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COVID-19 is a multi-system disorder with high variability in clinical outcomes among patients who are admitted to hospital. Although some cytokines such as interleukin (IL)-6 are believed to be associated with severity, there are no early biomarkers that can reliably predict patients who are more likely to have adverse outcomes. Thus, it is crucial to discover predictive markers of serious complications.In this retrospective cohort study, we analysed samples from 455 participants with COVID-19 who had had a positive SARS-CoV-2 RT-PCR result between April 14, 2020, and Dec 1, 2020 and who had visited one of three Mayo Clinic sites in the USA (Minnesota, Arizona, or Florida) in the same period. These participants were assigned to three subgroups depending on disease severity as defined by the WHO ordinal scale of clinical improvement (outpatient, severe, or critical). Our control cohort comprised of 182 anonymised age-matched and sex-matched plasma samples that were available from the Mayo Clinic Biorepository and banked before the COVID-19 pandemic. We did a deep profiling of circulatory cytokines and other proteins, lipids, and metabolites from both cohorts. Most patient samples were collected before, or around the time of, hospital admission, representing ideal samples for predictive biomarker discovery. We used proximity extension assays to quantify cytokines and circulatory proteins and tandem mass spectrometry to measure lipids and metabolites. Biomarker discovery was done by applying an AutoGluon-tabular classifier to a multiomics dataset, producing a stacked ensemble of cutting-edge machine learning algorithms. Global proteomics and glycoproteomics on a subset of patient samples with matched pre-COVID-19 plasma samples was also done.We quantified 1463 cytokines and circulatory proteins, along with 902 lipids and 1018 metabolites. By developing a machine-learning-based prediction model, a set of 102 biomarkers, which predicted severe and clinical COVID-19 outcomes better than the traditional set of cytokines, were discovered. These predictive biomarkers included several novel cytokines and other proteins, lipids, and metabolites. For example, altered amounts of C-type lectin domain family 6 member A (CLEC6A), ether phosphatidylethanolamine (P-18:1/18:1), and 2-hydroxydecanoate, as reported here, have not previously been associated with severity in COVID-19. Patient samples with matched pre-COVID-19 plasma samples showed similar trends in muti-omics signatures along with differences in glycoproteomics profile.A multiomic molecular signature in the plasma of patients with COVID-19 before being admitted to hospital can be exploited to predict a more severe course of disease. Machine learning approaches can be applied to highly complex and multidimensional profiling data to reveal novel signatures of clinical use. The absence of validation in an independent cohort remains a major limitation of the study.Eric and Wendy Schmidt.

Background: The kinases ataxia-telangiectasia-mutated-and-Rad3-related (ATR), checkpoint kinase 1 (CHK1) and WEE1 participate in the response to replication stress. Inhibitors of ATR, WEE1 and CHK1 are being tested for antineoplastic activity as monotherapies and in combination with chemotherapy. Although these agents impair cell cycle checkpoints, fork stabilization, origin firing and homologous recombination (HR), little is known about the apoptotic pathways that are engaged and the critical mediators activating cell death mechanisms. The present study utilizes high grade serous ovarian cancer (HGSOC) cell lines and patient derived xenografts (PDXs) to identify the primary mechanism of cell death in ovarian cancer and assess changes involved in the resistance setting after treatment with these inhibitors. Methods: A panel of HGSOC cell lines was examined for responses to ceralasertib (ATRi), prexasertib (CHK1i) or adavosertib (WEE1i) using colony forming assays, immunoblotting and assays for apoptosis. Mechanisms involved in acquired resistance and downstream changes in cell fate were evaluated by generating prexasertib-resistant HGSOC cell lines. Subsequent NGS analysis was performed in the sensitive and resistant pairs. Results: Irrespective of the homologous recombination (HR) status, ovarian cell lines and PDXs underwent cell death during prolonged exposure at clinically achievable concentrations. Treatment of ovarian cancer cell lines with ATRi, CHK1i or WEE1i activated the replication checkpoint, consistent with previously published reports. The subsequent apoptotic response involved PMAIP1 and BCL2L11 upregulation rather than the TNFa-induced death receptor-mediated apoptosis we recently described in acute leukemia (Cancer Res. PMID: 33414171). Additionally, ovarian lines selected for CHK1i resistance failed to die in response to either ATRi or WEE1i. Conclusions: Prexasertib, ceralasertib, and adavosertib exhibit monotherapy activity in both HR deficient and proficient HGSOC cell lines and PDX models. Unlike AML, death receptor mediated apoptosis was not observed; instead, the primary mechanism of cell killing in HGSOC lines involves activation of the mitochondrial apoptotic pathway. Cross-resistance of these DNA damage repair modulators is likely multifactorial and mainly involves diminished replication stress response. Citation Format: Annapoorna Venkatachalam, Kevin L. Peterson, Cristina Correia, Karen S. Flatten, Xianon Hou, Paula A. Schneider, Emily Balczewski, Cordelia McGehee, Rachel M. Hurley, Xue W. Meng, Chance Sine, Rameen Shah, Nicole Vincelettte, Husheng Ding, Hu Li, Saravut (John) Weroha, Scott H. Kaufmann. CHK1 inhibitor prexasertib induces NOXA-dependent apoptosis in ovarian cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 1832.

As the world endures the coronavirus disease 2019 (COVID-19) pandemic, the conditions of 35 million vulnerable individuals struggling with substance use disorders (SUDs) worldwide have not received sufficient attention for their special health and medical needs. Many of these individuals are complicated by underlying health conditions, such as cardiovascular and lung diseases and undermined immune systems. During the pandemic, access to the healthcare systems and support groups is greatly diminished. Current research on COVID-19 has not addressed the unique challenges facing individuals with SUDs, including the heightened vulnerability and susceptibility to the disease. In this systematic review, we will discuss the pathogenesis and pathology of COVID-19, and highlight potential risk factors and complications to these individuals. We will also provide insights and considerations for COVID-19 treatment and prevention in patients with SUDs.