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Acute GVHD biomarkers predict long-term outcomes after GVHD diagnosis. Our group has validated the MAGIC algorithm which uses the concentrations of 2 GVHD biomarkers, ST2+REG3a, to predict lethal GVHD (defined as death from GVHD without relapse) [Hartwell 2017]. Other published biomarker combinations that predict GVHD outcomes include ST2+ REG3a+TNFR1 [Levine 2015], ST2+TIM3 [Abu Zaid 2017], ST2+TNFR1 [McDonald 2015], TIM3+TNFR1+IL6 [McDonald 2015], as well as AREG alone [Holtan 2018]. It is not clear which biomarker combination best predicts lethal GVHD because these algorithms were developed using different patient cohorts with different endpoints. To answer this question, we compared the predictive accuracy of different biomarker combinations as well as these six specific combinations in the same patient cohort. We studied 522 patients with serum samples at GVHD diagnosis who were transplanted at 19 Mount Sinai Acute GVHD International Consortium (MAGIC) centers between January 1, 2004 and April 30, 2017. Patients were divided into training (n = 253) and validation (n = 269) sets; validation patients were transplanted after November 1, 2015 and had not previously been used to generate an algorithm. Biomarkers were measured by ELISA and log-transformed values were used to predict 1-year lethal GVHD by competing risk regression. Four of the 6 biomarkers (ST2, REG3a, TNFR1, and TIM3) independently predicted lethal GVHD in the training set. We then developed algorithms that predicted lethal GVHD using all possible combinations of these 4 biomarkers as well as the published combination of TIM3+TNFR1+IL6 and AREG alone. The best algorithms of 1-4 biomarkers for predicting lethal GVHD were REG3a alone, ST2+REG3a, ST2+REG3a+TNFR1, and ST2+REG3a+TNFR1+TIM3. While ST2+REG3a was the most accurate combination based on the lowest Akaike Information Criterion (AIC), the other published biomarker combinations produced similar AICs. Therefore, we used the independent validation set to compare all six published algorithms. We generated area under the receiver operating characteristic curves (AUC) for each algorithm (FIG 1). We next determined the threshold that maximized sensitivity and specificity for each algorithm and calculated the cumulative incidence of lethal GVHD in the resulting high and low risk strata (FIG 2). Highly similar results were obtained when the cumulative incidence of non-relapse mortality was used as the endpoint. In a validation set of previously unanalyzed patients, several biomarker combinations reproducibly stratified patients with GVHD for risk of death. The best 4 algorithms, all of which included ST2, produced comparable outcomes. Adding TNFR1 to ST2+ REG3a did not improve accuracy. The ST2+REG3a algorithm best identifies patients at onset of GVHD for high risk of lethal GVHD and NRM, and thus remains a standard for biomarker-based risk prediction. [ABSTRACT FROM AUTHOR]

There are no validated biomarkers that measure a patient's response to therapy for acute graft-versus-host disease (GVHD), the leading cause of non-relapse mortality (NRM) after allogeneic hematopoietic cell transplant (HCT). Recent studies from the Mount Sinai Acute GVHD International Consortium (MAGIC) have validated an algorithm probability (MAP) that combines serum concentrations of two biomarkers of GVHD (REG3α and ST2) to generate an estimated probability of 6 month NRM for individual patients. The MAP estimates GVHD-mediated damage to crypts throughout the lower GI tract at single time points (Hartwell et al., JCI Insight , 2017; Major- Monfried et al., Blood , 2018). We hypothesized that the change in MAP between start of treatment and 28 days later could serve as a response biomarker and would compare favorably to clinical response, the gold standard which is widely used as a surrogate for long term survival and is the primary endpoint in most GVHD treatment trials (Martin et al., BBMT , 2009; MacMillan et al., Blood , 2010). We prospectively collected serum samples and clinical staging from 368 sequential HCT patients who received systemic treatment for acute GVHD in one of 20 MAGIC centers between January 2016 and February 2018. We computed MAPs and clinical responses for each patient. MAPs of patients who experienced 6 month NRM increased significantly compared to MAPs of patients who survived (p=0.0004). In patients whose initial MAPs were low (Ann Arbor 1, MAP < 0.141) or intermediate (Ann Arbor 2, 0.141 ≤ MAP ≤ 0.290), 6 month NRM clustered among those with the greatest increases in MAP after 28 days (Fig 1A,B). In patients with high initial MAPs (Ann Arbor 3, MAP > 0.290), those who survived tended to have the largest decreases in MAP (Fig 1C). We found that patients whose MAPs rose above the previously determined high-risk threshold MAP of 0.290 had significantly worse survival compared to those who remained below it, whereas the large number patients with initially high MAPs that remained above the threshold had a large increase in mortality (Fig 2). When measured at day 28, MAPs predicted NRM more accurately than clinical responses, with areas under the receiver operating characteristic curve (AUC) of 0.86 and 0.70, respectively (p<0.0001). An algorithm that combined clinical response with biomarkers generated the same AUC as the MAP alone (0.83 v 0.86, p = NS). The same MAP threshold predicted risk of NRM within both clinical responders and non-responders after four weeks of treatment (Fig 3), and in those with and without significant lower GI symptoms (Fig 4). We conclude that the MAP is, to our knowledge, the first laboratory test validated as a response biomarker for acute GVHD treatment and more accurately predicts survival than clinical response after 28 days of treatment. The MAP may serve as a novel endpoint in future trials of GVHD treatment. [ABSTRACT FROM AUTHOR]