Wudhikarn, Kitsada

e19556 Background: CD19 chimeric antigen receptor T cell therapy possesses unique side effects including cytokine release syndrome (CRS) and immune effector cell associated neurotoxicity syndrome (ICANS). Age is a major risk factor for ICANS. However, whether ICANS in older patients is different compared to younger patients is unknown. Herein, we report clinical course, outcomes and risk factors for ICANS in older patients with large B cell lymphoma (LBCL) treated with axicabtagene ciloleucel (axi-cel). Methods: We comprehensively reviewed detailed clinical courses of ICANS in 78 adult patients with LBCL treated with axi-cel between June 2016 and October 2020. Incidence, manifestation, risk factors, treatment, and outcomes of ICANS were compared between patients age ≥60 (n=32) and <60 (n=46) years old. Results: Baseline characteristics were comparable between older and younger patients except higher proportion of high international prognostic index and underlying cerebral microvascular disease in older patients. ICANS was observed in 16 patients in the older and 24 patients in the younger age group, with a 30-day incidence of 52% and 50%, respectively. Median time to CRS and ICANS were similar between 2 age groups. The most common initial neurological findings included aphasia, dysgraphia and encephalopathy in both age groups. Table summarizes the characteristics, clinical course and interventions of ICANS in older and younger patients. In Cox regression model, the presence of CRS was the only factor associated with ICANS in both age groups. Age, history of central nervous system involvement and cerebral microvascular disease were not associated with ICANS. Importantly, all patients had complete resolution of ICANS. No elderly patients in our cohort experienced seizure as a manifestation of ICANS. Conclusions: In our study, older age was not a risk factor for ICANS. CRS was the only factor associated with ICANS in both younger and older patients. Incidence, clinical course and neurological outcomes of ICANS in older patients treated with axi-cel were comparable to younger patients. [Table: see text]

e19554 Background: Obesity is associated with a pro-inflammatory state and immune dysregulation. Retrospective studies indicate that obesity could affect toxicities and outcomes after immunotherapies including checkpoint inhibitors and allogeneic stem cell transplant. Currently, there are no data specifically on outcomes for obese patients who receive chimeric antigen receptor T (CAR-T) cells. We described the clinical outcome in obese patients with large B cell lymphoma (LBCL) who received axicabtagene ciloleucel (axi-cel). Methods: We analyzed the effect of body weight (BW) and body mass index (BMI) on toxicities and outcomes of 78 adults with LBCL who received axi-cel between June 2016 and October 2020 at Mayo Clinic. Obesity was defined as having BMI of 30 or higher. Results: Of 78 patients, 22 (28%) and 19 (24%) were classified as overweight (BMI 25-29.99 kg/m2) and obese (BMI ≥30 kg/m2), respectively. Baseline characteristics were not statistically significantly different between non-obese and obese patients. The median delivered dose of fludarabine was similar between non-obese and obese patients (89 [0-105] vs 88 [56-94] mg/m2, P=0.32) whereas the median delivered dose of cyclophosphamide was lower in non-obese patients (1503 [1077-1525] vs 1512 [1021-1660] mg/m2, P=0.01). The 30-days cumulative incidence of CRS and ICANS were similar between non-obese and obese patients. BW and BMI were not associated with CRS or ICANS. The overall response rate was 66% (CR 47%) and 68% (CR 53%) in non-obese and obese group, respectively ( P=0.83). The 1-year event free survival (EFS) and overall survival (OS) was 34.6% and 64.5%, neither were different between non-obese and obese patients (EFS 35.8% vs. 30.7%, P=0.60; OS 59.4% vs. 83.9%, P=0.18). The 1-year cumulative incidence of relapse and non-relapse mortality was comparable (60.8% vs. 69.0%, P=0.40 and 3.4% vs. 0%, P=0.42). In the Cox proportional hazards model, higher dose of fludarabine, but not cyclophosphamide, was associated with better EFS and OS; however, neither obesity nor BW were associated with toxicities and outcomes. Conclusions: In our study, Obesity was not associated with risk of toxicities or adverse survival outcomes. The effect of obesity on the pattern of LD chemotherapy dosing including toxicities and outcomes after CAR-T warrants further exploration. [Table: see text]

Abstract Introduction: Antiphospholipid antibody syndrome (APS) is characterized by arterial or venous thrombosis, and/or adverse obstetric outcomes in the presence of persistent antiphospholipid antibodies (APab). Between 2–10% of all patients with APS are reported to present solely with anti beta-2 glycoprotein 1 antibodies (b2GP1 abs) (ref 1). In 2006 the Sapporo criteria were revised to include b2GP1 abs to the panel of diagnostic tests. While presence of a lupus anticoagulant (LA) has been found to correlate well with occurrence of thromboses, the prognostic and predictive roles of b2GP1 abs and anticardiolipin antibodies (ACLA) need better definition. Previous studies suggest both the severity of the initial event and the risk for recurrent events is greater among patients demonstrating presence of more than one APab on laboratory testing (ref 2 and 3), but this aspect needs further confirmation. Objectives: To identify the spectrum of clinical thrombotic and obstetric manifestations associated with the different APab profiles; To determine the prevalence and clinical significance of the b2GP1 ab positive and ACLA/LA negative phenotype; and To evaluate the significance of IgM b2GP1 ab and IgM ACLA. Methods: Medical records of patients tested for all three antiphospholipid antibodies at the University of Minnesota Medical Center from Dec 2000 to Dec 2005 were reviewed. Patients that met the modified criteria for APS were included. IgG and IgM ACLA were measured using the Diamedix kit (VAX diagnostics) and IgG and IgM b2GP1 abs using the Inovalite kit (NOVA diagnostics). LA was determined using standard protocol. Results: Over the 5 year period, all three laboratory assays were obtained in 560 individuals, but only 76 (13.5%) patients who met both the clinical and laboratory criteria for APS were included in this study. Seven of the 76 patients (9.2%) had isolated, persistently positive b2GP1 abs. LA alone was present in 21 patients (27.6%). Twenty nine patients (38%) had all three APabs, and had a higher incidence of thrombosis at multiple sites at the time of diagnosis as well as increased rate of recurrent events, in comparison to those with two or less than two APab. Isolated ACLA was present in only 2 patients (2.6%). Interestingly, both IgM (47%) and IgG (50%) b2GP1 abs were associated with thrombosis, whereas only IgG ACLA was found to be associated with thrombosis. Conclusion: The presence of b2GP1 abs as the sole laboratory anomaly is uncommon, but is noted in patients with APS, thus warranting it’s testing at the time of clinical suspicion. Amongst the different antibody types LA seems to be the strongest predictor for thrombotic events and ACLA seems to have a weaker link, consistent with the results of previous studies (ref 4). The presence of all three AP antibodies does increase the risk for recurrent and multiple thromboses.

8563 Background: Optimal initial treatment of follicular lymphoma (FL) is unknown. Rituximab as monotherapy (R) or as a component of immunochemotherapy (R+Chemo) is established as effective and it is now reasonable to re-examine the role of chemotherapy dosing. We explored clinical features, systemic treatment and chemotherapy delivery with comparative effectiveness of delivered dose intensity (DDI) on outcomes. Methods: We reviewed the University of Iowa/Mayo Clinic SPORE Molecular Epidemiology Resource database along with medical records on newly diagnosed grade I-IIIa FL who received systemic therapy from 2002 to 2009. Presenting clinicopathologic factors, outcomes and systemic therapy details including doses of chemotherapy were collected. The event-free (EFS) and overall survival (OS) effects of systemic therapy and chemotherapy DDI were analyzed with multivariate Cox regression. Confounding effects of FLIPI, grade, stage, and age were considered in the analysis. Results: From 2002 to 2009, 631 newly diagnosed FL were enrolled. Median follow up duration was 52.7 months. We identified 322 grade I-IIIa FL treated with systemic therapy including 93 R and 229 R+Chemo. Age and stage were similarly distributed between the R and R+Chemo groups; however, patients in the R group had lower grade (p<0.01) and FLIPI (p=0.03). Multivariate analysis showed no significant differences in EFS (HR=1.24, p=0.28) or OS (HR=0.55, p=0.13) for R compared to R+Chemo. Among R-CVP or R-CHOP treated FL, DDI data were collected for 73 doxorubicin (dox) and 137 cyclophosphamide (cyc) patients. Eighty-five percent of patients received 90% or more pre-planned DDI. After controlling for confounding factors, higher cycDDI was associated with improved EFS (HR 0.55, P=0.04) and OS (HR 0.74, P=0.03). No significant OS or EFS effects of doxDDI were observed. Conclusions: Addition of chemotherapy to rituximab was not associated with a detectable difference in survival outcomes in grade I-IIIa FL at a median follow-up of 52.7 months. Among R+Chemo treated FL, chemotherapy was delivered completely in most patients and more completed delivery of cyclophosphamide was associated with improved EFS and OS.

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 &lt; 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 &gt; 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&lt;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). We next tested whether the same MAP threshold of 0.290 could predict risk within clinical response subsets. A significant minority (10%) of clinical responders had high MAPs and experienced three-fold greater NRM than those with low MAPs (40% v 12%, p&lt;0.0001) whereas the majority (57%) of non-responders had low MAPs and experienced almost three-fold lower NRM than those with high MAPs (24% v 65%, p&lt;0.0001) (Fig 3). Thus the MAP provides important prognostic information over and above the change in clinical symptoms, further stratifying both responders and non-responders at four weeks of treatment. The MAP threshold classified patients both with and without significant lower GI symptoms because the MAP is a more specific measure of irreversible cryptic damage in patients with copious diarrhea and more sensitive in patients with less than 0.5 liters of daily diarrhea (Fig 4). 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.

Key Points The MAGIC algorithm probability, computed from 2 serum biomarkers, predicts mortality in all GVHD grades after 4 weeks of treatment. Dynamic changes in the MAGIC algorithm probability occur within all biomarker risk groups and can guide therapy.

Abstract 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<0.001, Figure 1). Relapse rates were the same in both probability groups, and thus LP patients experienced significantly better relapse free survival (RFS) (69% vs 53%, p<0.001). As expected, significantly fewer LP patients experienced severe GVHD at onset (as measured by Minnesota risk), maximum grade III/IV GVHD, or steroid resistant GVHD by day 180 after HCT (Figure 2, p<0.001 for all three parameters). To measure the accuracy of prediction, at each timepoint we calculated the area under the curve (AUC) of receiver operating characteristic curves; the AUC increased significantly with each subsequent evaluation, from 0.59 at one timepoint (dotted line) to 0.74 at the third timepoint (solid line) (p<0.001), with a final sensitivity of 65% and specificity of 74% (Figure 3A). 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.

Abstract Graft-versus-host disease (GVHD), the primary cause of non-relapse mortality (NRM) following allogeneic hematopoietic stem cell transplantation, does not always respond to treatment with high dose systemic corticosteroids. We have recently shown that a combination of three biomarkers (TNFR1, ST2, and REG3α) measured at onset of GVHD can predict day 28 response to treatment and 6-month NRM (Levine, Lancet Haem, 2015). Our goal in the current study was to determine if the same biomarker-based Ann Arbor GVHD algorithm can alsopredict treatment response andmortality whenapplied after one week of systemic corticosteroid treatment. The study population consisted of 378 patients (pts) with acute GVHD from 11 centers in the Mount Sinai Acute GVHD International Consortium. All pts were treated with systemic steroids and provided a plasma or serum sample obtained after one week of treatment (±3 days). The median starting dose of systemic steroids for Grade II-IV GVHD was 2.0 mg/kg/day and for Grade I was 1.0 mg/kg/day, after which treatment varied. Patients were divided into test (n=236) and validation (n=142) cohorts. We applied the Ann Arbor GVHD algorithm to concentrations of TNFR1, ST2, and REG3α measured after one week of treatment to generate a predicted probability of 6-month NRM, which we term the treatment score (TS). We employed unsupervised k-medoidclustering to partition TS values from the test cohort into two groups (high and low). This unbiased approach identified a high score group made up of 25% of pts (n=58) in the test cohort. We observed that the day 28 response rate (complete, CR + partial, PR) was significantly lower in pts with high scores compared to low scores in the test cohort (24% vs 65%, p<0.0001) (Fig 1A). Analysis of the validation cohort using the same TS definitions showed similar differences in response rates (22% vs 61%, p<0.0001) (Fig 1B). Further, nearly four times as many pts with high scores in both cohorts died within 6 months from non-relapse causes compared to pts with low scores (test: 57% vs 17%, p<0.0001; validation: 57% vs 14%, p<0.0001) (Fig 1C/D). As expected, the majority of non-relapse deaths in pts treated for GVHD were directly attributable to GVHD (test: 95%; validation: 89%). Relapse rates for high and low score pts were similar (data not shown), and thus pts with a high TS experienced significantly worse overall survival in both cohorts (test: 37% vs 72%, p<0.0001; validation: 38% vs 79%, p<0.0001) (Fig 1E/F). Approximately half of the pts in each cohort (test: 48%; validation: 44%) responded (CR+PR) to the first week of steroids and these ptshad significantly lower 6-month NRM than non-responders (NR) (test: 17% vs 36%, p=0.0002; validation: 13% vs 36%, p=0.0014). Yet the TS continued to stratify mortality risk independently of clinical response. In the test cohort, pts with a high score comprised 16% of all early responders and experienced more than twice the NRM of early responders with a low score (33% vs 13%, p=0.022) (Fig 2A). Conversely, test cohort pts who did not respond by day 7, but had a low score, fared much better than non-responders with a high score (NRM 21% vs 68%, p<0.0001) (Fig 2B). Two thirds of early non-responders comprised this more favorable group. These highly significant results reproduced in the independent validation cohort in similar proportions (CR+PR: 45% vs 6%, p=0.0003; NR: 61% vs 22%, p=0.0001) (Fig 2C/D). Finally, a subset analysis revealed that pts classified as NR after one week of steroids due to isolated, yet persistent, grade I skin GVHD (24/378, 6%) overwhelmingly had low treatment scores (22/24, 92%) and experienced rates of NRM (9%) comparable to responders with low scores, thus forming a distinct, albeit small, subset of pts with non-responsive GVHD that fares particularly well (Fig 3). In conclusion, a treatment score based on three GVHD biomarkers measured after one week of steroids stratifies pts into two groups with distinct risks for treatment failure and 6-month NRM. It is particularly noteworthy that the TS identifies two subsets of pts with steroid refractory (SR) GVHD who have highly different outcomes (Fig 2B/D). The much larger group, approximately two thirds of all SR pts, may not need the same degree of treatment escalation as is traditional for clinical non-response, and thus overtreatment might be avoided. Because the TSis measured at a common decision making time point, it may prove useful to guide risk-adapted therapy. Disclosures Mielke: Novartis: Consultancy; MSD: Consultancy, Other: Travel grants; Celgene: Other: Travel grants, Speakers Bureau; Gilead: Other: Travel grants; JAZZ Pharma: Speakers Bureau. Kroeger:Novartis: Honoraria, Research Funding. Chen:Incyte Corporation: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding; Novartis: Research Funding. Jagasia:Therakos: Consultancy. Kitko:Therakos: Honoraria, Speakers Bureau. Ferrara:Viracor: Patents & Royalties: GVHD biomarker patent. Levine:Viracor: Patents & Royalties: GVHD biomarker patent.