A Molecular Classification Paradigm to Predict Therapeutic Response in Systemic Sclerosis
Abstract
Systemic sclerosis (SSc) is one of the deadliest autoimmune diseases. Although treatments have improved, clinical trials are still confounded by significant variability that may affect their interpretation and impact. The goal of this project is to develop a stratification method using easily accessible blood samples, based on methods we pioneered in skin, that can be used to identify the patients most likely to improve with a given treatment. We will further analyze SSc patient samples and data using cutting-edge technologies to determine how the skin, and even individual cells, are different or the same between various groups of patients. The Scleroderma Research Program Translational Research Partnership Award focus areas that will be addressed here are to: (1) understand the how different genes are expressed together in different groups of patients (i.e., what is a patient’s genetic fingerprint and how many different fingerprints are there?); (2) use approaches that take a “10,000-foot view” of the changes in the cells of a patient (i.e., systems biology) to understand the processes that cause disease; (3) define genes that we can use as markers of disease; and (4) identify the specific cells that may cause disease or make the disease worse. The ultimate application of our work will be an approach that will allow a physician to identify those patients that are the most likely to benefit from a specific therapy. This will also increase the likelihood of finding existing drugs that may improve their condition, or new future drugs that may treat their SSc entirely. If we are successful in identifying subsets of patients who could be treated with a specific therapy, then the data from this study could help develop a diagnostic test to identify who should be treated with which drug (i.e., precision medicine). This approach is already being used in some clinical trials, supporting the enthusiasm that new therapies could be identified in as little as a few years. Our approach addresses several gaps in knowledge. First, it addresses the problem of extreme variability across patients with SSc. Second, it will identify cells that may cause disease so we can understand how they work and how they can be therapeutically targeted. Third, by developing methods that can be applied across current, future and possibly prior therapeutic studies, we will validate several key research findings suggesting that subsets are predictive of response to various therapies. By identifying the specific cells that are the root cause of SSc and, subsequently, the characteristics of various subgroups of SSc patients, we may also realize that there are some existing therapies that were previously deemed failures, but may now be considered a success for some fraction of patients. The research we propose here will help us to understand the underlying, molecular cause(s) of SSc in different groups of patients and to identify therapies that may best treat them. This knowledge would have a major impact on the health, feeling, and quality of life of SSc patients, and on our ability to design more effective clinical trials into the future.
Document Details
- Document Type
- DoD Grant Award
- Publication Date
- Dec 05, 2021
- Source ID
- W81XWH2110878
Entities
People
- Michael Whitfield
Organizations
- Dartmouth College
- United States Army