An artificial intelligence system applied to recurrent cytogenetic aberrations and genetic progression scores predicts MYC rearrangements in large B-cell lymphoma.

Diffuse large B-cell lymphoma (DLBCL), the most common type of non-Hodgkin lymphoma, is characterized by MYC rearrangements (MYC R) in up to 15% of cases, and these have unfavorable prognosis. Due to cryptic rearrangements and variations in MYC breakpoints, MYC R may be undetectable by conventional methods in up to 10%-15% of cases. In this study, a retrospective proof of concept study, we sought to identify recurrent cytogenetic aberrations (RCAs), generate genetic progression scores (GP) from RCAs and apply these to an artificial intelligence (AI) algorithm to predict MYC status in the karyotypes of published cases. The developed AI algorithm is validated for its performance on our institutional cases. In addition, cytogenetic evolution pattern and clinical impact of RCAs was performed. Chromosome losses were associated with MYC-, while partial gain of chromosome 1 was significant in MYC R tumors. MYC R was the sole driver alteration in MYC-rearranged tumors, and evolution patterns revealed RCAs associated with gene expression signatures. A higher GPS value was associated with MYC R tumors. A subsequent AI algorithm (composed of RCAs + GPS) obtained a sensitivity of 91.4 and specificity of 93.8 at predicting MYC R. Analysis of an additional 59 institutional cases with the AI algorithm showed a sensitivity and specificity of 100% and 87% each with positive predictive value of 92%, and a negative predictive value of 100%. Cases with a MYC R showed a shorter survival.

Identification of potential antiviral compounds against SARS-CoV-2 structural and non structural protein targets: A pharmacoinformatics study of the CAS COVID-19 dataset.

SARS-CoV-2 is a newly discovered virus which causes COVID-19 (coronavirus disease of 2019), initially documented as a human pathogen in 2019 in the city of Wuhan China, has now quickly spread across the globe with an urgency to develop effective treatments for the virus and emerging variants. Therefore, to identify potential therapeutics, an antiviral catalogue of compounds from the CAS registry, a division of the American Chemical Society was evaluated using a pharmacoinformatics approach. A total of 49,431 compounds were initially recovered. After a biological and chemical curation, only 23,575 remained. A machine learning approach was then used to identify potential compounds as inhibitors of SARS-CoV-2 based on a training dataset of molecular descriptors and fingerprints of known reported compounds to have favorable interactions with SARS-CoV-2. This approach identified 178 compounds, however, a molecular docking analysis revealed only 39 compounds with strong binding to active sites. Downstream molecular analysis of four of these compounds revealed various non-covalent interactions along with simultaneous modulation between ligand and protein active site pockets. The pharmacological profiles of these compounds showed potential drug-likeness properties. Our work provides a list of candidate anti-viral compounds that may be used as a guide for further investigation and therapeutic development against SARS-CoV-2.