Employing Feature Selection Algorithms to Determine the Immune State of a Mouse Model of Rheumatoid Arthritis
This work addresses the challenge of determining immune states for effective immunotherapy in rheumatoid arthritis, but it is incremental as it applies existing feature selection methods to a specific biomedical dataset.
The study tackled the problem of predicting immunotherapy efficacy in a mouse model of rheumatoid arthritis by using feature selection algorithms on high-dimensional T cell data to identify immune states, achieving a predictive model for treatment outcomes.
The immune response is a dynamic process by which the body determines whether an antigen is self or nonself. The state of this dynamic process is defined by the relative balance and population of inflammatory and regulatory actors which comprise this decision making process. The goal of immunotherapy as applied to, e.g. Rheumatoid Arthritis (RA), then, is to bias the immune state in favor of the regulatory actors - thereby shutting down autoimmune pathways in the response. While there are several known approaches to immunotherapy, the effectiveness of the therapy will depend on how this intervention alters the evolution of this state. Unfortunately, this process is determined not only by the dynamics of the process, but the state of the system at the time of intervention - a state which is difficult if not impossible to determine prior to application of the therapy. To identify such states we consider a mouse model of RA (Collagen-Induced Arthritis (CIA)) immunotherapy; collect high dimensional data on T cell markers and populations of mice after treatment with a recently developed immunotherapy for CIA; and use feature selection algorithms in order to select a lower dimensional subset of this data which can be used to predict both the full set of T cell markers and populations, along with the efficacy of immunotherapy treatment.