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.

A liver-specific Alb-FlpO mouse line was generated. By coupling it with other existing CreERT or Cre lines, the dual recombinase approach can enable sequential gene modifications within the same cell or across various cell types in an organism for liver research through temporal and spatial gene manipulations.

This study presents a streamlined, cost-effective genotyping workflow for clustered regularly interspaced short palindromic repeats (CRISPR)-CRISPR-associated protein 9 (Cas9) edited mouse models, focusing on trypsinogen genes. It simplifies initial F0 mouse screening using PCR and Sanger sequencing without needing exact sequences. For F1 mice, precise editing is identified through Sanger sequencing and algorithm decoding. The workflow includes a novel PCR strategy for distinguishing homozygous and heterozygous statuses in subsequent generations, effective for small deletions, multiple-gene knockouts, and knockins.

Abstract Accumulating evidence suggests that changes in the tumor microenvironment caused by radiotherapy are closely related to the recurrence of glioma. However, the mechanisms by which such radiation‐induced changes are involved in tumor regrowth have not yet been fully investigated. In the present study, how cranial irradiation‐induced senescence in non‐neoplastic brain cells contributes to glioma progression is explored. It is observed that senescent brain cells facilitated tumor regrowth by enhancing the peripheral recruitment of myeloid inflammatory cells in glioblastoma. Further, it is identified that astrocytes are one of the most susceptible senescent populations and that they promoted chemokine secretion in glioma cells via the senescence‐associated secretory phenotype. By using senolytic agents after radiotherapy to eliminate these senescent cells substantially prolonged survival time in preclinical models. The findings suggest the tumor‐promoting role of senescent astrocytes in the irradiated glioma microenvironment and emphasize the translational relevance of senolytic agents for enhancing the efficacy of radiotherapy in gliomas.

Abstract Background Efficient and accurate delineation of organs at risk (OARs) is a critical procedure for treatment planning and dose evaluation. Deep learning‐based auto‐segmentation of OARs has shown promising results and is increasingly being used in radiation therapy. However, existing deep learning‐based auto‐segmentation approaches face two challenges in clinical practice: generalizability and human‐AI interaction. A generalizable and promptable auto‐segmentation model, which segments OARs of multiple disease sites simultaneously and supports on‐the‐fly human‐AI interaction, can significantly enhance the efficiency of radiation therapy treatment planning. Purpose Meta's segment anything model (SAM) was proposed as a generalizable and promptable model for next‐generation natural image segmentation. We further evaluated the performance of SAM in radiotherapy segmentation. Methods Computed tomography (CT) images of clinical cases from four disease sites at our institute were collected: prostate, lung, gastrointestinal, and head & neck. For each case, we selected the OARs important in radiotherapy treatment planning. We then compared both the Dice coefficients and Jaccard indices derived from three distinct methods: manual delineation (ground truth), automatic segmentation using SAM's ’segment anything’ mode, and automatic segmentation using SAM's ‘box prompt’ mode that implements manual interaction via live prompts during segmentation. Results Our results indicate that SAM's segment anything mode can achieve clinically acceptable segmentation results in most OARs with Dice scores higher than 0.7. SAM's box prompt mode further improves Dice scores by 0.1∼0.5. Similar results were observed for Jaccard indices. The results show that SAM performs better for prostate and lung, but worse for gastrointestinal and head & neck. When considering the size of organs and the distinctiveness of their boundaries, SAM shows better performance for large organs with distinct boundaries, such as lung and liver, and worse for smaller organs with less distinct boundaries, like parotid and cochlea. Conclusions Our results demonstrate SAM's robust generalizability with consistent accuracy in automatic segmentation for radiotherapy. Furthermore, the advanced box‐prompt method enables the users to augment auto‐segmentation interactively and dynamically, leading to patient‐specific auto‐segmentation in radiation therapy. SAM's generalizability across different disease sites and different modalities makes it feasible to develop a generic auto‐segmentation model in radiotherapy.

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.

Abstract Ruxolitinib reduces spleen volume, improves symptoms, and increases survival in patients with intermediate- or high-risk myelofibrosis. However, suboptimal response may occur, potentially because of signaling via the phosphoinositide 3-kinase (PI3K)/protein kinase B pathway. This phase 2 study evaluated dosing, efficacy, and safety of add-on PI3Kδ inhibitor parsaclisib for patients with primary or secondary myelofibrosis with suboptimal response to ruxolitinib. Eligible patients remained on a stable ruxolitinib dose and received add-on parsaclisib 10 or 20 mg, once daily for 8 weeks, and once weekly thereafter (daily-to-weekly dosing; n = 32); or parsaclisib 5 or 20 mg, once daily for 8 weeks, then 5 mg once daily thereafter (all-daily dosing; n = 42). Proportion of patients achieving a ≥10% decrease in spleen volume at 12 weeks was 28% for daily-to-weekly dosing and 59.5% for all-daily dosing. Proportions of patients achieving ≥50% decrease at week 12 in Myelofibrosis Symptom Assessment Form and Myeloproliferative Neoplasms Symptom Assessment Form symptom scores were 14% and 18% for daily-to-weekly dosing, and 28% and 32% for all-daily dosing, respectively. Most common nonhematologic treatment-emergent adverse events were nausea (23%), diarrhea (22%), abdominal pain and fatigue (each 19%), and cough and dyspnea (each 18%). New-onset grade 3 and 4 thrombocytopenia were observed in 19% of patients, each dosed daily-to-weekly, and in 26% and 7% of patients dosed all-daily, respectively, managed with dose interruptions. Hemoglobin levels remained steady. The addition of parsaclisib to stable-dose ruxolitinib can reduce splenomegaly and improve symptoms, with manageable toxicity in patients with myelofibrosis with suboptimal response to ruxolitinib. This trial was registered at www.clinicaltrials.gov as #NCT02718300.

320 Background: sRT is used in men with prostate cancer (PC) recurrence following radical prostatectomy (RP) signaled by a persistent or delayed elevation in PSA. It was previously reported that the use of AAT with RT improved cancer control and overall survival (OS). Long-term follow-up (FU) results are presented here. Methods: From 1998to 2003, 760 eligible post-RP patients with Stage pT3N0 or with pT2N0 and positive margins and PSA from 0.2 to 4.0 ng/mL were randomly assigned on a double-blinded, placebo-controlled trial of RT + placebo vs. RT + anti-androgen therapy (AAT) (24 months of bicalutamide, 150mg daily) during and after RT (64.8 Gy in 36 fractions prostate bed). The primary endpoint was OS estimated using Kaplan-Meier method. Time to PC death and metastatic PC (competing risk of death without an event) was estimated using cumulative incidence. The hazard ratio (HR) was obtained using Cox models (OS) and subdistribution HRs (sHRs) used the Fine-Gray model (time to PC death and metastatic PC). All tests are one-sided based on study design. Results: Median FU for surviving patients was 19 years. OS at 18 years was 53% [95% confidence interval (CI): 47-58%] for RT + AAT and 43% [95% CI: 38-48%] for RT + placebo (adjusted HR 0.82 [95% CI: 0.67-1.00], p = 0.025). The 18-year incidence of centrally reviewed PC deaths were 18% [95% CI: 14-22%] RT + AAT and 28% [95% CI: 23-32%] RT + placebo (adjusted sHR = 0.57 [95% CI: 0.42-0.78], p<0.001). The 18-year incidence of metastatic PC was 22% [95% CI: 18-26%] and 31% [95% CI: 26-35%] for AAT and placebo arms respectively with adjusted sHR = 0.59 [95% CI: 0.44-0.80, p<0.001]. Subgroup analyses is shown in the Table. Conclusions: Long term results of 24-month duration AAT during and after sRT are consistent with the primary report with significantly improved long-term OS, reduced incidence of metastatic PC and PC death for RT+AAT. Statistically significant metastatic PC was noted for Gleason score 8-10, study entry PSA>1.5 and margin positive patients and borderline significance was observed in patients with study entry PSA 0.7-1.5. Clinical trial information: NCT00002874 . [Table: see text]