Search results for "Immunomonitoring"
showing 3 items of 3 documents
Immunological features of coronavirus disease 2019 in patients with cancer.
2020
Background Coronavirus disease 2019 (COVID-19), caused by severe acute respiratory syndrome coronavirus 2, has caused a major pandemic. Patients with cancer are at higher risk of severe COVID-19. We aimed to describe and compare the immunological features of cancer patients hospitalised for COVID-19 or other concomitant, cancer-related illness. Methods In this prospective study, the clinical and immunological characteristics of 11 cancer patients with COVID-19 and 11 non–COVID-19 cancer patients hospitalised in the same unit at the same period for other medical issues were analysed. We also used 10 healthy volunteers as controls. Peripheral immune parameters were analysed using multiparamet…
Development of an RNA-based kit for easy generation of TCR-engineered lymphocytes to control T-cell assay performance.
2018
Cell-based assays to monitor antigen-specific T-cell responses are characterized by their high complexity and should be conducted under controlled conditions to lower multiple possible sources of assay variation. However, the lack of standard reagents makes it difficult to directly compare results generated in one lab over time and across institutions. Therefore TCR-engineered reference samples (TERS) that contain a defined number of antigen-specific T cells and continuously deliver stable results are urgently needed. We successfully established a simple and robust TERS technology that constitutes a useful tool to overcome this issue for commonly used T-cell immuno-assays. To enable users t…
A machine learning model based on tumor and immune biomarkers to predict undetectable MRD and survival outcomes in multiple myeloma
2022
Abstract Purpose: Undetectable measurable residual disease (MRD) is a surrogate of prolonged survival in multiple myeloma. Thus, treatment individualization based on the probability of a patient achieving undetectable MRD with a singular regimen could represent a new concept toward personalized treatment, with fast assessment of its success. This has never been investigated; therefore, we sought to define a machine learning model to predict undetectable MRD at the onset of multiple myeloma. Experimental Design: This study included 487 newly diagnosed patients with multiple myeloma. The training (n = 152) and internal validation cohorts (n = 149) consisted of 301 transplant-eligible patients…