A machine learning approach in a monocentric cohort for predicting primary refractory disease in Diffuse Large B-cell lymphoma patients.

Introduction: Primary refractory disease affects 30-40% of patients diagnosed with DLBCL and is a significant challenge in disease management due to its poor prognosis. Predicting refractory status could greatly inform treatment strategies, enabling early intervention. Various options are now available based on patient and disease characteristics.

Patients with treated indolent lymphomas immunized with BNT162b2 have reduced anti-spike neutralizing IgG to SARS-CoV-2 variants, but preserved antigen-specific T cell responses.

Patients with indolent lymphoma undertaking recurrent or continuous B cell suppression are at risk of severe COVID-19. Patients and healthy controls (HC; N = 13) received two doses of BNT162b2: follicular lymphoma (FL; N = 35) who were treatment naïve (TN; N = 11) or received immunochemotherapy (ICT; N = 23) and Waldenström's macroglobulinemia (WM; N = 37) including TN (N = 9), ICT (N = 14), or treated with Bruton's tyrosine kinase inhibitors (BTKi; N = 12). Anti-spike immunoglobulin G (IgG) was determined by a high-sensitivity flow-cytometric assay, in addition to live-virus neutralization.