Assessment of systemic and gastrointestinal tissue damage biomarkers for GVHD risk stratification
Aaron Etra, Stephanie Gergoudis, George Morales, Nikolaos Spyrou, Jay Shah, Steven Kowalyk, Francis Ayuk, Janna Baez, Chantiya Chanswangphuwana, Yi‐Bin Chen, Hannah Choe, Zachariah DeFilipp, Isha Gandhi, Elizabeth O. Hexner, William J. Hogan, Ernst Holler, Urvi Kapoor, Carrie L. Kitko, Sabrina Kraus, Jung-Yi Lin, Monzr M. Al Malki, Pietro Merli, Attaphol Pawarode, Michael A. Pulsipher, Muna Qayed, Ran Reshef, Wolf Rösler, Tal Schechter, Grace Van Hyfte, Daniela Weber, Matthias Wölfl, Rachel Young, Umut Özbek, James L.M. Ferrara, John E. Levine
Abstract
We used a rigorous PRoBE (prospective-specimen collection, retrospective-blinded-evaluation) study design to compare the ability of biomarkers of systemic inflammation and biomarkers of gastrointestinal (GI) tissue damage to predict response to corticosteroid treatment, the incidence of clinically severe disease, 6-month nonrelapse mortality (NRM), and overall survival in patients with acute graft-versus-host disease (GVHD). We prospectively collected serum samples of newly diagnosed GVHD patients (n = 730) from 19 centers, divided them into training (n = 352) and validation (n = 378) cohorts, and measured TNFR1, TIM3, IL6, ST2, and REG3α via enzyme-linked immunosorbent assay. Performances of the 4 strongest algorithms from the training cohort (TNFR1 + TIM3, TNFR1 + ST2, TNFR1 + REG3α, and ST2 + REG3α) were evaluated in the validation cohort. The algorithm that included only biomarkers of systemic inflammation (TNFR1 + TIM3) had a significantly smaller area under the curve (AUC; 0.57) than the AUCs of algorithms that contained ≥1 GI damage biomarker (TNFR1 + ST2, 0.70; TNFR1 + REG3α, 0.73; ST2 + REG3α, 0.79; all P < .001). All 4 algorithms were able to predict short-term outcomes such as response to systemic corticosteroids and severe GVHD, but the inclusion of a GI damage biomarker was needed to predict long-term outcomes such as 6-month NRM and survival. The algorithm that included 2 GI damage biomarkers was the most accurate of the 4 algorithms for all endpoints.