Proteomics-Based Regression Model for Assessing the Development of Chronic Lymphocytic Leukemia

Abstract

The clinical course of chronic lymphocytic leukemia (CLL) is very ambiguous, showing either an indolent nature of the disease or having latent dangerous progression, which, if diagnosed, will require an urgent therapy. The prognosis of the course of the disease and the estimation of the time of therapy initiation are crucial for the selection of a successful treatment strategy. A reliable estimating index is needed to assign newly diagnosed CLL patients to the prognostic groups. In this work, we evaluated the comparative expressions of proteins in CLL blood cells using a label-free quantification by mass spectrometry and calculated the integrated proteomic indexes for a group of patients who received therapy after the blood sampling over different periods of time. Using a two-factor linear regression analysis based on these data, we propose a new pipeline for evaluating model development for estimation of the moment of therapy initiation for newly diagnosed CLL patients.

Keywords

• chronic lymphocytic leukemia
• CLL
• linear regression
• proteomics
• mass spectrometry
• label-free quantification