Citations

Objective To assess the effect of Moses™ 2.0 (Lumenis Be Ltd., Yokneam Illit, Israel; Boston Scientific, Marlborough, MA, USA) for lithotripsy of renal stones of 8–20 mm in diameter on operative time, surgeon workload, and retropulsion. Subjects/Patients and Methods In our multicentre, randomised, double‐blind clinical trial, patients were randomised 1:1 to standard or Moses 2.0 laser lithotripsy and stratified by stone size. Patients and surgeons were blinded to laser modality. Primary outcomes were total operative time as well as surgeon workload assessed by the National Aeronautics and Space Administration‐Task Load Index (NASA‐TLX). Secondary outcomes were stone retropulsion evaluated by blinded video review and postoperative clinical outcomes. Linear regression modelling was used for analysis. Results A total of 153 patients (79 Standard, 74 Moses 2.0) with similar clinical, stone, and operative characteristics were evaluated. There was no significant difference in operative times between the Standard and Moses 2.0 groups ( P = 0.73). Moses 2.0 was associated with lower effort, frustration, mental, physical, and temporal workload, as well as higher rated overall performance compared to the standard laser. Video review demonstrated no difference in rates of retropulsion ( P = 0.13). Complication rates (Standard: 13%; Moses 2.0: 10%) and stone‐free rates (Standard: 51%; Moses: 2.0 55%) were comparable. Conclusion Our study evaluated the impact of Moses 2.0 technology on high‐powered renal stone dusting, constituting the largest double‐blind trial of its kind to date. There was insufficient evidence to suggest any difference in operative times or clinical outcomes when using Moses 2.0 compared to standard laser lithotripsy. Moses 2.0 was associated with reduced surgeon workload, which has implicit impact on surgeon wellness.

Abstract Background: Deletions in MTAP occur in 10-15% of cancers and are associated with poor prognosis. MTAP deletion (MTAPdel) leads to accumulation of MTA which causes partial inhibition of PRMT5, creating a synthetic lethality approach with PRMT5 inhibitors. Multiple investigational MTA-cooperative PRMT5 inhibitors have demonstrated early clinical efficacy in MTAPdel NSCLC and other cancers. MTAPdel, PRMT5 expression, and clinical outcomes to standard of care immune checkpoint inhibitors (ICI) and chemotherapy (chemo) have not been well characterized. Methods: The Tempus Lens Platform (Tempus AI, Inc., Chicago, IL) was used to query the Tempus multimodal de-identified database and establish and subsequently analyze a cohort of patients (pts)with NSCLC and DNA (Tempus xT) and RNA (Tempus xR) testing, treated with front-line (1L) ICI with or without chemo (N=3,676). PRMT5 expression was normalized as transcripts per million (TPM) and reported as log2(TPM + 1). Pts were classified into high vs low groups based on median PRMT5 expression and into MTAPdel vs intact (MTAPwt) based on MTAP biallelic loss. Immune infiltration was estimated using Quantiseq. Real-world overall survival (rwOS) was defined as the time from 1L start to death or last known follow-up. Real-world progression-free survival (rwPFS) was defined as the time from 1L start to death, progression or last known follow-up. Hazard ratios (HR) were calculated using Cox proportional hazards models and adjusted for age, sex, histology, smoking status and PDL1 status. Results: PRMT5 expression was moderately increased in pts with MTAPdel (6.65 v 6.70, p=0.012). Alterations in MYC, SMARCA4 and KEAP1 were enriched in PRMT5-high tumors in both MTAPdel and MTAPwt pts (p<0.03). Pts with PRMT5-high tumors had decreased infiltrate of multiple immune cells, including B cells, macrophages (M1 and M2), NK cells, CD4 and CD8 T cells (p<0.001). Pts with MTAPdel/PRMT5-high expression had shorter rwOS compared to MTAPwt/PRMT5-low when treated with ICI alone (HR 1.58, 95% CI 1.11-2.24, p=0.011) or with ICI + chemo (HR 1.65, 95% CI 1.34-2.03, p<0.001). These associations persisted on multivariate analysis. Pts with MTAPdel/PRMT5-high expression were also associated with worse rwPFS when treated with ICI + chemo (HR 1.5, 95% CI 1.22-1.86, p<0.001) on univariate and multivariate analysis, but did not associate with rwPFS in pts treated with ICI alone (HR=0.97, 95% CI 0.66-1.42, p=0.9). Conclusion: PRMT5 expression defines an immune suppressed subpopulation of pts with NSCLC. Pts with elevated PRMT5 expression and MTAPdel have poor outcomes with ICI+/- chemo, indicating PRMT5 RNA expression levels can be used to inform clinical trial designs, particularly those that are testing novel combinatorial strategies. Citation Format: Natalie Vokes, Stamatina Fragkogianni, Pooja A. Shah, Santiago G. Trevino, Konstantinos Leventakos, Lei Deng, Matthew Lee, Tali Azenkot, KAYLA LAYNG, JACOB MERCER, Jyoti Patel, Jordi Rodon Ahnert. High PRMT5 expression is associated with decreased immune infiltrate and worse outcomes to immune checkpoint inhibitors in non-small cell lung cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2026; Part 1 (Regular Abstracts); 2026 Apr 17-22; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2026;86(7 Suppl):Abstract nr 2446.

Obesity is a persistent public health issue, often resulting in metabolic complications such as insulin resistance (IR). The secretion of pro-inflammatory cytokines from adipose tissue (AT) is increased during obesity, contributing to the impairment of systemic insulin sensitivity. While interventions in animal models have shown that reducing inflammation restores insulin sensitivity, human studies reducing systemic inflammation have produced inconsistent results. We recently demonstrated that three months of high-dose (4 g/daily) ω-3PUFA (fish oil, FO) supplementation improved insulin sensitivity, and decreased systemic and AT inflammation in individuals with obesity (BMI ≥ 30 kg/m2). Given recent studies highlighting the involvement of microRNA (miRNA) in inflammatory cytokine production, we investigated the effect of ω-3PUFA supplementation on AT miRNA expression in this cohort. AT biopsies were collected before and after ω-3PUFA supplementation. miRNA was processed on the Affymetrix miRNA 4.0 GeneChip and analyzed using existing inflammatory gene sets sourced from MSigDB. Unbiased, differentially expressed miRNA analysis identified miR-4498 and miR-5689 as significantly increased after three months of ω-3PUFA supplementation. Real-time PCR confirmed bioinformatic analysis findings. Our study reports the modulation of miRNA in AT, reductions in systemic and AT markers of inflammation, and the improvement of IR post ω-3PUFA supplementation. Further research is needed to elucidate the link between miR-4498, miR-5689, and whole-body insulin sensitivity.

Abstract Understanding tumor cell dynamics can improve prognosis and treatment but remains limited for lung adenocarcinoma in people who have never smoked (NS-LUAD). With RNA sequencing data from 684 NS-LUAD cases and validation in an independent dataset, we identified three subtypes with distinct phenotypic traits and cell compositions. Additional genomic and histologic data further characterized the subtypes. “Steady,” marked by low proliferation, high alveolar cell fraction, moderate-to-well differentiation, and fewer driver gene alterations, is linked to prolonged survival and low immune evasion. “Proliferative” shows high proliferation markers, TP53 mutations, and gene fusions. “Chaotic,” with high epithelial-to-mesenchymal transition markers, has the worst prognosis, even within stage I tumors. Lacking known molecular or histologic characteristics, this aggressive subtype is solely identified by transcriptomic data. A 60-gene signature recapitulates the classification and predicts survival even within subgroups based on tumor stage or known genomic features, emphasizing its potential for improving early-stage NS-LUAD prognostication in clinical settings. Significance: The transcriptome of 684 NS-LUAD identifies three subtypes with different cellular dynamics and genomic and morphologic features. A 60-gene signature accurately stratifies subjects for mortality risk, even in stage I, offering a potential clinically applicable tool for treatment decision-making in patients with NS-LUAD. See related commentary by Azizi et al., p. 423

Abstract Recent research suggests higher circulating lymphocyte counts may protect against colorectal cancer (CRC). However, the role of specific lymphocyte subtypes and activation states remain unclear. CD4+ T cells—a highly dynamic lymphocyte subtype—undergo gene expression changes upon activation that are critical to their effector function. Previous studies using bulk tissue have limited our understanding of their role in CRC risk to static associations. We applied Mendelian randomization (MR) and genetic colocalisation to evaluate causal relationships of gene expression on CRC risk across multiple CD4+ T cell subtypes and activation states. Genetic proxies were obtained from single-cell transcriptomic data, allowing us to investigate the causal effect of expression of 1,805 genes across CD4+ T cell activation states on CRC risk (78,473 cases; 107,143 controls). Analyses were stratified by CRC anatomical subsites and sex, with sensitivity analyses assessing whether the observed effect estimates were likely to be CD4+ T cell-specific. We identified 6 genes—FADS2, FHL3, HLA-DRB1, HLA-DRB5, RPL28, and TMEM258—with strong evidence for a causal role in CRC development (FDR-P < 0.05; colocalisation H4 > 0.8). Causal estimates varied by CD4+ T cell subtype, activation state, CRC subsite and sex. However, many of genetic proxies used to instrument gene expression in CD4+ T cells also act as eQTLs in other tissues, highlighting the challenges of using genetic proxies to instrument tissue-specific expression changes. We demonstrate the importance of capturing the dynamic nature of CD4+ T cells in understanding CRC risk, and prioritize genes for further investigation in cancer prevention.

Abstract Introduction There is convincing evidence that overall adiposity increases the risks of several cancers. Whether the distribution of adiposity plays a similar role is unclear. Methods We used 2-sample Mendelian randomization (MR) to examine causal relationships of 5 adiposity distribution traits (abdominal subcutaneous adipose tissue (ASAT); visceral adipose tissue (VAT); gluteofemoral adipose tissue (GFAT); liver fat; and pancreas fat) with the risks of 12 obesity-related cancers (endometrial, ovarian, breast, colorectal, pancreas, multiple myeloma, liver, kidney (renal cell), thyroid, gallbladder, esophageal adenocarcinoma, and meningioma). Results Sample size across all genome-wide association studies (GWAS) ranged from 8407 to 728 896 (median: 57 249). We found evidence that higher genetically predicted ASAT increased the risks of endometrial cancer, liver cancer, and esophageal adenocarcinoma (odds ratios (OR) and 95% confidence intervals (CI) per standard deviation (SD) higher ASAT = 1.79 (1.18 to 2.71), 3.83 (1.39 to 10.53), and 2.34 (1.15 to 4.78), respectively). Conversely, we found evidence that higher genetically predicted GFAT decreased the risks of breast cancer and meningioma (ORs and 95% CIs per SD higher genetically predicted GFAT = 0.77 (0.62 to 0.97) and 0.53 (0.32 to 0.90), respectively). We also found evidence for an effect of higher genetically predicted VAT and liver fat on increased liver cancer risk (ORs and 95% CIs per SD higher genetically predicted adiposity trait = 4.29 (1.41 to 13.07) and 4.09 (2.29 to 7.28), respectively). Discussion Our analyses provide novel insights into the relationship between adiposity distribution and cancer risk. These insights highlight the potential importance of adipose tissue distribution alongside maintaining a healthy weight for cancer prevention.

Abstract Background The isocitrate dehydrogenase (IDH) mutation status is a key molecular marker in diagnosing and treating brain tumors. Currently, it is determined via invasive tissue biopsy. Recent advances in deep learning (DL) have offered promising non-invasive alternatives for determining IDH status. However, their clinical translation is hindered by a significant gap between DL predictions and their clinical applicability. The limited transparency of many DL-networks and inadequate evaluation metrics hinders trust and adoption, as clinicians require clear and validated insights for determining IDH status. These challenges highlight the need for robust validation and measures of predictive reliability to make DL-predictions clinically actionable. Methods We developed a unique approach for non-invasive prediction of IDH status using MRI. We combine a voxel-wise-segmentation network(MC-net) with Bayesian logistic regression (BLR) to provide an IDH status and estimate confidence scores. We utilized a comprehensive dataset of 2,481 glioma cases from eight institutions. Results Our framework(MC-net + BLR) demonstrated robust performance achieving 96.4% and 95.1% classification accuracies on diverse databases, with an AUC of 0.98. The BLR was implemented exclusively on held-out test data, ensuring that the derived confidence scores are independent of the training or validation phases. The derived confidence scores showed a low Brier score of 0.0125, highlighting its superior calibration and uncertainty quantification. Conclusion The developed framework provides an IDH status and a confidence score, offering clinicians an additional layer of assurance in prediction reliability. It bridges the gap between high-performing DL models and their clinical applicability by addressing the challenges in prediction reliability. Our framework is a significant advancement in non-invasive determination of IDH-status and confidence-informed therapeutic decision-making in neuro-oncology.

Abstract Pulmonary large cell neuroendocrine carcinoma (LCNEC) is a rare, aggressive lung tumor marked by significant molecular heterogeneity. In a study of 590 patients across two independent cohorts, we observed comparable overall survival across treatment regimens (chemotherapy, chemoimmunotherapy, immunotherapy) without unexpected adverse events. Genomic analysis identified distinct NSCLC-like (KEAP1, KRAS, STK11 mutations) and SCLC-like (RB1, TP53 mutations) LCNEC subtypes, with 80% aligning with SCLC transcriptional profiles. Serial sampling revealed stable mutational but shifting transcriptomic landscapes over time. NSCLC-like LCNECs showed elevated FGL-1 (a LAG-3 ligand) and SPINK1 expression, while SCLC-like subtypes expressed higher levels of DLL3. Immunofluorescence confirmed FGL-1 in NSCLC-like LCNECs, and H&E slide analyses indicated fewer tumor-infiltrating lymphocytes in LCNECs versus other lung cancers. These findings highlight LCNEC’s distinct immunogenomic profile, supporting future investigations into LAG-3, SPINK1, and DLL3-targeted therapies.

Abstract Objective To use an artificial intelligence (AI)‐powered large language model (LLM) to improve readability of patient handouts. Study Design Review of online material modified by AI. Setting Academic center. Methods Five handout materials obtained from the American Rhinologic Society (ARS) and the American Academy of Facial Plastic and Reconstructive Surgery websites were assessed using validated readability metrics. The handouts were inputted into OpenAI's ChatGPT‐4 after prompting: “Rewrite the following at a 6th‐grade reading level.” The understandability and actionability of both native and LLM‐revised versions were evaluated using the Patient Education Materials Assessment Tool (PEMAT). Results were compared using Wilcoxon rank‐sum tests. Results The mean readability scores of the standard (ARS, American Academy of Facial Plastic and Reconstructive Surgery) materials corresponded to “difficult,” with reading categories ranging between high school and university grade levels. Conversely, the LLM‐revised handouts had an average seventh‐grade reading level. LLM‐revised handouts had better readability in nearly all metrics tested: Flesch‐Kincaid Reading Ease (70.8 vs 43.9; P < .05), Gunning Fog Score (10.2 vs 14.42; P < .05), Simple Measure of Gobbledygook (9.9 vs 13.1; P < .05), Coleman‐Liau (8.8 vs 12.6; P < .05), and Automated Readability Index (8.2 vs 10.7; P = .06). PEMAT scores were significantly higher in the LLM‐revised handouts for understandability (91 vs 74%; P < .05) with similar actionability (42 vs 34%; P = .15) when compared to the standard materials. Conclusion Patient‐facing handouts can be augmented by ChatGPT with simple prompting to tailor information with improved readability. This study demonstrates the utility of LLMs to aid in rewriting patient handouts and may serve as a tool to help optimize education materials. Level of Evidence Level VI.