Citations

Restless legs syndrome (RLS) is a common sleep‐related movement disorder characterised by an uncomfortable urge to move the legs that occurs during periods of inactivity. Although there have been many case reports on antipsychotic‐induced RLS, ziprasidone has never been reported as a cause of RLS. We present a case of a female patient with schizophrenia who presented with symptoms of RLS following the administration of high doses of ziprasidone added to quetiapine and valproate. The patient’s symptoms of RLS occurred following the administration and titration of ziprasidone to 160 mg, and were relieved upon reducing the dose to 120 mg/day. Other potential causative medications and differential diagnoses that could have caused similar symptoms were excluded. Clinicians should be aware of the potential for ziprasidone‐induced RLS. Dopamine and serotonin interaction could be the mechanism underlying ziprasidone‐induced RLS.

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<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<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<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.

Background: Axicabtagene ciloleucel (axi-cel) is an autologous anti-CD19 chimeric antigen receptor (CAR) T cell therapy approved for the treatment of adult patients who have relapsed/refractory large B cell lymphoma (LBCL) and have had ≥ 2 prior systemic therapies. In ZUMA-1, the registrational study of axi-cel in patients with refractory LBCL, the objective response rate was 83% (complete response rate, 58%), with ongoing responses in 39% after a median follow-up of 27.1 months (Locke FL, et al. Lancet Oncol. 2019). Despite the success of axi-cel, approximately 60% of patients have no response or relapse after treatment, indicating that additional strategies are needed for patients with relapsed/refractory LBCL. Preclinical murine studies have shown that rituximab augmented the tumor-suppressing effects of anti-CD19 CAR T cells, and the combination led to higher rates of tumor reduction (Mihara K, et al. Br J Haematol. 2010; Rufener GA, et al. Cancer Immunol Res. 2016). The IMiD® immunomodulatory agent lenalidomide has shown activity in patients with relapsed/refractory diffuse large B cell lymphoma and has also been shown to enhance the antitumor functions of anti-CD19 and anti-CD20 CAR T cells in mice (Otahal P, et al. Oncoimmunology. 2016). In ZUMA-14, the aim is to investigate the efficacy and safety of axi-cel in combination with rituximab or lenalidomide in adult patients with refractory LBCL. Methods: This Phase 2 study (NCT04002401) has a planned enrollment of approximately 60 patients aged ≥ 18 years with refractory LBCL, defined as a response of either progressive disease or stable disease to previous chemotherapy or progressive disease or relapse ≤ 12 months after an autologous stem cell transplant. Patients with prior IMiD® treatment, including lenalidomide, prior CAR T cell therapy, and/or prior CD19-targeted therapy are excluded. After leukapheresis, patients will be assigned 1:1 to receive axi-cel with either rituximab (Cohort 1) or lenalidomide (Cohort 2). Patients will receive lymphodepleting chemotherapy of fludarabine (30 mg/m2) and cyclophosphamide (500 mg/m2) on days -5 to -3 before axi-cel infusion (2 × 106 cells/kg) on Day 0. Cohort 1 will receive rituximab (375 mg/m2) every 28 days starting on Day -5 for a total of 6 doses. Cohort 2 will receive lenalidomide (10 mg) daily starting 7 days after leukapheresis through Day 3 and for 5 additional cycles (20 mg, first 21 days of each 28-day cycle) beginning after axi-cel infusion starting on Day 21. Patients may not receive any therapy other than conditioning therapy and rituximab or lenalidomide, as specified by cohort, between leukapheresis and axi-cel infusion. The primary endpoint is investigator-assessed complete response rate per the Lugano classification (Cheson BD, et al. J Clin Oncol. 2014). Key secondary endpoints include safety, objective response rate, duration of response, progression-free survival, overall survival, and pharmacokinetics (levels of blood CAR T cells over time). Exploratory endpoints for both cohorts include pharmacodynamic assessment of cytokine profiles and the rate of CD19-negative relapses. An additional exploratory endpoint for Cohort 2 is to investigate immunomodulation of the tumor microenvironment, including the number and activation of T cells and natural killer cells. Enrollment is expected to start in September 2019. Disclosures Neelapu: Cell Medica: Consultancy; Pfizer: Consultancy; BMS: Research Funding; Kite, a Gilead Company: Consultancy, Research Funding; Novartis: Consultancy; Precision Biosciences: Consultancy; Acerta: Research Funding; Karus: Research Funding; Incyte: Consultancy; Merck: Consultancy, Research Funding; Unum Therapeutics: Consultancy, Research Funding; Celgene: Consultancy, Research Funding; Cellectis: Research Funding; Allogene: Consultancy; Poseida: Research Funding. Kharfan-Dabaja:Daiichi Sankyo: Consultancy; Pharmacyclics: Consultancy. Oluwole:Pfizer: Consultancy; Spectrum: Consultancy; Gilead Sciences: Consultancy; Bayer: Consultancy. Krish:Celgene: Consultancy, Research Funding, Speakers Bureau; Genetech: Consultancy, Speakers Bureau; Pharmacyclics: Consultancy, Research Funding, Speakers Bureau; Takeda: Research Funding; Curis: Research Funding; MEI Pharma: Research Funding; Bristol Meyers Squibb: Research Funding; Xencor: Research Funding; Roche: Research Funding; AstraZeneca: Consultancy, Research Funding, Speakers Bureau; Sunesis: Consultancy, Research Funding. Reshef:BMS: Consultancy; Shire: Research Funding; Incyte: Consultancy, Research Funding; Kite, a Gilead Company: Consultancy, Honoraria, Research Funding; Atara: Consultancy, Research Funding; Magenta: Consultancy; Pfizer: Consultancy; Pharmacyclics: Consultancy, Research Funding; Celgene: Research Funding. Riedell:Bayer: Honoraria, Speakers Bureau; Kite/Gilead: Honoraria, Research Funding, Speakers Bureau; Verastem: Membership on an entity's Board of Directors or advisory committees; Celgene: Membership on an entity's Board of Directors or advisory committees, Research Funding; Novartis: Research Funding. Stiff:Amgen: Research Funding; Gamida-Cell: Research Funding; Incyte: Research Funding; Cellectar: Research Funding; Unum: Research Funding; Gilead/Kite Pharma: Consultancy, Honoraria, Research Funding. Goyal:Kite, a Gilead Company: Employment. Kawashima:Kite, a Gilead Company: Employment, Equity Ownership. Milletti:Roche: Employment, Equity Ownership, Other: Travel Expenses, Patents & Royalties; Gilead: Employment, Equity Ownership, Other: Travel Expenses, Patents & Royalties; Kite, a Gilead Company: Employment. Oliva:Kite, a Gilead Company: Employment. Sun:Kite, A Gilead Company: Employment. Munoz:Pharmacyclics /Janssen: Consultancy, Research Funding, Speakers Bureau; Bayer: Consultancy, Speakers Bureau; Merck: Consultancy; Kyowa: Consultancy, Honoraria, Speakers Bureau; Seattle Genetics: Consultancy, Honoraria, Research Funding, Speakers Bureau; Celgene/Juno: Consultancy, Research Funding; Genentech: Consultancy, Research Funding, Speakers Bureau; Kite/Gilead: Consultancy, Research Funding, Speakers Bureau; Bristol-Myers Squibb: Consultancy; Alexion: Consultancy; Pfizer: Consultancy; Fosunkite: Speakers Bureau; AstraZeneca: Speakers Bureau; Portola: Research Funding; Incyte: Research Funding. OffLabel Disclosure: ZUMA-14 is a clinical trial evaluating the investigational combination of axicabtagene ciloleucel with either rituximab or lenalidomide in refractory large B cell lymphoma

Background: Relapsed/refractory (R/R) large B cell lymphoma (LBCL) is associated with poor outcomes to standard salvage therapy (Crump M, et al. Blood. 2017). In SCHOLAR-1, a large multicenter, patient-level, retrospective study, patients with R/R diffuse LBCL had a 26% objective response rate (ORR) to the next line of therapy, a 7% complete response (CR) rate, and a median overall survival of 6.3 months (Crump M, et al. Blood 2017). Axicabtagene ciloleucel (axi-cel) is an autologous anti-CD19 chimeric antigen receptor (CAR) T cell therapy approved for patients with R/R LBCL with ≥ 2 prior systemic therapies. With a median follow-up of 27.1 months in ZUMA-1, the ORR with axi-cel was 83% (58% CR rate) in patients with refractory LBCL (Locke FL, et al. Lancet Oncol. 2019). Activation of the costimulatory receptor 4-1BB (CD137) on CAR T cells may enhance axi-cel antitumor activity by enhancing T cell proliferation, function, and survival. Utomilumab (uto), an investigational monoclonal antibody agonist of the 4-1BB pathway, enhanced T cell function and survival in preclinical studies (Fisher TS, et al. Cancer Immunol Immunother. 2012) and had favorable single-agent safety in patients (Segal NH, et al. Clin Cancer Res. 2018). Possible mechanisms of resistance to axi-cel are thought to be suboptimal CAR T cell expansion an exclusionary tumor microenvironment and CD19 target antigen loss (Neelapu SS, et al. Blood 2017, Rossi JM, et al J Immunother Cancer. 2018). Combination strategies that increase proliferation, expansion, and persistence of CAR T cells or prevent activation-induced cell death of CAR T cells may improve clinical outcomes observed with axi-cel. ZUMA-11 is a Phase 1/2 study investigating the efficacy and safety of axi-cel + uto in patients with refractory LBCL. Methods: The primary objectives of this study are to determine the safety, recommended Phase 2 dosing and timing (Phase 1), and efficacy (Phase 2) of axi-cel + uto in adult patients with refractory LBCL. Patients with progressive or stable disease as the best response to second-line chemotherapy or relapse ≤ 12 months after autologous stem cell transplantation, a prior anti-CD20 antibody and anthracycline-containing regimen, and Eastern Cooperative Oncology Group performance status 0-1 are eligible. Patients with histologically proven primary mediastinal B cell lymphoma, history of Richter's transformation or chronic lymphocytic lymphoma, prior CAR T cell therapy, or central nervous system involvement of lymphoma are ineligible. In Phase 1, ≈24 patients in ≤ 3 cohorts will receive a single dose of axi-cel and escalating doses of uto (10, 30, or 100 mg) using a 3 + 3 design in up to 4 of 6 cohorts. The recommended uto dose will be based on dose-limiting toxicities and other factors. Patients will be leukapheresed and may receive optional, nonchemotherapy bridging therapy per investigator decision. After conditioning chemotherapy, patients will receive a single infusion of axi-cel (target dose, 2 × 106 CAR T cells/kg) on Day 0 followed by uto on Day 1 and every 4 weeks for 6 months or until progressive disease. Patients will be treated one at a time during Phase 1, and patients treated with axi-cel will be staggered by ≥ 2 weeks. Day 21 uto administration will be explored if toxicity is unacceptable with Day 1 administration. The primary endpoints are incidence of dose-limiting toxicities in Phase 1 and CR rate in Phase 2. Secondary endpoints include ORR, duration of response, progression-free survival, overall survival, safety, and levels of CAR T cells and cytokines in blood. This study uses a single-arm design to estimate the true CR rate; with a sample size of 27 patients, of which ≤ 3 patients will have been treated in the Phase 1 portion, the maximum half-width of the 95% confidence interval about response will be ≥ 21%. ZUMA-11 is open and accruing patients. Disclosures Reshef: Kite, a Gilead Company: Consultancy, Honoraria, Research Funding; Celgene: Research Funding; Incyte: Consultancy, Research Funding; Shire: Research Funding; BMS: Consultancy; Atara: Consultancy, Research Funding; Magenta: Consultancy; Pfizer: Consultancy; Pharmacyclics: Consultancy, Research Funding. Miklos:Pharmacyclics: Consultancy, Patents & Royalties, Research Funding; Precision Bioscience: Consultancy; Adaptive Biotechnologies: Consultancy, Research Funding; Miltenyi: Consultancy, Research Funding; Becton Dickinson: Consultancy; Janssen: Consultancy; AlloGene: Consultancy; Novartis: Consultancy; Kite, A Gilead Company: Consultancy, Research Funding; Celgene-Juno: Consultancy. Timmerman:Spectrum Pharmaceuticals: Research Funding; Kite, A Gilead Company: Consultancy, Honoraria, Other: travel support, Research Funding; ImmunGene: Research Funding; Merck: Research Funding; Bristol-Myers Squibb: Consultancy, Honoraria, Other: travel support, Research Funding. Jacobson:Novartis: Consultancy, Honoraria, Other: travel support; Bayer: Consultancy, Other: travel support; Precision Biosciences: Consultancy, Other: travel support; Humanigen: Consultancy, Other: travel support; Celgene: Consultancy, Other: travel support; Pfizer: Research Funding; Kite, a Gilead Company: Consultancy, Honoraria, Other: travel support. Bennani:Kite, A Gilead Company: Consultancy, Research Funding. Rossi:Kite, A Gilead Company: Employment. Sherman:Kite, A Gilead Company: Employment. Sun:Kite, A Gilead Company: Employment. Palluconi:Kite, A Gilead Company: Employment. Kim:Kite, A Gilead Company: Employment. Jain:Kite/Gilead: Consultancy.

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.