Citations

Abstract Introduction: Underlying tumor biology in metastatic castration-resistant prostate cancer (mCRPC) is highly complex and heterogeneous. To date, mCRPC state biomarker-enriched clinical trial designs incorporating either prognostic or predictive biomarkers are limited to alterations in homologous recombination repair pathway genes. In this study, we use an integrated clinico-molecular prognostic score in mCRPC that combines plasma prognostic proteins with a plasma-based copy number alteration (CNA) risk algorithm (PMID: 40036789; PMID: 36230636) to simulate a biomarker-enriched clinical trial design. Methods: A plasma cell-free DNA (cfDNA) prognostic risk algorithm from eight CNAs (gains in AR, MYC, PIK3CA, PIK3CB; deletions in NKX3-1, TMPRSS2, TP53, ZBTB16) is combined with clinical prognostic proteins (alkaline phosphatase, lactate dehydrogenase) to generate a prognostic risk-score (RS) in three independent treatment-naïve mCRPC cohorts. Using the RS classifier, we estimate the number of high-RS patients (pts) required to be screened from an unselected mCRPC population and the number needed for enrollment to achieve adequate statistical power in a randomized trial for a predefined effect size (Hazard Ratio- 0.70; 80% power). The trial design aims to enrich High-RS pts for targeting lethal mCRPC. Trade-offs are evaluated between number of pts screened and enrolled under varying RS enrichment thresholds and degrees of enrichment. Results: The range of RS distribution in 561 mCRPC pts from three pooled datasets was 0.362-11.737. The median overall survival (OS) (months; 95% Confidence Intervals) based on risk stratification for Low-RS (≤0.8) is 46.2 (38.8-55.4); “Intermediate-risk” (RS: 0.8-2.6) is 27.1(24.3-31.5); High-Risk (RS: >2.6) is 14.9 (11.9-17.6). For a 2-year OS mCRPC endpoint, a biomarker-enriched trial restricted to pts with RS ≥2, which corresponds to the 65th percentile of the RS distribution, would require enrollment of 398 pts, after screening 1,101 mCRPC pts to achieve 80% power to detect a 0.7 treatment vs. control hazard ratio (HR). In contrast, a traditional non-enrichment clinical trial (no molecular classifier) would require 574 pts to achieve to achieve 80% power and still not achieve adequate power to detect strong treatment by risk interactions. As a pragmatic alternative, we evaluated a hybrid enrichment strategy enrolling 50% of pts from the general mCRPC population and 50% of pts with RS ≥1 (41st percentile). This biomarker enriched design which takes 50% pts with a RS of 1.0 (or more) in a trial with 24-month follow-up will require a sample size 525 patients for which 676 pts will be screened to achieve an 80% power to detect a 0.7 treatment vs. control hazard ratio. Conclusion: A plasma-based molecular classifier can be used to design an enrichment trial in biologically defined lethal mCRPC, incorporating 50% of pts with High-RS and yet balance the required sample size and number of pts to be screened enabling an adequately powered molecular classifier-based biomarker-enriched clinical trial. Citation Format: Manish Kohli, Liang Wang, Aik C. Tan, Muhammad Zaki Fadlullah, Anushka Shankar, Sumithra Mandrekar, Benjamin Haaland. Clinico-Genomic Risk Score-Based Biomarker Enrichment Clinical Trial Designs in Poor Prognosis Metastatic Castrate Resistant Prostate Cancer [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Innovations in Prostate Cancer Research and Treatment; 2026 Jan 20-22; Philadelphia PA. Philadelphia (PA): AACR; Cancer Res 2026;86(2_Suppl):Abstract nr B039.

Abstract Background In prostate cancer care, artificial intelligence (AI) models are increasingly used to generate personalized risk predictions by analyzing diverse data inputs, including clinical variables, imaging findings, and genomic indicators such as polygenic risk scores. However, effectively communicating these complex AI-derived risk estimates to patients presents significant challenges. Patients often struggle to understand probabilistic risk information, particularly when advanced algorithms generate it. Traditional cancer risk communication methods might not consider the complexities of AI outputs or differences in patient health literacy and numeracy. We propose a conceptual framework to guide patient-centered communication of AI-driven risk predictions. Methods We reviewed the existing literature on AI-based risk modeling, health risk communication, numeracy, dual-process information processing, and related theories to develop a communication framework designed explicitly for AI-based prostate cancer risk prediction tools. Results The proposed framework emphasizes clear, patient-specific risk communication strategies to enhance understanding and foster trust. It highlights a dual-process approach: offering intuitive visual aids and narrative explanations for quick, instinctive understanding, while also providing accurate statistics and explanations of the AI model’s reasoning for more deliberate processing. By addressing various patient numeracy levels and clearly explaining the AI model’s inputs, assumptions, and uncertainty in plain language, the framework aims to improve understanding and trust in AI-generated risk estimates. Additionally, to promote equity, it includes culturally responsive and accessible communication strategies tailored to patients’ linguistic, literacy, and cultural backgrounds. Conclusion This conceptual framework offers guidance on integrating AI-based risk prediction tools into prostate cancer consultations in a manner that is patient-friendly. By enhancing patients’ understanding of personalized risk information, building greater trust in AI-driven predictions, and addressing various communication needs, the framework helps facilitate more informed decision-making. Ultimately, patient-focused communication can help reduce disparities and promote more equity in prostate cancer care. Citation Format: Tingyu Zou, Folakemi Odedina. A Conceptual Framework for Communicating AI-Based Prostate Cancer Risk Predictions [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Innovations in Prostate Cancer Research and Treatment; 2026 Jan 20-22; Philadelphia PA. Philadelphia (PA): AACR; Cancer Res 2026;86(2_Suppl):Abstract nr A083.

Abstract INTRODUCTION: Prostate cancer (PC) shows marked heterogeneity in clinical outcomes, from indolent to highly aggressive disease. Alterations in TP53, PTEN, RB1, and DNA damage repair genes are linked to poor patient outcomes, yet how the interaction and co-occurrence of these alterations impact prognosis remains unclear. We leveraged the AACR Project GENIE prostate cancer cohort to assess the prognostic impact of individual and concomitant gene alterations, focusing on key PC drivers (BRCA2, TP53, PTEN, RB1). METHODS: the study was conducted in two stages: (i) identification of candidate alterations at individual gene level through a systematic literature review, followed by analyzing the prevalence and co-occurrence of genes in the AACR Project GENIE Public Release (PR) dataset. (ii) Prognostic analysis of the pre-selected biomarkers in the AACR Project GENIE Biopharma Collaborative (BPC) cohort. Analyses were performed in three clinical settings (entire BPC cohort, n=1114; subgroup of patients with localized disease at diagnosis, n=837; and subgroup of patients with metastatic PC with a biopsy collected before or at the time of mPC, n=455) using two statistical approaches: univariate analysis (UA) and inverse probability weighting using propensity scores (PS). Cox models were applied to estimate overall survival hazard ratios (HRs). RESULTS: after integrating the findings from the systematic review with the analysis of the GENIE PR (n=5684), 107 individual genes and 122 co-occurring alterations were selected for study. In the GENIE BPC (n=1114 patients) 75% were M0 at diagnosis, and 67% presented metastatic PC at any time. Alteration prevalence was: TP53, 25%; PTEN, 15%; BRCA2, 4%; RB1, 4%; co-alterations included, PTEN-TP53 (6.4%), RB1-TP53 (2.1%), PTEN-RB1 (1.3%), and BRCA2-TP53 (1.2%). All four key genes showed a statistically significant prognostic impact in the entire cohort with UA (BRCA2, HR:1.9; TP53, HR:2.6; PTEN, HR:2.4; RB1, HR:4.1, all p<0.001). Results were consistent in the subgroup analysis and when using PS. Additional genes such as FANCA, PIK3CA, and MYC were also associated with poor outcomes. Among co-alterations, BRCA2-TP53 (HR:2.0, p=0.05), RB1-TP53 (HR:3.6, p<0.001), PTEN-TP53 (HR:2.4, p<0.001); PTEN-RB1 (HR:5.5, p<0.001), and PTEN-TP53-RB1 (HR:9.0, p<0.001) were the strongest predictors of poor survival, with consistent effects across subgroups and analytical methods. Other combinations including MYC-TP53 (HR 2.2, p<0.001) and PIK3CA-TP53 (HR 3.2, p<0.001), also showed independent prognostic value. CONCLUSIONS: Genomic alterations in TP53, PTEN, RB1, and BRCA2, alone and in combination, independently associate with poor prognosis. Co-alteration patterns such as PTEN-TP53-RB1 define aggressive molecular subsets. This evidence supports integrating genomic interaction patterns for enhancing clinical risk models and guiding precision patient management. Furthermore, this study highlights the potential of AACR Project GENIE to enable data-driven development of biomarker-informed patient care strategies. Citation Format: Pablo Cresta Morgado, Victor Navarro, Guillermo Vilacampa, Haitham Alatoom, Manuel Ramos del Rio, Irbaz Riaz, Shawn Sweeney, Nikolaus Schultz, Ken Kehl, Gregory Riely, Wasim Abida, Katherine Panageas, Deborah Schrag, Philippe Bedard, Christine Micheel, Xindi Guo, Chelsea Nayan, Rodrigo Dientsmann, Joaquin Mateo. Genomic prognostic biomarkers in prostate cancer from the AACR GENIE cohort [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Innovations in Prostate Cancer Research and Treatment; 2026 Jan 20-22; Philadelphia PA. Philadelphia (PA): AACR; Cancer Res 2026;86(2_Suppl):Abstract nr A013.

Background. Acute cellular rejection (ACR) remains a major cause of morbidity after heart transplantation despite advances in immunosuppression. Whole genome transcriptomic profiling offers a systems-based, unbiased approach to elucidate the molecular mechanisms underlying ACR. However, noninvasive, longitudinal biomarker assessments capable of capturing the temporal dynamics of rejection biology remain scarce. Methods. RNA sequencing of peripheral blood from heart transplant recipients before, during, and after ACR was compared with nonrejection controls. Pathway analysis was conducted using differentially expressed genes (DEGs), and a machine learning approach was applied to assess gene-based prediction of ACR. Results. A total of 235 rejection-specific significant DEGs and 863 postrejection DEGs (false discovery rate < 0.05) were identified. During ACR, DEGs were enriched for T-cell activation/differentiation, apoptosis, and B-cell receptor signaling pathways. By combining the 2 sets of DEGs, a panel of 71 common genes was identified that reflected the significant, longitudinal transcriptomic dynamics of ACR. In an elastic net machine learning–based classifier, DYNLL1 and SERF2 were identified as ACR predictive genes, and achieved a cross-validated area under the receiver operating characteristic curve of 0.63. Conclusions. Peripheral blood transcriptomics identify dynamic temporal responses in ACR including T- and B-cell pathways with potential ACR predictive genes that warrant further investigation.

OBJECTIVE: Volatile anesthetics (VAs) are gaining renewed interest as a sedation strategy in the intensive care, offering an alternative to traditional IV agents. VAs provide several pharmacologic advantages, including rapid onset and offset, minimal systemic metabolism, and favorable recovery profiles. Advances in delivery systems enabled the safe and practical administration of volatile agents in the ICU. Thus, we aimed to describe the pharmacology and safety aspects of inhaled agents as well as the systems designed to deliver VAs in the ICU. DATA SOURCES: Relevant literature was identified through PubMed and MEDLINE databases. STUDY SELECTION: Original research, review articles, commentaries, and guidelines addressing safety, efficacy, and use of VAs in adult ICU patients were included. DATA EXTRACTION: Studies were reviewed by the authors, with key findings summarized and organized by pharmacologic properties, delivery systems, and safety domains. DATA SYNTHESIS: VAs are halogenated hydrocarbons whose mechanism of action is not fully understood. Although the CNS is the primary site of action, the end-tidal concentration of exhaled anesthetic is used to monitor clinical effects such as immobility to a noxious stimulus. Inhaled agents have unique pharmacokinetics, minimal metabolisms, and distinct recovery. The side effect profile is also unique, with malignant hyperthermia being the most feared, yet rare complication. Two systems for inhalational sedation delivery are available internationally, with one currently under evaluation in the United States. The systems are composed of a miniature vaporizer, delivery controller, and a monitor. The systems have distinct safety considerations, such as tidal volume limits. CONCLUSIONS: VAs can be used as sedative agents in the ICU. This article comprehensively reviews the pharmacology of VAs along with their safety profile and describes the structure and function of miniature vaporizers currently available on the world market.

Abstract Objective To measure the relative levels of signal and noise in expert diagnosis of epilepsy. Methods Twenty multinational epileptologists independently reviewed 50 vignettes of adult and pediatric patients presenting with suspected seizure(s) on two separate occasions with a ≥30‐day washout period. Experts provided a diagnosis of epilepsy or non‐epilepsy based on clinical information and, if requested, routine EEG and neuroimaging data. Cases had an established clinical diagnosis of epilepsy or non‐epilepsy based on capture of habitual paroxysmal events on video‐EEG or long‐term clinical follow‐up. Experts' judgments were analyzed to decompose variability into different sources: signal (objective differences between cases), level noise (experts' bias toward over/under‐diagnosis), pattern noise (experts' idiosyncratic reactions to specific case features), and occasion noise (inconsistency across occasions). Results The probability of an expert making a different diagnosis for a given case on two different occasions was 16%. The probability of two different experts making a different diagnosis for the same case was 26%. Signal (case “difficulty”) accounted for 66–69% of total variation, with 31–34% attributable to noise. Level noise was the largest contributor in the absence of EEG/neuroimaging results (23%), while pattern noise dominated when test results were available (24%). Occasion noise contributed relatively little (1%) but was still sufficient to cause diagnostic reversals in 16–22% between occasions. Significance The degree of noise in expert diagnosis of epilepsy is substantial, stemming primarily from physicians' idiosyncratic interpretations of case features and variable dispositions toward over‐ or under‐diagnosis. Strategies to improve reliability are needed, including standardized data collection protocols and structured decision algorithms. For “difficult cases,” where expert reliability and accuracy are lowest, our findings support current clinical practice which favors early referral for video‐EEG monitoring over reliance on diagnostic anchoring. This diagnostic pathway may become more accessible with advances in EEG technology (e.g., wearable devices) and artificial intelligence.

Abstract SRD5A3-CDG is a rare autosomal recessive congenital disorder of glycosylation characterized by multisystemic dysfunction, including neurological, psychomotor, cognitive, and visual impairments. Approximately 60 cases have been reported, with treatment limited to symptomatic management. SRD5A3 encodes a polyprenal reductase enzyme essential for synthesizing dolichol, a lipid carrier of the oligosaccharide precursor in N-glycosylation. To address the lack of effective treatments and disease models suitable for high-throughput screening, we developed the first C. elegans model of SRD5A3-CDG, harboring the homozygous W19X nonsense mutation commonly observed in patients. This model recapitulates disease-relevant phenotypes, including developmental delays, neurological dysfunction, and mevalonate pathway dysregulation. Using this model, we conducted a high-throughput motility-based drug repurposing screen and identified atorvastatin, an FDA-approved HMG-CoA reductase inhibitor, as a repurposing candidate. Atorvastatin rescued disease-relevant phenotypes in the worm model and restored polyprenol-to-dolichol ratios in patient fibroblasts. These findings highlight atorvastatin as a promising drug repurposing candidate for SRD5A3-CDG.

ABSTRACT Background The COVID‐19 pandemic presented unprecedented challenges to hospital system and critical care resources, leading to significant changes to operations and patient care. There are limited national data on these changes and instances of unsanctioned deviations from patient care, yet understanding the COVID response is key to future preparedness efforts. We sought to understand how hospitals and states navigated scarcity during COVID‐19, particularly in the absence of a declaration of crisis standards of care. Methods Between February 2022 and September 2022 we conducted 34 interviews with 36 leaders of U.S. states' COVID‐19 planning and response efforts. Interviews were transcribed verbatim and verified. We analyzed interviews using iterative inductive thematic analysis for descriptions of resource scarcity and changes to policies and procedures to prevent rationing lifesaving care. Results Nearly all participants described equipment and personnel scarcity in their home institution or state during COVID‐19. Hospitals across regions and states developed formal and informal coordination processes for load and resource sharing in response to influxes of high‐acuity patients, avoiding formal rationing of lifesaving resources in many regions. Participants also described unsanctioned patient triage, early discharge, and patients counseled to accept less aggressive care (e.g., premature transition to hospice) in states that had not declared crisis standards of care. Conclusions Extending limited resources and inter‐institutional collaboration helped avoid formal rationing. Yet, patient care was unquestionably impacted due to scarcity, both real and perceived. Reports of using hospital triage protocols to deny patients lifesaving care outside of formally recognized crisis conditions and attempts to nudge patients to accept less‐resource‐intensive care are concerning. This may have had disproportionate effects on older adults, individuals with disabilities, and racial and ethnic minoritized groups. To avoid unsanctioned deviations from standard practice in future health emergencies, we recommend that transparent and equitable triage protocols are implemented with robust oversight.

Background In pediatric patients presenting after blunt trauma, the incidence of clinically significant cervical spine injury (CSI) is approximately 1%. Although multidetector CT (MDCT) is widely accepted for cervical spine clearance in adults, its utility in children remains uncertain. The purpose of this study is to evaluate the sensitivity of MDCT in identifying clinically significant CSI in pediatric patients, stratified by age groups: adolescents (12 years to 17 years) and children (0 years to 11 years). Methods We designed a national, multicenter, prospective observational study to assess the diagnostic performance of MDCT in detecting clinically significant CSI in pediatric trauma patients. Data are being collected from participating centers on imaging findings, clinical outcomes, and the presence of confirmed CSIs after blunt trauma. The analysis plan includes stratification by age group and calculation of sensitivity, specificity, and predictive values of MDCT for CSI. Results This abstract outlines the background, methodology, and analytical framework of the study. Data collection and analysis are ongoing. This study represents the largest known cohort of pediatric patients undergoing cervical spine imaging after blunt trauma and is the first prospective study of its kind focused exclusively on this population. Conclusions This study will provide critical data to inform guidelines for pediatric cervical spine clearance after blunt trauma. By evaluating the sensitivity of MDCT in detecting clinically significant CSI, the findings aim to support evidence-based imaging strategies and improve the safety and efficiency of pediatric trauma care.

Abstract Background Pregnancies complicated by gestational diabetes mellitus (GDM) are associated with increased risks of adverse perinatal outcomes, and with long-term metabolic and cardiovascular consequences for both mother and child. The original EMERGE randomized controlled trial (RCT) evaluated the effectiveness of early metformin in addition to usual care in women with GDM on glycaemic control and perinatal outcomes. The EMERGE Mothers and Kids study is a longitudinal follow-up of the EMERGE trial participants, designed to investigate the long-term metabolic, cardiovascular, and neurodevelopmental outcomes in mothers and their offspring. Methods This is a prospective, observational cohort study of participants and their children from the EMERGE trial (NCT06327191). A single follow-up visit will be conducted up to 6 years after the index pregnancy. Maternal assessments include anthropometric data, glucose tolerance, and metabolic parameters. Child assessments include growth metrics, adiposity measured via skinfold thickness, and neurodevelopmental status assessed through validated questionnaires. Additional data on quality of life, mental health, breastfeeding, and health economics will also be collected. Discussion This study aims to provide insights into the long-term safety and efficacy of metformin use during pregnancy and its potential impact on the long term maternal and child health outcomes, contributing to evidence-based management of GDM. Trial registration: ClinicalTrials.gov; Identifier: NCT06327191