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Background: Left ventricular (LV) systolic dysfunction is associated with a >8-fold increased risk of heart failure and a 2-fold risk of premature death. The use of ECG signals in screening for LV systolic dysfunction is limited by their availability to clinicians. We developed a novel deep learning-based approach that can use ECG images for the screening of LV systolic dysfunction., Methods: Using 12-lead ECGs plotted in multiple different formats, and corresponding echocardiographic data recorded within 15 days from the Yale New Haven Hospital between 2015 and 2021, we developed a convolutional neural network algorithm to detect an LV ejection fraction <40%. The model was validated within clinical settings at Yale New Haven Hospital and externally on ECG images from Cedars Sinai Medical Center in Los Angeles, CA; Lake Regional Hospital in Osage Beach, MO; Memorial Hermann Southeast Hospital in Houston, TX; and Methodist Cardiology Clinic of San Antonio, TX. In addition, it was validated in the prospective Brazilian Longitudinal Study of Adult Health. Gradient-weighted class activation mapping was used to localize class-discriminating signals on ECG images., Results: Overall, 385 601 ECGs with paired echocardiograms were used for model development. The model demonstrated high discrimination across various ECG image formats and calibrations in internal validation (area under receiving operation characteristics [AUROCs], 0.91; area under precision-recall curve [AUPRC], 0.55); and external sets of ECG images from Cedars Sinai (AUROC, 0.90 and AUPRC, 0.53), outpatient Yale New Haven Hospital clinics (AUROC, 0.94 and AUPRC, 0.77), Lake Regional Hospital (AUROC, 0.90 and AUPRC, 0.88), Memorial Hermann Southeast Hospital (AUROC, 0.91 and AUPRC 0.88), Methodist Cardiology Clinic (AUROC, 0.90 and AUPRC, 0.74), and Brazilian Longitudinal Study of Adult Health cohort (AUROC, 0.95 and AUPRC, 0.45). An ECG suggestive of LV systolic dysfunction portended >27-fold higher odds of LV systolic dysfunction on transthoracic echocardiogram (odds ratio, 27.5 [95% CI, 22.3-33.9] in the held-out set). Class-discriminative patterns localized to the anterior and anteroseptal leads (V 2 and V 3 ), corresponding to the left ventricle regardless of the ECG layout. A positive ECG screen in individuals with an LV ejection fraction ≥40% at the time of initial assessment was associated with a 3.9-fold increased risk of developing incident LV systolic dysfunction in the future (hazard ratio, 3.9 [95% CI, 3.3-4.7]; median follow-up, 3.2 years)., Conclusions: We developed and externally validated a deep learning model that identifies LV systolic dysfunction from ECG images. This approach represents an automated and accessible screening strategy for LV systolic dysfunction, particularly in low-resource settings., Competing Interests: Disclosures Dr Mortazavi reported receiving grants from the National Institute of Biomedical Imaging and Bioengineering, National Heart, Lung, and Blood Institute, US Food and Drug Administration, and the US Department of Defense Advanced Research Projects Agency outside the submitted work; in addition, Dr Mortazavi has a pending patent on predictive models using electronic health records (US20180315507A1). Dr A.H. Ribeiro is funded by the Kjell och Märta Beijer Foundation. Dr Krumholz works under contract with the Centers for Medicare & Medicaid Services to support quality measurement programs, was a recipient of a research grant from Johnson & Johnson, through Yale University, to support clinical trial data sharing; was a recipient of a research agreement, through Yale University, from the Shenzhen Center for Health Information for work to advance intelligent disease prevention and health promotion; collaborates with the National Center for Cardiovascular Diseases in Beijing; receives payment from the Arnold & Porter Law Firm for work related to the Sanofi clopidogrel litigation, from the Martin Baughman Law Firm for work related to the Cook Celect IVC filter litigation, and from the Siegfried and Jensen Law Firm for work related to Vioxx litigation; chairs a cardiac scientific advisory board for UnitedHealth; was a member of the IBM Watson Health Life Sciences board; is a member of the advisory board for element science, the advisory board for Facebook, and the physician advisory board for Aetna; and is co-founder of Hugo Health, a personal health information platform, and co-founder of Refactor Health, a health care artificial intelligence–augmented data management company. Dr A.L.P. Ribeiro is supported in part by CNPq (465518/2014-1, 310790/2021-2, and 409604/2022-4) and by FAPEMIG (PPM-00428-17, RED-00081-16, and PPE-00030-21). V. Sangha and Dr Khera are the coinventors of US provisional patent application No. 63/346,610, “Articles and methods for format-independent detection of hidden cardiovascular disease from printed electrocardiographic images using deep learning.” Dr Khera receives support from the National Heart, Lung, and Blood Institute of the National Institutes of Health (under award K23HL153775) and the Doris Duke Charitable Foundation (under award 2022060). He receives support from the Blavatnik Foundation through the Blavatnik fund for innovation at Yale. He also receives research support, through Yale, from Bristol-Myers Squibb, and Novo Nordisk. He is an associate editor for JAMA. In addition to 63/346,610, Dr Khera is a coinventor of US provisional patent applications 63/177,117, 63/428,569, and 63/484,426. He is also a founder of Evidence2Health, a precision health platform to improve evidence-based cardiovascular care.

Purpose: Even when diagnosed prior to metastasis, pancreatic ductal adenocarcinoma (PDAC) is a devastating malignancy with almost 90% lethality, emphasizing the need for new therapies optimally targeting the tumors of individual patients., Experimental Design: We first developed a panel of new physiologic models for study of PDAC, expanding surgical PDAC tumor samples in culture using short-term culture and conditional reprogramming with the Rho kinase inhibitor Y-27632, and creating matched patient-derived xenografts (PDX). These were evaluated for sensitivity to a large panel of clinical agents, and promising leads further evaluated mechanistically., Results: Only a small minority of tested agents was cytotoxic in minimally passaged PDAC cultures in vitro Drugs interfering with protein turnover and transcription were among most cytotoxic. Among transcriptional repressors, triptolide, a covalent inhibitor of ERCC3, was most consistently effective in vitro and in vivo causing prolonged complete regression in multiple PDX models resistant to standard PDAC therapies. Importantly, triptolide showed superior activity in MYC-amplified PDX models and elicited rapid and profound depletion of the oncoprotein MYC, a transcriptional regulator. Expression of ERCC3 and MYC was interdependent in PDACs, and acquired resistance to triptolide depended on elevated ERCC3 and MYC expression. The Cancer Genome Atlas analysis indicates ERCC3 expression predicts poor prognosis, particularly in CDKN2A-null, highly proliferative tumors., Conclusions: This provides initial preclinical evidence for an essential role of MYC-ERCC3 interactions in PDAC, and suggests a new mechanistic approach for disruption of critical survival signaling in MYC-dependent cancers. Clin Cancer Res; 22(24); 6153-63. ©2016 AACR., Competing Interests: The authors disclose no potential conflicts of interest., (©2016 American Association for Cancer Research.)

Inhibitors of the DNA damage checkpoint kinase, Chk1, are highly effective as chemo- and radio-sensitizers in preclinical studies but are not well-tolerated by patients. We exploited the promiscuous nature of kinase inhibitors to screen 9 clinically relevant kinase inhibitors for their ability to sensitize pancreatic cancer cells to a sub-lethal concentration of gemcitabine. Bosutinib, dovitinib, and BEZ-235 were identified as sensitizers that abrogated the DNA damage checkpoint. We further characterized bosutinib, an FDA-approved Src/Abl inhibitor approved for chronic myelogenous leukemia. Unbeknownst to us, we used an isomer (Bos-I) that was unknowingly synthesized and sold to the research community as "authentic" bosutinib. In vitro and cell-based assays showed that both the authentic bosutinib and Bos-I inhibited DNA damage checkpoint kinases Chk1 and Wee1, with Bos-I showing greater potency. Imaging data showed that Bos-I forced cells to override gemcitabine-induced DNA damage checkpoint arrest and destabilized stalled replication forks. These inhibitors enhanced sensitivity to the DNA damaging agents' gemcitabine, cisplatin, and doxorubicin in pancreatic cancer cell lines. The in vivo efficacy of Bos-I was validated using cells derived directly from a pancreatic cancer patient's tumor. Notably, the xenograft studies showed that the combination of gemcitabine and Bos-I was significantly more effective in suppressing tumor growth than either agent alone. Finally, we show that the gatekeeper residue in Wee1 dictates its sensitivity to the 2 compounds. Our strategy to screen clinically relevant kinase inhibitors for off-target effects on cell cycle checkpoints is a promising approach to re-purpose drugs as chemosensitizers.