By generating 194 epigenomic and transcriptomic datasets from 57 human tissue samples using H3K27ac and HIF2α chromatin immunoprecipitation sequencing (ChIP-seq), assay for transposase-accessible chromatin using sequencing (ATAC-seq), and RNA sequencing, we provide a comprehensive, integrated characterization of clear cell renal cell carcinoma (ccRCC) across normal, tumor, and metastatic states. Our analyses provide several insights into ccRCC biology. First, we demonstrate significant reprogramming of enhancer and HIF2α cistromes as well as chromatin accessibility during the normal-to-tumor transition, whereas localized and metastatic tumors show minimal epigenomic differences. Second, we show reactivation of kidney-specific developmental pathways driving malignancy. Third, we perform a cistrome-wide association study in ccRCC, validating five established RCC risk loci and identifying six novel loci, including a locus at 12q24 linked to SCARB1 that was functionally validated. These datasets provide new perspectives on the role of developmental pathways in ccRCC tumorigenesis, insights into epigenetic mechanisms of ccRCC heritability, and a comprehensive epigenomic atlas for the research community.

Supplementary Figure S5: Outcomes based on cell-free DNA tumor fraction

The journal retracts the article titled "Analysis of PMEPA1 Isoforms (a and b) as Selective Inhibitors of Androgen and TGF-β Signaling Reveals Distinct Biological and Prognostic Features in Prostate Cancer" [...].

Supplementary Figure S4: Lollipop plot of ATM pathogenic variants (PVs) in patients with PFS greater than 6 months (A) or less than 6 months

Metabolic reprogramming is a defining feature of cancer; however, how it contributes to therapeutic resistance remains incompletely understood. Here we show that loss of aldo-ketoreductase 1A1 (AKR1A1) in renal cell carcinoma (RCC) and hepatocellular carcinoma (HCC) disrupts terminal glycolytic flux and lactate production through S-nitrosylation-mediated inhibition of pyruvate kinase, resulting in the accumulation of methylglyoxal (MGO). In multiple AKR1A1-deficient models, but not in those endogenously expressing the C423/424 A mutant of pyruvate kinase M2, elevated MGO triggers autophagic degradation of Kelch-like ECH-associated protein 1, leading to Nuclear factor erythroid 2-Related Factor 2 (NRF2) activation and transcriptional reprogramming. This NRF2-driven response enhances chemoresistance and promotes tumor cell migration, two hallmarks of aggressive cancer. Therapeutically, we demonstrate that pharmacological inhibition of the glyoxalase system—the major pathway for MGO detoxification—restores drug sensitivity in patient-derived cells and xenograft models, revealing a context-dependent metabolic vulnerability in AKR1A1 loss conditions. These findings identify AKR1A1 as a metabolic tumor suppressor and uncover crosstalk between S-nitrosylation and glycation as a key regulatory axis linking metabolic reprogramming to NRF2-driven therapy resistance, offering glyoxalase inhibition as a potential precision treatment strategy for RCC and HCC. Aldo-ketoreductase 1A1 (AKR1A1), a detoxifying enzyme, is reported to have an alternative role in regulating S-nitrosylation. Here, the authors show that AKR1A1 regulates S-nitrosylation of PKM2, and its loss leads to metabolic changes that promote chemoresistance and cell migration in liver and renal cancers.

Next generation sequencing based mutational signatures are frequently used to identify tumors with specific DNA repair deficiencies for targeted therapeutic strategies. Although mutational signatures are most commonly derived from whole exome (WES) or whole genome sequencing (WGS) data, more patients currently undergo tumor sequencing using more limited targeted panels that typically encompass several hundred cancer-associated genes. Identifying clinically relevant mutational signatures from targeted panel data requires new approaches capable of deriving signatures from the more limited sequencing data. Here, we derive and validate a panel sequencing-based composite mutational signature associated with nucleotide excision repair (NER) deficiency induced by inactivating ERCC2 mutations in bladder cancer. Using publicly available panel sequencing data, we find that ERCC2 wild type (WT) bladder cancer cases that have high levels of this mutational signature respond better to neoadjuvant platinum therapy and have improved overall survival compared to ERCC2 WT cases with low levels of the signature. We also find that other solid tumor types with ERCC2 mutations also show the characteristic mutational signature seen in NER-deficient ERCC2-mutant bladder cancers, suggesting a novel approach to therapeutically target these ERCC2-mutant solid tumors beyond bladder cancer.

Summary of Treatment Emergent Adverse Events

Gastrointestinal (GI) polyposis is a major risk factor for colorectal cancer (CRC) and a defining feature of hereditary polyposis syndromes such as familial adenomatous polyposis (FAP). Therapy-associated polyposis (TAP), however, is a rare and incompletely characterized condition that develops decades after treatment for childhood or young adult cancers (CYAC), most often following abdominopelvic radiation or exposure to alkylating agents. As long-term CYAC survival improves, the burden of late GI toxicity, including markedly elevated risks of polyps, CRC, and secondary cancers, continues to rise, yet the molecular features of TAP remain poorly understood. Here, we present the largest clinicopathological and genomic study of TAP to date, comprising 29 patients diagnosed at a median age of 49 years and a median latency of 29 years after primary cancer therapy. Most patients (78%) had received alkylating agents and exhibited high rates of secondary malignancies. Histopathology revealed mixed polyp subtypes with a predominance of adenomas. Given these features and the presence of family history in a subset of patients, we investigated the possibility of Hereditary Mixed Polyposis Syndrome (HMPS). Whole-genome sequencing excluded HMPS by demonstrating absence of the canonical 40-kb GREM1 duplication and lack of consistent GREM1 overexpression. Comparative genomic analysis revealed that TAP adenomas exhibit more extensive genome fragmentation and a higher burden of large structural variants than FAP adenomas. Mutational signature profiling identified strong contributions from age-associated signatures (SBS1, SBS5) and a strong, pervasive contribution of the alkylating-agent signature SBS25, even in samples lacking matched normal tissue, whereas platinum-associated SBS31 was minimal. Patient-derived organoids from TAP adenomas showed impaired differentiation, suggesting persistent therapy-induced stem cell dysfunction. Together, these findings define TAP as a distinct polyposis syndrome marked by heterogeneous histology, long latency, profound structural genomic injury, and chemotherapy-specific mutational scars. This work supports early and tailored GI surveillance for CYAC survivors and provides mechanistic insight into the long-term consequences of cytotoxic therapy on intestinal epithelial homeostasis.

Acetaldehyde is the primary metabolite of ethanol, and routes of exposure include endogenous sources, food and cigarette smoke. To explore whether the mutagenic effect of acetaldehyde is responsible for the carcinogenicity of ethanol, we use whole genome sequencing on four human cell lines subjected to long-term, physiologically relevant, analytically validated acetaldehyde treatments. Unexpectedly, the treatments do not induce increased base substitution and short insertion/deletion mutagenesis, nor the appearance of the alcohol-associated cancer mutation signature SBS16. In contrast, we observe large genomic alterations in most cell lines, which parallel the association of 32 kb to 1 Mb deletions and duplications with alcohol consumption in a Japanese gastric cancer cohort. Observations of DNA damage response and a specific requirement for the homologous recombination pathway to tolerate acetaldehyde suggest that DNA breaks are responsible for structural genomic alterations in both cell line and tumour samples, and these may contribute to the carcinogenic effect of acetaldehyde. Acetaldehyde treatment and whole genome sequencing of human cells reveal no increased base substitution mutagenesis but an induction of structural genomic alterations mirrored by the association of similar events with alcohol consumption in cancer.

Identifying clinically relevant synthetic lethal interactions has great potential for uncovering novel therapeutic vulnerabilities in cancer. Current approaches rely on machine learning models that estimate probabilities of synthetic lethal interactions, without supplying explicit knowledge of the underlying biology and lack the human-readable interpretation leading to the prediction. Large Language Models (LLMs) represent a new class of tools capable of reasoning and leveraging extensive biological knowledge acquired from relevant literature during their pretraining. Here, we tested multiple open-weight LLMs for their ability to predict known and novel synthetic lethal interactions. We found that most of the tested models were better at reconstructing the results of three known genome-wide CRISPR knockout screens than random chance, while observed that their performance was related to the parameter-size of the model, and on average benefited little from additional pathway and genetic information apart from what they already possess when estimating the likelihood of a synthetic lethal relationship. After selecting the best-performing and most computationally efficient model for our use case (Qwen2.5-32B-Instruct, 0.715 AUROC), we performed an in silico screen of 398,277 gene pairs from 893 clinically relevant genes. Our goal was to highlight the potential of open-weights LLMs as scalable, context-aware prioritization tools for synthetic lethal interactions, and to lay the groundwork for predicting higher-order genetic interactions. ### Competing Interest Statement Aurel Prosz and Bogumil Zimon are co-founders of PharosBio ApS. Novo Nordisk Foundation, NNF25OC0104818 Breast Cancer Research Foundation, BCRF-23-159 Kræftens Bekæmpelse, R325-A18809, R342-A19788 Det Frie Forskningsr˚ad Sundhed og Sygdom, 2034-00205B Basser Foundation Susan G. Komen Breast Cancer Foundation, https://ror.org/02nadbe75 Ovarian Cancer Research Alliance, CRDGAI-2025-3-1992 National Research, Development, and Innovation Office of Hungary, NKKP-153428

Supplementary Figure S6: Genomic characterization of BRCA2 reversion mutations

Supplementary Figure S2: Kaplan-Meier estimates of overall survival (OS) in patients treated with niraparib across the entire trial population

The standard treatment for stage I lung adenocarcinoma is surgical resection, in most cases without additional systemic adjuvant treatment. A significant proportion of stage I cases recur with a less than 50% 5-year survival rate. There are clinical data suggesting that adjuvant treatment may improve survival in such recurrent cases. However, previously evaluated predictors such as the IASLC grading system from histological sections and transcriptomic profiles have not been sufficiently accurate and consistent for risk stratification and to guide therapeutic interventions. We hypothesized that these previously investigated diverse diagnostic measurements carry complementary information that may provide higher prognostic power when combined. Here we describe a multimodal deep learning method, PATH-ORACLE. This biomarker is built on top of the prospectively validated transcriptomic-based ORACLE score with the addition of routine histological sections processed by pre-trained foundation models. PATH-ORACLE predicts recurrence with an accuracy of over 85% in two independent cohorts. Given further validation this predictor could be used to prioritize stage IB patients for adjuvant chemotherapy in a more consistent fashion. Furthermore, for stage IA cases, PATH-ORACLE, combined with liquid biopsy-based monitoring may help identify high-risk patients suitable for adjuvant targeted therapy.

Patients with High-Grade Serous Ovarian Cancer (HGSOC) exhibit varied responses to treatment, with 20-30% showing de novo resistance to platinum-based chemotherapy. While hematoxylin-eosin (H&E) pathological slides are used for routine diagnosis of cancer type, they may also contain diagnostically useful information about treatment response. Our study demonstrates that combining H&E-stained Whole Slide Images (WSIs) with proteomic signatures using a multimodal deep learning framework significantly improves the prediction of platinum response in both discovery and validation cohorts. This method outperforms the Homologous Recombination Deficiency (HRD) score in predicting platinum response and overall patient survival. The study sets new performance benchmarks and explores the intersection of histology and proteomics, highlighting phenotypes related to treatment response pathways, including homologous recombination, DNA damage response, nucleotide synthesis, apoptosis, and ER stress. This integrative approach has the potential to improve personalized treatment and provide insights into the therapeutic vulnerabilities of HGSOC.

Accurate single-cell phenotypic classification in histopathological tissue sections is essential for understanding tumor behavior, identifying potential therapeutic targets, and improving prognostic assessments in cancer research. In this study, we applied deep learning to classify lung cancer cell phenotypes in hematoxylin and eosin-stained tissue sections. Using 11 whole slide images from 11 patients, we annotated nearly 20,000 cells into seven distinct phenotypes for training and validation. We used a fisheye transformation technique, which modifies images to mimic fisheye camera effects in order to incorporate cellular microenvironment information to enhance deep learning models. We evaluated its effectiveness on lung cancer tissue sections, optimizing transformation parameters and assessing classification performance through multiple cross-validation strategies. Our results demonstrate that the transformation significantly improves classification accuracy, approaching human level performance, particularly for phenotypes that rely on subtle morphological differences. The approach enhances model generalizability across patient samples, highlighting the importance of integrating spatial context in computational pathology. These findings suggest that incorporating adaptive image transformations can significantly improve automated histopathological analysis, with potential implications for more robust and clinically applicable AI-driven diagnostics. ### Competing Interest Statement P.H. is the founder and shareholder of Single-cell technologies Ltd. Department of Defence, Congressionally Directed Medical Research Programs, W81XWH-18-1-0751, W81XWH-22-1-0089 Breast Cancer Research Foundation, BCRF-24-159 TKP2021-EGA09 Horizon-BIALYMPH Horizon-SYMMETRY Horizon-SWEEPICS H2020-Fair-CHARM HAS-NAP3 OTKA-SNN, 139455/ARRS, OTKA-Excellence 2025 Finnish Cancer Society

Human tumors are diverse in their natural history and response to treatment, which in part results from genetic and transcriptomic heterogeneity. In clinical practice, single-site needle biopsies are used to sample this diversity, but cancer biomarkers may be confounded by spatiogenomic heterogeneity within individual tumors. Here we investigate clonally expressed genes as a solution to the sampling bias problem by analyzing multiregion whole-exome and RNA sequencing data for 450 tumor regions from 184 patients with lung adenocarcinoma in the TRACERx study. We prospectively validate the survival association of a clonal expression biomarker, Outcome Risk Associated Clonal Lung Expression (ORACLE), in combination with clinicopathological risk factors, and in stage I disease. We expand our mechanistic understanding, discovering that clonal transcriptional signals are detectable before tissue invasion, act as a molecular fingerprint for lethal metastatic clones and predict chemotherapy sensitivity. Lastly, we find that ORACLE summarizes the prognostic information encoded by genetic evolutionary measures, including chromosomal instability, as a concise 23-transcript assay.

Tumor gene alterations can serve as predictive biomarkers for therapy response. The nucleotide excision repair (NER) helicase ERCC2 carries heterozygous missense mutations in approximately 10% of bladder tumors, and these may predict sensitivity to cisplatin treatment. To explore the clinical actionability of ERCC2 mutations, we assembled a multinational cohort of 2,012 individuals with bladder cancer and applied the highly quantitative CRISPR-Select assay to functionally profile recurrent ERCC2 mutations. We also developed a single-allele editing version of CRISPR-Select to assess heterozygous missense variants in their native context. From the cohort, 506 ERCC2 mutations were identified, with 93% being heterozygous missense variants. CRISPR-Select pinpointed deleterious, cisplatin-sensitizing mutations, particularly within the conserved helicase domains. Importantly, single-allele editing revealed that heterozygous helicase-domain mutations markedly increased cisplatin sensitivity. Integration with clinical data confirmed that these mutations were associated with improved response to platinum-based neoadjuvant chemotherapy. Comparison with computational algorithms showed substantial discrepancies, highlighting the importance of precision functional assays for interpreting mutation effects in clinically relevant contexts. Our results demonstrate that CRISPR-Select provides a robust platform to advance biomarker-driven therapy in bladder cancer and supports its potential integration into precision oncology workflows.

Polymerase theta (POLθ) inhibitors were developed to overcome resistance to PARP inhibitor treatment in homologous recombination (HR) deficient cancer. Biomarkers identifying PARP inhibitor-resistant cancer cases that specifically rely on POLθ activity for cancer cell survival will help to identify the sensitive patient population. From the TCGA RNAseq data, we determined POLθ expression levels, and from whole-exome and whole-genome sequencing data, we determined the number of POLθ-associated mutational signatures in solid tumors with various HR deficiency status. We found that POLθ expression levels did not differ significantly between HR-proficient and HR-deficient cancers. However, POLθ expression correlated strongly with proliferation-associated gene expression signatures and was predominantly observed in the S and G2/M phases of the cell cycle. POLθ-associated mutational signatures are correlated with POLθ expression levels only in BRCA2-deficient cancers. POLθ expression level and POLθ-associated mutational signatures may be indicative of POLθ inhibitor sensitivity in BRCA2-deficient tumors, but are unlikely to be informative in other cancers.