Mielke, Stephan

Outcomes of allogeneic hematopoietic cell transplantation (allo-HCT) for adult acute lymphoblastic leukemia (ALL) have improved over time. Studies have shown that total body irradiation (TBI) is the preferable type of myeloablative conditioning (MAC). However, outcomes based on central nervous system (CNS) involvement, namely CNS-positive versus CNS-negative, have not been compared. Here, we evaluated outcomes of 547 patients (CNS-positive = 96, CNS-negative = 451) who were allografted in the first complete remission (CR1) between 2009 and 2019. Primary endpoint was leukemia-free survival (LFS). Median follow-up was not different between the CNS-positive and CNS-negative groups (79 versus 67.2 months, P = 0.58). The CNS-positive group were younger (median age 31.3 versus 39.7 years, P = 0.004) and were allografted more recently (median year 2012 versus 2010, P = 0.003). In both groups, MAC was the preferred approach (82.3% versus 85.6%, P = 0.41). On multivariate analysis, the CNS-positive group had higher incidence of relapse (RI) (hazard ratio [HR] = 1.58 [95% confidence interval (CI) = 1.06-2.35], P = 0.025), but no adverse effect on LFS (HR = 1.38 [95% CI = 0.99-1.92], P = 0.057) or overall survival (OS) (HR = 1.28 [95% CI = 0.89-1.85], P = 0.18). A subgroup multivariate analysis limited to CNS-positive patients showed that a TBI-based MAC regimen resulted in better LFS (HR = 0.43 [95% CI = 0.22-0.83], P = 0.01) and OS (HR = 0.44 [95% CI = 0.21-0.92], P = 0.03) and lower RI (HR = 0.35 [95% CI = 0.15-0.79], P = 0.01). Another subgroup analysis in CNS-negative patients showed that MAC-TBI preparative regimens also showed a lower RI without a benefit in LFS or OS. While a MAC-TBI allo-HCT regimen may not be suitable to all, particularly for older patients with comorbidities, this approach should be considered for patients who are deemed fit and able to tolerate.

Abstract Background: A recent study from the Acute Leukemia Working Party of EBMT demonstrated that outcomes of allogeneic hematopoietic cell transplantation (allo-HCT) for adults with acute lymphoblastic leukemia (ALL) have improved significantly over time and that total body irradiation (TBI) should be considered as the preferable type of myeloablative conditioning (MAC). This study, however, did not compare outcomes of allo-HCT in patients with CNS involvement (CNS-pos) vs. those without CNS disease (CNS-neg). Study population: Here, we evaluate post allo-HCT outcomes of 547 patients (CNS-pos at initial presentation=96, CNS-neg=451) who underwent the procedure in first complete remission (CR1) between 2009 and 2019 at an EBMT participating transplant center. The distribution of ALL subtypes were as follows: CNS-pos (Ph-neg B ALL=28%, Ph-pos B ALL=27%, and T-cell ALL=45%) and for CNS-neg (Ph-neg B ALL=21%, Ph-pos B ALL=44%, and T-cell ALL=35%), p=0.01. The primary endpoint was leukemia-free survival (LFS). Results: The median follow up was not statistically different between the CNS-pos (78.7 months) and the CNS-neg group (67.2 months), p=0.58. Patients in the CNS-pos group were younger (median age 31.3 vs. 39.7 years, p=0.004), received the procedure more recently (median year 2012 vs. 2010, p=0.003), were less likely to have a Karnofsky score of equal or higher than 90 (70.8% vs. 81.9%, p=0.017), or to have received peripheral blood stem cells (PBSC) (61.5% vs. 72.7%, p=0.028). The groups did not differ in regards to donor source (URD, 50% vs. 56.5%, p=0.24) or the intensity of the preparative regimen (MAC, 82.3% vs. 85.6%, p=0.41). In multivariate analysis, CNS-pos were associated with higher cumulative incidence of relapse (HR=1.58 (95%CI=1.06-2.35), P=0.025) and a trend for an inferior leukemia-free survival (LFS) (HR=1.38 (95%CI=0.99-1.92), p=0.057), but did not adversely impact overall survival (OS) (HR=1.28 (95%CI=0.89-1.85), p=0.18). A subgroup multivariate analysis limited to patients with CNS-pos showed that prescribing a TBI MAC regimen (vs. others) results in a lower cumulative incidence of relapse (HR=0.35 (95%CI=0.15-0.79), p=0.012) and better LFS (HR=0.43 (95%CI=0.22-0.83), p=0.01) and OS (HR=0.44 (95%CI=0.21-0.92), p=0.03). Use of PBSC (vs. BM) was also independently associated with better OS (HR=0.53 (95%CI=0.29-0.99), p=0.046). Conclusion: Notwithstanding the inherent limitations of registry data, particularly ascertaining the absence of CNS involvement in the CNS-neg group, our results show CNS involvement as an independent risk factor for relapse following allo-HCT. Our data support, nonetheless, the choice of a TBI-based MAC regimen in this group of patients but stresses the need for close monitoring of relapse after allo-HCT. Disclosures Labopin: Jazz Pharmaceuticals: Honoraria. Bazarbachi: Novartis: Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding, Speakers Bureau; Roche: Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding, Speakers Bureau; Amgen: Honoraria, Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Abbvie: Honoraria, Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Hikma: Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding, Speakers Bureau; Janssen: Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding, Speakers Bureau; Pfizer: Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding, Speakers Bureau; Takeda: Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding, Speakers Bureau; Astra Zeneca: Membership on an entity's Board of Directors or advisory committees. Mielke: Immunicum: Other: Data safety monitoring board; DNA Prime SA: Speakers Bureau; Gilead/KITE: Other: Travel support, Expert panel ; Miltenyi: Other: Data safety monitoring board; Novartis: Speakers Bureau; Celgene/BMS: Speakers Bureau. Socie: Alexion: Research Funding. Huynh: Jazz Pharmaceuticals: Honoraria. Yakoub-Agha: Jazz Pharmaceuticals: Honoraria. Giebel: Janssen: Honoraria, Speakers Bureau; Pfizer: Consultancy, Honoraria, Speakers Bureau; Novartis: Consultancy, Honoraria, Speakers Bureau; Amgen: Consultancy, Honoraria, Speakers Bureau. Peric: Therakos, Servier, MSD, Astellas, Novartis, Abbvie, Pfizer: Honoraria. Mohty: Sanofi: Honoraria, Research Funding; Pfizer: Honoraria; Novartis: Honoraria; Takeda: Honoraria; Jazz: Honoraria, Research Funding; Janssen: Honoraria, Research Funding; Gilead: Honoraria; Celgene: Honoraria, Research Funding; Bristol Myers Squibb: Honoraria; Astellas: Honoraria; Amgen: Honoraria; Adaptive Biotechnologies: Honoraria.

Abstract Background: Pts with LBCL primary refractory to or relapsed ≤ 12 mo after first-line (1L) therapy may have poor outcomes with SOC, including salvage CT and ASCT, which underscores a critical unmet need. Liso-cel is an autologous CD19-directed, defined composition, 4-1BB CAR T cell product administered at equal target doses of CD8 + and CD4 + CAR + T cells. In the TRANSCEND NHL 001 study (NCT02631044) in pts with R/R LBCL (≥ 2 prior lines of therapy), liso-cel treatment resulted in an ORR of 73% (CR rate, 53%), 2% grade ≥ 3 cytokine release syndrome (CRS), and 10% grade ≥ 3 neurological events (NE) (Abramson et al. Lancet 2020). Here we present a prespecified interim analysis of TRANSFORM (NCT03575351; SOC vs liso-cel as 2L therapy in pts with R/R LBCL). Methods: TRANSFORM is a pivotal, global, randomized, multicenter, phase 3 study comparing efficacy and safety of SOC (Arm A; R-DHAP, R-ICE, or R-GDP per investigator choice followed by BEAM + ASCT) vs liso-cel (Arm B). Pts were adults (aged ≤ 75 years), eligible for ASCT, and with LBCL primary refractory to or relapsed ≤ 12 mo after 1L therapy. Key inclusion criteria were ECOG PS ≤ 1 and adequate organ function (LVEF ≥ 40%; serum CrCl > 45 mL/min); pts with secondary CNS lymphoma were allowed. Key exclusion criteria were prior gene or anti-CD19-targeted therapy, and active infection. Pts in Arm A were to receive 3 cycles of CT. Responding pts (CR or PR) were to proceed to BEAM + ASCT. Pts in Arm B were to undergo lymphodepletion with fludarabine/cyclophosphamide followed by liso-cel at a target dose of 100 × 10 6 CAR + T cells. Bridging therapy with an Arm A CT regimen was allowed. Crossover to receive liso-cel was allowed in Arm A for pts not achieving CR or PR after 3 cycles of CT or not in CR after ASCT, or demonstrating PD at any time. Primary endpoint is event-free survival (EFS) based on independent review committee per Lugano 2014 criteria, defined as time from randomization to death from any cause, PD, failure to achieve CR or PR by 9 weeks after randomization, or start of new antineoplastic therapy, whichever occurred first. Key secondary endpoints included in the testing strategy are CR rate, PFS, and OS. P value significance threshold for endpoints to reject the null hypothesis was ≤ 0.012. Results: A total of 184 pts were randomized, with 92 pts in each arm. Baseline characteristics were well balanced between both arms (Table). Of 91 treated pts in Arm A (1 pt withdrew consent), 43 received BEAM + ASCT, of which 28 achieved CR with CT. Fifty pts crossed over to receive liso-cel. In Arm B, 90 pts received liso-cel infusion; 58 pts (63%) received bridging therapy. Two Arm B pts were not infused (1 each due to manufacturing failure and rapid progression). Median EFS and PFS were significantly longer, and CR rate was significantly improved for Arm B vs Arm A. For Arms A and B, respectively, median EFS was 2.3 vs 10.1 mo (HR, 0.349; P < 0.0001), median PFS was 5.7 vs 14.8 mo (HR, 0.406; P = 0.0001), and CR rate was 39% vs 66% (P < 0.0001). OS data were immature at the time of this analysis with a median follow-up of 6.2 mo (range, 0.9-20.0), but a numerical trend favored Arm B (HR, 0.509; 95% CI, 0.258-1.004; P = 0.0257). Cellular kinetics in Arm B showed a median t max of 10 d (range, 6‒22). No new liso-cel safety signals were detected in the 2L setting. In Arm B, any-grade CRS was reported in 49% of pts, with grade 1 in 37% and grade 2 in 11%. Only 1 pt had grade 3 CRS (onset at Day 9, which resolved in 2 days). Any-grade NEs were reported in 12% of pts and were also primarily low grade (grade 3, 4%). No grade 4 or 5 CRS or NEs were reported. In Arm B, 24% of pts received tocilizumab, 17% received corticosteroids, and none received vasopressors. The most common TEAEs in both arms were cytopenias. Prolonged cytopenias in Arm B (ie, grade ≥ 3 at 35 d after infusion) were reported in 43% of pts; the majority recovered within 2 mo after infusion. Conclusions: In the TRANSFORM study, liso-cel demonstrated statistically significant and clinically meaningful improvement in the primary endpoint, EFS, as well as in key secondary efficacy endpoints (CR rate and PFS) compared with SOC as 2L therapy in pts with LBCL primary refractory to or relapsed ≤ 12 mo after 1L therapy. Safety results in the 2L setting were consistent with the liso-cel safety profile in 3L or later LBCL, and no new safety concerns were identified. Liso-cel improved outcomes vs SOC and exhibited a favorable safety profile, providing support for liso-cel as a potential new SOC for 2L treatment in pts with R/R LBCL. Figure 1 Figure 1. Disclosures Kamdar: SeaGen: Speakers Bureau; ADC Therapeutics: Consultancy; Celgene: Other; TG Therapeutics: Research Funding; KaryoPharm: Consultancy; Celgene (BMS): Consultancy; Genetech: Other; Genentech: Research Funding; Adaptive Biotechnologies: Consultancy; AbbVie: Consultancy; Kite: Consultancy; AstraZeneca: Consultancy. Arnason: Juno/BMS: Honoraria. Glass: BMS: Consultancy; Roche: Consultancy, Research Funding, Speakers Bureau; Riemser: Research Funding; Kite: Consultancy; Novartis: Consultancy; Helios Klinik Berlin-Buch: Current Employment. Bachanova: KaryoPharma: Membership on an entity's Board of Directors or advisory committees; Incyte: Research Funding; FATE: Membership on an entity's Board of Directors or advisory committees, Research Funding; Gamida Cell: Membership on an entity's Board of Directors or advisory committees, Research Funding. Ibrahimi: Karyopharm Theraputics: Divested equity in a private or publicly-traded company in the past 24 months. Mielke: Immunicum: Other: Data safety monitoring board; Novartis: Speakers Bureau; Miltenyi: Other: Data safety monitoring board; DNA Prime SA: Speakers Bureau; Gilead/KITE: Other: Travel support, Expert panel ; Celgene/BMS: Speakers Bureau. Mutsaers: BMS: Consultancy; AstraZeneca: Research Funding. Hernandez-Ilizaliturri: Kite: Other: Advisory Boards; Amgen: Other: Advisory Boards; Pharmacyclics: Other: Advisory Boards; BMS: Other: Advisory Boards; Celgene: Other: Advisory Boards; Incyte: Other: Advisory Boards; AbbVie: Other: Advisory Boards; Gilead: Other: Advisory Boards; Epyzime: Other: Advisory Boards. Izutsu: Novartis: Honoraria, Research Funding; MSD: Research Funding; Kyowa Kirin: Honoraria, Research Funding; Janssen: Honoraria, Research Funding; Incyte: Research Funding; Huya Biosciences: Research Funding; Genmab: Honoraria, Research Funding; Fuji Film Toyama Chemical: Honoraria; Eisai: Honoraria, Research Funding; Daiichi Sankyo: Honoraria, Research Funding; Chugai: Honoraria, Research Funding; Celgene: Honoraria, Research Funding; Beigene: Research Funding; Bayer: Research Funding; AstraZeneca: Honoraria, Research Funding; Allergan Japan: Honoraria; AbbVie: Honoraria; Ono Pharmaceutical: Honoraria, Research Funding; Pfizer: Research Funding; Solasia: Research Funding; Symbio: Honoraria; Takeda: Honoraria, Research Funding; Yakult: Research Funding. Morschhauser: Celgene: Membership on an entity's Board of Directors or advisory committees; Incyte: Membership on an entity's Board of Directors or advisory committees; Genentech, Inc.: Consultancy; Janssen: Honoraria; AbbVie: Consultancy, Membership on an entity's Board of Directors or advisory committees; Epizyme: Consultancy, Membership on an entity's Board of Directors or advisory committees; Roche: Consultancy, Speakers Bureau; Chugai: Honoraria; Servier: Consultancy; AstraZenenca: Membership on an entity's Board of Directors or advisory committees; Novartis: Consultancy, Membership on an entity's Board of Directors or advisory committees; BMS: Consultancy, Membership on an entity's Board of Directors or advisory committees; Genmab: Membership on an entity's Board of Directors or advisory committees; F. Hoffmann-La Roche Ltd: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees; Gilead: Consultancy, Membership on an entity's Board of Directors or advisory committees. Lunning: Karyopharm: Consultancy; AstraZeneca: Consultancy; Legend: Consultancy; Verastem: Consultancy; Janssen: Consultancy; Myeloid Therapeutics: Consultancy; Daiichi-Sankyo: Consultancy; Novartis: Consultancy; Spectrum: Consultancy; Celgene, a Bristol Myers Squibb Co.: Consultancy; AbbVie: Consultancy; Acrotech: Consultancy; Beigene: Consultancy; ADC Therapeutics: Consultancy; TG Therapeutics: Consultancy; Morphosys: Consultancy; Kite, a Gilead Company: Consultancy; Kyowa Kirin: Consultancy. Maloney: Amgen: Honoraria; Celgene: Honoraria, Other: Rights to royalties from Fred Hutchinson Cancer Research Center for patents licensed to Juno Therapeutics/Bristol Myers Squibb; A2 Biotherapeutics: Honoraria, Other: Stock options; BMS: Honoraria, Other: Rights to royalties from Fred Hutchinson Cancer Research Center for patents licensed to Juno Therapeutics/Bristol Myers Squibb; Celgene: Other: Research funding was paid to my institution, Research Funding; Umoja: Honoraria; Janssen: Honoraria; Legend Biotech: Honoraria; Genentech: Honoraria; Novartis: Honoraria; MorphoSys: Honoraria; Juno therapeutics: Other: Research funding was paid to my institution, Research Funding; Navan Technologies: Honoraria, Other: Stock options; Kite Pharma: Honoraria, Other: Research funding was paid to my institution, Research Funding; Juno Therapeutics: Honoraria, Other: Rights to royalties from Fred Hutchinson Cancer Research Center for patents licensed to Juno Therapeutics/Bristol Myers Squibb. Crotta: Bristol Myers Squibb: Current Employment, Current equity holder in publicly-traded company. Montheard: Bristol Myers Squibb: Current Employment, Current equity holder in publicly-traded company. Previtali: Bristol Myers Squibb: Current Employment, Current equity holder in publicly-traded company. Stepan: Bristol Myers Squibb: Current Employment, Current equity holder in publicly-traded company. Ogasawara: Bristol Myers Squibb: Current Employment, Current equity holder in publicly-traded company. Mack: Bristol Myers Squibb: Current Employment, Current equity holder in publicly-traded company. Abramson: Bluebird Bio: Consultancy; EMD Serono: Consultancy; Kymera: Consultancy; BeiGene: Consultancy; Incyte Corporation: Consultancy; Astra-Zeneca: Consultancy; Allogene Therapeutics: Consultancy; Seagen Inc.: Research Funding; AbbVie: Consultancy; Novartis: Consultancy; Kite Pharma: Consultancy; Morphosys: Consultancy; C4 Therapeutics: Consultancy; Bristol-Myers Squibb Company: Consultancy, Research Funding; Genmab: Consultancy; Karyopharm: Consultancy; Genentech: Consultancy. OffLabel Disclosure: Liso-cel is a CAR T cell therapy approved for use in the third line for R/R LBCL. This trial reports data from the pivotal trial in the second line.

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.

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.

Abstract No laboratory test can predict non-relapse mortality (NRM) after hematopoietic cellular transplantation (HCT) prior to the onset graft-versus-host disease (GVHD). Recently, we have shown that a signature of three GVHD plasma biomarkers (TNFR1, ST2, and REG3α) can predict response to GVHD therapy and NRM at the onset of clinical GVHD (Levine, Lancet Haem, 2015). Our goal in the current study was to identify a blood biomarker signature that could predict lethal GVHD and six-month NRM well in advance of the onset of GVHD symptoms. Patient samples on day +7 after HCT were obtained from 1,287 patients from 11 HCT centers in the Mount Sinai Acute GVHD International Consortium (MAGIC). Samples from two large centers (n = 929) were combined and randomly assigned to a training set (n = 620) and test set (n = 309). 358 patients from nine others centers constituted an independent validation set. The overall cumulative incidences of 6-month NRM were 11%, 12%, and 13% for the training, test, and validation sets respectively. The incidence of lethal GVHD, defined as death without preceding relapse while under steroid treatment for acute GVHD, were 18%, 24%, and 14% in the same groups, respectively. The median day of GVHD onset was 28 days in the training set and 29 days in the test and validation sets. We measured four GVHD related biomarkers [ST2, REG3α, TNFR1, and IL2Rα] in all samples and used the training set alone to develop competing risks regression models that used all 13 possible combinations of one to four biomarkers to predict 6-month NRM. The best algorithm, which we rigorously confirmed through Monte Carlo cross-validation of 75 different combinations of training sets, included ST2 and REG3α. No combination of one, three, or four biomarkers was superior to the combination of these two biomarkers. The day 7 algorithm identified high risk (HR) and low risk (LR) groups with 6-month NRMs of 28% and 7%, respectively (p<0.001) (Fig 1A). The relapse rates did not differ between risk groups so that overall survival (OS) was 60% for HR and 84% for LR (p<0.001) (Fig 1B). When applied to the test set (Fig 1C/D), the algorithm identified 54/309 (17%) of the patients as HR with an NRM of 33% vs 7% for LR patients (p<0.001) and 6-month OS of 57% and 81% for HR and LR patients, respectively (p<0.001). In the independent validation set (Fig 1 E/F), the algorithm identified 72/358 (20%) of the patients as HR with an NRM of 26% vs 10% for LR patients (p<0.001) and OS of 68% and 85% for HR and LR patients, respectively (p<0.001). High risk patients were three times more likely to die from GVHD than LR patients in each cohort (p<0.001) (Fig 2). The GI tract is the GVHD target organ that is most resistant to treatment and represents a major cause of NRM, and we observed twice as much severe GI GVHD (stage 3 or 4) in HR patients as in LR patients (p<0.001, data not shown). The algorithm successfully separated HR and LR strata for 6 month NRM in several groups with differing risks for GVHD and NRM, including donor type, degree. of HLA-match, age group, and conditioning regimen intensity (Fig 3). In conclusion, we have developed a blood biomarker algorithm that predicts the development of lethal GVHD seven days after HCT, which performed successfully in large multicenter validation sets. The GVH reaction is already in progress by day +7, even though clinical symptoms may not occur until days or weeks later. We speculate that the blood biomarker concentrations at this early time point reflect subclinical GI pathology, a notion that is reinforced by the fact that ST2 and REG3α, the two biomarkers in the algorithm, are closely associated with GI GVHD. The algorithm identified HR and LR strata in several patient groups with different overall risk for lethal GVHD (donor, HLA match, conditioning regimen intensity, age). This day +7 algorithm should prove useful in clinical BMT research by identifying patients at high risk for lethal GVHD who might benefit from aggressive preemptive treatment strategies. Disclosures Chen: Novartis: Research Funding; Incyte Corporation: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding. Jagasia:Therakos: Consultancy. Kitko:Therakos: Honoraria, Speakers Bureau. Kroeger:Novartis: Honoraria, Research Funding. Levine:Viracor: Patents & Royalties: GVHD biomarkers patent. Ferrara:Viracor: Patents & Royalties: GVHD biomarkers patent.

BACKGROUND. No laboratory test can predict the risk of nonrelapse mortality (NRM) or severe graft-versus-host disease (GVHD) after hematopoietic cellular transplantation (HCT) prior to the onset of GVHD symptoms. METHODS. Patient blood samples on day 7 after HCT were obtained from a multicenter set of 1,287 patients, and 620 samples were assigned to a training set. We measured the concentrations of 4 GVHD biomarkers (ST2, REG3α, TNFR1, and IL-2Rα) and used them to model 6-month NRM using rigorous cross-validation strategies to identify the best algorithm that defined 2 distinct risk groups. We then applied the final algorithm in an independent test set ( n = 309) and validation set ( n = 358). RESULTS. A 2-biomarker model using ST2 and REG3α concentrations identified patients with a cumulative incidence of 6-month NRM of 28% in the high-risk group and 7% in the low-risk group ( P < 0.001). The algorithm performed equally well in the test set (33% vs. 7%, P < 0.001) and the multicenter validation set (26% vs. 10%, P < 0.001). Sixteen percent, 17%, and 20% of patients were at high risk in the training, test, and validation sets, respectively. GVHD-related mortality was greater in high-risk patients (18% vs. 4%, P < 0.001), as was severe gastrointestinal GVHD (17% vs. 8%, P < 0.001). The same algorithm can be successfully adapted to define 3 distinct risk groups at GVHD onset. CONCLUSION. A biomarker algorithm based on a blood sample taken 7 days after HCT can consistently identify a group of patients at high risk for lethal GVHD and NRM. FUNDING. The National Cancer Institute, American Cancer Society, and the Doris Duke Charitable Foundation.