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Multisystem inflammatory syndrome in children (MIS-C) is a syndrome associated with coronavirus disease 2019. Various phenotypes of MIS-C have been described including Kawasaki disease (KD). Although perineal desquamation is a known early sign of KD, to our knowledge, this rash has not yet been described in the KD phenotype of MIS-C. In this article, we report two patients in whom perineal desquamation was an early clue for the KD phenotype of MIS-C.

The graft-versus-leukemia (GVL) effect after allogeneic hematopoietic cell transplant (HCT) can prevent relapse but the risk of severe graft-vs-host disease (GVHD) leads to prolonged intensive immunosuppression and possible blunting of the GVL effect. Strategies to reduce immunosuppression in order to prevent relapse have been offset by increases in severe GVHD and non-relapse mortality (NRM). We recently validated the MAGIC algorithm probability (MAP) that predicts the risk for severe GVHD and NRM in asymptomatic patients using serum biomarkers. In this study we tested whether the MAP could identify patients whose risk for relapse is higher than their risk for severe GVHD and NRM. The multicenter study population (n=1604) was divided into two cohorts: historical (2006–2015, n=702) and current (2015–2017, n=902) with similar non-relapse mortality, relapse, and survival. On day 28 post-HCT, patients who had not developed GVHD (75% of the population) and who possessed a low MAP were at much higher risk for relapse (24%) than severe GVHD and NRM (16% and 9%); this difference was even more pronounced in patients with a high disease risk index (relapse 33%, NRM 9%). Such patients are good candidates to test relapse prevention strategies that might enhance GVL.

PURPOSE Severe (grade 3-4) acute graft-versus-host disease (AGVHD) is a major cause of death after unrelated-donor (URD) hematopoietic cell transplant (HCT), resulting in particularly high mortality after HLA-mismatched transplantation. There are no approved agents for AGVHD prevention, underscoring the critical unmet need for novel therapeutics. ABA2 was a phase II trial to rigorously assess safety, efficacy, and immunologic effects of adding T-cell costimulation blockade with abatacept to calcineurin inhibitor (CNI)/methotrexate (MTX)-based GVHD prophylaxis, to test whether abatacept could decrease AGVHD. METHODS ABA2 enrolled adults and children with hematologic malignancies under two strata: a randomized, double-blind, placebo-controlled stratum (8/8-HLA-matched URD), comparing CNI/MTX plus abatacept with CNI/MTX plus placebo, and a single-arm stratum (7/8-HLA-mismatched URD) comparing CNI/MTX plus abatacept versus CNI/MTX CIBMTR controls. The primary end point was day +100 grade 3-4 AGVHD, with day +180 severe-AGVHD-free-survival (SGFS) a key secondary end point. Sample sizes were calculated using a higher type-1 error (0.2) as recommended for phase II trials, and were based on predicting that abatacept would reduce grade 3-4 AGVHD from 20% to 10% (8/8s) and 30% to 10% (7/8s). ABA2 enrolled 142 recipients (8/8s, median follow-up = 716 days) and 43 recipients (7/8s, median follow-up = 708 days). RESULTS In 8/8s, grade 3-4 AGVHD was 6.8% (abatacept) versus 14.8% (placebo) ( P = .13, hazard ratio = 0.45). SGFS was 93.2% (CNI/MTX plus abatacept) versus 82% (CNI/MTX plus placebo, P = .05). In the smaller 7/8 cohort, grade 3-4 AGVHD was 2.3% (CNI/MTX plus abatacept, intention-to-treat population), which compared favorably with a nonrandomized matched cohort of CNI/MTX (30.2%, P < .001), and the SGFS was better (97.7% v 58.7%, P < .001). Immunologic analysis revealed control of T-cell activation in abatacept-treated patients. CONCLUSION Adding abatacept to URD HCT was safe, reduced AGVHD, and improved SGFS. These results suggest that abatacept may substantially improve AGVHD-related transplant outcomes, with a particularly beneficial impact on HLA-mismatched HCT.

A disease risk index (DRI) that was developed for adults with hematologic malignancy who were undergoing hematopoietic cell transplantation is also being used to stratify children and adolescents by disease risk. Therefore, to develop and validate a DRI that can be used to stratify those with AML and ALL by their disease risk, we analyzed 2569 patients aged 8) risk groups was 78%, 53%, 40%, and 25%, respectively (P < .0001). For ALL, 3 risk groups were identified based on age and disease status, including minimal residual disease status at transplantation. The 5-year leukemia-free survival for low (0 points), intermediate (2-4), and high (≥5) risk groups was 68%, 51%, and 33%, respectively (P < .0001). We confirmed that the risk groups could be applied to overall survival, with 5-year survival ranging from 80% to 33% and 73% to 42% for AML and ALL, respectively (P < .0001). This validated pediatric DRI, which includes age and residual disease status, can be used to facilitate prognostication and stratification of children with AML and ALL for allogeneic transplantation.

Background Large granular lymphocytic (LGL) leukemia is a rare hematological malignancy in children. The two types of LGL leukemia that have been described are T-cell and Natural Killer cell leukemia. It is most commonly diagnosed in older adults, average age of 60-year-old. About 20 cases of LGL leukemia have been reported in children and young adults. All the patients in the reported cases had immune dysregulation conditions, such as chronic graft versus host disease, common variable immunodeficiency disorder, Crohn's disease and autoimmune hemolytic anemia. Here we report a case of T-cell LGL leukemia in a 11-year-old boy without underlying condition who presented with chronic neutropenia associated with gingival hypertrophy, recurrent skin abscesses, aphthous ulcers, clubbing of nails and low bone density. Methods Multi-institution collaboration and literature review. Case Description 11-year-old male with two years history of episodic gum bleeding with gingival hypertrophy, skin abscesses, aphthous ulcers, chronic neutropenia and lymphocytosis presented to our clinic for further evaluation. Initial workup demonstrated moderate to severe neutropenia (absolute neutrophil count between 400/ul to 800/ul) with low segmented neutrophils of 2-4% and high lymphocytes of more than 80%, but normal white blood cell count, hemoglobin for age and platelet count. Peripheral blood smear showed several variant lymphocytes with cytoplasmic blebs and no immature cells present. Expansion of T-cell large granular lymphocytes were detected in peripheral blood by flow cytometry. Due to new symptom of lower back pain, a lumbar Magnetic Resonance Imaging was performed. Results showed low bone density with mild compression deformity of L1 and abnormal heterogeneous marrow signal with heterogeneous contrast enhancement. The abnormal bone marrow signal promoted the investigation of bone marrow aspiration and biopsy. Flow cytometry detected forty-five percent of lymphocytes with immuno-phenotype of CD3+, CD8+, CD57+, CD16+, CD7+ and CD5-. The morphology of minimal cytoplasm and mature chromatin along with immunophenotype were consistent with clonal T-cell large granular lymphocytic proliferation/leukemia. Further cytogenetic tests showed TCR gamma and beta genes rearrangement, STAT3 N647I mutation with normal male karyotype. A peripheral blood congenital neutropenia panel, which included a total of 18 genes, found a heterozygous mutation c 279 G>A in the Gata2 gene; a variant of uncertain significance. Next generation sequencing showed somatic mutations of TRGV10, TRGV8 TRGJ1, TNFAIP3 and STAT3. However, there was no germline mutations detected in sample from skin biopsy. Comprehensive evaluation by immunology, rheumatology and gastroenterology failed to detect any underlying conditions. Conclusion Due to rarity of LGL leukemia in pediatrics, standard of care guidelines are currently unavailable. Extrapolated from limited literature, two management options are considered: watch and wait approach versus early initiation of immunosuppressant chemotherapy. Improved diagnostics can aide management strategies in this patient population. Disclosures No relevant conflicts of interest to declare.

The Mount Sinai Acute GVHD International Consortium (MAGIC) algorithm probability (MAP), derived from 2 serum biomarkers, measures damage to crypts in the gastrointestinal tract during graft-versus-host disease (GVHD). We hypothesized that changes in MAP after treatment could validate it as a response biomarker. We prospectively collected serum samples and clinical stages of acute GVHD from 615 patients receiving hematopoietic cell transplantation in 20 centers at initiation of first-line systemic treatment and 4 weeks later. We computed MAPs and clinical responses and compared their abilities to predict 6-month nonrelapse mortality (NRM) in the validation cohort (n = 367). After 4 weeks of treatment, MAPs predicted NRM better than the change in clinical symptoms in all patients and identified 2 groups with significantly different NRM in both clinical responders (40% vs 12%, P < .0001) and nonresponders (65% vs 25%, P < .0001). MAPs successfully reclassified patients for NRM risk within every clinical grade of acute GVHD after 4 weeks of treatment. At the beginning of treatment, patients with a low MAP that rose above the threshold of 0.290 after 4 weeks of treatment had a significant increase in NRM, whereas patients with a high MAP at onset that fell below that threshold after treatment had a striking decrease in NRM that translated into clear differences in overall survival. We conclude that a MAP measured before and after treatment of acute GVHD is a response biomarker that predicts long-term outcomes more accurately than change in clinical symptoms. MAPs have the potential to guide therapy for acute GVHD and may function as a useful end point in clinical trials.

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 0.290), 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 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.

Relapse is most common cause of death after HCT. However, given the heterogeneity of diseases and disease states among patients (pts), risk for relapse is often difficult to manage in clinical trials when survival is the endpoint. The Disease Risk Index (DRI) predicts overall survival (OS) based on pre-HCT disease characteristics such as type of disease, remission status, and cytogenetic abnormalities and can be used to group heterogeneous diseases into 4 risk strata: very high risk [VHR, 1% of pts], high risk [HR, 23%], intermediate risk [IR, 64%], and low risk [LR, 12%] (Armand, Blood, 2014). OS for each of these strata is distinctly different. The DRI has been validated for adults, but not for children. Furthermore, the DRI cannot be calculated for some pediatric diseases like juvenile myelomonocytic leukemia (JMML). To determine its validity for children we calculated the DRI for 280 pediatric pts transplanted between 2008 and 2018 at Mount Sinai Acute GVHD International Consortium centers. The median age was 8.9 years (range, 0.4 - 17). The most common indications for HCT were ALL (51%), AML (31%), MDS (10%), and JMML (4%). Donors were adult unrelated donors (48%), unrelated cord blood (20%), haploidentical donors (17%), and matched siblings (15%). We first determined 2y survival by DRI for the 270 pts without JMML. Survival for JMML was closest to the VHR strata and we grouped JMML with VHR for all further analyses. The size of each of the risk strata were different for children compared to adults, VHR (n=32, 11%), HR (n=108, 39%), IR (n=128, 46%), and LR (n=12, 4%). Given the small number of LR pts, we excluded them from further analyses. We then determined that DRI effectively stratified pts into distinct strata for relapse (FIG 1A) and disease-free survival (FIG 1B). Because transplant practices are different for children compared to adults (e.g., more myeloablative conditioning), we examined whether DRI remained predictive of outcomes after adjustment for age, donor type, stem cell source, and conditioning intensity. We used IR as the reference group for these multivariate analyses. Children with HR DRI were significantly more likely to relapse (HR 2.1, 95% CI 1.2-3.7) and have worse DFS (HR 1.6, 95% CI 1.0-2.5). The risks for relapse and worse DFS were even greater for children with VHR DRI (relapse, HR 3.4, 95% CI 1.6-7.3, DFS 3.59, 95% CI 2.0-6.5). There were no significant differences in 2y non-relapse mortality by DRI. VHR pts had significantly worse 2y OS (HR 3.2, 95% CI 1.6-6.4) but HR pts were not significantly different for 2y OS than IR pts (HR 1.3, 95% CI 0.7-2.2). OS for HR and IR pts may diverge with longer follow-up. In summary, the LR strata is small and more pts are needed to determine their outcomes. However, for >95% of children the DRI creates 3 distinct risk strata. The DRI can be used in pediatric clinical trials to stratify for risk of relapse.

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.

Systemic glucocorticoids are the principal treatment for acute graft-versus-host disease (GVHD), which remains the major cause of non-relapse mortality (NRM) after allogeneic hematopoietic cell transplantation (HCT). However, there are no validated biomarkers that measure a patient's response to glucocorticoid therapy, and thus response is evaluated by the change in clinical symptom severity. A major weakness in the predictive power of clinical responses is that changes to all organs are weighted equally even though the major driver of NRM is irreversible damage to the crypts of the GI tract. 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 has been considered a "liquid biopsy" that estimates the damage caused by GVHD to crypts throughout the lower GI tract (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 for GVHD and might compare favorably to the change in clinical symptoms that measures response to GVHD treatment, 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 measured the serum concentrations of REG3α and ST2 before and after systemic therapy for acute GVHD and computed MAPs, the changes in MAPs, and clinical responses for each patient. MAPs of patients who experienced 6 month NRM showed significantly greater increases than MAPs of patients who survived (p=0.0004). In patients whose MAPs at the start of treatment were low (Ann Arbor 1, MAP < 0.141) or intermediate (Ann Arbor 2, 0.141 ≤ MAP ≤ 0.290), 6 month NRM clustered among those who had the greatest increases in MAP after 28 days (Fig 1A,B). In patients with high MAPs at the start of treatment (Ann Arbor 3, MAP > 0.290), those who survived tended to have the largest decreases in MAP (Fig 1C). These changes in MAP suggested crossing a single threshold could predict risk of mortality. We found that patients whose MAPs rose above a threshold MAP of 0.290 (5% of Ann Arbor 1, 27% of Ann Arbor 2) 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 (66% of Ann Arbor 3) had a large increases in mortality (Fig 2). When measured at day 28, the MAP was significantly more accurate in predicting NRM than the gold standard of the clinical response, with areas under the receiver operating characteristic curve (AUC) of 0.86 and 0.70, respectively (p We conclude that the MAP is, to our knowledge, the first validated laboratory test to serve as response biomarker for the treatment for acute GVHD and a more accurate predictor of survival than clinical response after four weeks of treatment. The MAP may serve as a novel endpoint and an important complement to changes in clinical symptom severity in future trials of GVHD treatment. Disclosures Srinagesh: National Institutes of Health: Research Funding. Ozbek:Viracor: Patents & Royalties: Biomarker Patent. Ayuk:Novartis: Honoraria, Other: Advisory Board, Research Funding. Aziz:Doris Duke Charitable Foundation: Research Funding. Defilipp:Incyte: Research Funding. Grupp:Novartis: Consultancy, Research Funding; Roche: Consultancy; GSK: Consultancy; CBMG: Consultancy; Novartis: Research Funding; Kite: Research Funding; Servier: Research Funding; Jazz: Other: study steering committees or scientific advisory boards; Adaptimmune: Other: study steering committees or scientific advisory boards; Cure Genetics: Consultancy; Humanigen: Consultancy. Hexner:novartis: Research Funding. Kitko:Mallinckrodt: Honoraria; Novartis: Consultancy, Honoraria. Mielke:EBMT/EHA: Other: Travel support; ISCT: Other: Travel support; Miltenyi: Consultancy, Honoraria, Other: Travel and speakers fee (via institution), Speakers Bureau; Jazz Pharma: Honoraria, Other: Travel support, Speakers Bureau; IACH: Other: Travel support; Kiadis Pharma: Consultancy, Honoraria, Other: Travel support (via institution), Speakers Bureau; DGHO: Other: Travel support; Bellicum: Consultancy, Honoraria, Other: Travel (via institution); GILEAD: Consultancy, Honoraria, Other: travel (via institution), Speakers Bureau; Celgene: Honoraria, Other: Travel support (via institution), Speakers Bureau. Merli:Sobi: Consultancy; Amgen: Honoraria; Novartis: Honoraria; Bellicum: Consultancy. Pulsipher:Amgen: Other: Lecture; Miltenyi: Research Funding; Bellicum: Consultancy; Novartis: Consultancy, Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Jazz: Other: Education for employees; CSL Behring: Membership on an entity's Board of Directors or advisory committees; Adaptive: Membership on an entity's Board of Directors or advisory committees, Research Funding; Medac: Honoraria. Qayed:Bristol-Myers Squibb: Honoraria. Reshef:Pfizer: Consultancy; Magenta: Consultancy; Kite: Consultancy, Research Funding; Atara: Consultancy, Research Funding; BMS: Consultancy; Pharmacyclics: Consultancy, Research Funding; Incyte: Consultancy, Research Funding; Celgene: Research Funding; Shire: Research Funding. Levine:Incyte: Consultancy, Research Funding; Biogen: Other: non-financial support; Viracor: Patents & Royalties: biomarker patent; Ironwood: Honoraria; bluebird bio: Consultancy; National Cancer Institute: Research Funding; Novartis: Honoraria; Kamada: Research Funding. Ferrara:National Institutes of Health: Research Funding; ViraCor: Consultancy; Incyte: Consultancy; Kamada: Consultancy; Mallinckrodt: Consultancy; Enlivex: Consultancy; Xenikos: Consultancy; CSL Behring: Consultancy.

OBJECTIVES/SPECIFIC AIMS: The first aim of the study is to evaluate the accuracy of serum biomarkers of acute GVHD measured after four weeks of corticosteroid therapy to predict 6 month NRM. The second aim of this study is to compare the accuracy of the biomarker algorithm to that of clinical response to corticosteroids after four weeks. The third aim of the study is to develop a novel regression model that uses weekly biomarker measurements over the first month of corticosteroid therapy to predict 6 month NRM. METHODS/STUDY POPULATION:. Patients who received HCT at one of 22 IRB-approved centers and provided blood samples to the Mount Sinai Acute GVHD International Consortium (MAGIC) biorepository and developed GVHD between January 2008 to May 2018 are included in this study. Patients were divided by time into a training set (Jan 2008-Dec 2015, n=233) for model development and a validation set (Jan 2015-May 2018, n=357) to evaluate the predictive performance of the model. The later time of the validation set was chosen deliberately to model contemporaneous GVHD treatment practices. The size of each group was designed so that there would be roughly equal numbers of deaths in both groups. RESULTS/ANTICIPATED RESULTS:. Serum concentrations of GVHD biomarkers after one month of corticosteroid therapy were measured in the validation set, and the predicted probability of NRM ($\hat{\rm p}$) was computed according to the previously published algorithm: $\log[-\log(1 - \hat{\rm p})]=-11.263 + 1.844({\rm logST}2)+ 0.577({\rm logREG}3\alpha)$. The performance of the biomarker algorithm was evaluated by creating receiver operating characteristic (ROC) curves and calculating the area under the curve (AUC) in the validation set. The AUC of the biomarker algorithm was a significantly better predictor of 6 month NRM than clinical response to treatment after four weeks of corticosteroids (0.84 vs. 0.64, p

Relapse of malignancy and lethal graft versus host disease (GVHD) are the principal causes of failure of allogeneic hematopoietic cell transplant (HCT). Recently we have shown that at seven days after HCT an algorithm using two serum biomarkers (ST2 and REG3α) can predict severe GVHD (Hartwell et al. JCI Insight 2017). We determined whether serial testing (in the first month following HCT) of patients with low probability biomarkers would improve the predictive accuracy of the algorithm and identify patients with different risks of relapse and lethal GVHD. Patients who received an HCT at 18 centers in the Mount Sinai Acute GVHD International Consortium (MAGIC) for hematologic malignancy and who supplied three blood samples were divided into a training set and validation set with equal numbers of lethal GVHD events, which was defined as death from GVHD or infection during treatment for GVHD. Patients in the training set (n=702) underwent HCT from January 1, 2006 until June 30, 2015, whereas patients in the validation set (n=906) underwent HCT from July 1, 2015 to May 1, 2017. Serum samples were analyzed using the previously published algorithm of two biomarkers up to three times (day 7, day 14, day 28 or GVHD onset, if onset occurred within the first 28 days). The algorithm generates a predicted probability of lethal GVHD between 0 and 1 for each patient. Patients were categorized as either low probability (LP) or high probability (HP) for lethal GVHD. HP thresholds of 0.20 and 0.16 were used to classify patients with and without GVHD symptoms, respectively (once categorized as HP, patients remained in that category and were not retested). All results were similar between training and validation sets, and we present here the validation set results. Serial testing identified 28% of patients as HP with a three-fold greater cumulative incidence of lethal GVHD at one year (13% vs 4%, p Early development of GVHD (by day 28) is a risk factor for lethal GVHD. Therefore, we next plotted RFS (dashed line), relapse (solid line), and lethal GVHD (dotted line) rates in patients who developed GVHD by day 28. 25% of patients with GVHD were categorized as HP and had a cumulative incidence of lethal GVHD more than four times higher (28%) than that of relapse (6%); however the risks were reversed for the 75% of patients who were LP, where relapse (15%) occurred twice as often as lethal GVHD (7%) (Figure 3B). In patients who did not develop GVHD in the first month, this reversal of risks was even more dramatic. Approximately half (53%) of the entire validation cohort did not develop GVHD by day 28 and was LP at all three evaluations. These patients had an exceptionally low risk of lethal GVHD and thus they relapsed (25%) much more often than they died from GVHD (3%). When malignancies were classified according to risk for relapse by the disease risk index (DRI) (Figure 3C), the probability of relapse was three fold higher than lethal GVHD in malignancies with a low DRI (12%), six fold higher for intermediate DRI (20%), and eleven fold higher for high/very high DRI (33%). We conclude that a serial monitoring strategy using GVHD biomarkers for one month after HCT is able to identify two groups of patients with very different risks of lethal GVHD and relapse. For these patients, the intensity of immunosuppression after day 28 could be tailored according to the probabilities of developing lethal GVHD and relapse in the context of clinical trials. Disclosures Aziz: Doris Duke Charitable Foundation: Research Funding. Ayuk:Therakos (Mallinckrodt): Honoraria; Novartis: Honoraria; Celgene: Consultancy; Gilead: Consultancy. Chen:REGiMMUNE: Consultancy; Incyte: Consultancy, Membership on an entity's Board of Directors or advisory committees; Magenta Therapeutics: Consultancy; Takeda Pharmaceuticals: Consultancy. Merli:Neovii Biotech: Honoraria; AMGEN: Honoraria. Roesler:Sanofi: Other: Travel, Accommodations, Expenses; Amgen: Equity Ownership; Jazz Pharmaceuticals: Other: Travel, Accommodations, Expenses; Immunomedics: Equity Ownership; Biogen: Equity Ownership; Merck: Consultancy; Pfizer: Consultancy. Kitko:Novartis: Consultancy, Honoraria; Mallinckrodt: Honoraria, Other: Travel, Accommodations, Expenses. Qayed:Novartis: Consultancy. Wölfl:Bristol-myers Squibb: Equity Ownership; Novartis: Equity Ownership; Taheda: Equity Ownership; Juno: Equity Ownership; Neovii: Other: Travel, Accommodations, Expenses. Mielke:Celgene: Speakers Bureau; DGHO: Speakers Bureau; EHA: Speakers Bureau; Kiadis Pharma: Speakers Bureau; Miltenyi: Speakers Bureau. Wudhikarn:Takeda Oncology: Other: Travel, Accommodations, Expenses. Nakamura:Celgene: Honoraria; Molmed: Honoraria; Merck: Consultancy; Pharmacyclics: Consultancy; Atara: Consultancy; Jazz Pharmaceuticals: Consultancy. Pulsipher:CSL Behring: Consultancy; Novartis: Consultancy, Honoraria, Speakers Bureau; Adaptive Biotech: Consultancy, Research Funding; Amgen: Honoraria. Reshef:Pfizer: Consultancy; Atara Biotherapeutics: Consultancy; Kite Pharma: Consultancy; Takeda Pharmaceuticals: Consultancy; Bristol-Myers Squibb: Consultancy; Incyte: Consultancy. Levine:Therakos: Consultancy; Novartis: Consultancy; Bluebird: Consultancy; Incyte: Consultancy; Kamada: Research Funding; Viracor: Patents & Royalties. Ferrara:Incyte: Consultancy, Honoraria, Other: Travel, Accommodations, Expenses; Xenikos: Consultancy, Other: Travel, Accommodations, Expenses; Kamada: Consultancy, Research Funding; Viracor: Consultancy, Patents & Royalties.