Citations

Spatially resolved sequencing technologies help us dissect how cells are organized in space. Several available computational approaches focus on the identification of spatially variable genes (SVGs), genes whose expression patterns vary in space. The detection of SVGs is analogous to the identification of differentially expressed genes and permits us to understand how genes and associated molecular processes are spatially distributed within cellular niches. However, the expression activities of SVGs fail to encode all information inherent in the spatial distribution of cells. Here, we devised a deep learning model, Spatially Informed Artificial Intelligence (SPIN-AI), to identify spatially predictive genes (SPGs), whose expression can predict how cells are organized in space. We used SPIN-AI on spatial transcriptomic data from squamous cell carcinoma (SCC) as a proof of concept. Our results demonstrate that SPGs not only recapitulate the biology of SCC but also identify genes distinct from SVGs. Moreover, we found a substantial number of ribosomal genes that were SPGs but not SVGs. Since SPGs possess the capability to predict spatial cellular organization, we reason that SPGs capture more biologically relevant information for a given cellular niche than SVGs. Thus, SPIN-AI has broad applications for detecting SPGs and uncovering which biological processes play important roles in governing cellular organization.

Abstract Aims To investigate the impact of statins on plaque progression according to high-risk coronary atherosclerotic plaque (HRP) features and to identify predictive factors for rapid plaque progression in mild coronary artery disease (CAD) using serial coronary computed tomography angiography (CCTA). Methods and results We analyzed mild stenosis (25–49%) CAD, totaling 1432 lesions from 613 patients (mean age, 62.2 years, 63.9% male) and who underwent serial CCTA at a ≥2 year inter-scan interval using the Progression of AtheRosclerotic PlAque DetermIned by Computed TomoGraphic Angiography Imaging (NCT02803411) registry. The median inter-scan period was 3.5 ± 1.4 years; plaques were quantitatively assessed for annualized percent atheroma volume (PAV) and compositional plaque volume changes according to HRP features, and the rapid plaque progression was defined by the ≥90th percentile annual PAV. In mild stenotic lesions with ≥2 HRPs, statin therapy showed a 37% reduction in annual PAV (0.97 ± 2.02 vs. 1.55 ± 2.22, P = 0.038) with decreased necrotic core volume and increased dense calcium volume compared to non-statin recipient mild lesions. The key factors for rapid plaque progression were ≥2 HRPs [hazard ratio (HR), 1.89; 95% confidence interval (CI), 1.02–3.49; P = 0.042], current smoking (HR, 1.69; 95% CI 1.09–2.57; P = 0.017), and diabetes (HR, 1.55; 95% CI, 1.07–2.22; P = 0.020). Conclusion In mild CAD, statin treatment reduced plaque progression, particularly in lesions with a higher number of HRP features, which was also a strong predictor of rapid plaque progression. Therefore, aggressive statin therapy might be needed even in mild CAD with higher HRPs. Clinical trial registration ClinicalTrials.gov NCT02803411

Understanding of the evolution of metazoans from their unicellular ancestors is a fundamental question in biology. In contrast to fungi which utilize the Mon1–Ccz1 dimeric complex to activate the small GTPase RAB7A, metazoans rely on the Mon1–Ccz1–RMC1 trimeric complex. Here, we report a near-atomic resolution cryogenic-electron microscopy structure of the Drosophila Mon1–Ccz1–RMC1 complex. RMC1 acts as a scaffolding subunit and binds to both Mon1 and Ccz1 on the surface opposite to the RAB7A-binding site, with many of the RMC1-contacting residues from Mon1 and Ccz1 unique to metazoans, explaining the binding specificity. Significantly, the assembly of RMC1 with Mon1–Ccz1 is required for cellular RAB7A activation, autophagic functions and organismal development in zebrafish. Our studies offer a molecular explanation for the different degree of subunit conservation across species, and provide an excellent example of how metazoan-specific proteins take over existing functions in unicellular organisms.

AbstractCardiac physiologic pacing (CPP), encompassing cardiac resynchronization therapy (CRT) and conduction system pacing (CSP), has emerged as a pacing therapy strategy that may mitigate or prevent the development of heart failure (HF) in patients with ventricular dyssynchrony or pacing‐induced cardiomyopathy. This clinical practice guideline is intended to provide guidance on indications for CRT for HF therapy and CPP in patients with pacemaker indications or HF, patient selection, pre‐procedure evaluation and preparation, implant procedure management, follow‐up evaluation and optimization of CPP response, and use in pediatric populations. Gaps in knowledge, pointing to new directions for future research, are also identified.

Anticipating and understanding cancers’ need for specific gene activities is key for novel therapeutic development. Here we utilized DepMap, a cancer gene dependency screen, to demonstrate that machine learning combined with network biology can produce robust algorithms that both predict what genes a cancer is dependent on and what network features coordinate such gene dependencies. Using network topology and biological annotations, we constructed four groups of novel engineered machine learning features that produced high accuracies when predicting binary gene dependencies. We found that in all examined cancer types, F1 scores were greater than 0.90, and model accuracy remained robust under multiple hyperparameter tests. We then deconstructed these models to identify tumor type-specific coordinators of gene dependency and identified that in certain cancers, such as thyroid and kidney, tumors’ dependencies are highly predicted by gene connectivity. In contrast, other histologies relied on pathway-based features such as lung, where gene dependencies were highly predictive by associations with cell death pathway genes. In sum, we show that biologically informed network features can be a valuable and robust addition to predictive pharmacology models while simultaneously providing mechanistic insights.

Emerging evidence suggest that transcription factors play multiple roles in the development of pancreatitis, a necroinflammatory condition lacking specific therapy. Estrogen-related receptor γ (ERRγ), a pleiotropic transcription factor, has been reported to play a vital role in pancreatic acinar cell (PAC) homeostasis. However, the role of ERRγ in PAC dysfunction remains hitherto unknown. Here, we demonstrated in both mice models and human cohorts that pancreatitis is associated with an increase in ERRγ gene expression via activation of STAT3. Acinar-specific ERRγ haploinsufficiency or pharmacological inhibition of ERRγ significantly impaired the progression of pancreatitis both in vitro and in vivo. Using systematic transcriptomic analysis, we identified that voltage-dependent anion channel 1 (VDAC1) acts as a molecular mediator of ERRγ. Mechanistically, we showed that induction of ERRγ in cultured acinar cells and mouse pancreata enhanced VDAC1 expression by directly binding to specific site of the Vdac1 gene promoter and resulted in VDAC1 oligomerization. Notably, VDAC1, whose expression and oligomerization were dependent on ERRγ, modulates mitochondrial Ca 2+ and ROS levels. Inhibition of the ERRγ–VDAC1 axis could alleviate mitochondrial Ca 2+ accumulation, ROS formation and inhibit progression of pancreatitis. Using two different mouse models of pancreatitis, we showed that pharmacological blockade of ERRγ–VDAC1 pathway has therapeutic benefits in mitigating progression of pancreatitis. Likewise, using PRSS1 R122H -Tg mice to mimic human hereditary pancreatitis, we demonstrated that ERRγ inhibitor also alleviated pancreatitis. Our findings highlight the importance of ERRγ in pancreatitis progression and suggests its therapeutic intervention for prevention and treatment of pancreatitis.

Myelin oligodendrocyte glycoprotein (MOG) antibody–associated disease (MOGAD) is an inflammatory demyelinating CNS condition characterized by the presence of MOG autoantibodies. We sought to investigate whether human MOG autoantibodies are capable of mediating damage to MOG-expressing cells through multiple mechanisms. We developed high-throughput assays to measure complement activity (CA), complement-dependent cytotoxicity (CDC), antibody-dependent cellular phagocytosis (ADCP), and antibody-dependent cellular cytotoxicity (ADCC) of live MOG-expressing cells. MOGAD patient sera effectively mediate all of these effector functions. Our collective analyses reveal that (a) cytotoxicity is not incumbent on MOG autoantibody quantity alone; (b) engagement of effector functions by MOGAD patient serum is bimodal, with some sera exhibiting cytotoxic capacity while others did not; (c) the magnitude of CDC and ADCP is elevated closer to relapse, while MOG-IgG binding is not; and (d) all IgG subclasses can damage MOG-expressing cells. Histopathology from a representative MOGAD case revealed congruence between lesion histology and serum CDC and ADCP, and we identified NK cells, mediators of ADCC, in the cerebrospinal fluid of relapsing patients with MOGAD. Thus, MOGAD-derived autoantibodies are cytotoxic to MOG-expressing cells through multiple mechanisms, and assays quantifying CDC and ADCP may prove to be effective tools for predicting risk of future relapses.

Background: Recognition of the causes of early mortality after atrial fibrillation (AF) catheter ablation is essential for the improvement of patient safety. This study sought to determine the causes of early mortality (≤90 days) after AF ablation. Methods: We performed a retrospective analysis of AF ablation from January 1, 2013, to December 1, 2021 at the Mayo Clinic (Rochester, Phoenix, and Jacksonville). Causes of death were identified through a comprehensive chart review of the electronic health record from within the Mayo Clinic system and outside records when available. Results: A total of 6723 patients were included in the study. The 90-day all-cause mortality rate was 0.22% (n=15). Among all 90-day deaths, majority of the deaths (73.3%) did not have a direct relationship with the procedure. Sudden death was the most common cause of early death (20%), followed by peri-procedural stroke (13%), respiratory failure (13%), atrioesophageal fistula (13%), infection (7%), heart failure (7%), and traumatic brain injury (7%). The 90-day mortality rate directly due to AF ablation procedural complications was 0.06% (n=4). Conclusions: AF ablation procedure has a 90-day mortality of 0.22%, and the most common cause of early mortality was sudden death. The majority (73.3%) of early mortality was not directly associated with a procedural complication, and the mortality rate due to complications associated with the AF ablation procedure was low at 0.06%. Further studies are required to investigate causes and risk factors associated with sudden death in this patient population.

Abstract Background: Isocitrate dehydrogenase 1/2 (IDH1/2) is mutated in a subset of cholangiocarcinoma (CCA), gliomas, and other solid tumors. LY3410738 is a potent, selective, covalent, dual inhibitor of IDH1/2 mutations (IDH1/2m). LY3410738 binds covalently at a novel binding site, enabling continued potency in preclinical models in the setting of second site IDH resistance mutations. We present initial results from the first-in-human phase 1 study of oral LY3410738 in patients (pts) with IDH1/2m CCA, and IDH1m glioma or other solid tumors. Methods: Dose escalation (3+3 design) evaluated LY3410738 monotherapy in advanced IDHm CCA and other solid tumors (NCT04521686). Key objectives included determining the RP2D, safety, PK, PD (inhibition of plasma D-2-HG), and preliminary antitumor activity. Results: As of 28 July 2022, 80 pts including 42 with CCA (33 IDH1m, 9 IDH2m), 27 with glioma (IDH1m), and 11 other tumor types (IDH1m) received LY3410738 dosed at 25-600 mg QD or 300 mg BID. Pts were median 52 years of age (range, 23-80) with a median of 2 prior therapies (range, 1-7). 19% of CCA pts had received prior IDH1 inhibitor. Median time on treatment was 3.7 months (range, 0.1-19). No DLTs or treatment related deaths were observed; the MTD was not reached. Treatment emergent adverse events (TEAEs) ≥15% included nausea (35%), vomiting (21%), and decreased appetite (19%) and were mostly grade 1-2. Most frequent grade ≥3 TEAEs >2% were anemia (4%), cholangitis (3%), headache (3%), decreased lymphocyte count (3%), and hyponatremia (3%). LY3410738 exposure was dose proportional. In pts with IDH1m cancers, LY3410738 achieved sustained D-2-HG inhibition at all dose levels, including in pts who received prior IDH1 inhibitor. In pts with IDH2m cancers, a higher dose (≥150 mg daily dose) was required for D-2-HG inhibition. Among the 42 pts with R/R CCA, the best response included 1 PR and 22 SD. Of the 22 glioma pts with contrast enhancing tumors, best response included 3 PR and 9 SD. Conclusions: LY3410738 demonstrated a favorable safety profile with potent and sustained D-2-HG inhibition in pts with IDH1/2m advanced solid tumors. Consistent with the expectations for IDH inhibitor monotherapy in this setting, CCA and glioma pts exhibited prolonged stable disease. RP2D evaluation is ongoing. Updated data on LY3410738 monotherapy will be presented at the meeting. Citation Format: Jordi Rodon, Lipika Goyal, Teresa Macarulla Mercade, Masafumi Ikeda, Shunsuke Kondo, Do-Youn Oh, Li-Yuan Bai, Makoto Ueno, Antoine Italiano, Kyriakos Papadopoulos, David Spigel, Sani H. Kizilbash, Rasha Cosman, Joon Oh Park, Li-Tzong Chen, Tomoya Yokota, Anita A. Turk, Chih-Yi Liao, Rachna Shroff, Anthony El-Khoueiry, Taroh Satoh, Antoine Hollebecque, Mitesh J. Borad, Nilofer Azad, Kurt A. Jaeckle, Herbert H. Loong, Jorge Adeva, Wei Peng Yong, Junjie Zhao, Hui Liu, Anna M. Szpurka, Ivelina Gueorguieva, Kamnesh R. Pradhan, Xiaojian Xu, James J. Harding. A first-in-human phase 1 study of LY3410738, a covalent inhibitor of mutant IDH, in advanced IDH-mutant cholangiocarcinoma and other solid tumors [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 2 (Clinical Trials and Late-Breaking Research); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(8_Suppl):Abstract nr CT098.

Abstract Introduction As total neoadjuvant therapy for locally advanced rectal cancer (LARC) emerged, the possibility of skipping radiotherapy for poorly responsive patients arose. Machine-learning algorithms have focused on the radiopathologic features of tumor segmentation in order to predict responsiveness to radiotherapy. However, in addition to the tumor itself, there are several factors related to responsiveness, such as vasculature affecting hypoxia or MRI-detected extramural vascular invasion status. We aimed to predict poor responders using pretreatment rectal MRI images without segmentation and to identify which factors mainly contribute to the prediction algorithm. Methods Between Jan 1, 2000, and Dec 30, 2020, 689 consecutive patients were retrospectively included in two hospitals. Poor responders were defined by tumor regression grades (TRG) 2 and 3 that were determined through surgical resection. The ResNet-50 model was trained to predict poor responders from pretreatment rectal MRIs (T2-weighted axial, sagittal, and coronal images). We adopted a tenfold cross-validation for training and testing the model and used Gradient-weighted Class Activation Mapping (Grad-CAM) to highlight the important regions in the MRI scans that help predict poor responders. Results The number in each group of TRG was 108 (15.7%), 250 (36.3%), 265 (38.5%), and 66 (9.6%) for TRG0, TRG1, TRG2, and TRG3, respectively. There were 618 patients in the training cohort and 71 patients in the validation cohort. In the training and validation cohort, the accuracy for the prediction of poor responders was 85.6% (area under the curve (AUC) 0.856 [95% CI 0.761-0.950]) and 70.2% (AUC 0.703 [0.682-0.724]), although without segmentation. Our prediction model achieved a sensitivity of 0.724 (95% CI 0.700-0.748), a specificity of 0.684 (0.658-0.710), a positive predictive value of 0.697 (0.656-0.737), and a negative predictive value of 0.708 (0.666-0.751) in the validation cohort. Grad-CAM showed that the most important part of the accurately predicted images to contribute to the prediction was not the tumor (7/355, 1.9%) but the pelvic vasculature (353/355, 99.4%), including iliac vessels, femoral vessels, and presacral plexus, and followed by the mesorectum (38/355, 10.7%). Conclusion The pelvic vasculature contributes more to predicting poor responders to radiotherapy than the tumor itself. Therefore, when creating a prediction model for responsiveness to radiotherapy in LARC, this should be considered. Citation Format: Rumi Shin, Byunho Jo, Inyeop Jang, Cheong-Il Shin, Jin Sun Choi, Seung-Yong Jeong, Seung Chul Heo, Ji Won Park, Min Jung Kim, Tae Hyun Hwang. Why only focus on the tumor?: The crucial role of the extra-tumor environment to predict poor responders for locally advanced rectal cancer. [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 5374.