Background and Objectives: Postoperative atrial fibrillation (POAF) is a common complication following cardiac surgery, associated with increased morbidity and prolonged hospital stays. Oxidative stress has been implicated in POAF pathogenesis, with malondialdehyde (MDA), a marker of lipid peroxidation, proposed as a potential biomarker. However, conflicting evidence exists regarding its predictive value. This study aimed to assess the association between serum MDA levels and POAF incidence in patients undergoing cardiac surgery. Materials and Methods: This prospective observational study included 99 consecutive patients undergoing elective on-pump cardiac surgery. Patients with preoperative atrial fibrillation, chronic kidney disease requiring dialysis, or emergency surgery were excluded. Blood samples for MDA measurement were collected at six perioperative time points: preoperatively, intraoperatively after aortic clamp release, and at 8, 24, 48, and 72 h postoperatively. Patients were monitored for new-onset POAF during the first three postoperative days. Statistical analyses included independent samples t-tests, Mann–Whitney U-tests, and Fisher’s exact tests, with significance set at p < 0.05. Results: POAF occurred in 33 (33%) patients. Patients who developed POAF were significantly older (p = 0.017) and had higher EuroSCORE II values (p = 0.019). No significant differences were observed in serum MDA concentrations between POAF and non-POAF patients at any measured time point. The incidence of POAF was higher in patients undergoing valvular surgery (p = 0.014). Conclusions: Serum MDA levels were not associated with POAF development, suggesting that lipid peroxidation alone may not play a central role in POAF pathogenesis. These findings challenge the predictive value of MDA for POAF risk stratification. Future research should explore alternative oxidative stress markers and their potential therapeutic implications in POAF prevention.

ccRCC is marked by niches of immune evasion in late-stage disease, that are associated with resistance to immune checkpoint inhibitors (CPI). Intratumoral microbes are emerging as key modulators of the tumor immune microenvironment and may therefore play a yet unrecognised role in ccRCC disease progression and therapy resistance, marking them as potential biomarkers, and novel therapeutic targets. We extracted bacterial reads from three cohorts of treatment-naïve primary tumors and one cohort of pre- and post-CPI treated tumors: Genomics England Renal (GEL) (636 patients, WGS), TRACERx Renal (154 patients, 629 samples, WGS); TCGA Renal (494 patients, RNA) & ADAPTeR (15 patients, 56 samples, RNA). Bespoke denoising, and decontamination were applied. Live presence of intratumoral bacteria was confirmed through culture and RNAscope. Genera associated with survival were identified using ElasticNet feature selection. We observed significant heterogeneity in bacterial abundance within and across tumors, driven by differences in Cutibacterium abundance. Cutibacterium makes up ∼90% of bacteria in each tumor sample and is enriched in tumors compared to adjacent normal tissue (p=7.1e-10). 9 of 11 colonies grown from two positive tumors were genotyped as Cutibacterium acnes, confirming its live presence and relative abundance in ccRCC tumors. Cutibacterium is higher in late-stage tumors (III&IV) than early-stage tumors (I&II)(p=7.8e-3). Its abundance also separates patients by progression free survival (PFS) and overall survival (OS) (p=9.9e-3, p=0.016) and is higher in CPI non-responders than responders (p=0.028). Association with survival is confirmed in the TCGA cohort independently of stage (PFS = 0.031, OS = 7.3e-4). While other genera associate with survival in either TRACERx or TCGA, only Cutibacterium is prognostic in both. Enrichment analyses of bulk RNA (TRACERx & TCGA), show upregulation of leukocyte taxis and innate immune response with Cutibacterium abundance. To further probe this interaction with the immune microenvironment we are performing single cell spatial transcriptomics, and in vitro co-cultures. Results of these ongoing experiments will be presented at the conference. Cutibacterium either creates or exploits a disease- and CPI-resistance-promoting environment in ccRCC tumors and can therefore act as a prognostic and predictive biomarker. The genus is known for causing chronic inflammation in sebaceous skin follicles, induces Nf-kB signalling, and M2-macrophage differentiation in vitro, and associates with myeloid response in ccRCC tumors. Together this supports a compelling hypothesis that Cutibacterium reduces survival in ccRCC by creating immunosuppressive niches, thereby fostering disease progression and CPI-resistance. Alice C. Martin, Anne-Laure Cattin, Irene Lobon, Zayd Tippu, Fiona Byrne, Charlotte Spencer, Clara Becker, Martha Zepeda Rivera, Krupa Thakkar, Hongui Cha, Angel Fernandez-Sanroman, Annika Fendler, Taja Barber, Leo Bickley, Daqi Deng, Scott Shepherd, Parise Lockwood, Maximiliano Gutierrez, Susan Bullman, Kevin Litchfield, Samra Turajlic. Intratumoral bacteria predict survival in clear cell renal cell carcinoma (ccRCC) [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2025; Part 1 (Regular Abstracts); 2025 Apr 25-30; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2025;85(8_Suppl_1):Abstract nr 2207.

Flow cytometry is a well-established method to analyze cell populations using antibody-based fluorescent detection of protein biomarkers. In this study, we demonstrate the ability to generate intact single cells and perform flow cytometry analysis with two types of formalin fixed tissue: FFPE curls and Representative Samples (RS). RS are homogenized, well-mixed tissue samples from formalin fixed tumors dissected from leftover surgical material. We demonstrate biomarker expression results which correlate with IHC scores. This new method for biomarker quantification may be considered alongside other methods (e.g. digital pathology). Intact single cells were dissociated from RS and FFPE curls using a non-enzymatic, mechanical dissociation method. Cells were stained in suspension for Cytokeratin 8&18 (CK8&18), Ki67, Her2, and DNA content was assessed via DAPI staining and analyzed by flow cytometry. Samples were analyzed on a BD FACSMelody or BD LSR II flow cytometer, and analysis was performed using FCS Express 7 software. Immunohistochemistry (IHC) was performed on the Benchmark ULTRA to compare Ki67 expression (n=78) and Her2 expression (n=16) to the flow analysis. Ki67 expression by IHC was assessed using the international Ki67 working group scoring methods to generate a positive percentage. Her2 expression by IHC was assessed by a pathologist and assigned a score ranging 0 to 3+. Formalin fixed tissue such as RS and FFPE curls can be mechanically dissociated into single cells with intact surface biomarkers. These single cells can be stained in suspension, analyzed via flow cytometry and generate correlating data with both weighted IHC-based scores and clinical IHC scores. The flow analysis of Her2 positive percentage correlates with the IHC scores showing an increasing trend and significant difference between scores for both FFPE and RS. Ki67 expression varied by tumor region using IHC analysis, however by flow cytometry showed a strong correlation with a weighted average across multiregional quantification of Ki67 expression. We demonstrate that millions of intact single cells can be generated from RS and FFPE curls for breast tissue, using non-enzymatic mechanical dissociation methods. Staining in suspension and flow cytometry analysis can be performed in a day for rapid biomarker quantification. Flow cytometry has the potential to analyze FFPE samples using workflows and technologies that have existed in the hematopathology space for decades. Samantha M. Hill, Hannah L. Veloz, Brian Hanley, Tracy Davis, Lisa L. Gallegos, Harold Sasano, Samra Turajlic, Nelson R. Alexander. Optimizing fixed flow cytometry for breast cancer biomarker expression in representative samples and FFPE curls [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2025; Part 1 (Regular Abstracts); 2025 Apr 25-30; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2025;85(8_Suppl_1):Abstract nr 670.

Most human cancers arise from somatic alterations, ranging from single nucleotide variations to structural variations (SVs) that can alter the genomic organization. Pathogenic SVs are identified in various cancer types and subtypes, and they play a crucial role in diagnosis and patient stratification. However, the studies on structural variations have been limited due to biological and computational challenges, including tumor heterogeneity, aneuploidy, and the diverse spectrum of SVs from simpler deletions and focal amplifications to catastrophic events shuffling large fragments from one or multiple chromosomes. Long-read sequencing provides the advantage of improved mappability and direct haplotype phasing. Yet, no tool currently exists to comprehensively analyze complex rearrangements within the cancer genome using long-read sequencing. Here, we present Severus, a tool for somatic SV calling and complex SV characterization using long reads. Severus first detects individual SV junctions from phased split alignments, then constructs a phased breakpoint graph to cluster junctions into complex rearrangement events. We first benchmarked the somatic SV calling performance using six tumor/normal cell line pairs (HCC1395, H1437, H2009, HCC1937, HCC1954, Hs578T). We sequenced all cell lines with Illumina, ONT, and PacBio HiFi. We then established a set of high-confidence calls supported by multiple technologies and tools. Severus consistently had the highest F1 scores compared to the HiFi, ONT, and Illumina methods against this high-confidence SV call set. We then extend our analysis to complex SVs. Severus accurately detected complex events, i.e., chromothripsis and chromoplexy, and templated insertion cycles/chains (TIC), reported for these cell lines. We then compared Severus’ performance with Jabba and Linx, two widely used tools for complex SV calling in short-read sequencing. Our comparison revealed that Severus showed higher agreement with Linx, while Jabba failed to detect most of the SV clusters identified by both Severus and Linx. Severus also outperformed the other tools in characterizing complex reciprocal translocations and TICs. Most of the junctions in complex SVs called by either of the tools but not Severus were either simple SVs with a single long-read junction or were not present in long-read sequencing. In contrast, Severus effectively resolved overlapping SVs by utilizing long-read connectivity, allowing for more accurate clustering of smaller genomic segments. We have also applied Severus to seventeen pediatric leukemia cases. Severus identified two chromoplexy and two cryptic translocations, which were missed by FISH and karyotype panels and were incomplete in Illumina SV calls, further validated by RNA-seq. This highlights the potential of the long-read whole genome sequencing approach for diagnosing complex cases driven by SVs. Ayse Keskus, Asher Bryant, Tanveer Ahmad, Anton Goretsky, Byunggil Yoo, Sergey Aganezov, Ataberk Donmez, Lisa A. Lansdon, Isabel Rodriguez, Jimin Park, Yuelin Liu, Xiwen Cui, Joshua Gardner, Brandy McNulty, Samuel Sacco, Jyoti Shetty, Yongmei Zhao, Bao Tran, Giuseppe Narzisi, Adrienne Helland, Daniel Cook, Pi-Chuan Chang, Alexey Kolesnikov, Andrew Carroll, Erin Molloy, Chengpeng Bi, Adam Walter, Margaret Gibson, Irina Pushel, Erin Guest, Tomi Pastinen, Kishwar Shafin, Karen Miga, Salem Malikic, Chi-Ping Day, Nicolas Robine, Cenk Sahinalp, Michael Dean, Midhat S. Farooqi, Benedict Paten, Mikhail Kolmogorov. Severus: A tool for detecting and characterizing complex structural variants in cancer using long-read sequencing [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2025; Part 1 (Regular Abstracts); 2025 Apr 25-30; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2025;85(8_Suppl_1):Abstract nr 2848.

Melanoma, a highly heterogeneous cancer, evolves through a complex interplay of genetic alterations, including both single nucleotide variants (SNVs) and structural variants (SVs). To study the evolutionary trajectory of melanoma, we established a model system composed of 24 single-cell-derived clonal sublines (C1-C24) from the M4 melanoma model, developed in a genetically engineered hepatocyte growth factor (HGF)-transgenic mouse. While SNVs have been extensively used to construct phylogenetic trees using Trisicell (Triple-toolkit for single-cell intratumor heterogeneity inference), a tool that analyzes intratumor heterogeneity and single-cell RNA mutations, the role and timing of SVs in melanoma evolution remain less well understood. This study integrates SV data with an SNV-driven phylogeny to investigate whether SV patterns align with SNV-based evolutionary trajectories in the mouse melanoma model, providing insights into the functional impact of SVs during tumor progression. We performed long-read sequencing on the 24 clonal sublines and detected SVs using Severus, a tool optimized for phasing in long-read sequencing. The SVs were mapped to the SNV-driven phylogeny using R and classified as either concordant (aligning with the SNV-based tree) or discordant (deviating from the SNV phylogeny). Gene ontology enrichment analysis revealed that concordant SVs were significantly enriched in genes associated with the hepatocyte growth factor receptor signaling pathway and the negative regulation of peptidyl-threonine phosphorylation, both of which represent core drivers of tumor progression. In contrast, discordant SVs were associated with a broader range of functional pathways, including the positive regulation of antigen receptor-mediated signaling and the regulation of natural killer cell-mediated cytotoxicity, though the exact mechanisms underlying these associations remain unclear. By integrating these SVs with an established SNV-driven phylogeny, this study highlights the distinct and critical roles SVs play in melanoma evolution. Concordant SVs appear to drive core oncogenic processes, while discordant SVs may contribute to other aspects of tumor evolution. These findings underscore the importance of considering SVs alongside SNVs to fully capture the complexity of melanoma evolution. Ongoing investigations will continue to explore the functional implications of these SVs and how the gene disruption patterns they cause shape the evolutionary trajectory of melanoma, offering potential targets for future therapeutic strategies. Xiwen Cui, Ayse G. Keskus, Salem Malikic, Yuelin Liu, Anton Goretsky, Chi-Ping Day, Farid R. Mehrabadi, Mikhail Kolmogorov, Glenn Merlino, S. Cenk Sahinalp. Integrating structural variants and single nucleotide variants to uncover evolutionary trajectories in melanoma [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2025; Part 1 (Regular Abstracts); 2025 Apr 25-30; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2025;85(8_Suppl_1):Abstract nr 3898.

Melanoma is the most serious form of skin cancer, developed by the malignant evolution of melanocytes. Malignant melanoma incidence is increasing faster than most other cancers. While stage zero melanoma is highly treatable, survivability dramatically decreases in its advanced stages. Melanoma has shown to be one of the most heterogeneous cancers from RNA and exome analyses by The Cancer Genome Atlas and other groups. A better understanding of the key genomic and epigenomic events that characterize the diverse subclonal populations in melanoma may reveal key insights into what drives its progression and therapeutic resistance. In this study, we leveraged Nanopore long-read sequencing to study the evolution of the mouse B2905 melanoma cell line. Twenty-four distinct clonal sublines were derived in vitro from single cells of the cell line, and the genetically homogeneous population from each subline was sequenced using PromethION R10 flow cells. Enabled by long reads to perform haplotype phasing and accurate structural variation detection, our goal is to integrate small and structural variants to better our understanding of melanoma evolution, and build upon prior analyses of short-read sequenced sublines. We employed multiple SNV calling approaches, including DeepVariant and Clair, in order to provide highly accurate variants for phylogeny reconstruction using Trisicell. We performed structural variant calling with our cancer somatic structural variant (SV) caller Severus as well as copy-number alteration (CNA) analysis with our method Wakhan. Lastly, we placed SNVs, SVs, and CNAs on our reconstructed phylogeny to examine the progression of different types of variants during subline evolution. We identified approximately 560k unique SNVs and around 2, 400 unique SVs. The majority of SNVs (19%) are either clonal or private (73%); however, a meaningful fraction of subclonal variants were available for phylogenetic tree reconstruction. SVs are distributed across the phylogenetic tree branches similarly to SNVs. We identified loss of heterozygosity (LOH) events throughout the subline evolution as well as subclonal CNAs resulting from chromosomal translocations. We find clonal and subclonal evidence of densely clustered SNVs and SV, resembling kataegis; however, our analysis of mutational signatures did not reveal APOBEC-mediated mutations. By analyzing mutational signatures within individual branches of the phylogenetic tree, we observed relative timing of different mutational processes, such as early clonal signatures of UV damage. By incorporating structural variations, copy number changes, and small variant data in the phylogenetic reconstruction, our analysis offers a better characterization of the genetic landscape of subclonal evolution in melanoma. Anton Goretsky, Yuelin Liu, Ayse Keskus, Tanveer Ahmad, Salem Malikic, Glenn Merlino, Chi-Ping Day, Erin Molloy, S. Cenk Sahinalp, Mikhail Kolmogorov. Nanopore sequencing of single-cell derived sublines provides insights into melanoma heterogeneity and evolution [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2025; Part 1 (Regular Abstracts); 2025 Apr 25-30; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2025;85(8_Suppl_1):Abstract nr 7497.

Ethnic villages are examples of tourism products based on historical representations of the region. Within these villages, tourists participate in various customs and traditions, gaining insights into the heritage of local communities. As heritage should be the basis for improving rural tourism, this research was conducted to investigate the extent to which ethnic villages safeguard their heritage. The examination of cultural heritage was carried out by experts who evaluated the importance of the criteria for assessing heritage and the application of cultural heritage in these ethnic villages. A fuzzy approach was used to assess the criteria and ethnic villages using fuzzy Logarithm Methodology of Additive Weights (LMWA) and fuzzy Additive Ratio Assessment (ARAS). The sampling process included an initial pool of 28 ethnic villages identified through various associations and agencies. The villages included in this research were chosen by randomly selecting eight villages using a random number generator. Through collaboration with experts and thorough literature research, 12 criteria were established for evaluating heritage use degree in these villages. Results highlighted tourist participation as the most significant criterion, with the Lubac Valley ethnic village demonstrating superior performance. As this research has shown, applying heritage in tourism provides a unique experience, which is the base for developing rural tourism, and the Lubac Valley could serve as an example to other ethnic villages on building a tourist offer based on heritage. In addition, this research contributes to understanding the current landscape and strengthening the promotion of heritage in ethnic villages by developing a sustainable tourist offer.

We discuss a set of precision observables that can probe the existence of a light particle $X$ coupled to electrons in the mass range of 1--100 MeV. As a case study, we consider the recent excess of $e^+e^-$ final-state events at $\sqrt{s} = 16.9$ MeV reported by the PADME collaboration. Interestingly, this mass is tantalizingly close to the invariant mass at which anomalous $e^+e^-$ pair production has previously been observed in nuclear transitions from excited to ground states by the ATOMKI collaboration. For the scenario in which the new particle has a vector coupling to electrons, we show that the PADME excess is already in tension with constraints from the anomalous magnetic moment of the electron and the non-observation of the exotic pion and muon decays $\pi^+\to e^+ \nu X$ and $\mu^+ \to e^+ \bar\nu_\mu\nu_e X$ at the SINDRUM experiment. Further improvements in the measurement of the electron $g$-2, together with upcoming results from the Mu3e and PIONEER experiments, are expected to definitively probe this scenario in the near future. We also explore alternative possibilities where the new particle has scalar, pseudoscalar, or axial-vector couplings.

The widespread deployment of AI systems has led to overlapping concerns around technological impact and governance, often resulting in conceptual ambiguities and policy confusion. We propose a structured and context-sensitive framework for addressing the ethical implications of artificial intelligence. We argue that ethical frameworks must distinguish between the intended domain of AI deployment and the scale of its societal effects.To resolve these tensions, we introduce a two-dimensional matrix based on (1) the extent of AI’s impact and (2) the scope of its governance, which together form four distinct ethical contexts. Within each quadrant, we explore specific risks, values, and regulatory considerations. This matrix not only clarifies the conceptual terrain of AI ethics but also offers a practical roadmap for anticipating ethical risks, developing normative guidance, and informing domain-specific governance strategies.Our goal is not to prescribe a single ethical doctrine but to provide decision-makers with a structured lens through which AI systems can be evaluated in context. This approach promotes adaptive and anticipatory governance while remaining responsive to local, institutional, and cultural variations.

Background and aim Public health and social measures (PHSM) are critical aspects of limiting the spread of infections in pandemics. Compliance with PHSM depends on a wide range of factors, including behavioral determinants such as emotional response, trust in institutions or risk perceptions. This study examines self-reported compliance with PHSM during the COVID-19 pandemic in the Federation of Bosnia and Herzegovina (FBIH). Materials and methods We analyze the association between compliance and behavioral determinants, using data from five cross-sectional surveys that were conducted between June 2020 and August 2021 in FBIH. Quota-based sampling ensured that the 1000 people per wave were population representative regarding age, sex, and education level based on the data from the latest census in Bosnia and Herzegovina. One-way analysis of variance (ANOVA) was used to identify significant changes between studies on determinants and PHSM measures. Regression was used to find relations between behavioral determinants and PHSM. Results Participants reported strong emotional responses to the rapid spread of the virus and its proximity to them. Risk perception was spiking in December 2020 when rates of infection and death were particularly high. Trends in policy acceptance were divergent; participants did not rate PHSM as exaggerated, but perceived fairness was low. Trust in institutions was low across all waves and declined for specific institutions such as the health ministry. In five wave-specific regression analyses, emotional response (βmin/max = .11*/.21*), risk perception (βmin/max = .06/.18*), policy acceptance (βmin/max = .09/.20*), and trust in institutions (βmin/max = .06/.21*) emerged as significant predictors of PHSM. Conclusions This study contributes to the body of research on factors influencing compliance with PHSM. It emphasizes the importance of behavioral monitoring through repeated surveys to understand and improve compliance. The study also affirms the impact of public trust on compliance, the risk of eroding compliance over time, and the need for health literacy support to help reinforce protective behaviors.