Patients with autoimmune disorders (AD) have altered cancer risks compared to the general population. Systemic lupus erythematosus and multiple sclerosis lead to a heightened risk for hematological malignancies and decreased risk for breast, ovarian, and prostate malignancies. Often patients with autoimmune disease have dysregulated antiviral immune responses, including against oncogenic viruses. To uncover the relationship between viral incidence and cancer risk in the context of autoimmune disease, we extracted electronic health records (EHR) from Vanderbilt University. ICD-9/10 codes and laboratory values were collected for hematological, lung, anal-vaginal, thyroid, hepatobiliary, bladder, prostate, and breast cancers; and viruses including Epstein Barr virus (EBV), Human papilloma virus (HPV), and Hepatitis A/B/C (Hep). Only viral infections that led to a physician visit or laboratory test were entered into the EMR; therefore, only clinically relevant cases were noted and considered positive in this study. The relationship between virus infection and cancer in an SLE cohort (SLE-cases n = 2,313, and SLE-controls n = 5,702) and an MS cohort (MS-case n = 7,277, MS-control n = 7,277) was examined by multilinear logistic regression. Viral infection was strongly associated with increased risk for cancer overall. SLE and MS patients were more susceptible to all viral infections. MS patients trended toward increased risk for cancers overall, while decreased risk for hormone-based cancers in SLE patients non-significantly reduced their risk for overall cancer. Both SLE and MS patients had increased clinically relevant EBV infection, which was associated with risk for hematological cancers. Preventing viral infections by vaccination may be especially helpful in controlling risk for cancer in SLE and MS patients.

Multiple sclerosis (MS) is an immune-mediated, demyelinating disease of the central nervous system. In this study, an MS cohort and healthy controls were stratified into Caucasian and African American groups. Patient hematological profiles—composed of complete blood count (CBC) and complete metabolic panel (CMP) test values—were analyzed to identify differences between MS cases and controls and between patients with different MS subtypes. Additionally, random forest models were used to determine the aggregate utility of common hematological tests in determining MS disease status and subtype. The most significant and relevant results were increased bilirubin and creatinine in MS cases. The random forest models achieved some success in differentiating between MS cases and controls (AUC values: 0.725 and 0.710, respectively) but were not successful in differentiating between subtypes. However, larger samples that adjust for possible confounding variables, such as treatment status, may reveal the value of these tests in differentiating between MS subtypes.

Electronic medical records (EMRs) are being quickly adopted in clinics around the world. This advancement can greatly enhance the clinical care of patients with multiple sclerosis (MS) by providing formats that allow easier review of medical documents and more structured avenues to store relevant information. MS clinicians should be involved with implementing and updating EMRs at their institutions to ensure EMR formats that benefit MS clinics. EMRs also provide opportunities for research studies of MS to access detailed, longitudinal data of MS disease course that would otherwise be difficult to collect.

The rapid evolution of SARS-CoV-2 has fueled its global proliferation since its discovery in 2019, with several notable variants having been responsible for increases in cases of coronavirus disease 2019 (COVID-19). Analyses of codon bias and usage in these variants between phylogenetic clades or lineages may grant insights into the evolution of SARS-CoV-2 and identify target codons indicative of evolutionary or mutative trends that may prove useful in tracking or defending oneself against emerging strains. We processed a cohort of 120 SARS-CoV-2 genome sequences through a statistical and bioinformatic pipeline to identify codons presenting evidence of selective pressure as well as codon coevolution. We report the identification of two codon sites in the orf8 and N genes demonstrating such evidence with real-world impacts on pathogenicity and transmissivity.