Predicting treatment responses in patients with systemic lupus
Although there are a number of lupus treatments available, there is no way to predict which drugs will work for a specific patient without relying on trial and error. One lupus drug called abatacept works by impairing the function of a type of immune cells known as T cells. In people with lupus, T cells cause inflammation that harms healthy human cells and results in symptoms. By interfering with T cells, abatacept lowers the level of inflammation and alleviates symptoms. Even though abatacept works for many lupus patients, for unknown reasons this drug does not help everyone. Dr. Guthridge’s study is using emerging technology to compare the blood samples from patients whose lupus does respond to treatment with that of lupus patients who do not improve with abatacept therapy. By comparing the immune cells in each sample, he aims to determine what is different about the immune response in patients who respond to treatment compared to those who don’t.
What this study means for people with lupus
This study will identify differences in the immune system that can predict which patients are likely to benefit from treatment with abatacept.
The goal of this proposal is to develop a practical approach to gage the likelihood of responses of individual SLE patients to specific immune modulating treatments. The heterogeneity of lupus patients is manifest at many levels, including diverse genetics, environmental exposures, clinical disease history and previous or ongoing treatments. This combination of hard-coded genetic propensities with current and past exposures, treatments and adaptive responses must produce complex impacts on how the disease presents at a given time, as well as whether an individual patient responds to a given treatment. Previous studies, including our own work, have focused on defining immunologically similar subsets of SLE by patterns of gene expression of immune pathways. We have recently demonstrated that it is possible to sort patients into similar phenotypes of manageable size and that these specific immune phenotypes are reproducible in non-overlapping populations of lupus patients. We will now leverage our access to an extensive biorepository of SLE patient samples which have already had extensive clinical characterization and whole blood multi-omic biomarker/phenotyping performed, in order to perform deeper cell-type specific epigenetic and transcript isoform biomarker assays. We will leverage preliminary data from a recently completed investigator-initiated trial of abatacept vs placebo (1:1 randomization) in 66 SLE patients with background medications withdrawn. Baseline phenotypes in responsive subsets from this trial will be used to choose patients in the same phenotypic clusters from our cohort. Previously collected multi-omics data will be integrated with new cell-specific epigenetic/methylation and transcript alternative isoform data to better understand differences between disease propensity factors and more transient, reversible elements predictive of abatacept response. A multiplex model will be built and concentrated for testing on the prospectively collected samples from the active treatment arm of the abatacept trial to determine whether these may help to distinguish patients with clinical response to abatacept.