Point Mutation Specific Antibodies in B-Cell and T-Cell Lymphomas and Leukemias: Targeting IDH2, KRAS, BRAF and Other Biomarkers RHOA, IRF8, MYD88, ID3, NRAS, SF3B1 and EZH2.

B-cell and T-cell lymphomas and leukemias often have distinct genetic mutations that are diagnostically defining or prognostically significant. A subset of these mutations consists of specific point mutations, which can be evaluated using genetic sequencing approaches or point mutation specific antibodies. Here, we describe genes harboring point mutations relevant to B-cell and T-cell malignancies and discuss the current availability of these targeted point mutation specific antibodies. We also evaluate the possibility of generating novel antibodies against known point mutations by computationally assessing for chemical and structural features as well as epitope antigenicity of these targets. Our results not only summarize several genetic mutations and identify existing point mutation specific antibodies relevant to hematologic malignancies, but also reveal potential underdeveloped targets which merit further study.

A meta-analysis of SARS-CoV-2 patients identifies the combinatorial significance of D-dimer, C-reactive protein, lymphocyte, and neutrophil values as a predictor of disease severity.

BACKGROUND: Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), known to be the causative agent of COVID-19, has led to a worldwide pandemic. At presentation, individual clinical laboratory blood values, such as lymphocyte counts or C-reactive protein (CRP) levels, may be abnormal and associated with disease severity. However, combinatorial interpretation of these laboratory blood values, in the context of COVID-19, remains a challenge. METHODS: To assess the significance of multiple laboratory blood values in patients with SARS-CoV-2 and develop a COVID-19 predictive equation, we conducted a literature search using PubMed to seek articles that included defined laboratory data points along with clinical disease progression. We identified 9846 papers, selecting primary studies with at least 20 patients for univariate analysis to identify clinical variables predicting nonsevere and severe COVID-19 cases. Multiple regression analysis was performed on a training set of patient studies to generate severity predictor equations, and subsequently tested on a validation cohort of 151 patients who had a median duration of observation of 14 days. RESULTS: Two COVID-19 predictive equations were generated: one using four variables (CRP, D-dimer levels, lymphocyte count, and neutrophil count), and another using three variables (CRP, lymphocyte count, and neutrophil count). In adult and pediatric populations, the predictive equations exhibited high specificity, sensitivity, positive predictive values, and negative predictive values. CONCLUSION: Using the generated equations, the outcomes of COVID-19 patients can be predicted using commonly obtained clinical laboratory data. These predictive equations may inform future studies evaluating the long-term follow-up of COVID-19 patients.