(1) Background: The sensitivity of head and neck squamous cell carcinoma (HNSCC) to ionizing radiation, among others, is determined by the number of cells with high clonogenic potential and stem-like features. These cellular characteristics are dynamically regulated in response to treatment and may lead to an enrichment of radioresistant cells with a cancer stem cell (CSC) phenotype. Epigenetic mechanisms, particularly DNA and histone methylation, are key regulators of gene-specific transcription and cellular plasticity. Therefore, we hypothesized that specific epigenetic targeting may prevent irradiation-induced plasticity and may sensitize HNSCC cells to radiotherapy. (2) Methods: We compared the DNA methylome and intracellular concentrations of tricarboxylic acid cycle metabolites in radioresistant FaDu and Cal33 cell lines with their parental controls, as well as aldehyde dehydrogenase (ALDH)-positive CSCs with negative controls. Moreover, we conducted a screen of a chemical library targeting enzymes involved in epigenetic regulation in combination with irradiation and analyzed the clonogenic potential, sphere formation, and DNA repair capacity to identify compounds with both radiosensitizing and CSC-targeting potential. (3) Results: We identified the histone demethylase inhibitor GSK-J1, which targets UTX (KDM6A) and JMJD3 (KDM6B), leading to increased H3K27 trimethylation, heterochromatin formation, and gene silencing. The clonogenic survival assay after siRNA-mediated knock-down of both genes radiosensitized Cal33 and SAS cell lines. Moreover, high KDM6A expression in tissue sections of patients with HNSCC was associated with improved locoregional control after primary (n = 137) and post-operative (n = 187) radio/chemotherapy. Conversely, high KDM6B expression was a prognostic factor for reduced overall survival. (4) Conclusions: Within this study, we investigated cellular and molecular mechanisms underlying irradiation-induced cellular plasticity, a key inducer of radioresistance, with a focus on epigenetic alterations. We identified UTX (KDM6A) as a putative prognostic and therapeutic target for HNSCC patients treated with radiotherapy.

Overcoming PARPi resistance is a high clinical priority. We established and characterized comparative in vitro models of acquired PARPi resistance, derived from either a BRCA1-proficient or BRCA1-deficient isogenic background by long-term exposure to olaparib. While parental cell lines already exhibited a certain level of intrinsic activity of multidrug resistance (MDR) proteins, resulting PARPi-resistant cells from both models further converted toward MDR. In both models, the PARPi-resistant phenotype was shaped by (i) cross-resistance to other PARPis (ii) impaired susceptibility toward the formation of DNA-platinum adducts upon exposure to cisplatin, which could be reverted by the drug efflux inhibitors verapamil or diphenhydramine, and (iii) reduced PARP-trapping activity. However, the signature and activity of ABC-transporter expression and the cross-resistance spectra to other chemotherapeutic drugs considerably diverged between the BRCA1-proficient vs. BRCA1-deficient models. Using dual-fluorescence co-culture experiments, we observed that PARPi-resistant cells had a competitive disadvantage over PARPi-sensitive cells in a drug-free medium. However, they rapidly gained clonal dominance under olaparib selection pressure, which could be mitigated by the MRP1 inhibitor MK-751. Conclusively, we present a well-characterized in vitro model, which could be instrumental in dissecting mechanisms of PARPi resistance from HR-proficient vs. HR-deficient background and in studying clonal dynamics of PARPi-resistant cells in response to experimental drugs, such as novel olaparib-sensitizers.

PARP-inhibitors (PARPi) are an integral part of ovarian cancer treatment. However, overcoming acquired PARPi resistance or increasing the benefit of PARPi in patients without homologous recombination deficiency (HRD) remains an unmet clinical need. We sought to identify genetic modulators of PARPi response, guiding pharmacological PARPi sensitization. CRISPR-Cas9 mediated loss-of-function screen with a focused sgRNA library revealed that DNA-demethylases JMJD1B/JMJD1C, targetable by the small inhibitor methylstat, promote PARPi resistance. Methylstat synergistically interacted with olaparib, and (re-)sensitized ovarian cancer cells to PARPi treatment, surpassing the efficacy of common demethylase inhibitors. Genetic knockout of JMJD1B and/or JMJD1C phenocopied the effect of methylstat in an additive manner. Validation studies revealed methylstat to be a universal PARPi-sensitizing drug, effective, regardless of PARPi resistance status or BRCA1 mutational background. Methylstat modulated clonal cancer dynamics by mitigating positive selection of PARPi-resistant or BRCA1-proficient cells under olaparib treatment. Using a model of PARPi-induced cellular toxicity, we showed that methylstat impairs cellular DNA repair, indicated by an increased susceptibility of ovarian cancer cells to olaparib-induced DNA double strand breaks after methylstat exposure. This study proposes the histone demethylase inhibitor methylstat as an epigenetic drug for overcoming PARPi-resistance or for increasing efficacy of PARPi beyond HRD in ovarian cancer patients.

Comparative analysis of urinary versus plasma-based metabolomic signature. A, Pairwise correlations between metabolite concentrations in matched plasma and urine samples (n = 161); r denotes Pearson’s coefficient. Asterisks indicate P < 0.05. B, Schematic of model comparisons using urinary metabolites and a previously established plasma signature. C, Time-dependent ROC curves of the urinary model, plasma signature, and combined model. AUC values indicate discriminatory performance for 60-month OS. To account for censoring before 60 months, AUC estimates were calculated using inverse probability of censoring weighting. Analysis was restricted to the complete-case cohort. (B, Image created with BioRender.com. Schwarz, F. [2026] https://BioRender.com/dy8x8k4.)

Patient characteristics at primary diagnosis.

Purpose:Reliable prognostic tools in ovarian cancer are urgently needed to guide risk-adapted treatment decisions, yet the clinical utility of urinary metabolites for noninvasive risk stratification remains largely undefined. Here, we define a clinically relevant urinary metabolite signature that enables noninvasive prognostic risk stratification in ovarian cancer.Experimental Design:We used targeted 1H nuclear magnetic resonance spectroscopy to profile 149 metabolites related to energy metabolism, oxidative stress, mitochondrial function, nitrogen metabolism, amino acid degradation, gut microbiome activity, and inflammation. Metabolites were measured in preoperative urine samples from 199 consecutive patients with newly diagnosed ovarian cancer treated in routine clinical practice between 2013 and 2022.Results:Unsupervised clustering revealed biologically heterogeneous subgroups but lacked prognostic resolution and alignment with overt clinical phenotypes. However, single-metabolite analysis identified a condensed three-metabolite prognostic signature comprising glycine, alanine, and citrate. A final parsimonious model integrating this metabolite signature with clinical covariates outperformed established risk factors alone (Fédération Internationale de Gynécologie et d'Obstétrique stage and surgical outcome), accurately predicted 60-month overall survival (AUC = 0.839), and stratified risk. Patients in the highest-risk quartile (Q4) had markedly shorter progression-free survival [Δmedian ≈ 56 months; HR, 2.63; 95% confidence interval (CI), 1.54–4.52; P < 0.001] and overall survival (Δmedian ≈ 86 months; HR, 2.49; 95% CI, 1.39–4.46; P = 0.009) compared with the lowest-risk group (Q1).Conclusions:We define a urinary three-metabolite signature that enables noninvasive identification of patients with high-risk ovarian cancer beyond established clinical factors. This signature may support molecular stratification and risk-adapted clinical decisions, thereby underscoring the clinical scalability of urine as a matrix for metabolic risk profiling in ovarian cancer.