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Introduction: Genome-wide association studies have identified common genetic variants associated with hypertrophic (HCM) and dilated cardiomyopathy (DCM) and left ventricular (LV) characteristics. However, the association of these variants with myocardial fibrosis (MF), an important pathophysiological mediator of cardiomyopathy, is unknown. Hypothesis: We used the Multi-Ethnic Study of Atherosclerosis (MESA), to evaluate the association of these loci with interstitial MF measured by cardiac MRI (CMR). Methods: Unrelated MESA participants with T1-mapping CMR and no history of MI or heart failure were included (n=1,235). We used the extracellular volume (ECV) fraction as a well-established surrogate of interstitial MF. Genotyping was performed by Affymetrix Human SNP array 6.0, and imputation was performed using the TOPMED reference panel. Log ECV(%) was regressed on 50 candidate SNPs, in each ethnicity/race group, adjusting for age, sex, and population stratification principal component. The race-specific results were pooled by fixed-effect meta-analyses using METASOFT. Results: The SMARCB1 rs2186370 intronic variant, previously identified as a risk variant for DCM and HCM, was associated with ECV (p=0.0004). The rs2186370 A allele (frequency: 0.18 in Whites, and 0.50 in African Americans) was associated with a 1.6% increase in ECV. The rs2070458 A allele (another SMARCB1 intronic variant), in complete linkage disequilibrium with rs2186370, was known previously to be associated with increased LV wall thickness, mass, and concentricity. The direction of effect was similar in all four ancestry groups, but the effect was strongest in African Americans. These variants are strong expression quantitative loci (eQTLs) in human LV tissue and are associated with genotype-dependent expression of SMARCB1 ( p =7.3х10 -22 ) and MMP11 ( p =2.5х10 -24 ). Conclusions: In conclusion, variants in two linked SMARCB1 loci previously associated with HCM, DCM and LV traits, are associated with CMR-based interstitial MF. These loci are eQTL not only for SMARCB1 but also for MMP11 an important mediator of MF . These findings underscore the important link between MF and CM, and potentially provide novel mechanistic targets in understanding the development of CM.

Introduction: It has been previously demonstrated that cardiomyopathy predisposing common genetic variation in SMARCB1 is associated with interstitial myocardial fibrosis (MF) as determined by cardiac MRI (CMR) linking the development of cardiomyopathy with MF. SMARCB1 is known to be a tumor suppressor, but it’s role in the pathophysiology of MF and fibroblast physiology has not been functionally validated. Hypothesis: SMARCB1 plays an important role in regulating human cardiac fibroblast (HCF) differentiation/activation and collagen deposition, which are the critical steps in the development of MF. Methods: Cultured HCFs were treated with TGF-β1 (10ng/ml) for 48hrs to transdifferentiate into myofibroblasts, which are characterized by increased expression of α-smooth muscle actin (α-SMA, ACTA2) and collagen I (COL1A1). HCFs were transfected by siRNA using Lipofectamine™ RNAiMAX Transfection Reagent to knock-down (KD) the expression of SMARCB1 . Total RNA and protein were extracted from cell lysate followed by qPCR and Western blot (WB) to quantify the expression level of SMARCB1, ACTA2 and COL1A1. Triplicate experiments were performed using biologically independent samples. For statistical analysis, a paired two-tail t-test was used. Results: HCFs treatment with TGF-β1, resulted in a significant decrease in expression of SMARCB1 (p=0.031) with accompanying significant increase in expression of ACTA2 (p=0.025) and COL1A1 (p=0.0015) as compared to control by qPCR and WB. SMARCB1 KD resulted in a further significant increase in expression of ACTA2 (p=0.018) and COL1A1 (p=0.028) as compared to non-target siRNA control group at both the transcript and protein level. Conclusion: We demonstrate for the first time that a gene associated with the development of cardiomyopathy SMARCB1 mediates TGF-β1 induced cardiac fibrosis. These findings suggest SMARCB1 as a potential therapeutic target to attenuate MF in cardiomyopathy.

Introduction: Ebstein’s anomaly (EA) is a rare congenital anomaly of the tricuspid valve associated with heart failure and arrhythmias. Currently available imaging modalities like transthoracic echocardiography, cardiac magnetic resonance imaging (MRI), or computerized tomography (CT) are not viable for screening large asymptomatic populations. Hypothesis: A deep learning algorithm, implemented using a convolutional neural network (CNN) applied to the standard 12-lead ECG can be used as an accurate screening tool for EA in children and adults. Method: We identified 1,154 EA patients and 21,430 age- and sex-matched controls (1:20 ratio) between 1987 - 2020. A CNN was trained, validated and tested using digital 12-lead ECGs in 70%, 10% and 20% of the cohort respectively, divided by random shuffling. Results: The mean age was 26.68±19.23 years (41.8% aged < 18 years, 56.41% female) for the EA cohort and 28.36±18.65 years (37.2% aged <18 years, 55.67% female) for the control cohort. Following training and validation the area under the curve (AUC) for detecting EA in the testing cohort was 0.976 (95% CI 0.962 - 0.989) with prediction accuracy of 0.959. In subgroup analysis, the AUC was comparable in males (0.970, 95% CI 0.946-0.995) and females (0.979, 95% CI 0.964-0.994), those <18 (0.980, 95% CI 0.958-1.000) and ≥18 years old (0.972, 95% CI 0.954-0.989), as well as in those with right bundle branch block (RBBB) (0.969, 95% CI 0.950-0.989) and without RBBB (0.950, 95% CI 0.922-0.978) on ECG. Conclusion: Artificial intelligence enhanced ECG based detection of EA is possible with high diagnostic accuracy. Following further refinement and external validation, this algorithm can facilitate early detection of EA using ECGs, a widely available and inexpensive tool. Figure: Receiver operating characteristic (ROC) curve for the performance of the artificial intelligence CNN in detecting EA in the test cohort stratified by (A) age (< 18 and ≥ 18 years) and sex and (B) presence of RBBB.

Introduction: Prior AF screening trials demonstrated low yield, highlighting the need for more targeted approaches. An AI algorithm was developed to identify ECG signatures of AF risk during normal sinus rhythm, which has been validated in diverse external populations. Hypothesis: An AI-guided, targeted screening approach could improve the diagnosis of AF. Methods We conducted a pragmatic decentralized trial to prospectively recruit patients with stroke risk factors but no prior AF. The AI algorithm was applied to the ECGs performed in routine practice and divided patients into high- or low-risk groups. The primary endpoint was AF lasting ≥ 30 seconds on a subsequent 30-day continuous cardiac rhythm monitor. In a secondary analysis, trial participants were 1:1 propensity score-matched to real-world controls derived from the eligible but unenrolled population. Results A total of 1,003 patients from 40 U.S. states completed the study, with a mean age of 74 [SD 8.8] years. Over a mean of 22.3 days of continuous monitoring, AF was detected in 6 (1.6%) of low-risk patients and 48 (7.6%) of high-risk patients (OR 4.98 [2.11-11.75], p<0.001). Compared to usual care, AI-guided AF screening was associated with increased detection of AF (high-risk group: 4.2% vs. 11.1%, p<0.001; low-risk group: 0.9% vs. 2.4%, p=0.12) over a median follow-up of 10 months. Conclusions A prospective pragmatic study found that the AI a can risk-stratify a relatively uniform population (i.e., older adults at risk for stroke) to detect AF during short-term cardiac monitoring. Furthermore, when compared with usual care, AI-guided cardiac monitoring was associated with increased AF detection. As such, an AI-guided AF screening approach, leveraging existing EHR data and infrastructure, could be effective, patient-centered, and massively scalable, thereby reducing unnecessary health utilization and diagnosis-related anxiety (Clinicaltrials.gov: NCT04208971).

Glioblastoma (GBM) is a lethal brain cancer with a five-year survival rate of <5%. Approximately half of GBM tumors lack the DNA repair protein O6-methylguanine DNA methyltransferase (MGMT), which reverses O6-alkylguanine (O6G) lesions. Patients presenting MGMT– GBM are treated with surgery followed by radiation therapy and temozolomide (TMZ), an imidazotetrazine prodrug that produces O6-methylguanine (O6MeG) lesions. However, ~50% of these patients will develop TMZ resistance by silencing of the DNA mismatch repair (MMR) pathway. We recently reported that the novel N3-(2-fluoroethyl)imidazotetrazine “KL-50” is efficacious and well-tolerated in murine models of TMZ-resistant GBM (Lin et al. Science 2022, 377, 502). Herein, we rigorously establish that KL-50 generates DNA interstrand crosslinks (ICLs) by DNA alkylation to generate O6-(2-fluoroethyl)guanine (O6FEtG), displacement of fluoride to form an N1,O6-ethanoguanine (N1,O6EtG) intermediate, and ring-opening by the adjacent cytidine. 2-Chloroethylating agents, such as lomustine and mitozolomide (MTZ), generate the same ICL by an analogous mechanism. However, DNA ICLs form >10-fold more slowly from O6FEtG than O6ClEtG, and this slower rate of cross-linking allows MGMT to reverse the initial O6FEtG in healthy tissue while also reducing MGMT–DNA cross-links arising from addition of MGMT to the N1,O6EtG intermediate. KL- 50 is efficacious in an intracranial patient-derived murine xenograft of TMZ-resistant, MGMT–/MMR– GBM (mOS = 205, 28, and 26 d for KL-50, TMZ, and vehicle-treated control, respectively) and in murine models of newly-diagnosed MGMT–/MMR+ GBM, suggesting its use in recurrent and up-front settings, respectively. These studies underscore the significance of considering the rates of chemical DNA modification and biochemical DNA repair in the design of systemic DNA alkylation agents.

PURPOSE Radiation-associated cardiac disease is a major cause of morbidity/mortality among childhood cancer survivors. Radiation dose-response relationships for cardiac substructures and cardiac diseases remain unestablished. METHODS Using the 25,481 5-year survivors of childhood cancer treated from 1970 to 1999 in the Childhood Cancer Survivor Study, we evaluated coronary artery disease (CAD), heart failure (HF), valvular disease (VD), and arrhythmia. We reconstructed radiation doses for each survivor to the coronary arteries, chambers, valves, and whole heart. Excess relative rate (ERR) models and piecewise exponential models evaluated dose-response relationships. RESULTS The cumulative incidence 35 years from diagnosis was 3.9% (95% CI, 3.4 to 4.3) for CAD, 3.8% (95% CI, 3.4 to 4.2) for HF, 1.2% (95% CI, 1.0 to 1.5) for VD, and 1.4% (95% CI, 1.1 to 1.6) for arrhythmia. A total of 12,288 survivors (48.2%) were exposed to radiotherapy. Quadratic ERR models improved fit compared with linear ERR models for the dose-response relationship between mean whole heart and CAD, HF, and arrhythmia, suggesting a potential threshold dose; however, such departure from linearity was not observed for most cardiac substructure end point dose-response relationships. Mean doses of 5-9.9 Gy to the whole heart did not increase the risk of any cardiac diseases. Mean doses of 5-9.9 Gy to the right coronary artery (rate ratio [RR], 2.6 [95% CI, 1.6 to 4.1]) and left ventricle (RR, 2.2 [95% CI, 1.3 to 3.7]) increased risk of CAD, and to the tricuspid valve (RR, 5.5 [95% CI, 2.0 to 15.1]) and right ventricle (RR, 8.4 [95% CI, 3.7 to 19.0]) increased risk of VD. CONCLUSION Among children with cancer, there may be no threshold dose below which radiation to the cardiac substructures does not increase the risk of cardiac diseases. This emphasizes their importance in modern treatment planning.

AbstractAlthough pathogenic variants in PSEN1 leading to autosomal‐dominant Alzheimer disease (ADAD) are highly penetrant, substantial interindividual variability in the rates of cognitive decline and biomarker change are observed in ADAD. We hypothesized that this interindividual variability may be associated with the location of the pathogenic variant within PSEN1. PSEN1 pathogenic variant carriers participating in the Dominantly Inherited Alzheimer Network (DIAN) observational study were grouped based on whether the underlying variant affects a transmembrane (TM) or cytoplasmic (CY) protein domain within PSEN1. CY and TM carriers and variant non‐carriers (NC) who completed clinical evaluation, multimodal neuroimaging, and lumbar puncture for collection of cerebrospinal fluid (CSF) as part of their participation in DIAN were included in this study. Linear mixed effects models were used to determine differences in clinical, cognitive, and biomarker measures between the NC, TM, and CY groups. While both the CY and TM groups were found to have similarly elevated Aβ compared to NC, TM carriers had greater cognitive impairment, smaller hippocampal volume, and elevated phosphorylated tau levels across the spectrum of pre‐symptomatic and symptomatic phases of disease as compared to CY, using both cross‐sectional and longitudinal data. As distinct portions of PSEN1 are differentially involved in APP processing by γ‐secretase and the generation of toxic β‐amyloid species, these results have important implications for understanding the pathobiology of ADAD and accounting for a substantial portion of the interindividual heterogeneity in ongoing ADAD clinical trials.

AbstractBackgroundDeformable Image Registration (DIR) is an essential technique required in many applications of radiation oncology. However, conventional DIR approaches typically take several minutes to register one pair of 3D CT images and the resulting deformable vector fields (DVFs) are only specific to the pair of images used, making it less appealing for clinical application.PurposeA deep‐learning‐based DIR method using CT images is proposed for lung cancer patients to address the common drawbacks of the conventional DIR approaches and in turn can accelerate the speed of related applications, such as contour propagation, dose deformation, adaptive radiotherapy (ART), etc.MethodsA deep neural network based on VoxelMorph was developed to generate DVFs using CT images collected from 114 lung cancer patients. Two models were trained with the weighted mean absolute error (wMAE) loss and structural similarity index matrix (SSIM) loss (optional) (i.e., the MAE model and the M+S model). In total, 192 pairs of initial CT (iCT) and verification CT (vCT) were included as a training dataset and the other independent 10 pairs of CTs were included as a testing dataset. The vCTs usually were taken 2 weeks after the iCTs. The synthetic CTs (sCTs) were generated by warping the vCTs according to the DVFs generated by the pre‐trained model. The image quality of the synthetic CTs was evaluated by measuring the similarity between the iCTs and the sCTs generated by the proposed methods and the conventional DIR approaches, respectively. Per‐voxel absolute CT‐number‐difference volume histogram (CDVH) and MAE were used as the evaluation metrics. The time to generate the sCTs was also recorded and compared quantitatively. Contours were propagated using the derived DVFs and evaluated with SSIM. Forward dose calculations were done on the sCTs and the corresponding iCTs. Dose volume histograms (DVHs) were generated based on dose distributions on both iCTs and sCTs generated by two models, respectively. The clinically relevant DVH indices were derived for comparison. The resulted dose distributions were also compared using 3D Gamma analysis with thresholds of 3 mm/3%/10% and 2 mm/2%/10%, respectively.ResultsThe two models (wMAE and M+S) achieved a speed of 263.7±163 / 265.8±190 ms and a MAE of 13.15±3.8 / 17.52±5.8 HU for the testing dataset, respectively. The average SSIM scores of 0.987±0.006 and 0.988±0.004 were achieved by the two proposed models, respectively. For both models, CDVH of a typical patient showed that less than 5% of the voxels had a per‐voxel absolute CT‐number‐difference larger than 55 HU. The dose distribution calculated based on a typical sCT showed differences of ≤2cGy[RBE] for clinical target volume (CTV) D95 and D5, within ±0.06% for total lung V5, ≤1.5cGy[RBE] for heart and esophagus Dmean, and ≤6cGy[RBE] for cord Dmax compared to the dose distribution calculated based on the iCT. The good average 3D Gamma passing rates (> 96% for 3 mm/3%/10% and > 94% for 2 mm/2%/10%, respectively) were also observed.ConclusionA deep neural network‐based DIR approach was proposed and has been shown to be reasonably accurate and efficient to register the initial CTs and verification CTs in lung cancer.

Abstract Purpose A protocol was developed to evaluate the value of an NK-1 receptor antagonist for preventing nausea and vomiting resulting from highly emetogenic chemotherapy when an olanzapine-based antiemetogenic regimen was used. Materials and Methods A221602, a prospective double-blind, placebo-controlled clinical trial, was developed to compare 2 ­olanzapine-containing antiemetic regimens, one with an NK-1 receptor antagonist (aprepitant or fosaprepitant) and one without. Trial patients had a malignant disease for which they received intravenous highly emetogenic chemotherapy (single day cisplatin ≥ 70 mg/m2 or doxorubicin plus cyclophosphamide on 1 day). Patients on both arms received commonly administered doses of a 5-HT3 receptor antagonist, dexamethasone, and olanzapine. Additionally, patients were randomized to receive an NK-1 receptor antagonist (fosaprepitant 150 mg IV or aprepitant 130 mg IV) or a corresponding placebo. The primary objective was to compare the proportion of patients with no nausea for 5 days following chemotherapy between the 2 study arms. This trial was designed to test for the noninferiority of deleting the NK-1 receptor antagonist, with noninferiority defined as a decrease in freedom from nausea by less than 10%. Results A total of 690 patients were entered on this trial, 50% on each arm. The proportion of patients without nausea for the complete 5-day study period was 7.4% lower (upper limit of the one-sided 95% confidence interval was 13.5%) in the arm without an NK-1 receptor antagonist compared with the arm with an NK-1 receptor antagonist. Conclusion This trial did not provide sufficient evidence to support that deletion of the NK-1 receptor antagonist was as good as keeping it, as a part of a 4-drug antiemetic regimen for highly emetogenic chemotherapy (ClinicalTrials.gov Identifier: NCT03578081).

AbstractBackgroundIt is challenging to assess the accuracy of volume CT Dose Index (CTDIvol) when the axial scan modes corresponding to a helical scan protocol are not available. An alternative approach was proposed to directly measure using helical acquisitions and relatively small differences (< 20%) from CTDIvol were observed.PurposeTo visually demonstrate the 3D dose distribution for both axial and helical CT acquisitions and quantitively compare and CTDIvol.Methods3D dose distribution within the standard CTDI phantoms (16 and 32 cm diameter) from a single CT projection, Dp(x,y,z) was first generated using Monte Carlo simulation (GEANT4) with 9×108 photons per combination of tube voltage (80–140 kV), collimation width (1–8 cm), and z‐axis location of the central ray of the x‐ray beam, with a spatial resolution of 1 mm3. These dose distributions from one single projection were analytically ensembled to simulate 3D dose volumes DA(x,y,z) and DH(x,y,z) for axial and helical scans, respectively, with different helical pitches (0.3–2) and scan lengths (100–150 mm). 2D planar dose distributions were obtained by integrating the inside 100 mm of the dose volumes. CTDIvol and were calculated using the planar dose data at corresponding pencil chamber locations and the percentage differences (PD) were reported.ResultsHigh spatial resolution 3D CT dose volumes were generated and visualized. PDs between and CTDIvol had strong dependency on scan length and peripheral chamber locations, with subtle dependency on collimation width and pitch. PDs were mostly within the range of ± 3% for a scan length of 150 mm with four peripheral chamber locations.ConclusionsWith a scan length covering the entire phantom length, directly measured from helical scans can serve as an alternative to CTDIvol only if all four peripheral locations were measured.