalize following polypectomy (AUC 0.84 for GC-IMS) is compelling, indicating potential application in personalized, risk-stratified follow-up and, more broadly, in monitoring intervention efficacy. The identification of decanoic acid as a discriminatory metabolite is concordant with accumulating evidence that dysregulated lipid metabolism underpins the earliest stages of CRC. Importantly, the open release of R code for GC-IMS feature selection directly addresses reproducibility, a persistent barrier in biomarker discovery, and provides a transparent framework for validation across independent cohorts. Nonetheless, the diagnostic performance remains modest (65% sensitivity, 70% specificity), markedly lower than faecal VOCs3 reported in the same cohort (95% sensitivity). This discrepancy may reflect issues of dilution and metabolite stability in urine, as well as confounders such as diet, age, and smoking status. Moreover, the limited number of advanced lesions constrains extrapolation to clinically meaningful endpoints such as advanced adenoma or CRC. These limitations typify the translational “valley of death” that continues to challenge the development of single-platform biomarkers. Within the broader biomarker landscape, Potievskaya et al. recently underscored that no single class, whether circulating tumour DNA, exosomes, VOCs, or metabolomics, has yet demonstrated the reproducibility and diagnostic power required for stand-alone CRC detection.4 In parallel, Seum et al.5 emphasized the promise of pre-diagnostic metabolomics, particularly lipid-based panels, which have achieved AUCs of up to 0.95 for CRC precursors in prospective cohorts. Most recently, Abu Bakar et al. systematically reviewed serum metabolite biomarkers for colorectal adenomas (CRA), identifying discriminatory candidates such as benzoic acid, acetate, and lactate (CRA vs. normal), and adenosine, pentothenate, and linoleic acid (CRA vs. CRC), with reported AUCs ranging from 0.7 to >0.9,6 Table 1. Nonetheless, even these serum markers were limited by poor reproducibility and methodological heterogeneity, reflecting recurrent challenges across the biomarker discovery field. We believe that urinary VOCs may find their optimal role not as a solitary test7 but as part of integrated, patient-friendly biomarker panels. Combining VOCs with serum metabolites, polygenic risk scores, metabolomic lipid panels, or faecal immunochemical testing (FIT) could enhance specificity without sacrificing accessibility.8 Moreover, embedding such assays within prospective, multicenter Lynch syndrome registries would allow validation across diverse diets, lifestyles, and geographies, all critical for translation. In sum, van Liere et al. advance the field of volatilomics by demonstrating feasibility, biological plausibility, and transparency in LS surveillance. Their study complements ongoing work in serum and metabolomic biomarkers, reinforcing the view that progress towards precision prevention will require harmonized, multi-omic strategies. Anastasios Koulaouzidis: Conceptualization; supervision; writing – original draft; writing – review and editing. Wojciech Marlicz: Supervision. Ramesh P. Arasaradnam: Conceptualization; writing – review and editing. Artificial intelligence tools (ChatGPT5, OpenAI, San Francisco, CA, USA) were used solely to assist with language editing and style refinement during manuscript preparation. No AI-generated data or figures were included. All scientific content, interpretations, and conclusions were developed, verified, and approved entirely by the authors. Wojciech Marlicz is shareholder and co-founder of Sanprobi sp. z o.o. spk and Endoklinika sp. z o.o. Anastasios Koulaouzidis is a co-founder and shareholder of AJM Med-i-caps Ltd.; consultant for Jinshan Science & Technology Ltd.; recipient of research grants from Given Imaging Ltd. (via ESGE) and IntroMedic and a former co-founder and director of iCERV Ltd. Anastasios Koulaouzidis also received lecture honoraria from Covidien/Medtronic, Jinshan, Dr. Falk Pharma UK, Ferring, Aquilant, Almiral and has advisory roles for Tillots, Ankon, and Dr. Falk Pharma UK. Ramesh P. Arasaradnam declares no conflict of interest.

Patients with recurrent/metastatic nasopharyngeal carcinoma (RM-NPC) do not yet have a strong recommended regimen after failure of first-line systemic therapy. This study retrospectively analyzed the efficacy of immunotherapy regimens in RM-NPC that failed at least first-line therapy. From February 2014 to August 2023, a total of 794 patients with RM-NPC were included in this study, of which 75 patients received anti-programmed cell death protein-1 (PD-1) only (P), 130 patients received anti-PD-1 plus antiangiogenic therapy (AP), 210 patients received anti-PD-1 plus single-agent chemotherapy (C1P), 276 patients received anti-PD-1 plus two-agent chemotherapy (C2P), and 103 patients received anti-PD-1 plus antiangiogenic plus chemotherapy (CAP). Progression-free survival (PFS), overall survival (OS), and adverse events were analyzed. In the inverse probability of treatment weighting (IPTW) cohort, median follow-up time was 28.7 months, median PFS in the P, AP, C1P, C2P, and CAP were 3.6, 8.5, 7.6, 12.7, and 16.3 months, respectively. PFS was significantly better in the CAP, C2P, C1P, and AP than P (p values of <.001, <.001, .026, and .002). Median OS in the P, AP, C1P, C2P, and CAP were 35.8, 46.6, 50.9, 50.6, and 47.2 months, respectively. Grade 3-4 treatment-related adverse events (TRAEs) occurred in 288 patients, with rates significantly higher in the C1P than AP (34.3% vs. 16.9%) and higher in the C2P than CAP (54.0% vs. 42.7%). These results showed that, in RM-NPC patients who failed at least first-line therapy, anti-PD-1 combination therapy was associated with improved PFS compared with anti-PD-1 monotherapy.

Asia faces a rising cancer burden, with type 2 diabetes mellitus (T2DM) prevalence in cancer patients at 24.4% (2.3-fold higher than that of the general population). Antineoplastic therapies (e.g., anthracyclines and vascular endothelial growth factor receptor [VEGFR] inhibitors) increase cardiorenal toxicity, exacerbated by T2DM. Sodium-glucose cotransporter 2 inhibitors (SGLT2i) benefit non-oncologic populations but are understudied in T2DM-cancer patients. We assessed SGLT2i's effects on antineoplastic-related cardiorenal toxicity/survival via a multicenter retrospective cohort of 248 T2DM-cancer patients (56.0% male, mean age 66.7 years) 1:1 divided into SGLT2i/non-SGLT2i groups. Over 30.4 months of follow-up, SGLT2i reduced cardiovascular events by 86.1% (odds ratio [OR] = 0.139, 95% CI 0.027-0.710, p = .018) and all-cause mortality by 84.7% (HR = 0.153, 95% CI 0.055-0.431, p < .001). Echocardiography showed improved left ventricular ejection fraction (LVEF) and reduced left ventricular end-diastolic diameter (LVID)/left atrial (LA) dimensions (all p < .001). Despite lower baseline estimated glomerular filtration rate (eGFR) in the SGLT2i group, SGLT2i preserved renal function (post-treatment eGFR: 99.8 vs. 67.5 mL/min/1.73 m2; p = .054) and reduced uric acid (UA) levels by 23.6% (p < .001). Benefits persisted across solid tumor/hematological malignancies and anthracycline-based therapy, targeted antineoplastic therapy or immunotherapy subgroups, with no increase in infection. SGLT2i improve cardiorenal outcomes and survival in T2DM-cancer patients warranting prospective validation.

Oxaliplatin-based regimens are increasingly used in total neoadjuvant therapy (TNT) for locally advanced rectal cancer, frequently causing dose-limiting chemotherapy-induced peripheral neuropathy (CIPN), whose extent and impact on health-related quality of life (HrQoL) remain insufficiently characterized. The optimal duration and intensity of chemotherapy within TNT for tumor response and toxicity remain unclear. This retrospective multicenter study (DRKS00033000) assessed the dose-response relationship of CIPN with cumulative oxaliplatin dose (cOXAd) and its impact on HrQoL. A total of 227 patients (164 men) with a median age of 63 (Q1-Q3: 54-68) years, who underwent oxaliplatin-based TNT between 2015 and 2024, were analyzed. HrQoL was assessed cross-sectionally during follow-up using the EORTC QLQ-C30 and QLQ-CIPN20 questionnaires. Multivariable logistic regression was applied to evaluate the relationship between cOXAd and CIPN grade ≥2, and known-group comparisons examined differences in HrQoL scores. At follow-up, 61 patients (26.9%) experienced CIPN grade ≥2 after a median cOXAd of 758.8 mg/m2. Higher cOXAd was associated with an increased likelihood of CIPN grade ≥2 (adjusted OR 1.004, 95% CI: 1.002-1.007), corresponding to a 7.8% higher probability per additional FOLFOX cycle (+85 mg/m2 cOXAd). Patients with CIPN grade ≥2 reported lower mean QLQ-C30 Global Health Scores and Summary Scores (-13.0 and -12.4 points; Cohen's d = -0.59 and -0.68) and higher QLQ-CIPN20 Summary Scores (+23.9 points; Cohen's d = 1.65) compared to those without CIPN. These findings indicate that CIPN is a common dose-dependent toxicity of oxaliplatin-based TNT, associated with considerably reduced HrQoL and markedly increased neuropathy-related symptom burden at follow-up.

Epithelial ovarian cancer (EOC) is a highly aggressive malignancy with poor prognosis. Thus, new treatment options are needed. Recently, lipid metabolism in EOC has been highlighted. However, the specific lipid molecules activating lipid metabolism remain unclear. This study aimed to elucidate lipid metabolism in EOC and evaluate the potential of its inhibition as a therapeutic approach. We obtained high-fat diet (HFD)-fed mouse serum and performed metabolome analysis to identify lipid molecules contributing to cell proliferation of EOC. We also analyzed which signaling pathway was activated by the lipid molecule. Finally, we demonstrated the inhibition of lipid metabolism in EOC cells. HFD significantly promoted tumor growth of EOC cells in vivo, and HFD-fed mouse serum promoted EOC cell proliferation in vitro. Metabolome analysis identified cholesterol (C27H46O) as a key molecule in HFD-fed mouse serum. Cholesterol (C27H46O) activated the Akt/mTOR signaling pathway in vitro. Cholesterol (C27H46O) is an important component of lipid rafts, and its inhibitor, which extracts cholesterol (C27H46O) from lipid rafts, inactivated the Akt/mTOR signaling pathway and suppressed subsequent EOC cell proliferation. Exogenous cholesterol (C27H46O) contributed to cell proliferation of EOC via the lipid rafts-Akt/mTOR signaling pathway, and its inhibition undoubtedly presents a novel therapeutic strategy for EOC.

Estrogen receptor-positive (ER+) breast cancer accounts for the majority of breast cancer cases worldwide, yet the long-term efficacy of endocrine therapy is limited by resistance and recurrence. While tumor-intrinsic mechanisms of endocrine resistance are well established, growing evidence highlights the contributions of innate immune cells and the tumor microenvironment (TME) in shaping therapeutic outcomes. This review synthesizes recent advances into how tumor-associated macrophages (TAMs), natural killer (NK) cells, myeloid-derived suppressor cells (MDSCs), and tumor-associated neutrophils (TANs) collectively foster an immunosuppressive TME that undermines endocrine responsiveness. Central to this crosstalk is the STAT3 signaling pathway, which integrates inflammatory and metabolic stress signals to drive immune reprogramming, promotes tumor progression, and facilitates therapy resistance. By activating tolerogenic pathways and inhibiting anti-tumor immunity, STAT3 provides a mechanistic link between innate immune dysregulation and endocrine resistance. Preclinical studies demonstrate that STAT3 inhibition can restore tamoxifen sensitivity in resistant ER+ breast cancer models, highlighting its therapeutic potential. These insights reveal the immunological complexity of endocrine resistance and provide rationale for combinatorial strategies integrating endocrine therapy with immunomodulation. Future approaches that incorporate STAT3 inhibitor, immune checkpoint blockade, and biomarker-guided patient selection may transform the management of ER+ breast cancer, offering more durable and clinically meaningful outcomes by acknowledging the emerging interactions between immune dysregulation and metabolic stress in resistant ER+ breast cancer.

Lung cancer, the leading cause of global cancer-related mortality, is categorized into small-cell and non-small-cell subtypes. The heterogeneous non-small-cell lung cancer group is further subcategorized primarily into adenocarcinoma, squamous cell carcinoma, and large cell carcinoma, each underpinned by distinct molecular alterations. Although traditional serum biomarkers aid in subtype differentiation and treatment monitoring, their utility is limited by challenges such as poor specificity due to inflammatory confounders and the difficulty of dynamically tracking therapeutic resistance. Recent advances have identified emergent subtype-specific biomarkers that reflect metabolic reprogramming, epigenetic dysregulation, stemness signatures, and interactions within the immune microenvironment. By integrating analytes such as ctDNA, exosomal RNAs, and urinary DNA with multi-analyte panels and advanced imaging, liquid biopsies offer a promising avenue to enhance early detection accuracy, prognostication, and dynamic therapy monitoring. Nevertheless, the clinical adoption is hindered by several challenges, including incomplete validation, the need for technical standardization, intratumoral heterogeneity, and inter-ethnic variability. The convergence of artificial intelligence (AI)-enhanced multi-omics with biomarker-guided therapeutics represents a transformative strategy with the potential to overcome resistance, mitigate ethnic disparities, and ultimately transform lung cancer into a chronic, manageable disease. Therefore, prioritizing clinically validated AI-integrated platforms is pivotal to achieve precision oncology.