Citations

Purpose: Prostate-specific membrane antigen positron emission tomography (PSMA-PET) improves prostate cancer (PCa) localization, particularly in biochemical recurrence (BCR). The primary objective was to assess management patterns after PSMA-PET for BCR at 2 academic centers. Methods: This study evaluated clinical management within 6 months after first PSMA-PET for patients with BCR after local therapy. Management categories included no treatment, local therapy, systemic therapy, and metastasis-directed therapy (MDT). We also evaluated clinical management decision variation across strata of PSMA-PET results PSA levels (<2.0, 2.0-4.99, and ≥5.0 ng/mL) at time of imaging. Results: We identified 581 patients who received PSMA-PET from February 2021 to June 2023. PSMA-PET findings were negative in 142 (24%) and demonstrated localized recurrence in 86 (15%), regional nodal disease in 60 (10%), and distant metastasis in 293 (50%). Distant metastasis was identified in 105 (35%) patients with PSA < 2.0, 67 (60%) with PSA 2.0 to 4.99, and 115 (71%) with PSA ≥ 5.0. In patients with negative PSMA-PET, 63 (44%) received treatment. Conversely, 92% with detected metastases were treated (systemic therapy in 189 [64%], MDT in 75 [26%], and local therapy in 7 [2%]). Treatments differed by the PSA level within strata of PSMA-PET findings. Among 293 patients with metastases, MDT was used more commonly in patients with PSA < 2.0 (37%) than PSA 2.0 to 4.99 (30%) or PSA ≥ 5.0 (13%). Conclusions: Most patients undergoing PSMA-PET for BCR had identifiable disease and initiated therapy within 6 months. Identification of distant metastases and treatment choices differed by the PSA level at imaging. Prospective research is needed to determine whether earlier detection and treatment initiation improve clinical outcomes.

Abstract Background In 2021, the American Academy of Physical Medicine and Rehabilitation established the Multi‐Disciplinary Post‐Acute Sequelae of SARS‐CoV‐2 Infection Collaborative to provide guidance from established Long COVID clinics for the evaluation and management of Long COVID. The collaborative previously published eight Long COVID consensus guidance statements using a primarily symptom‐based approach. However, Long COVID symptoms most often do not occur in isolation. Aims This compendium aims to equip clinicians with an efficient, up‐to‐date clinical resource for evaluating and managing adults experiencing Long COVID symptoms. The primary intended audience includes physiatrists, primary care physicians, and other clinicians who provide first‐line assessment and management of Long COVID symptoms, especially in settings where subspecialty care is not readily available. This compendium provides a holistic framework for assessment and management, symptom‐specific considerations, and updates on prevalence, health equity, disability considerations, pathophysiology, and emerging evidence regarding treatments under investigation. Because Long COVID closely resembles other infection‐associated chronic conditions (IACCs) such as myalgic encephalomyelitis/chronic fatigue syndrome, the guidance in this compendium may also be helpful for clinicians managing these related conditions. Methods Guidance in this compendium was developed by the collaborative's established modified Delphi approach. The collaborative is a multidisciplinary group whose members include physiatrists, primary care physicians, pulmonologists, cardiologists, psychiatrists, neuropsychologists, neurologists, occupational therapists, physical therapists, speech and language pathologists, patients, and government representatives. Over 40 Long COVID centers are represented in the collaborative. Results Long COVID is defined by the National Academies of Sciences, Engineering, and Medicine as “an IACC that occurs after SARS‐CoV‐2 infection and is present for at least 3 months as a continuous, relapsing and remitting, or progressive disease state that affects one or more organ systems.” The current global prevalence of Long COVID is estimated to be 6%. Higher prevalence has been identified among female gender, certain racial and ethnic groups, and individuals who live in nonurban areas. However, anyone can develop Long COVID after being infected with the SARS‐CoV‐2 virus. Long COVID can present as a wide variety of symptom clusters. The most common symptoms include exaggerated fatigue and diminished energy windows, postexertional malaise (PEM)/postexertional symptom exacerbation (PESE), cognitive impairment (brain fog), dysautonomia, pain/myalgias, and smell and taste alterations. Holistic assessment should include a traditional history, physical examination, and additional diagnostic testing, as indicated. A positive COVID‐19 test during acute SARS‐CoV‐2 infection is not required to diagnose Long COVID, and currently, there is no single laboratory finding that is definitively diagnostic for confirming or ruling out the diagnosis of Long COVID. A basic laboratory assessment is recommended for all patients with possible Long COVID, and consideration for additional labs and diagnostic procedures is guided by the patient's specific symptoms. Current management strategies focus on symptom‐based supportive care. Critical considerations include energy conservation strategies and addressing comorbidities and modifiable risk factors. Additionally, (1) it is essential to validate the patient's experience and provide reassurance that their symptoms are being taken seriously because many patients have had their symptoms dismissed by loved ones and clinicians; (2) physical activity recommendations must be carefully tailored to the patient's current activity tolerance because overly intense activity can trigger PEM/PESE and worsened muscle damage; and (3) treatment recommendations should be delivered with humility because there are many persistent unknowns related to Long COVID. To date, there are limited data to guide medication management specifically in the context of Long COVID. As such, medication use generally follows standard practice regarding indications and dosing, with extra attention to prioritize (1) patient preference via shared decision‐making and (2) cautious use of medications that may improve some symptoms (eg, cognitive/attention impairment) but may worsen other symptoms (eg, PEM/PESE). Numerous clinical trials are investigating additional treatments. The return‐to‐work process for individuals with Long COVID can be challenging because symptoms can fluctuate, vary in nature, affect multiple functional areas (eg, physical and cognitive), and often manifest as an “invisible disability” that may not be readily acknowledged by employers or coworkers. Clinicians can help patients return to work by identifying suitable workplace accommodations and resources, providing necessary documentation, and recommending occupational or vocational therapy when needed. If these efforts are unsuccessful and work significantly worsens Long COVID symptoms or impedes recovery, applying for disability may be warranted. Long COVID is recognized as a potential disability under the Americans with Disabilities Act. Conclusion To contribute to the overall health and well‐being for all patients, Long COVID care should be delivered in a holistic manner that acknowledges challenges faced by the patient and uncertainties in the field. For more detailed information on assessment and management of specific Long COVID symptoms, readers can reference the collaborative's symptom‐specific consensus guidance statements.

Background: Hypertrophic cardiomyopathy (HCM) is a genetic cardiomyopathy marked by increased left ventricular wall thickness, leading in some cases to left ventricular outflow tract (LVOT) obstruction, heart failure, and arrhythmias. Mavacamten, a selective allosteric inhibitor of cardiac myosin, has demonstrated benefits in improving hemodynamics and reducing LVOT obstruction. However, its impact on arrhythmic burden remains unclear, with reports of early atrial fibrillation (AF) risk contrasting with long-term reductions in arrhythmias. This study assesses the temporal patterns of Holter-detected arrhythmias in HCM patients treated with mavacamten. Methods: This retrospective study included HCM patients from three Mayo Clinic sites. Baseline demographic, clinical, and echocardiographic data were collected. Holter monitoring was performed at baseline, short-term (<6 months), and long-term (>6 months) follow-up. Arrhythmic events, including premature atrial contractions (PACs), premature ventricular contractions (PVCs), and supraventricular tachycardia (SVT), were analyzed using standardized rates per 24 h. Statistical comparisons utilized the Wilcoxon signed-rank test. Results: Twenty-seven patients (56% female, median age 66 years) were included. PACs, PVCs, and SVT duration transiently but not significantly increased at short-term follow-up but returned to baseline at long-term follow-up. No sustained or high-risk ventricular arrhythmias were observed. Conclusions: Mavacamten is associated with transient arrhythmic fluctuations early in treatment, followed by stabilization. These findings support its long-term electrophysiological safety and underscore the need for early rhythm monitoring. Further research should explore its role in arrhythmic risk stratification in HCM patients.

Observational studies have demonstrated that aspirin and/or PTSG2 inhibitor (e.g., celecoxib) use, before or after colon cancer (CC) diagnosis, is associated with a lower risk of recurrence. However, a randomized trial of adjuvant celecoxib therapy in stage III CC did not show improved survival compared to placebo treatment. We therefore hypothesized that immune microenvironment features may identify patient subgroups with stronger survival benefits from anti-inflammatory celecoxib treatment. Drawing from an NCI-Intergroup trial (Cancer and Leukemia Group B [now Alliance for Clinical Trials in Oncology]/SWOG 80702) for patients with stage III resected CC that compared 3 versus 6 months of adjuvant fluorouracil, leucovorin, and oxaliplatin ± 3 years of celecoxib, we conducted tissue-based tumor microenvironment profiling on 1,098 resected tumors using a custom 9-plex, T-cell multiplex immunofluorescence assay coupled with digital image analysis and supervised machine learning. The Kaplan-Meier method was used to describe disease-free survival, based on T cell density, and log-rank testing was performed. Cox proportional hazards models were used to examine unadjusted and adjusted associations between T cell density and disease-free survival (DFS). Two-sided P values ≤0.05 were considered statistically significant. We found that higher CD3+ T cell density was associated with longer DFS with an adjusted hazard ratio (HR) of 0.69 (95% confidence interval [CI] 0.52-0.91) for patients with densities in the highest tertile as compared the lowest tertile (P=0.01). T cell subset analysis showed that stromal regulatory T helper cells (HR 0.42 comparing highest to lowest tertile, CI 0.31-0.58, P&lt;0.001) and stromal memory cytotoxic T cells (HR 0.55 comparing highest to lowest tertile, CI 0.42-0.74, P&lt;0.001) were most strongly associated with longer DFS. Compared to the placebo group, adjuvant celecoxib use associated with improved DFS for patients with low but not high stromal CD3+ T cell density (adjusted HRs of 0.45 [CI 0.31-0.65] for lowest tertile and 1.13 [CI 0.73-1.74] for highest tertile; Pinteraction=0.01). This association was also seen when the analysis was restricted to microsatellite stable tumors (HR 0.50 [CI 0.32-0.80] for lowest density tertile and 1.50 [CI 0.87-2.59] for highest density tertile; Pinteraction=0.01). T cell subset analysis showed that DFS benefit for celecoxib use most strongly associated with stromal memory helper T cell density (HR 0.38 [CI 0.26-0.56] for lowest density tertile and 1.26 [CI 0.80-1.97] for highest density tertile; Pinteraction&lt;0.001). Together, these results show that while higher T cell infiltration is associated with improved DFS, lower T cell infiltration is associated with improved DFS for patients treated with celecoxib. This unexpected finding may inform immunomodulatory treatment strategies for CC. Citation Format: Yasutoshi Takashima, Chao Ma, Qian Shi, Andressa Dias Costa, Tyler Twombly, Juha P. Väyrynen, Melissa Zhao, Ardaman Shergill, Pankaj Kumar, Felix Couture, Philip Kuebler, Smitha Krishnamurthi, Benjamin Tan, Eileen M. O'Reilly, Anthony F. Shields, Shuji Ogino, Charles S. Fuchs, Jeffrey A. Meyerhardt, Jonathan A. Nowak. Prognostic and predictive role of T cell infiltration in stage III colon cancer (CC) treated with celecoxib: CALGB/SWOG 80702 [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 5858.

Abstract Introduction: The binding of Programmed Death-Ligand 1 (PD-L1) on the surface of tumor cells to PD-1 on T cells prevents T-cell-mediated anti-cancer immune responses. Immune checkpoint blockades that target the PD-L1/PD-1 interaction have improved survival rates but have several critical limitations. Post-translational modifications (PTMs) of PD-L1, such as glycosylation, are crucial for its stability and engagement with PD-1. Thus, targeting PD-L1 PTMs may be a novel method to improve immune checkpoint blockade efficacy. The mevalonate pathway (MVP) generates various substances involved in PTMs process. Here, we explore the effect of MVP in altering the PTMs of PD-L1 and its impacts on cell surface PD-L1 function. Methods: The triple-negative human breast cancer (TNBC) cell lines MDA-MB-231 and BT549 were treated with the HMG-CoA reductase inhibitor lovastatin (LOV) with or without the addition of IFN-γ and/or MVP intermediates for 48 hours. PD-L1 transcription was evaluated by qPCR. Alterations in PD-L1 size were detected by Western blotting. PD-L1 surface levels were measured by flow cytometry. HMG-CoA reductase (HMGCR) knockdown was performed to validate the non-off-target effect of LOV. The binding of PD-1 protein to the cell surface PD-L1 was measured by PD-1/PD-L1 binding assay. PD-L1 glycosylation levels were measured by Concanavalin A (Con A) lectin binding assay with immunoprecipitated PD-L1 protein. Results: LOV treatment caused a significant molecular weight shift of PD-L1 from approximately 45 kDa to 40 kDa and reduced the cell surface PD-L1 levels. However, PD-L1 transcription was not affected by LOV. HMGCR knockdown produced similar changes, indicating LOV's impact on PD-L1 was not due to off-target effects. The addition of MVP intermediates mevalonate (MEV) or geranylgeraniol, but not farnesol prevented LOV-induced changes in PD-L1 molecular weight and surface levels. Interestingly, the addition of 25-hydroxycholesterol, but not dolichol, partially prevented LOV-induced decreases in PD-L1 surface levels. Geranylgeranyl transferase inhibitor GGTI-286, which prevents protein geranylgeranylation, also altered PD-L1 molecular weight and decreased surface levels. In addition, LOV impaired the binding of PD-1 protein to tumor cells' surface PD-L1, which was reversed by the addition of MVP substrate MEV. Con A lectin-binding of immunoprecipitated PD-L1 showed that the LOV did not reduce the N-glycosylation level of PD-L1. Conclusions: This study reveals the involvement of the MVP in regulating PD-L1 PTMs and highlights the MVP as a potential therapeutic target to modulate PD-L1’s molecular weight and function and improve the efficacy of immunotherapy interventions, presenting a promising avenue for further investigation. Citation Format: Ruoheng Zhang, Emily J. Koubek, Jessie L. Reed, Jeffrey D. Neighbors, Joel M. Reid, Raymond J. Hohl. The role of the mevalonate pathway in post-translational modifications of PD-L1 [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 4265.

Abstract Introduction: Breast cancer genome-wide association studies (GWAS) have identified over 200 susceptibility loci, many replicated in diverse populations. However, cross-ancestry evaluation of breast cancer genetic architecture remains limited. We examined breast cancer genetic architecture using GWAS summary results from European (EUR; cases (ca) = 188,474, controls (co) = 96,201), East Asian (EAS; ca = 20,393, co = 86,329), African American (AA; ca = 9,235, co = 10,184), and US Hispanic/Latina and Latin American (H/L; ca = 2,396, co =7,468) studies. Methods: GWAS results were derived from the Breast Cancer Association Consortium, the African Ancestry Breast Cancer Genetics Consortium (AABCG), Biobank Japan, and a meta-analysis of five studies of H/L women with breast cancer. Linkage disequilibrium (LD) scores were generated for EUR, EAS, and AMR using the 1000 Genomes Project, while AA LD scores were derived from a subset of AABCG controls. Heritability was estimated for each ancestry group using LD score regression. Genetic correlations between populations were assessed via Popcorn. Polygenicity was analyzed using GENESIS; however, due to the limited sample size in the H/L studies, these estimates were restricted to AA, EAS, and EUR. Enrichment of heritability across regulatory elements was evaluated using stratified LD score regression across all populations. Results: The logit-scale heritability and corresponding standard error (SE) were 0.466 (0.066) for EAS, 0.501 (0.050) for EUR, 0.588 (0.360) for H/L, and 0.614 (0.095) for AA. The estimated number of independent susceptibility loci was 4,446 for EAS, 5,235 for EUR, and 8,308 for AA. Using a clumping and thresholding approach, an optimal set of common variants were projected to explain 38.6% (EUR), 39.4% (EAS), and 26.2% (AA) of genetic variance for samples of 100,000 cases and 100,000 controls with AUCs from corresponding estimated polygenic risk scores (PRSs) of 0.621 (EUR), 0.634 (EAS), and 0.611 (AA). Genetic correlations were strongest between EUR and EAS (ρ = 0.79, SE = 0.08) and EUR and H/L (ρ = 0.68, SE = 0.21), and weakest between AA and EAS (ρ = 0.42, SE = 0.14) and AA and H/L (ρ = 0.26, SE = 0.24). Among 73 genomic features, we found significant (P &lt; 0.05/73) enrichment in heritability in EUR for ‘ancient promoter’ regions, transcription factor binding sites, H3K4me3, ‘super-enhancers’, H3K4me1, H3K27ac; in EAS for ‘super-enhancers’; and in AA for H3K27ac. No genomic features were significantly enriched in H/L, likely due to limited power. Conclusion: These findings suggest a shared breast cancer genetic architecture across diverse populations, as well as the potential for a similar level of breast cancer risk stratification by PRS in these populations. Expanding GWAS in underrepresented populations is essential to improve genetic risk predictions and foster equitable cancer prevention. Citation Format: James L. Li, Maria Zanti, Jacob Williams, Om Jahagirdar, Guochong Jia, Qiang Hu, Jean-Tristan Brandenburg, Li Yan, Weang-Kee Ho, Jingmei Li, José P. Miranda, Devika Godbole, Julie-Alexia Dias, Leila Dorling, Wenlong C. Chen, Nicholas Boddicker, Ying Wang, Alicia Martin, Martin J. Zhang, Yan Zhang, Joe Dennis, Esther M. John, Gabriela Torres-Mejia, Larry Kushi, Jeffrey Weitzel, Susan L. Neuhausen, Luis Carvajal-Carmona, Christopher Haiman, Elad Ziv, Laura Fejerman, Wei Zheng, Dezheng Huo, Douglas Easton, Nilanjan Chatterjee, Peter Kraft, Montserrat Garcia-Closas, Wendy Wong, Kyriaki Michailidou, Qianqian Zhu, Diptavo Dutta, Thomas U. Ahearn, Haoyu Zhang. Genetic architecture of breast cancer across diverse populations: Assessing heritability, genetic correlation, and polygenicity [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 2282.

ABSTRACT Reduced functional capacity and poor sleep quality are common in pulmonary arterial hypertension (PAH). Wearable devices are an emerging, user‐friendly tool to capture activity and sleep information. We aimed to determine whether Fitbit‐derived activity and sleep trends provide clinically meaningful information in patients with PAH. Our prospective observational study recruited patients with PAH from across the United States using remote enrollment strategies and in‐person efforts. Participants wore a Fitbit device for 12 weeks at baseline and a subgroup with 1‐year follow‐up. A matched control cohort was generated from the All of Us Research Program and we evaluated changes in patients with PAH compared to matched controls. Among 110 patients with baseline monitoring, average daily steps correlated with 6MWD ( r = 0.61, p < 0.001) and percent rapid eye movement (REM) sleep ( r = 0.28, p = 0.008). In 44 PAH participants who completed baseline and 1‐year monitoring, there was a group‐time interaction for percent light sleep ( p = 0.024) and percent REM sleep ( p = 0.034), which demonstrated that sleep quality worsened in patients with PAH over 1 year compared to matched controls. Average daily steps decreased in patients with PAH from 5200 [IQR 3212–7458] at baseline to 4651 [IQR 2912–6827] at 1 year ( p = 0.008). In conclusion, our study demonstrated the potential clinical value of wearable devices by showing that activity and sleep quality are reduced in PAH compared to matched controls and these measures decline over time. Future studies should investigate if monitoring these health behaviors detects early functional decline and whether targeted interventions may improve outcomes.

Abstract Background The lupus anticoagulant (LAC) is an important laboratory criterion in the diagnosis of antiphospholipid antibody syndrome. LAC testing at the Mayo Clinic Special Coagulation Laboratory includes up to 13 tests, which are interpreted by trained physicians to identify the presence of LAC and rule out anticoagulant interferences. This feasibility study explored the use of two deep neural network (DNN) architectures for multilabel classification of LAC profiles as a first step toward automating interpretation. Methods Seven thousand two hundred and two retrospective cases were randomly split (64:16:20) for training, validation, and test, respectively. LAC positivity by dilute Russell's viper venom time (LAC-DRVVT) and activated partial thromboplastin time (LAC-APTT) and the presence of warfarin (WAR) and heparin (HEP) were adjudicated by one expert. DNN architectures included: single-output DNNs using domain-knowledge input feature selection and a single-column multioutput DNN using all 13 inputs. Results Domain-knowledge-naïve multioutput DNN achieved similar or improved performance for all 4 label prediction tasks compared with domain knowledge optimized DNNs: F1 scores of 0.977 vs 0.968 for LAC-DRVVT, 0.954 vs 0.945 for LAC-APTT, 0.961 vs 0.957 for HEP, and 0.995 vs 0.977 for WAR, respectively. Conclusions The comparable performance of the 4 domain knowledge optimized DNNs and the multioutput DNN using all 13 input features suggests that the DNN may learn feature importance or mapping to a task without explicit input selection. Given its relative simplicity and versatility, the multioutput DNN is the preferred choice for implementation in a clinical laboratory to standardize LAC diagnosis.