Citations

ABSTRACTPurposeThis study aimed to assess whether kidney stone burden and risk factors at the time of kidney donor evaluation were associated with a symptomatic stone event post‐donor evaluation.MethodsWe identified adults evaluated at Mayo Clinic (two sites) (2000–2011) for living kidney donation and had either a personal history or radiological evidence of kidney stone disease. We analyzed demographics, stone risk factors, stone number/size, and the committee's donation decision and reasons. A follow‐up survey (2022–2023) assessed post‐evaluation symptomatic kidney stones and related morbidity.ResultsAmong 412 potential donors with kidney stone disease, 258 donated, 75 did not donate due to kidney stones, and 79 did not donate for other reasons. Multivariable analysis showed that candidates not donating due to stones had higher body mass index (BMI), prior symptomatic kidney stones, multiple stones on imaging, bilateral kidney stones, and diameter of largest stone ≥3 mm. Of 147 who completed the survey, 26 (18%) had a symptomatic kidney stone post‐donor evaluation. Younger age (p = 0.031) and multiple stones on imaging (p = 0.02) were significant predictors of post‐evaluation symptomatic stones regardless of donation status (p = 0.41).ConclusionsStone burden on imaging and prior symptomatic stone events were associated with not donating. Younger age and stone burden on imaging were the primary risk factors for a symptomatic kidney stone event after donor evaluation.

Pancreatic cystic lesions (PCLs) represent a spectrum of non-neoplasms and neoplasms with varying malignant potential, posing significant challenges in diagnosis and management. While some PCLs are precursors to pancreatic cancer, others remain benign, necessitating accurate differentiation for optimal patient care. Conventional approaches to PCL management rely heavily on radiographic imaging, and endoscopic ultrasound (EUS) guided fine-needle aspiration (FNA), coupled with clinical and biochemical data. However, the observer-dependent nature of image interpretation and the complex morphology of PCLs can lead to diagnostic uncertainty and variability in patient management strategies. This review critically evaluates current PCL diagnosis and surveillance practices, showing features of the different lesions and highlighting the potential limitations of conventional methods. We then explore the potential of artificial intelligence (AI) to transform PCL management. AI-driven strategies, including deep learning algorithms for automated pancreas and lesion segmentation, and radiomics for analyzing heterogeneity, can improve diagnostic accuracy and risk stratification. These advanced techniques can provide more objective and reproducible assessments, aiding clinicians in decision-making regarding follow-up intervals and surgical interventions. Early results suggest that AI-driven methods can significantly improve patient outcomes by enabling earlier detection of high-risk lesions and reducing unnecessary procedures for benign cysts. Finally, this review emphasizes that AI-driven approaches could potentially reshape the landscape of PCL management, ultimately leading to improved pancreatic cancer prevention.

Abstract As the healthcare burden caused by an increasingly aging population rapidly rises, a pressing need exists for innovative geroscience research that can elucidate aging mechanisms and precipitate the development of therapeutic interventions to support healthy aging. The Fifth Annual Midwest Aging Consortium Aging Research symposium, held from April 28 to 30, 2024, was hosted by The Ohio State University in Columbus, Ohio, and featured presentations from investigators across the Midwestern United States. This report summarizes the research presented at the symposium, whose topics included cellular senescence and the aging brain, metabolism and metabolic interventions, nutrition, redox mechanisms and biomarkers, and stress mechanisms. Abstract presentations and short talks highlighted early-stage and young investigators, whereas 2 keynote presentations anchored the symposium. Overall, this symposium showed the robustness of aging research in the Midwest and underscored the advantages of a collaborative approach to geroscience research.

Abstract Background The majority of patients with prostate cancer (PCa) exhibit intrinsic resistance to immune checkpoint blockade (ICB) following radiotherapy (RT). This resistance is generally attributed to the limited antigen presentation of heterogeneous cells within tumors. Here, we aimed to isolate and characterize these diverse subgroups of tumor post‐RT to understand the molecular mechanisms of their resistance to ICB. Methods Single‐cell RNA‐sequencing (scRNA‐seq) was used to profile senescent cancer cell clusters induced by RT in LNCaP cells. The expression and phosphorylation levels of ataxia telangiectasia and Rad3‐related protein (ATR) were assessed by immunohistochemistry in clinical samples from patients with or without RT. Co‐immunoprecipitation, mutagenesis, and Western blotting were used to measure the interactions between proteins. Xenograft experiments were performed to assess the tumor immune response in the mice. Results We identified a subset of PCa cells that exhibited resistance to RT, characterized by a reduced antigen presentation capability, which enhanced their ability to evade immune detection and resist cytotoxic T‐lymphocyte‐associated protein 4 (CTLA‐4) blockade. scRNA‐seq revealed that the senescent state was a transient phase of PCa cells post‐RT, particularly in CTLA‐4 blockade treatment‐resistant cells. This state was marked by increased cytosolic ATR level. Cytosolic ATR phosphorylated CD86 in its cytosolic domain and enhanced the interaction between CD86 and its E3 ligase MARCH1 through electrostatic attraction. Depletion or inhibition of Atr increased the sensitivity to immune attack and improved responses to anti‐Ctla‐4 antibody treatment in a mouse model. Conclusions Our findings indicate that the activation of cytosolic ATR, which is associated with cellular senescence, impedes the effectiveness of combined RT and ICB treatments. This discovery may provide valuable insights for improving the efficacy of combined RT and ICB therapies in PCa.

Abstract Objectives The impact of mild autonomous cortisol secretion (MACS) on cognition is incompletely characterized. We aimed to assess cognition in patients with MACS, identify factors associated with lower cognition, and determine the impact of adrenalectomy on cognition. Methods We conducted a cross-sectional study (4/2019 to 10/2022) and a longitudinal cohort study (10/2021 to 9/2023) of adults with MACS and referent subjects. Cognition was assessed with National Institute of Health Toolbox Cognition Battery and reported as standardized T scores (adjusted for age, sex, race/ethnicity, and education) for fluid, crystallized, and total composite cognition. Participants were assessed with urine steroid profiling (liquid chromatography-tandem mass spectrometry), frailty index, Beck’s Depression Inventory (BDI), and Short Form-36 (SF-36). Results A total of 84 patients (median age 57 years, 67% women) and 201 referent subjects (median age 63 years, 58% women) were included. Patients with MACS demonstrated lower mean (SD) total composite (50.9 [9.3] vs 55.4 [8.9], P < .001) and fluid composite T score (49.3 [10.6] vs 55.2 [10.2], P < .001), but not crystallized composite T score (52.3 [8.4] vs 54.0 [8.4], P = .130) when compared with referent subjects. Multivariable analysis adjusted for age, sex, BDI, SF-36, frailty index, total glucocorticoids, and androgens demonstrated that MACS status was associated with lower cognition. When cognition was reassessed in 37 patients post-adrenalectomy and 31 referent subjects, adjusted group differences of cognition change were similar except for a higher comparative increase in attention and executive functioning in patients with MACS. Conclusion Patients with MACS demonstrate lower cognition than referent subjects. Adrenalectomy for MACS had a minor positive impact on cognition.

Abstract Chemotherapy regimens for triple-negative breast cancer (TNBC) combine agents without knowing which agents drive response. Consequently, predictors derived from multi-agent regimens cannot be assumed to generalize to individual drugs or other regimens, motivating development of treatment-matched predictors. Here, we used TNBC patient-derived xenografts (PDXs) treated with carboplatin, docetaxel, or the combination to deconvolute drug-specific responses and identify associated molecular features. Combination treatment rarely improved upon the best single agent, with enhanced responses in only 13% of PDXs and antagonism in a comparable fraction. Proteogenomic analyses identified high cytokeratin-5 (KRT5) as a general marker of chemotherapy responsiveness and KRT5 immunohistochemistry discriminated responsive PDXs (AUROC, 0.83). To train treatment-specific predictors, we integrated these data with independent PDX and clinical cohorts with responses assessed after anthracycline-free platinum, taxane, or platinum-taxane therapy, ensuring response corresponded to the modeled treatment. Four feature selection strategies yielded largely nonoverlapping biomarker panels converging on treatment-relevant pathways. On independent test data, RNA-based predictors of complete response (CR) to platinum-based (carboplatin or cisplatin) and taxane-based (docetaxel or paclitaxel) chemotherapy achieved AUROCs of 0.80 and 0.86, respectively. For platinum-taxane regimens (carboplatin plus docetaxel or paclitaxel), proteomic-guided feature selection generated a 10-biomarker, protein- informed RNA predictor of pathologic complete response (pCR) that outperformed RNA-only feature selection and achieved an AUROC of 0.85 in an independent clinical cohort, while retaining practicality as an RNA-based assay. Treatment-matched integration of multi-omic PDX and clinical datasets provides a framework for chemotherapy-specific predictors with clinically relevant performance, supporting biomarker-guided precision selection and treatment optimization for patients with TNBC. Statement of significance Integration of multi-omic data from patient-derived xenografts with treatment-matched clinical cohorts yielded three retrospectively validated predictors of platinum, taxane, and platinum+taxane response, enabling biomarker-guided chemotherapy selection for patients with triple-negative breast cancer.