Citations

Abstract Background: Metastatic castration-resistant prostate cancer (mCRPC) resistant to androgen receptor (AR)-targeted agents is often lethal. Unfortunately, biomarkers for this deadly disease remain under investigation, and underpinning mechanisms are ill-understood. Here, we applied epigenomic approaches to identify cell-free (cf) DNA features associated with these lethal AR-resistant mCRPC patients. Methods: Plasma from 99 mCRPC patients was collected from two independent institutions, either prior to first-line AR-targeted therapy (n = 63) or during treatment (n = 36). We applied EnhanceAR-Seq to detect alterations in the AR locus including the upstream enhancer. We then applied genome-wide cfDNA epigenomics to 43 pre-treatment plasma samples and performed both fragmentomics and methylation sequencing to delineate the biology of AR/enhancer-altered lethal mCRPC. Finally, we identified stemness genes by applying CytoTRACE to external single-cell RNA-seq data from mCRPC, and applied these to cfDNA via promoter-level methylation analysis. We then assessed these stem-associated genes in tumor bulk RNA-seq data from an independent mCRPC cohort. Results: AR/enhancer alterations were detected in 44% of pre-treatment cfDNA samples and correlated with significantly worse progression-free survival (PFS) (HR = 2.12, P = 0.01) and overall survival (OS) (HR = 2.48; P = 0.02). AR/enhancer alterations were detected in 19% of on-treatment cfDNA samples and were also associated with profoundly worse PFS (HR = 15.38, P = 0.0003) and OS (HR = 15.53, P = 0.002). Further, genome-wide cfDNA fragmentomic analysis revealed that binding sites for developmentally relevant transcription factors such as FOXA1, NKX-3 and HOXB13 were significantly more accessible in AR/enhancer-altered lethal mCRPC patients. Gene set enrichment analysis of the 20 most accessible transcription factors revealed several pathways associated with stem cell development. Interestingly, the 10 most stem-associated genes obtained using CytoTRACE applied to an external cohort of single-cell RNA-seq data from mCRPC (He et al. 2021) were promoter-hypomethylated in the plasma cfDNA of AR/enhancer-altered lethal mCRPC patients (P = 0.005). Further, metagene analysis of these stem-associated genes revealed worse PFS by Kaplan-Meier analysis (P = 0.04), which was confirmed by multivariate cox proportional hazards modeling. Our stemness metagene further stratified overall survival (P = 0.03) when applied to an external cohort of 80 mCRPC patients from Abida et al. 2019. Conclusions: Alterations in AR including the upstream enhancer are remarkably prognostic in mCRPC with pre-treatment predictive potential. The mechanism can be inferred via cfDNA epigenomic analysis and appears to involve increased tumor cell stemness in AR-altered lethal mCRPC. Citation Format: Pradeep Singh Chauhan, Irfan Alahi, Savar Sinha, Ryan Mueller, Alexander L. Shiang, Jace Webster, Ha X. Dang, Debanjan Saha, Lilli Greiner, Breanna Yang, Elisa M. Ledet, Ramandeep K. Babbra, Wenjia Feng, Peter K. Harris, Faridi Qaium, Ellen B. Jaeger, Patrick J. Miller, Sydney A. Caputo, Oliver A. Sartor, Russell K. Pachynski, Christopher A. Maher, Aadel A. Chaudhuri. Epigenomic analysis of plasma cell-free DNA identifies stemness features associated with worse prognosis in AR-altered lethal metastatic castration resistant prostate cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 983.

Background The rapid evolution of artificial intelligence (AI) in conjunction with recent updates in dual antiplatelet therapy (DAPT) management guidelines emphasizes the necessity for innovative models to predict ischemic or bleeding events after drug‐eluting stent implantation. Leveraging AI for dynamic prediction has the potential to revolutionize risk stratification and provide personalized decision support for DAPT management. Methods and Results We developed and validated a new AI‐based pipeline using retrospective data of drug‐eluting stent‐treated patients, sourced from the Cerner Health Facts data set (n=98 236) and Optum's de‐identified Clinformatics Data Mart Database (n=9978). The 36 months following drug‐eluting stent implantation were designated as our primary forecasting interval, further segmented into 6 sequential prediction windows. We evaluated 5 distinct AI algorithms for their precision in predicting ischemic and bleeding risks. Model discriminative accuracy was assessed using the area under the receiver operating characteristic curve, among other metrics. The weighted light gradient boosting machine stood out as the preeminent model, thus earning its place as our AI‐DAPT model. The AI‐DAPT demonstrated peak accuracy in the 30 to 36 months window, charting an area under the receiver operating characteristic curve of 90% [95% CI, 88%–92%] for ischemia and 84% [95% CI, 82%–87%] for bleeding predictions. Conclusions Our AI‐DAPT excels in formulating iterative, refined dynamic predictions by assimilating ongoing updates from patients' clinical profiles, holding value as a novel smart clinical tool to facilitate optimal DAPT duration management with high accuracy and adaptability.

ImportanceIncreased white matter hyperintensity (WMH) volume is a common magnetic resonance imaging (MRI) finding in both autosomal dominant Alzheimer disease (ADAD) and late-onset Alzheimer disease (LOAD), but it remains unclear whether increased WMH along the AD continuum is reflective of AD-intrinsic processes or secondary to elevated systemic vascular risk factors.ObjectiveTo estimate the associations of neurodegeneration and parenchymal and vessel amyloidosis with WMH accumulation and investigate whether systemic vascular risk is associated with WMH beyond these AD-intrinsic processes.Design, Setting, and ParticipantsThis cohort study used data from 3 longitudinal cohort studies conducted in tertiary and community-based medical centers—the Dominantly Inherited Alzheimer Network (DIAN; February 2010 to March 2020), the Alzheimer’s Disease Neuroimaging Initiative (ADNI; July 2007 to September 2021), and the Harvard Aging Brain Study (HABS; September 2010 to December 2019).Main Outcome and MeasuresThe main outcomes were the independent associations of neurodegeneration (decreases in gray matter volume), parenchymal amyloidosis (assessed by amyloid positron emission tomography), and vessel amyloidosis (evidenced by cerebral microbleeds [CMBs]) with cross-sectional and longitudinal WMH.ResultsData from 3960 MRI sessions among 1141 participants were included: 252 pathogenic variant carriers from DIAN (mean [SD] age, 38.4 [11.2] years; 137 [54%] female), 571 older adults from ADNI (mean [SD] age, 72.8 [7.3] years; 274 [48%] female), and 318 older adults from HABS (mean [SD] age, 72.4 [7.6] years; 194 [61%] female). Longitudinal increases in WMH volume were greater in individuals with CMBs compared with those without (DIAN: t = 3.2 [P = .001]; ADNI: t = 2.7 [P = .008]), associated with longitudinal decreases in gray matter volume (DIAN: t = −3.1 [P = .002]; ADNI: t = −5.6 [P < .001]; HABS: t = −2.2 [P = .03]), greater in older individuals (DIAN: t = 6.8 [P < .001]; ADNI: t = 9.1 [P < .001]; HABS: t = 5.4 [P < .001]), and not associated with systemic vascular risk (DIAN: t = 0.7 [P = .40]; ADNI: t = 0.6 [P = .50]; HABS: t = 1.8 [P = .06]) in individuals with ADAD and LOAD after accounting for age, gray matter volume, CMB presence, and amyloid burden. In older adults without CMBs at baseline, greater WMH volume was associated with CMB development during longitudinal follow-up (Cox proportional hazards regression model hazard ratio, 2.63; 95% CI, 1.72-4.03; P < .001).Conclusions and RelevanceThe findings suggest that increased WMH volume in AD is associated with neurodegeneration and parenchymal and vessel amyloidosis but not with elevated systemic vascular risk. Additionally, increased WMH volume may represent an early sign of vessel amyloidosis preceding the emergence of CMBs.