ABSTRACT
Background: Coronavirus disease 2019 (COVID-19), caused by betacoronavirus SARS-CoV-2, is associated with an increased risk of severe infection or death in cancer patients compared to the general population. The CANVAX trial recently demonstrated that short term immune responses to SARS-CoV-2 vaccines are modestly impaired in patients with cancer- particularly those who receive myelosuppressive chemotherapy. Because little is known regarding longitudinal antibody or T-cell responses in cancer patients who receive cytotoxic chemotherapy or non-myelosuppressive targeted systemic therapy, the aim of this longitudinal study is to assess immune B and T cell responses to SARS-CoV-2 over a 12-month period in solid tumor patients who receive chemotherapy or non-immunosuppressive therapy compared to healthy individuals without cancer. Methods: This is an ongoing prospective non-interventional clinical trial (NCT05238467). Approximately 100 patients will be enrolled into three different arms. Accrual began in May 2021 and 37 patients have been enrolled. Eligible patients must not have prior COVID-19 infection < 6 months from study enrollment and have a diagnosis of a solid tumor (breast, genitourinary, or gastrointestinal cancers), who either: received myelosuppressive chemotherapy within 60 days prior to initial or booster COVID vaccination, or who started on chemotherapy within 30 to 60 days after the initial or booster COVID vaccination (Arm A);or received non-immunosuppressive treatments (Arm B);or have no history of cancer or prior history of cancer but beyond 12 months from completion of curative cancer treatment (Arm C, control cohort). Whole blood will be collected in accordance with standard operating procedures. Blood samples analyzed for the presence of antibodies against the major antigenic components of SARS-CoV-2 including the spike glycoprotein (S), receptor binding domain (R) and nucleocapsid phosphoprotein (N). Antibody levels will be quantified utilizing quantitative ELISA. T-cell responses will also be quantified. The primary endpoint is seroprotection rate with an antibody titer protective (1:40) at any point: baseline, 2, 6, and 12 months. The secondary endpoint is to evaluate differences in longitudinal immunological responses to SARSCoV- 2 over a 12-month period. The difference of the seroprotection rate among 3 cohorts of participants will be examined using chi-square test. Moreover, the effect of treatment (chemotherapy, endocrine, TKIs) on seroprotection will be estimated using multivariable logistic regression controlling the effects of confounders, such as age, gender and cancer type. COVID antibody titers measured over time (baseline, 8 weeks, 6, 9, 12 months after the second vaccination) will be analyzed using mixedeffect models. .
ABSTRACT
Background: COVID-19 has contributed to healthcare inequity amongst minorities and lower socioeconomic populations, while complicating present anti-cancer treatment regimens. Due to their immunocompromised status, cancer patients are at an increased risk of severe SARS-CoV-2 infection. While sentiment analysis via SM has seen vast growth among healthcare professional, deeper connection and management has been lacking. Given the higher usage of SM impressions and the increase in healthcare disparities especially at the intersection of oncology and COVID-19, the aim of this study was to develop a platform that can: (1) show that the relationships highlighted within these tweets can be realized in biomolecular interactions-specifically within the interaction between solid tumors and COVID-19;(2) use SM data to connect patients with clinical trials. Methods: To determine this relationship, ontologies, which are groupings of terms and related identifiers, such as genes, were created for general search terms, utilizing the Human Phenotype Ontology. They were then combined with “COVID-19” and used as search terms in Twitter's Standard Search tool. The keywords with the most matches were then queried through clinicaltrials.gov and European Bioinformatics Institute's (EBI) Protein Search Tool to find relevant clinical trials and proteins. Finally, the proteins found by the EBI protein search were run through the SwissModel Tool to find relevant protein structures before being used in binding using Polar+'s Binding Platform from Iff Technologies, which provides K values related to 50% inhibition for each medication or immunotherapy. This produced a set of disease-specific keywords that are related to top tweets, clinical trials, protein structures, and binding concentration values in relevant biomolecular pathways for the keyword set “Tumor COVID-19”. Results: The example shown in Table is produced via our platform, with keywords with tweet numbers greater than 95% of all tweets with connected keywords used. Conclusions: By utilizing SM with highly relevant keywords, this platform can combat healthcare inequity by connecting patients and their tweets to clinical trials and enhance literacy about their medical conditions, while providing a greater understanding of the biomolecular pathways involved.