tbuckley/cabot-search huggingface dataset:
Amplification of spatially isolated adenosine pathway by tumor–macrophage interaction induces anti-PD1 resistance in hepatocellular carcinoma
Authors: Jia-Cheng Lu, Peng-Fei Zhang, Xiao-Yong Huang, Xiao-Jun Guo, Chao Gao, Hai-Ying Zeng, Yi-Min Zheng, Si-Wei Wang, Jia-Bin Cai, Qi-Man Sun, Ying-Hong Shi, Jian Zhou, Ai-Wu Ke, Guo-Ming Shi, Jia Fan
Journal: Journal of Hematology & Oncology (2021)

Abstract Background Immune checkpoint blockade resistance narrows the efficacy of cancer immunotherapies, but the underlying mechanism remains elusive. Delineating the inherent mechanisms of anti-PD1 resistance is important to improve outcome of patients with advanced HCC. Method The level of cricTMEM181 was measured in HCC patients with anti-PD1 therapy by RNA sequencing and then confirmed by qPCR and Sanger sequencing. Immune status in tumor microenvironment of HCC patients or mice models was evaluated by flow cytometry and IHC. Exosomes from HCC cell lines were isolated by ultracentrifugation, and their internalization by macrophage was confirmed by immunofluorescence. The underlying mechanism of HCC-derived exosomal circTMEM181 to macrophage was confirmed by SILAC, RNA FISH and RNA immunoprecipitation. The ATP–ADO pathway amplified by HCC–macrophage interaction was evaluated through ATP, AMP and ADO measurement and macrophage-specific CD39 knockout mice. The role of circTMEM181 in anti-PD1 therapy and its clinical significance were also determined in our retrospective HCC cohorts. Results Here, we found that circTMEM181 was elevated in hepatocellular carcinoma (HCC) patients responding poorly to anti-PD1 therapy and in HCC patients with a poor prognosis after operation. Moreover, we also found that high exosomal circTMEM181 favored the immunosuppressive microenvironment and endowed anti-PD1 resistance in HCC. Mechanistically, exosomal circTMEM181 sponged miR-488-3p and upregulated CD39 expression in macrophages. Using macrophage-specific CD39 knockout mice and pharmacologic approaches, we revealed a novel mode of anti-PD1 resistance in HCC. We discovered that cell-specific CD39 expression in macrophages and CD73 expression in HCC cells synergistically activated the eATP–adenosine pathway and produced more adenosine, thereby impairing CD8 + T cell function and driving anti-PD1 resistance. Conclusion In summary, HCC-derived exosomal circTMEM181 contributes to immunosuppression and anti-PD1 resistance by elevating CD39 expression, and inhibiting the ATP–adenosine pathway by targeting CD39 on macrophages can rescue anti-PD1 therapy resistance in HCC. Graphical Abstract
Abstract 11971: Predicting Significant Cardiomyopathy in Coronary Artery Disease Through Application of Predictive Machine Learning (ML) Algorithms
Authors: Carter W English, Sairamvinay Vijayaraghavan, Jeffrey Flack, Prasant Mohapatra, Uma N Srivatsa
Journal: Circulation (2021)

Introduction: Left ventricular ejection fraction (LVEF) of ≤ 40% in patients with coronary artery disease (CAD) is associated with an increased risk of sudden cardiac death. Accurately predicting LVEF at clinical follow-up can identify patients needing an implantable cardioverter-defibrillator device. Hypothesis: To develop a predictive algorithm using ML methods for predicting LVEF on follow-up echocardiogram for individuals with CAD. Methods: We identified the 10 most significant predictors of follow-up LVEF using a variety of feature selection algorithms from 109 variables based on clinical, demographic, ECG, and echocardiography data from 2116 hospitalized patients diagnosed with CAD. Various predictive ML algorithms were constructed to predict follow-up LVEF at 60 to 730 days from admission through regression, binary classification (LVEF ≤ 40% and LVEF &gt; 40%), and multi-classification analysis. AUC scores, accuracy, precision, F Score, and recall were calculated for each model to evaluate outcome performance. Results: N = 421 patients had LVEF ≤ 40% on presentation and 535 had LVEF ≤ 40% on follow-up. We observed that binary classification tasks outperformed regression and multi-classification tasks (Graph 1). In a binary classification setting, the top-performing models were simple neural networks, random forests, or logistic regression. (Table 1). Discussion: We developed various ML algorithms in multiple settings to optimize predicting follow-up LVEF with patients with CAD. With an enhanced ability to predict reduced LVEF, we aspire to improve the timing and efficiency of appropriate ICD implantation in patients based on current guidelines.
Abstract 13830: Prediction of Cardiac Rejection Trajectories via Machine Learning Derived Features From Digital Endomyocardial Biopsy Images
Authors: Sara Arabyarmohammadi, Cai Yuan, Eliot G Peyster, Priti Lal, Michael Feldman, Kenneth B Margulies, Anant Madabhushi
Journal: Circulation (2021)

Introduction: Cellular rejection occurs in 20-40% of transplant recipients with an increased risk of graft failure. Previously, we developed a model for predicting ISHLT rejection grades via the automated extraction of morphologic features in Endomyocardial biopsy (EMB) images. Considering the frequent discordance between conventional rejection grade and clinical rejection severity, in this work, we sought to identify morphologic features that predict the clinical trajectory of rejection events. Methods: Our study comprised 299 EMBs with grade and trajectory labels. Trajectory labels are based on the development of overt clinical signs of allograft injury as “clinically evident” or “clinically silent”. Morphologic features describing number, spatial arrangement of lymphocytes, and shape, orientation of interstitial fibers were computationally extracted. To identify the top features associated with clinically evident disease, the T-test method was applied across 500 iterations of 3-fold cross validation. In each iteration, a quadratic discriminant analysis model was trained with top 10 features on 2 folds of dataset using trajectory labels (M trj ) and was validated on one hold out test set. This model was trained using grade labels (M grd ) to predict “high” (2R + 3R) or “low” (0R + 1R). To assess feature importance, in each model, the frequency of every feature appearing in the classifier was measured through iterations. Results: The mean area under the receiver operating curve of M trj and M grd was 0.80±0.04 and 0.84±0.02 correspondingly. The top features for predicting grades differ substantially from those for predicting clinical trajectory (Figure 1). Conclusions: The additional features required to predict clinical trajectory vs. rejection grade may explain the discordance between conventional histology and rejection syndrome observed in clinical practice, and highlights the translational potential of computer-assisted histologic analysis of EMBs.
NIMG-63. MACHINE LEARNING USING MRI RADIOMIC ANALYSIS TO PREDICT KI-67 IN WHO GRADE I MENINGIOMAS
Authors: Omaditya Khanna, Anahita Fathi Kazerooni, Jose A Garcia, Chiharu Sako, Sherjeel Arif, Wenyin Shi, Christos Davatzikos
Journal: Neuro-Oncology (2021)

Abstract PURPOSE Although WHO grade I meningiomas are considered ‘benign’ tumors, an elevated Ki-67 is one crucial factor that has been shown to influence clinical outcomes. In this study, we use standard pre-operative MRI and develop a machine learning (ML) model to predict the Ki-67 in WHO grade I meningiomas. METHODS A retrospective analysis was performed of 306 patients that underwent surgical resection. The mean and median Ki-67 of tumor specimens were 4.84 ± 4.03% (range: 0.3–33.6) and 3.7% (Q1:2.3%, Q3:6%), respectively. Pre-operative MRI was used to perform radiomic feature extraction (N=2,520) followed by ML modeling using least absolute shrinkage and selection operator (LASSO) wrapped with support vector machine (SVM) through nested cross-validation on a discovery cohort (N=230), to stratify tumors based on Ki-67 &amp;lt; 5% and ≥ 5%. A replication cohort (N=76) was kept ‘unseen’ in order to provide insights regarding the generalizability of our predictive model. RESULTS A total of 60 radiomic features extracted from seven different MRI sequences were used in the final model. With this model, an AUC of 0.84 (95% CI: 0.78-0.90), with associated sensitivity and specificity of 84.1% and 73.3%, respectively, were achieved in the discovery cohort. The selected features in the trained predictive model were then applied to the subjects of the replication cohort and the model was applied independently in this cohort. An AUC of 0.83 (95% CI: 0.73-0.94), with a sensitivity of 82.6% and specificity of 85.5% was obtained for this independent testing. Furthermore, the model performed commendably when applied to all skull base and non-skull base tumors in our patient cohort, evidenced by comparable AUC values of 0.86 and 0.83, respectively. CONCLUSION The results of this study may provide enhanced diagnostics to the surgeon pre-operatively such that it can guide surgical strategy and individual patient treatment paradigms.
Abstract 104: Deletion Of Myd88 In T-Cells Worsens Pathology In A Mouse Model Of Non-Ischemic Heart Failure Through Enhanced T-Cell Survival And Effector Function:
Authors: Abraham L Bayer, Njabulo Ngwenyama, Sasha Smolgovsky, Ana Hernández Martínez, Kuljeet Kaur, Pilar Alcaide
Journal: Circulation Research (2021)

Background: Heart failure (HF) is a leading cause of death worldwide, associated with cardiac and systemic inflammation. However, no anti-inflammatory therapies have shown success thus far. Damage associated molecular patterns (DAMPs) released in the heart can activate myeloid cells to promote antigen presentation to T-cells, which infiltrate the heart and participate in adverse cardiac remodeling. DAMP signaling converges onto the adaptor protein “Myeloid differentiation primary response 88” (MyD88). DAMP receptors and MyD88 are also expressed in T-cells, but their role in T-cell activation is unclear, and is unknown in the context of HF. We hypothesized that T-cell recognition of DAMPs through MyD88 causes “bystander activation” of T-cells and contributes to cardiac pathology in HF. Methods and Results: We reconstituted Tcra -/- mice, normally protected from HF, with WT or Myd88 -/- Type 1 helper T-cells (Th1) in the onset of transaortic constriction (TAC), a well-established model of HF. Surprisingly, we found that mice given Myd88 -/- Th1 cells exhibited significantly higher levels of cardiac T-cell infiltration, more severe fibrosis, and lower fractional shortening than mice given WT Th1 cells. We found that WT and Myd88 -/- Th1 cells had similar levels of IFNγ and Tbx21 by intracellular staining and RT-qPCR, indicating that MyD88 does not alter Th1 differentiation. However, Myd88 -/- Th1 cells secreted higher levels of IL-2 and TNFα, suggesting enhanced proliferative and pro-inflammatory effector function. We performed viability studies using live cell microscopy and measuring propidium iodide incorporation in real time, as well as by flow cytometry, and found that Myd88 -/- Th1 cells have a survival advantage compared to WT Th1 cells. Moreover, we found that Myd88 -/- Th1 cells exhibited higher levels of adhesion to ICAM-1 and VCAM-1, protein ligands involved in T-cell recruitment, compared to WT Th1 cells when perfused under conditions of shear flow. Conclusion: Together, these data demonstrate that T-cell MyD88 limits T-cell mediated pathology in HF by modulating Th1 effector function, survival, and adhesion ability. We identify novel role for T-cell MyD88 in cardiac inflammation that may be modulated in HF.
Abstract MP240: Site-specific O-Glcnacylation Of Transient Receptor Potential Canonical Type 6 (trpc6) Channel Is Required For Normal Structure And Function
Authors: Sumita Mishra, Junfeng Ma, Kei Akiyoshi, Federica Farinelli, Rick Page, Christian U Oeing, Natasha Zachara, Samarjit Das, Mark J Ranek, David A Kass
Journal: Circulation Research (2021)

O-GlcNAcylation is a dynamic, reversible posttranslational modification (PTM) that regulates a multitude of biological processes. Fluctuations in O-GlcNAC of various calcium handling proteins impact their functionality in cardiomyocytes. Here, we show for the first time that TRPC6, a nonselective receptor-operated cation channel and mediator of hypertrophy and fibrosis, is constitutively O-GlcNAcylated in the ankyrin repeat domain (AR4), at Ser 14, Thr 70, and Thr 221 within the N-terminal cytoplasmic segment. Of these, only substitution of Thr 221 with alanine (T221A) results in a change of function, notably a hyperactive TRPC6 channel with 5X greater increase in consequent NFAT promoter activity, which is a marker of TRPC6 calcium signaling. Patch-clamp analysis of T221A mutant channels found a 75-80% increased conductance compared to WT. Myocardial injection of T221A in homozygous TRPC6 KO mice by AAV-9 mediated gene transfer results in systolic dysfunction, hypertrophy, and cardiac fibrosis, by loss of OGlcNAc modification at site T221. T221 is highly conserved across species and found in the AR4 domain, which forms the core structure of TRPC6 intracellular domain. Mutating the site in its closest homologs, TRPC3 and TRPC7, also activates channel activity. T221 O-GlcNAcylation also protects the nascent protein from premature proteasomal degradation. Molecular modeling from the crystal structure of human TRPC6 predicts that OGlcNACylation stabilizes electrostatic interactions with the 193-203 loop near AR4, and loop connecting AR4 to the linker helix 1 (LH1) at S199, E200, and E246. Mutating these sites to alanine also increases TRPC6-NFAT signaling similar to what was observed in the T221A mutant. In summary, this study highlights that O-GlcNAcylation of TRPC6 is an important PTM needed to stabilize channel function, and its decline results in gain-of-function related diseases.
Abstract 11784: Machine Learning Analysis of Progression From Moderate to Severe Tricuspid Regurgitation
Authors: Olivia Hudson, Maria Gabriela Gastanadui, Miguel Sotelo, loren wagner, Chris Rogers, Andrikopoulou Efstathia
Journal: Circulation (2021)

Background: Tricuspid regurgitation (TR) is linked to poor outcomes and its progression is associated with higher mortality. Left heart disease, age and sex are factors related to TR progression. We assessed echocardiographic (Echo) and demographic predictors of moderate TR progression using Machine Learning Natural Language Processing (ML NLP). Methods: 54,315 patients were retrospectively assessed for moderate TR with an Echo between May 2017 to October 2020 and a follow-up Echo 30 days to 113 months. Exclusion criteria included prior pacemaker/ICD, tricuspid prosthesis or band/ring, history of Ebstein, tetralogy of Fallot and tricuspid endocarditis. Cardiac Intelligence TM (Mpirik, Milwaukee, WI) identified moderate TR, demographic and Echo indices using ML NLP of the Echo report. Our endpoint was progression from moderate to severe TR within 1 or 2 years. Univariate Cox proportional hazard model was used with univariate analysis. Results: Of 3,489 moderate TR patients, 1,564 had a subsequent Echo and 771 met study criteria. Table 1 shows baseline demographic and Echo data. 15% and 24% of moderate TR patients progressed to severe TR within 1 and 2 years, respectively. Black race, left ventricular size, moderate/severe right ventricular (RV) dysfunction and Doppler-based RV systolic pressure were found to be univariate predictors of progression (Figure 1). Conclusion: ML NLP based on input from Echo-derived parameters using a real-world sample can offer insights into risk stratification of moderate TR regarding risk of progression to severe disease. Including further clinical and demographic variables will allow to improve performance of a disease prediction ML model.
Abstract 13261: Utility of a Novel Ensemble Based Deep Learning Network for the Automatic Detection of Pulmonary Hypertension and Right Ventricular Dilatation: A Study Based on Data From 783 Individuals From Two Tertiary Centres
Authors: Maria Luisa Benesch Vidal, Aleksander Kempny, Wei Li, Konstantinos Dimopoulos, John Wort, Alicia Fischer, Stefan Orwat, Helmut Baumgartner, Michael A Gatzoulis, Gerhard Diller
Journal: Circulation (2021)

Background: Pulmonary arterial hypertension (PAH) is associated with high morbidity and mortality. Echocardiography represents the main screening tool but requires specific expertise and diagnosis is often delayed. We hypothesized that an ensemble of deep learning (EDL) networks could reliably detect PAH in routine echocardiograms and improve patient management. Methods: Consecutive patients with PAH, Tetralogy of Fallot (TOF) or atrial septal defects (ASD) and right ventricular (RV) dilation, as well as normal controls were included. An ensemble of deep convolutional networks incorporating apical and parasternal views as well as tricuspid regurgitation gradients (TRG) into one unified network was trained to classify patients by presence of (invasively confirmed) PAH. Results: Overall, 408 PAH patients (median age 59 years, 74% female; 34% idiopathic, 66% associated PAH, RV inflow diameter 4.7±0.9 cm), 308 patients with RV dilatation (201 TOF and 107 ASD patients (med. age 41 years, 81% female, RV-inflow diameter 4.6±1.0 cm) and 67 normal controls (med. age 49 years, RV-inflow 3.4±0.6 cm) were included. Overall, 84% of PAH patients had usable TRGs and the sensitivity of detecting PAH by conventional RV diameters and TRG (estimated RV systolic pressure &gt;40 mmHg) was 68.6% (81.7% in patients with adequate TRGs). In contrast, only 35% of normal controls and 64% of RV dilatation patients had interrogable TRGs. The EDL algorithm trained on 53,832 frames and validated on 26,474 frames not used for training achieved an on-frame accuracy of 95.0% in the validation set. On a per patient basis all normal controls and PAH patients were correctly classified, while two patients with RV-dilatation, elevated RV pressure and poor image quality were assigned to the PAH group, yielding an overall accuracy of 97.6% and a sensitivity of detecting PAH of 100%. Conclusions: The study is the first to demonstrate the utility of ensemble based deep learning algorithms, trained on routine echocardiograms, to detect PAH based on large tertiary center data. These algorithms outperform by far conventional echocardiographic evaluation for detecting PAH. Due to the automated process involved, they could be deployed to the community and contribute to the earlier diagnosis of PAH.
Abstract P415: Etv2 Interacts With Vezf1 And Co-regulate Endothelial Gene Expression
Authors: Satyabrata Das, Vinayak Gupta, Wuming Gong, Javier Sierra-pagan, Erik Skie, Mary G Garry, Daniel J Garry
Journal: Circulation Research (2021)

Background: Ets transcription factors function as important developmental regulators and are known to be modulators of cell fate. Previously, in conjunction with co-factors, we have described Ets variant 2 (Etv2) as an essential regulator of the hematopoietic and endothelial lineages. But the mechanism and the Etv2 interacting partners involved in achieving this critical function remains poorly understood. Results: Using a yeast two-hybrid assay, we identified Vascular Endothelial Zinc Finger 1 (Vezf1) as an interacting factor with Etv2. Vezf1 is a conserved C2H2 zinc finger transcription factor known to regulate the formation, proliferation, and migration of endothelial cells. We verified Vezf1 as a binding partner of Etv2 through co-immunoprecipitation and GST-pull down studies. Bioinformatics analysis of ChIP-seq and Etv2-expressing single cell RNA sequencing was conducted to identify candidate genes containing both Etv2 and Vezf1 binding motifs in their regulatory regions. Histone deacetylase 7 (Hdac7) and angiomotin like protein 2 (Amotl2) were identified as candidate genes and have previously been shown to function as regulators of endothelial development. RT-qPCR analysis showed upregulation of Hdac7 and Amotl2 in response to doxycycline inducible Etv2 and Vezf1; whereas significant reduction of expression of these two genes was observed in the Etv2 and Vezf1 knockout cells. Chromatin immunoprecipitation (ChIP) and electrophoresis mobility shift assays (EMSA) confirmed Etv2-Vezf1 adjacent binding sites in the promoters of Hdac7 and Amotl2. Histone Acetyl transferase (HAT) assays was performed to investigate Etv2-Vezf1 on global histone acetylation conditions in doxycycline inducible embryoid bodies. Vezf1 overexpression results in a significant reduction of acetylated histones. Additionally, we have undertaken ATAC-Seq (Assay for Transposase-Accessible Chromatin using sequencing) and Vezf1 ChIP-Seq studies to profile the epigenetic modifications and genome-wide occupancy of Vezf1 in the presence or absence of Etv2 in endothelial progenitor cells. Conclusion: In summary, this study identifies Vezf1 as a novel binding partner of Etv2 and we further demonstrate their combined role in the regulation of downstream target genes, Amotl2 and Hdac7, during endothelial development.
Risk factors for systemic reactions in typical cold urticaria: Results from the COLD‐CE study
Authors: Mojca Bizjak, Mitja Košnik, Dejan Dinevski, Simon Francis Thomsen, Daria Fomina, Elena Borzova, Kanokvalai Kulthanan, Raisa Meshkova, Dalia Melina Ahsan, Mona Al‐Ahmad, Sabine Altrichter, Andrea Bauer, Maxi Brockstädt, Célia Costa, Semra Demir, Roberta Fachini Criado, Luis Felipe Ensina, Asli Gelincik, Ana Maria Giménez‐Arnau, Margarida Gonçalo, Maia Gotua, Jesper Grønlund Holm, Naoko Inomata, Alicja Kasperska‐Zajac, Maryam Khoshkhui, Aliya Klyucharova, Emek Kocatürk, Rongbiao Lu, Michael Makris, Natalya Maltseva, Jovan Miljković, Maria Pasali, Marisa Paulino, David Pesqué, Jonny Peter, German Dario Ramón, Carla Ritchie, Solange Oliveira Rodrigues Valle, Michael Rudenko, Agnieszka Sikora, Eduardo M. de Souza Lima, Nicola Wagner, Paraskevi Xepapadaki, Xiaoyang Xue, Zuotao Zhao, Dorothea Terhorst‐Molawi, Marcus Maurer
Journal: Allergy (2021)

Cold urticaria (ColdU), that is, the occurrence of wheals or angioedema in response to cold exposure, is classified into typical and atypical forms. The diagnosis of typical ColdU relies on whealing in response to local cold stimulation testing (CST). It can also manifest with cold-induced anaphylaxis (ColdA). We aimed to determine risk factors for ColdA in typical ColdU.An international, cross-sectional study COLD-CE was carried out at 32 urticaria centers of reference and excellence (UCAREs). Detailed history was taken and CST with an ice cube and/or TempTest® performed. ColdA was defined as an acute cold-induced involvement of the skin and/or visible mucosal tissue and at least one of: cardiovascular manifestations, difficulty breathing, or gastrointestinal symptoms.Of 551 ColdU patients, 75% (n = 412) had a positive CST and ColdA occurred in 37% (n = 151) of the latter. Cold-induced generalized wheals, angioedema, acral swelling, oropharyngeal/laryngeal symptoms, and itch of earlobes were identified as signs/symptoms of severe disease. ColdA was most commonly provoked by complete cold water immersion and ColdA caused by cold air was more common in countries with a warmer climate. Ten percent (n = 40) of typical ColdU patients had a concomitant chronic spontaneous urticaria (CSU). They had a lower frequency of ColdA than those without CSU (4% vs. 39%, p = .003). We identified the following risk factors for cardiovascular manifestations: previous systemic reaction to a Hymenoptera sting, angioedema, oropharyngeal/laryngeal symptoms, and itchy earlobes.ColdA is common in typical ColdU. High-risk patients require education about their condition and how to use an adrenaline autoinjector.