Anti-SARS-CoV-2 antibody containing plasma improves outcome in patients with hematologic or solid cancer and severe COVID-19 via increased neutralizing antibody activity. A randomized clinical trial (preprint)

Cancer patients are at high risk of severe COVID-19 with high morbidity and mortality. Further, impaired humoral response renders SARS-CoV-2 vaccines less effective and treatment options are scarce. Randomized trials using convalescent plasma are missing for high-risk patients. Here, we performed a multicenter trial (https://www.clinicaltrialsregister.eu/ctr-search/trial/2020-001632-10/DE) in hospitalized patients with severe COVID-19 within four risk groups (1, cancer; 2, immunosuppression; 3, lab-based risk factors; 4, advanced age) randomized to standard of care (CONTROL) or standard of care plus convalescent/vaccinated anti-SARS-CoV-2 plasma (PLASMA). For the four groups combined, PLASMA did not improve clinically compared to CONTROL (HR 1.29; p=0.205). However, cancer patients experienced shortened median time to improvement (HR 2.50, p=0.003) and superior survival in PLASMA vs. CONTROL (HR 0.28; p=0.042). Neutralizing antibody activity increased in PLASMA but not in CONTROL cancer patients (p=0.001). Taken together, convalescent/vaccinated plasma may improve COVID-19 outcome in cancer patients unable to intrinsically generate an adequate immune response.

Machine Learning Based Prediction of COVID-19 Mortality Suggests Repositioning of Anticancer Drug for Treating Severe Cases (preprint)

Despite available vaccinations COVID-19 case numbers around the world are still growing, and effective medications against severe cases are lacking. In this work, we developed a machine learning model which predicts mortality for COVID-19 patients using data from the multi-center Lean European Open Survey on SARS-CoV-2-infected patients (LEOSS) observational study (>100 active sites in Europe, primarily in Germany), resulting into an AUC of almost 80%. We showed that molecular mechanisms related to dementia, one of the relevant predictors in our model, intersect with those associated to COVID-19. Most notably, among these molecules was tyrosine kinase 2 (TYK2), a protein that has been patented as drug target in Alzheimers Disease but also genetically associated with severe COVID-19 outcomes. We experimentally verified that anti-cancer drugs Sorafenib and Regorafenib showed a clear anti-cytopathic effect in Caco2 and VERO-E6 cells and can thus be regarded as potential treatments against COVID-19. Altogether, our work demonstrates that interpretation of machine learning based risk models can point towards drug targets and new treatment options, which are strongly needed for COVID-19.