Interpretative Information

The diagnosis of aGVHD in allogeneic HCT patients has largely relied on clinical assessment, often in combination with biopsy. However, this approach is problematic since damage is often severe by the onset of clinical signs and biopsies are invasive, expensive and carry the risk of severe complications. Non-invasive approaches, such as the use of biomarkers to test for aGVHD have been the subject of clinical research for a number of years, but suffer from a lack of clinical validation, established algorithmic value cutoffs and standardization between laboratories. However, recent research by Drs. James Ferrara and John Levine, working with an international consortium through the Icahn School of Medicine at Mount Sinai has led to the development and clinical validation of an algorithm (based on ST2 and REG3α levels) that provides prediction of risk for severe aGVHD and non-relapse mortality (NRM) in allo-HCT patients at critical times post-transplant. Use of the results from the aGVHD Algorithm tests may aid in treatment decisions and modifications prior to or early in the disease process, and thus improve outcomes for patients.

To evaluate the risk of severe aGVHD and NRM post-transplant:

• The aGVHD Pre-Symptomatic Algorithm for use post-transplant and before the patient shows onset of aGVHD symptoms.
• The aGVHD Symptomatic Onset Algorithm for use post-transplant, and after the patient displays symptoms of aGVHD.
• The aGVHD Post-Treatment Algorithm for use after systemic treatment for aGVHD has been initiated.

The aGVHD predictive algorithm tests utilize serum levels of ST2 and REG3α, and have been clinically validated to help health care professionals better predict the risk of NRM prior to the onset of aGVHD. While there are multiple clinical factors that increase the risk of NRM post-transplant include HLA mismatch, non-family donors, recipient age and GVHD prophylactic therapy, use of the predictive algorithm has been demonstrated to be more accurate despite variations in these clinical factors. The two biomarkers used in the algorithm are both important to the pathobiology of aGVHD. ST2 is member of the Toll-interleukin 1 receptor family, and functions as a down-regulator of the pro-inflammatory cytokines IL-1, IL-6 and TNF-α. ST2 has been shown to be elevated in inflammatory conditions. REG3α, an anti-inflammatory/anti-bacterial protein expressed in Paneth cells within the epithelium of the small intestine, has been shown to be elevated in medical conditions where immune dysregulation causes damage to the mucosal epithelial barrier.