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Background: Dysfunction of systemic vascular endothelial cells (ECs) is common in heart failure with preserved ejection fraction (HFpEF). We sought to determine if organ-specific EC translational heterogeneity is present in HFpEF. Using a transgenic mouse model (RiboTag EC ), we analyzed the translatome (the mRNAs bound with ribosomes) of organ-specific ECs in a mouse HFpEF model induced by hypertensive/metabolic stresses. Methods: Six-month-old male RiboTag EC ( Cdh5 CreERT2+ / Rpl22 HA/+ ) mice, expressing a hemagglutinin tag in EC ribosomal protein Rpl22, were fed a high-fat diet and L-NAME water (N=4) or a normal diet and water (N=5) for 7 weeks. Organs were collected for Translating Ribosome Affinity Purification, RNA isolation, and RNA-seq. RNA-seq data were visualized using uniform manifold approximation and projection (UMAP). Differentially expressed genes (DEGs; absolute fold change ≥ 2 and false discovery rate (FDR) adjusted p-value ≤ 0.05). Pathway enrichment analyses were conducted (Ingenuity Pathways Analysis (IPA)). Results: In UMAP, the EC translatome clustered by organ (heart, lung, skeletal muscle, and liver) highlighting organ-specific EC heterogeneity and by disease (HFpEF) status with cardiac ECs showing the most dramatic changes with HFpEF (Fig A). Cardiac ECs in HFpEF (vs Control) had 283 downregulated and 23 upregulated DEG (Fig B) out of 14,923 genes identified. There were significant differences in 142 canonical pathways (16 upregulated, 126 downregulated), including LXR/RXR activation, DHCR 24 signaling, regulation of insulin-like growth factors, eNOS signaling, acute phrase response (all down regulated) and IL-1b-mediated inhibition of RXR and unfolded protein response (both up regulated) (Fig C). Conclusion: HFpEF induction caused extensive translational changes in EC but particularly cardiac ECs. In cardiac ECs, metabolism, anti-inflammatory, vasodilation, tissue repair, and angiogenesis pathways were inhibited, while pro-inflammatory response, insulin resistance, and proteostasis pathways were activated.

Background and Objective: Decline of cardiovascular (CV) function is commonly observed in patients with severe acute pancreatitis (AP), which is associated with high mortality and morbidity. While the pathological changes in the pancreas during severe AP have been extensively studied, the impact on the CV system has not been well characterized. Furthermore, obesity is a major risk factor for severe AP. Hereby we utilized an obesity-associated mouse AP model to assess potential alterations in vascular integrity and cardiac function, two major components of circulatory failure. Methods: Wild-type male C57BL6J mice were fed a normal or a high fat (to induce obesity) diet. Both groups received intraperitoneal injection of cerulein (100 mg/kg/h x12) to induce AP. H&E and TUNEL staining of pancreas were performed to assess histology and cell death. To measure vascular integrity, we measured the leakiness of a fluorescein-conjugated dextran (70 kDa) in tissue parenchyma and serum levels of soluble vascular endothelial (VE)-cadherin, which has recently shown to be shed from endothelial cells with disrupted adherens junctions. Cardiac systolic and diastolic function were assessed by echocardiography at baseline and post-AP induction. Results: Compared to lean mice, obese mice developed more severe pancreatic edema, cell death and inflammatory cell infiltration, along with significantly increased accumulation of fluorescent dextran in multiple organs including pancreas, heart, lung, and kidney, suggesting systemic disruption of vascular integrity. Increased serum levels of soluble VE-cadherin in obese mice (vs. lean) after AP induction further supported compromised endothelial cell barrier function. At baseline, obese mice exhibited similar cardiac function to lean mice. However, obese mice, but not lean mice, developed impaired systolic and diastolic function post-AP induction, as supported by decreased ejection fraction and e’ velocity respectively. Conclusion: In this obesity-associated severe AP mouse model, systemic vascular permeability is increased, and cardiac function is substantially compromised. Systemic vascular hyperpermeability and cardiac dysfunction may represent therapeutic targets to enhance the outcomes in severe AP.

Heart failure with preserved ejection fraction (HFpEF), characterized by cardiac diastolic dysfunction, leads to premature mortality with limited effective treatments. A recent study in HFpEF patients suggested neuropilin 1 (NRP1), an endothelial cell (EC) originated prognostic marker, might contribute to the pathogenesis of HFpEF. However, the functional role of NRP1 in the pathogenesis of HFpEF remains unclear. Purpose: To determine the functional role of NRP1 in the pathogenesis of HFpEF. Hypothesis: NRP-1 contributes to the pathogenesis of HFpEF, potentially mediated through chronic inflammation. Methods: HFpEF was induced by hypertensive and metabolic stresses in control (n=14) and tamoxifen-induced, endothelial cell-specific NRP1 knockout (NRP1 iEC-KO ) mice (n=13). We assessed cardiac diastolic function via echocardiography and pressure-volume loop through invasive catheters. To determine the inflammation level, we tested leukocyte adhesion, inflammatory marker vascular cell adhesion molecule 1 (VCAM-1), and intercellular adhesion molecule 1 (ICAM-1) mRNA level in control and NRP1 shRNA knockdown (NRP1 KD) human umbilical vein endothelial cells (HUVECs) with a stimulation of TNF-α. Student t-test (two tailed) was used and the criterion level of significance was set at p<0.05. Results: We found a significantly improved diastolic function in NRP1 iEC-KO mice, evidenced by a significantly reduced end diastolic pressure in NRP1 iEC-KO mice (8±2mmHg; n=7) compared to the controls (17±2mmHg; n=9; p=0.01) after HFpEF induction. E/A ratio was also significantly lower in NRP1 iEC-KO mice (2.0±0.2; n=10) compared to controls (2.7±0.3; n=12; p=0.04). Moreover, the isovolumic relaxation time was also significantly reduced in NRP1 iEC-KO mice (18±0.8ms; n=13) compared to controls (20±0.8ms; n=14; p=0.03). In addition, In HUVECs, NRP1 KD significantly reduced inflammatory marker VCAM-1 mRNA expression (95±16; n=5) compared to controls (293±36; n=5; p<0.01) upon TNF-α treatment. Similarly, NRP1 KD significantly reduced ICAM-1 mRNA expression (44±14; n=5) compared to controls (83±9; n=5; p=0.03) upon TNF-α treatment. Furthermore, leukocyte adhesion was significantly lower in the NRP1 KD group (57.0 ±8.6 cells/field) compared to the control group (90.8 ±9.1 cells/field; p=0.04). Conclusion: Endothelial NRP1 contributes to cardiac diastolic dysfunction and the pathogenesis of HFpEF, potentially mediated through the activation of chronic low-grade inflammation.

Abstract Innovation in medical imaging artificial intelligence (AI)/machine learning (ML) demands extensive data collection, algorithmic advancements, and rigorous performance assessments encompassing aspects such as generalizability, uncertainty, bias, fairness, trustworthiness, and interpretability. Achieving widespread integration of AI/ML algorithms into diverse clinical tasks will demand a steadfast commitment to overcoming issues in model design, development, and performance assessment. The complexities of AI/ML clinical translation present substantial challenges, requiring engagement with relevant stakeholders, assessment of cost-effectiveness for user and patient benefit, timely dissemination of information relevant to robust functioning throughout the AI/ML lifecycle, consideration of regulatory compliance, and feedback loops for real-world performance evidence. This commentary addresses several hurdles for the development and adoption of AI/ML technologies in medical imaging. Comprehensive attention to these underlying and often subtle factors is critical not only for tackling the challenges but also for exploring novel opportunities for the advancement of AI in radiology.

Abstract The adoption of artificial intelligence (AI) tools in medicine poses challenges to existing clinical workflows. This commentary discusses the necessity of context-specific quality assurance (QA), emphasizing the need for robust QA measures with quality control (QC) procedures that encompass (1) acceptance testing (AT) before clinical use, (2) continuous QC monitoring, and (3) adequate user training. The discussion also covers essential components of AT and QA, illustrated with real-world examples. We also highlight what we see as the shared responsibility of manufacturers or vendors, regulators, healthcare systems, medical physicists, and clinicians to enact appropriate testing and oversight to ensure a safe and equitable transformation of medicine through AI.

Background: The use of CAR-T cell therapy has transformed treatment paradigms in B-cell malignancies. Ciltacabtagene autoleucel (cilta-cel) was approved for use in RRMM after four or more prior lines of therapy in 2022. While the pivotal studies exclusively treated patients in an inpatient setting, robust infrastructure planning at some centers across US has fostered outpatient administration of the commercially available product. Outpatient administration of CAR-T is more desirable due to superior financial sustainability, lower healthcare resource utilization, and greater patient autonomy during the treatment. Hence, we conducted a qualitative study to evaluate clinical and administrative perspectives and trends in outpatient administration of commercialized cilta-cel with an aim to understand best practices from certified CAR-T centers in the USA. Methods: A targeted literature review was conducted to better understand current practices around outpatient administration of CAR-T, and inform the development of primary research materials for designing subsequent interviews. Next, two 60-minute exploratory interviews were conducted with multi-stakeholder participants (n=5) from two certified cilta-cel centers, each with significant volume of outpatient administration of cilta-cel. Following the qualitative analysis of interviews, two 90-minute panels were conducted with multi-stakeholder participants (n=14) from six certified cilta-cel centers, including centers from exploratory interviews, focusing on clinical and administrative topics across the patient journey. Results: Each center in the study treated approximately 15-30 patients with cilta-cel in the outpatient setting. Using Likert scales, participants from all centers ranked space/capacity as the most important factor in decision-making process for outpatient cilta-cel administration, and ranked reimbursement incentives as a highly (67% of participants) or a moderately (33%) important factor. All participants agreed that having a dedicated outpatient infrastructure (dedicated space such as bed availability and internal processes including standard operating procedures (SOPs)), 24/7 support, outpatient bone marrow transplant (BMT) or cell therapy experience, and robust multidisciplinary teams were key to setting-up successful outpatient centers for cilta-cel therapy. The participants agreed that cilta-cel can be administered safely in an outpatient setting due to generally predictable, delayed onset of potential AEs, except in patients with significant comorbidities or organ dysfunction. Participants also considered caregiver support and lodging availability as factors of importance for outpatient administration. Institutionally established AE monitoring protocols are critical for safe use of cilta-cel in an outpatient setting. Over 50% of the centers interviewed have recently started to administer tocilizumab to treat mild grade 1 cytokine release syndrome (CRS) in an outpatient setting. Based on the findings across the phases of research, the patient journey for outpatient administration of cilta-cel was updated (Figure 1). While patients may be admitted for toxicity events, 100% of centers interviewed had 24-hour coverage and bed availability for these patients if needed. Conclusions: Overall, this study further supports the notion that centers can safely administer cilta-cel in an outpatient setting, if appropriate infrastructure is in place. Additional best practices for new outpatient sites include adequate infrastructure, SOPs, a well-trained and multidisciplinary team, and education of patients and caregivers (Table 1). Outpatient management and earlier intervention for low-grade AEs is currently evolving, with more real-world research required. Continued expert sharing of best practices can aid centers in implementing outpatient administration of CAR-T therapies. Funding: This analysis was supported by Janssen Research & Development, LLC and Legend Biotech USA, Inc

e13552 Background: Real-world data from Electronic Health Records (EHR) have been widely used for patient identification to build study cohorts for clinical research. Traditionally, diagnosis codes in the EHR, such International Classification of Diseases (ICD), are used to identify the target patients. However, the accuracy of this approach is dependent on the accuracy of ICD coding, with potential errors especially for tumor types that are frequent locations for metastases (which may contribute to mis-coding). In this study, we attempted to develop a Machine Learning (ML) based approach on EHR data to improve the accuracy of identification of patients with lung cancer. Methods: We used survey respondents in the Enhanced, EHR-facilitated Cancer Symptom Control (E2C2, NCT03892967) cluster-randomized trial at Mayo Clinic as our initial pan-cancer cohort. E2C2 includes adults receiving Medical Hematology/Oncology care for a solid or liquid tumor at Mayo Clinic. We collected cancer diagnoses from the individually abstracted Mayo Clinic Cancer Registry to annotate the cancer type for the patients. Lung cancer related ICD-9 (162.X) and ICD-10 (C34.X) codes were used to build a search query on the Mayo’s EHR to find target patients from the E2C2 cohort, and to investigate the ICD-based lung cancer patient identification performance. Diagnosis, radiation oncology treatment (CPT 77261 - 77799), and antineoplastic drug administration data were collected from EHR as variables. Logistic Regression (LR), Support vector machine (SVM), Random Forest (RF), and eXtreme Gradient Boosting (XGB) were selected to build models for lung cancer patient identification. 10-fold cross-validation was implemented to assess the models. Precision, Recall, F1 Score, and area under the curve (AUC) were selected to measure the performance. Results: We collected 13,893 patients with a specific cancer diagnosis, and 1,394 were identified as having lung cancer. The identification performance across different methods is shown. The ICD-based method only had 0.65 precision. It means we collected a lot of false positive cases (other cancer but no lung cancer patient), as we mentioned in the background. SVM gets the best precision results, but its recall and F1 score are not good enough. XGB shows the best F1 Score and AUC, which also means this method achieved the best and most balanced performance. Conclusions: In this study, we found that XGB-based methods achieved the best identification performance for lung cancer. In the future, we will investigate if this is also true for the identification of other cancer types. [Table: see text]

Abstract Purpose: Treatment options are limited beyond JAK inhibitors for patients with primary myelofibrosis (MF) or secondary MF. Preclinical studies have revealed that PI3Kδ inhibition cooperates with ruxolitinib, a JAK1/2 inhibitor, to reduce proliferation and induce apoptosis of JAK2V617F-mutant cell lines. Patients and Methods: In a phase I dose-escalation and -expansion study, we evaluated the safety and efficacy of a selective PI3Kδ inhibitor, umbralisib, in combination with ruxolitinib in patients with MF who had a suboptimal response or lost response to ruxolitinib. Enrolled subjects were required to be on a stable dose of ruxolitinib for ≥8 weeks and continue that MTD at study enrollment. The recommended dose of umbralisib in combination with ruxolitinib was determined using a modified 3+3 dose-escalation design. Safety, pharmacokinetics, and efficacy outcomes were evaluated, and spleen size was measured with a novel automated digital atlas. Results: Thirty-seven patients with MF (median age, 67 years) with prior exposure to ruxolitinib were enrolled. A total of 2 patients treated with 800 mg umbralisib experienced reversible grade 3 asymptomatic pancreatic enzyme elevation, but no dose-limiting toxicities were seen at lower umbralisib doses. Two patients (5%) achieved a durable complete response, and 12 patients (32%) met the International Working Group-Myeloproliferative Neoplasms Research and Treatment response criteria of clinical improvement. With a median follow-up of 50.3 months for censored patients, overall survival was greater than 70% after 3 years of follow-up. Conclusions: Adding umbralisib to ruxolitinib in patients was well tolerated and may resensitize patients with MF to ruxolitinib without unacceptable rates of adverse events seen with earlier generation PI3Kδ inhibitors. Randomized trials testing umbralisib in the treatment of MF should be pursued.

Abstract Background The shared inherited genetic contribution to risk of different cancers is not fully known. In this study, we leverage results from 12 cancer genome-wide association studies (GWAS) to quantify pairwise genome-wide genetic correlations across cancers and identify novel cancer susceptibility loci. Methods We collected GWAS summary statistics for 12 solid cancers based on 376 759 participants with cancer and 532 864 participants without cancer of European ancestry. The included cancer types were breast, colorectal, endometrial, esophageal, glioma, head and neck, lung, melanoma, ovarian, pancreatic, prostate, and renal cancers. We conducted cross-cancer GWAS and transcriptome-wide association studies to discover novel cancer susceptibility loci. Finally, we assessed the extent of variant-specific pleiotropy among cancers at known and newly identified cancer susceptibility loci. Results We observed widespread but modest genome-wide genetic correlations across cancers. In cross-cancer GWAS and transcriptome-wide association studies, we identified 15 novel cancer susceptibility loci. Additionally, we identified multiple variants at 77 distinct loci with strong evidence of being associated with at least 2 cancer types by testing for pleiotropy at known cancer susceptibility loci. Conclusions Overall, these results suggest that some genetic risk variants are shared among cancers, though much of cancer heritability is cancer-specific and thus tissue-specific. The increase in statistical power associated with larger sample sizes in cross-disease analysis allows for the identification of novel susceptibility regions. Future studies incorporating data on multiple cancer types are likely to identify additional regions associated with the risk of multiple cancer types.