Abstract: Achievement of complete remission signifies a crucial milestone in the therapy of acute myeloid leukemia (AML) while refractory disease is associated with dismal outcomes. Hence, accurately identifying patients at risk is essential to tailor treatment concepts individually to disease biology. We used nine machine learning (ML) models to predict complete remission and 2-year overall survival in a large multicenter cohort of 1,383 AML patients who received intensive induction therapy. Clinical, laboratory, cytogenetic and molecular genetic data were incorporated and our results were validated on an external multicenter cohort. Our ML models autonomously selected predictive features including established markers of favorable or adverse risk as well as identifying markers of so-far controversial relevance. De novo AML, extramedullary AML, double-mutated CEBPA, mutations of CEBPA-bZIP, NPM1, FLT3-ITD, ASXL1, RUNX1, SF3B1, IKZF1, TP53, and U2AF1, t(8;21), inv(16)/t(16;16), del(5)/del(5q), del(17)/del(17p), normal or complex karyotypes, age and hemoglobin concentration at initial diagnosis were statistically significant markers predictive of complete remission, while t(8;21), del(5)/del(5q), inv(16)/t(16;16), del(17)/del(17p), double-mutated CEBPA, CEBPA-bZIP, NPM1, FLT3-ITD, DNMT3A, SF3B1, U2AF1, and TP53 mutations, age, white blood cell count, peripheral blast count, serum lactate dehydrogenase level and hemoglobin concentration at initial diagnosis as well as extramedullary manifestations were predictive for 2-year overall survival. For prediction of complete remission and 2-year overall survival areas under the receiver operating characteristic curves ranged between 0.77–0.86 and between 0.63–0.74, respectively in our test set, and between 0.71–0.80 and 0.65–0.75 in the external validation cohort. We demonstrated the feasibility of ML for risk stratification in AML as a model disease for hematologic neoplasms, using a scalable and reusable ML framework. Our study illustrates the clinical applicability of ML as a decision support system in hematology.

Abstract: Background In newly diagnosed acute myeloid leukemia (AML), the general recommendation is to start treatment immediately after the diagnosis has been made. This paradigm is based both on the observation that untreated acute leukemia has a poor prognosis and on retrospective analyses demonstrating a shorter survival in younger AML patients (pts) in whom treatment was delayed by more than 5 days (Sekeres et al., 2009). A more recent single-center analysis came to a different conclusion, showing no prognostic effect for the time from diagnosis to treatment (TDT; Bertoli et al., 2013). We explored the relationship between TDT and prognosis on a large set of real-world data from the AML registry of the Study Alliance Leukemia (SAL) and compared it to the published cohorts. Methods The SAL runs a transregional AML registry in 46 treatment centers across Germany (NCT03188874). All registered patients with an intensive induction treatment, a minimum follow-up time of 12 months and no acute promyelocytic leukemia were selected (n=2,200). Treatment start was defined by the first day of cytarabine, whereas single agent hydroxyurea (HU) was labeled as pretreatment. We analyzed the influence of TDT on complete remission (CR), early death (ED) and overall survival (OS) in univariable analyses for each day of treatment delay, in groups of 0-5, 6-10, 11-15 and & gt;15 days of TDT, and by using the restricted cubic spline (RCS) method for data modelling. In order to adjust for the influence of established prognostic variables on the outcomes, we used multivariable regression models and propensity score weighting. The influence of HU pretreatment on outcomes was investigated by introducing an interaction term between TDT and the presence of HU pretreatment. Results The median age was 59 years (y) (IQR 50-68), the proportion of pts with favorable, intermediate and adverse genetic risk according to ELN was 27%, 53%, and 20%; & gt;95% of pts received induction treatment with standard 7+3. HU pretreatment was administered in 4% of pts. The median TDT was 3 days (IQR 2-6). Descriptive statistics after grouping of pts showed the highest median age and the lowest proportion of NPM1 mutated and favorable risk in the TDT group 11-15. Of all pts, 79% achieved a CR/CRi; unadjusted CR rates for the patient groups with TDT of 0-5, 6-10, 11-15 and & gt;15 days were 80%, 77%, 74% and 76%, respectively (p=0.317). In multivariable analysis accounting for the influence of ELN risk, age, WBC, LDH, de novo versus secondary AML and ECOG, the OR for each additional day of TDT was 0.99 (95%-CI, 0.97-1.00; p=0.124). Four percent of pts died within the first 30 days from treatment start. The respective rates in the four TDT categories were 4.0%, 3.8%, 5.1% and 4.1% (p=0.960). In multivariable analysis, the OR for TDT was 1.01 (95%-CI, 0.98-1.05; p=0.549). After a median follow-up of 40 months, the 2-y OS of all pts was 51%. The unadjusted 2-y OS rates stratified by TDT of 0-5, 6-10, 11-15, & gt;15 days were 52, 49, 46, and 51% (see Table 1 and Figure 1). The hazard ratio (HR) for each day of treatment delay was 1.00 (95%-CI; 0.99-1.01; p=0.317). In multivariable Cox regression analysis, the HR for TDT as continuous variable was 1.00 (95%-CI, 0.99-1.01; p=0.689). When OS was analyzed separately stratified for age ≤60 and & gt;60 ys and for high versus lower initial WBC defined by a threshold of 50 x 109/L, no significant differences between TDT groups were observed. Multivariable models using TDT as a grouped variable or with RCS did not provide evidence for a significant influence of TDT on outcomes. Propensity score matching of pts in the four TDT groups did not reveal an influence on outcomes. The use of HU was not associated with CR, ED nor OS. Conclusion Our study on 2,200 newly diagnosed registry pts receiving consistent intensive induction with standard-dose cytarabine plus daunorubicin (7+3) suggests that TDT is not related to response or survival, neither in younger nor in older pts. Despite multivariable analyses, a bias towards longer TDT intervals in pts judged to be clinically stable by the treating physician cannot be excluded entirely. As treatment stratification in intensive first-line treatment of AML evolves, the TDT data suggests that it may be a safe and reasonable approach to wait for genetic and other laboratory test results in order to assign clinically stable pts to the best available treatment option before the start of intensive treatment. Disclosures Krämer: Daiichi-Sankyo: Honoraria, Membership on an entity's Board of Directors or advisory committees; Bayer: Research Funding; BMS: Research Funding; Roche: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees. Hänel:Roche: Honoraria; Amgen: Honoraria; Celgene: Other: advisory board; Novartis: Honoraria; Takeda: Other: advisory board. Jost:Daiichi: Honoraria; Sanofi: Honoraria; Gilead: Other: travel grants; Jazz Pharmaceuticals: Honoraria. Brümmendorf:Merck: Consultancy; Janssen: Consultancy; Novartis: Consultancy, Research Funding; Pfizer: Consultancy, Research Funding; University Hospital of the RWTH Aachen: Employment; Ariad: Consultancy. Krause:Siemens: Research Funding; Takeda: Honoraria; MSD: Honoraria; Gilead: Other: travel; Celgene Corporation: Other: Travel. Scholl:Novartis: Other: Project funding; Pfizer: Other: Advisory boards; Gilead: Other: Project funding; Daiichi Sankyo: Other: Advisory boards; AbbVie: Other: Advisory boards. Hochhaus:Pfizer: Research Funding; Novartis: Research Funding; BMS: Research Funding; Incyte: Research Funding; MSD: Research Funding. Kiani:Novartis: Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding, Speakers Bureau. Middeke:Sanofi: Research Funding, Speakers Bureau; Roche: Speakers Bureau; AbbVie: Consultancy, Speakers Bureau; Gilead: Consultancy; Janssen: Consultancy, Speakers Bureau; MSD: Consultancy. Thiede:AgenDix GmbH: Employment, Equity Ownership; Novartis: Research Funding, Speakers Bureau; Bayer: Research Funding; Daiichi-Sankyo: Speakers Bureau. Stoelzel:JAZZ Pharmaceuticals: Consultancy; Neovii: Other: Travel funding; Shire: Consultancy, Other: Travel funding. Platzbecker:Celgene: Consultancy, Honoraria, Research Funding; Abbvie: Consultancy, Honoraria; Novartis: Consultancy, Honoraria, Research Funding.

Abstract: Background: Monitoring of measurable residual disease (MRD) in patients (pts) with advanced myelodysplastic syndromes (MDS) or acute myeloid leukemia (AML) who achieve complete remission (CR) can predict hematological relapse. Recently published data from the first cohort of the RELAZA2-trial have shown that pre-emptive therapy with azacitidine (AZA) can prevent or substantially delay an overt relapse in MRD-positive pts with MDS or AML (Platzbecker et al. Lancet Oncol. 2018). Aims: To evaluate outcome of the entire patient cohort of the RELAZA2-trial and determine whether MRD-guided pre-emptive AZA treatment could prevent relapse in molecularly defined cohorts. Methods: Between 12/2011 and 07/2018 380 pts with advanced MDS or AML, who had achieved CR after conventional chemotherapy or allogeneic hematopoietic stem-cell transplantation (allo-HCT) were prospectively screened for MRD in monthly intervals either in bone marrow (BM) or peripheral blood (PB). A total of 94 pts (AML, n=83; MDS, n=11) became MRD positive during 24 months from baseline by either quantitative PCR (qPCR) or analysis of CD34+ donor-chimerism and entered the treatment phase. Preemptive MRD-triggered treatment consisted of AZA 75 mg/m2 per day subcutaneously on days 1-7 of a 29-day cycle for up to 24 cycles. After six cycles, MRD status was reassessed and pts with MRD negativity were eligible for treatment de-escalation. Primary endpoint was relapse-free survival (RFS) six months after start of pre-emptive treatment. For mutational analysis next generation sequencing (NGS) with a panel of 54 genes was performed (Illumina Trusight Myeloid). Results: Median age was 60 yrs (range: 22-80 yrs); 52 (55%) of the pts were female. Prior therapy consisted of chemotherapy in 42 (45%) and allo-HCT in 52 (55%) of the pts. Cytogenetics could be analyzed in 93 (99%) of the 94 pts. Risk categorization according to ELN 2017 was favorable in 30 (37%), intermediate in 31 (38%) and adverse in 21 (26%) of the AML pts, respectively. Type of MDS was advanced in all 11 pts and all were previously transplanted. Fifty-two (61%) of 85 pts with available NPM1 status were positive. NGS on 64 (68%) pts with available DNA at the time of first diagnosis revealed additional mutations in DNMT3A (n=25), TET2 (n=15), FLT3-ITD (n=12), IDH1 (n=9), FLT3-TKD (n=8), ASXL1, NRAS, TP53 (n=7, each), IDH2 (n=6), PTPN11, WT1 (n=5, each), GATA2, U2AF1 (n=4, each), CBL (n=3), CEBPA, CSFR3, CUX1, EZH2, KIT, RAD21, RUNX1, SF3B, STAG2, ZRSR2 (n=2, each), and KRAS (n=1). MRD data were correlated with outcome in 45 pts for NPM1, in 3 for RUNX1-RUNX1T1, whereas CD34-donor-chimerism was analyzed in 39 pts (missing, n=7). There was a significant faster and deeper decline of MRD in PB as compared to BM (P=0.03). The same held true with regard to the increase of donor-chimerism, which was achieved faster in PB as compared to BM (P=0.05). Secondary molecular abnormalities (MAs) had no impact on MRD response as measured by qPCR, which was also true if MAs were categorized functionally. Similarly, additional chromosomal abnormalities had no impact on MRD response in both MRD methods. However, in pts with measurement of donor-chimerism ASXL1 mutations were a negative factor for MRD response (P & lt;0.001). At hematological relapse, only 1 (2%) of 45 pts with NPM1 measurement was not congruently MRD positive. Six months after start of MRD-guided therapy, 56 (60%) of 94 pts were still in CR while a total of 38 pts (40%) developed a hematological relapse after median of 3 AZA cycles. 38 (40%) pts responded with either a decline of MRD below a predefined threshold (increasing donor-chimerism to ≥80% or PCR MRD & lt;1%), while a stabilization in the absence of relapse was achieved in 18 (19%) pts. Overall response rate was not statistically different between pts with (63%) or without (55%) antecedent allo-HCT (P=0.5). RFS rate at 6 months was 60% (56/94 pts). With a median follow-up of 9 months after start of MRD-guided pre-emptive treatment 12-months overall and progression-free survival rates were 94% and 44%, respectively. Conclusions: AZA as a pre-emptive therapy was effective in delaying hematological relapse of advanced MDS or AML pts, regardless of the underlying genetic signature. Based on these encouraging results, intensifying treatment with AZA in combination with pembrolizumab is currently investigated as MRD-guided treatment in NPM1 positive AML (PEMAZA; ClinicalTrials.gov Identifier: NCT03769532). Disclosures Wolf: Celgene: Honoraria, Research Funding. Bug:Novartis: Membership on an entity's Board of Directors or advisory committees, Research Funding; Hexal: Membership on an entity's Board of Directors or advisory committees; Pfizer: Membership on an entity's Board of Directors or advisory committees; Eurocept: Membership on an entity's Board of Directors or advisory committees; Celgene: Honoraria, Membership on an entity's Board of Directors or advisory committees, Other: Travel; Jazz: Honoraria; Neovii: Other: Travel; Gilead: Membership on an entity's Board of Directors or advisory committees, Other: Travel; Sanofi: Other: Travel. Götze:Celgene: Research Funding. Stelljes:Amgen: Consultancy, Speakers Bureau; Pfizer: Consultancy, Honoraria, Research Funding, Speakers Bureau. Subklewe:Celgene: Consultancy, Honoraria; Pfizer: Consultancy, Honoraria; Seattle Genetics: Research Funding; Morphosys: Research Funding; Janssen: Consultancy; AMGEN: Consultancy, Honoraria, Research Funding; Roche AG: Consultancy, Research Funding; Novartis: Consultancy, Research Funding; Gilead Sciences: Consultancy, Honoraria, Research Funding. Haenel:Amgen, Novartis, Roche, Celgene, Takeda, Bayer: Honoraria. Rollig:Amgen, Astellas, BMS, Daiichi Sankyo, Janssen, Roche: Consultancy; Abbvie, Novartis, Pfizer: Consultancy, Research Funding. Müller-Tidow:Pfizer: Research Funding, Speakers Bureau; Daiichi Sankyo: Research Funding; BiolineRx: Research Funding; Janssen-Cilag GmbH: Speakers Bureau. Platzbecker:Novartis: Consultancy, Honoraria, Research Funding; Amgen: Honoraria, Research Funding; AbbVie: Consultancy, Honoraria; BMS: Consultancy, Honoraria; Janssen: Consultancy, Honoraria, Research Funding; Takeda: Consultancy, Honoraria; Geron: Consultancy, Honoraria. Thiede:AgenDix GmbH: Other: Co-owner and CEO. OffLabel Disclosure: Off-label: treatment with azacitidine to prevent or substantially delay an overt relapse in MRD-positive patients with MDS or AML

Abstract: Acute myeloid leukemia (AML) is characterized by recurrent genetic events. The BCL6 corepressor (BCOR) and its homolog, the BCL6 corepressor-like 1 (BCORL1), have been reported to be rare but recurrent mutations in AML. Previously, smaller studies have reported conflicting results regarding impacts on outcomes. Here, we retrospectively analyzed a large cohort of 1529 patients with newly diagnosed and intensively treated AML. BCOR and BCORL1 mutations were found in 71 (4.6%) and 53 patients (3.5%), respectively. Frequently co-mutated genes were DNTM3A, TET2 and RUNX1. Mutated BCORL1 and loss-of-function mutations of BCOR were significantly more common in the ELN2017 intermediate-risk group. Patients harboring loss-of-function mutations of BCOR had a significantly reduced median event-free survival (HR = 1.464 (95%-Confidence Interval (CI): 1.005–2.134), p = 0.047), relapse-free survival (HR = 1.904 (95%-CI: 1.163–3.117), p = 0.01), and trend for reduced overall survival (HR = 1.495 (95%-CI: 0.990–2.258), p = 0.056) in multivariable analysis. Our study establishes a novel role for loss-of-function mutations of BCOR regarding risk stratification in AML, which may influence treatment allocation.

Abstract: The evaluation of bone marrow morphology by experienced hematopathologists is essential in the diagnosis of acute myeloid leukemia (AML); however, it suffers from a lack of standardization and inter-observer variability. Deep learning (DL) can process medical image data and provides data-driven class predictions. Here, we apply a multi-step DL approach to automatically segment cells from bone marrow images, distinguish between AML samples and healthy controls with an area under the receiver operating characteristic (AUROC) of 0.9699, and predict the mutation status of Nucleophosmin 1 ( NPM1 )—one of the most common mutations in AML—with an AUROC of 0.92 using only image data from bone marrow smears. Utilizing occlusion sensitivity maps, we observed so far unreported morphologic cell features such as a pattern of condensed chromatin and perinuclear lightening zones in myeloblasts of NPM1 -mutated AML and prominent nucleoli in wild-type NPM1 AML enabling the DL model to provide accurate class predictions.

Abstract: In fit patients with newly diagnosed acute myeloid leukemia (AML), immediate treatment start is recommended due to the poor prognosis of untreated acute leukemia. We explored the relationship between time from diagnosis to treatment start (TDT) and prognosis in a large real-world data set from the German Study Alliance Leukemia–Acute Myeloid Leukemia (SAL-AML) registry. All registered non–acute promyelocytic leukemia patients with intensive induction treatment and a minimum 12 months of follow-up were selected (n = 2263). We analyzed influence of TDT on remission, early death, and overall survival (OS) in univariable analyses for each day of treatment delay, in groups of 0 to 5, 6 to 10, 11 to 15, and & gt;15 days of TDT, adjusted for influence of established prognostic variables on outcomes. Median TDT was 3 days (interquartile range, 2-7). Unadjusted 2-year OS rates, stratified by TDT of 0 to 5, 6 to 10, 11 to 15, and & gt;15 days, were 51%, 48%, 44%, and 50% (P = .211). In multivariable Cox regression analysis accounting for established prognostic variables, the TDT hazard ratio as a continuous variable was 1.00 (P = .617). In OS analyses, separately stratified for age ≤60 and & gt;60 years and for high vs lower initial white blood cell count, no significant differences between TDT groups were observed. Our study suggests that TDT is not related to survival. As stratification in intensive first-line AML treatment evolves, TDT data suggest that it may be a feasible approach to wait for genetic and other laboratory test results so that clinically stable patients are assigned the best available treatment option. This trial was registered at www.clinicaltrials.gov as #NCT03188874.

Abstract: Acute promyelocytic leukemia (APL) is considered a hematologic emergency due to high risk of bleeding and fatal hemorrhages being a major cause of death. Despite lower death rates reported from clinical trials, patient registry data suggest an early death rate of 20%, especially for elderly and frail patients. Therefore, reliable diagnosis is required as treatment with differentiation-inducing agents leads to cure in the majority of patients. However, diagnosis commonly relies on cytomorphology and genetic confirmation of the pathognomonic t(15;17). Yet, the latter is more time consuming and in some regions unavailable. Methods In recent years, deep learning (DL) has been evaluated for medical image recognition showing outstanding capabilities in analyzing large amounts of image data and provides reliable classification results. We developed a multi-stage DL platform that automatically reads images of bone marrow smears, accurately segments cells, and subsequently predicts APL using image data only. We retrospectively identified 51 APL patients from previous multicenter trials and compared them to 1048 non-APL acute myeloid leukemia (AML) patients and 236 healthy bone marrow donor samples, respectively. Results Our DL platform segments bone marrow cells with a mean average precision and a mean average recall of both 0.97. Further, it achieves high accuracy in detecting APL by distinguishing between APL and non-APL AML as well as APL and healthy donors with an area under the receiver operating characteristic of 0.8575 and 0.9585, respectively, using visual image data only. Conclusions Our study underlines not only the feasibility of DL to detect distinct morphologies that accompany a cytogenetic aberration like t(15;17) in APL, but also shows the capability of DL to abstract information from a small medical data set, i. e. 51 APL patients, and infer correct predictions. This demonstrates the suitability of DL to assist in the diagnosis of rare cancer entities. As our DL platform predicts APL from bone marrow smear images alone, this may be used to diagnose APL in regions were molecular or cytogenetic subtyping is not routinely available and raise attention to suspected cases of APL for expert evaluation.

Abstract: Achievement of complete remission (CR) signifies a crucial milestone in the therapy of acute myeloid leukemia (AML) while refractory disease is associated with dismal outcomes. Hence, accurately identifying patients at risk is essential to tailor treatment concepts individually to disease biology. Machine Learning (ML) is a branch of computer science that can process large data sets for a plethora of purposes. The underlying mechanism does not necessarily begin with a manually drafted hypothesis model. Rather the ML algorithms can detect patterns in pre-processed data and derive abstract information. We used ML to predict CR and 2-year overall survival (OS) in a large multi-center cohort of 1383 AML patients who received intensive induction therapy using clinical, laboratory, cytogenetic and molecular genetic data. To enable a customizable and reusable technological approach and achieve optimal results, we designed a data-driven platform with an embedded, automated ML pipeline integrating state-of-the-art software technology for data management and ML models. The platform consists of five scalable modules for data import and modelling, data transformation, model refinement, machine learning algorithms, feature support and performance feedback that are executed in an iterative manner to approach step-wisely the optimal configuration. To reduce dimensionality and the the risk of overfitting, dynamic feature selection was used, i.e. features were selected according to their support by feature selection algorithms. To be included in an ML model, a feature had to pass a pre-determined threshold of overall predictive power determined by summing the normalized scores of the feature selection algorithms. Features below the threshold were automatically excluded from the ML models for the respective iteration. In that way, features of high redundancy or low entropy were automatically filtered out. Our classification algorithms were completely agnostic of pre-existing risk classifications and autonomously selected predictive features both including established markers of favorable or adverse risk as well as identifying markers of so-far controversial relevance. De novo AML, extramedullary AML, double-mutated (dm) CEBPA, mutations of CEBPA-bZIP, NPM1, FLT3-ITD, ASXL1, RUNX1, SF3B1, IKZF1, TP53, U2AF1, t(8;21), inv(16)/t(16;16), del5/del5q, del17, normal or complex karyotypes, age and hemoglobin at initial diagnosis were statistically significant markers predictive of CR while t(8;21), del5/del5q, inv(16)/t(16;16), del17, dm CEBPA, CEBPA-bZIP, NPM1, FLT3-ITD , DNMT3A, SF3B1, U2AF1, TP53, age, white blood cell count, peripheral blast count, serum LDH and Hb at initial diagnosis as well as extramedullary manifestations were predictive for 2-year OS. For prediction of CR and 2-year OS, AUROCs ranged between 0.77 - 0.86 and 0.63 - 0.74, respectively. We provide a method to automatically select predictive features from different data types, cope with gaps and redundancies, apply and optimize different ML models, and evaluate optimal configurations in a scalable and reusable ML platform. In a proof-of-concept manner, our algorithms utilize both established markers of favorable or adverse risk and also provide further evidence for the roles of U2AF1, IKZF1, SF3B1, DNMT3A and bZIP mutations of CEBPA in AML risk prediction. Our study serves as a fundament for prospective validation and data-driven ML-guided risk assessment in AML at initial diagnosis for the individual patient. Image caption: Patient features were automatically selected by machine learning to predict complete remission (CR) and 2-year overall survival (OS) after intensive induction therapy. Based on a continuous feature support metric with a predefined cut-off of 0.5 (determined by optimal classification performance), 27 and 25 features were automatically selected for prediction of CR (A) and 2-year OS (C), respectively. For each of these features predicted by machine learning, odds ratios and 95% confidence intervals (CI) were calculated for CR (B) and 2 year OS (D). BMB: bone marrow blast count; FLT3h/low: FLT3-ITD ratio, h=high & gt;0.5; Hb: hemoglobin; karyotype, c: complex aberrant karyotype (≥ 3 aberrations); karyotype, n: normal karyotype (no aberrations); LDH: lactate dehydrogenase; PBB: peripheral blood blast count; PLT: platelet count; WBC: white blood cell count. Figure 1 Figure 1. Disclosures Schetelig: Roche: Honoraria, Other: lecture fees; Novartis: Honoraria, Other: lecture fees; BMS: Honoraria, Other: lecture fees; Abbvie: Honoraria, Other: lecture fees; AstraZeneca: Honoraria, Other: lecture fees; Gilead: Honoraria, Other: lecture fees; Janssen: Honoraria, Other: lecture fees . Platzbecker: Janssen: Honoraria; Celgene/BMS: Honoraria; AbbVie: Honoraria; Novartis: Honoraria; Takeda: Honoraria; Geron: Honoraria. Müller-Tidow: Pfizer: Research Funding; Janssen: Consultancy, Research Funding; Bioline: Research Funding. Baldus: Celgene/BMS: Honoraria; Amgen: Honoraria; Novartis: Honoraria; Jazz: Honoraria. Krause: Siemens: Research Funding; Takeda: Honoraria; Pfizer: Honoraria; art-tempi: Honoraria; Kosmas: Honoraria; Gilead: Other: travel support; Abbvie: Other: travel support. Haenel: Bayer Vital: Honoraria; Jazz: Consultancy, Honoraria; GSK: Consultancy; Takeda: Consultancy, Honoraria; Novartis: Consultancy, Honoraria; Roche: Consultancy, Honoraria; Amgen: Consultancy; Celgene: Consultancy, Honoraria. Schliemann: Philogen S.p.A.: Consultancy, Honoraria, Research Funding; Abbvie: Consultancy, Other: travel grants; Astellas: Consultancy; AstraZeneca: Consultancy; Boehringer-Ingelheim: Research Funding; BMS: Consultancy, Other: travel grants; Jazz Pharmaceuticals: Consultancy, Research Funding; Novartis: Consultancy; Roche: Consultancy; Pfizer: Consultancy. Middeke: Roche: Consultancy, Honoraria; Janssen: Consultancy, Honoraria, Research Funding; Abbvie: Consultancy, Honoraria; Pfizer: Consultancy, Honoraria; Jazz: Consultancy; Astellas: Consultancy, Honoraria; Sanofi: Honoraria, Research Funding; Novartis: Consultancy; Gilead: Consultancy; Glycostem: Consultancy; UCB: Honoraria.

Abstract: Background: In relapsed or refractory acute myeloid leukemia (AML), long-term disease-free survival may only be achieved with allogeneic hematopoietic stem cell transplantation (HSCT). Within the BRIDGE Trial, the safety and efficacy of a clofarabine salvage therapy as a bridge to HSCT was studied. Here, we report long-term survival data and the impact of donor availability at the time of study enrollment. The BRIDGE trial (NCT 01295307) was a phase II, multicenter, intent-to-transplant study. Patients and Methods: Between March 2011 and May 2013, 84 patients with relapsed or refractory AML older than 40 years were enrolled. Patients were scheduled for at least one cycle of induction therapy with CLARA (clofarabine 30 mg/m2 and cytarabine 1 g/m2, days 1-5). Patients with a donor received HSCT in aplasia after first CLARA. In case of a prolonged donor search, HSCT was performed as soon as possible. The conditioning regimen consisted of clofarabine 30 mg/m2, day -6 to -3, and melphalan 140 mg/m2 on day -2. In patients with partially matched unrelated donors, ATG (Genzyme) at a cumulative dose of 4.5 mg/kg was recommended. GvHD prophylaxis consisted of CsA and mycophenolate mofetil. Results: Forty-four patients suffered from relapsed AML and 40 patients had refractory disease. The median patient age was 61 years (range 40 – 75). According to the current ELN risk stratification 17% of pts were classified as favorable risk, 35% as intermediate I, 17% as intermediate II and 20% as adverse risk. The overall response rate assessed at day 15 after start of CLARA was 80% (defined as at least a marked reduction in BM blasts or BM cellularity and absence of blasts in the peripheral blood) with 31% of patients having less than 5% BM blasts at that time. Seventeen patients did not respond to CLARA, and were subsequently treated off-study. Due to early death, three patients were not evaluable for treatment response. Overall, 66% of the patients received HSCT within the trial. Donors were HLA-identical siblings in eight cases (14%), HLA-compatible unrelated donors in 30 cases (55%) and unrelated donors with one mismatch in 17 cases (31%). Treatment success was defined as complete remission (CR), CR with incomplete recovery (CRi) or CRchim (BM donor chimerism 〉 95% and absolute neutrophil count 〉 0.5/nL) on day 35 after HSCT. Treatment success was achieved in 61% of the patients. With a median follow up of 25 months, the OS for all enrolled patients at two years was 42% (95% CI, 32% to 54%). (Figure 1) The Leukemia-free survival at two years for those 51 patients who achieved the primary endpoint was 52% (95% CI, 40% to 69%). (Figure 2) At the time of enrollment, 14% of patients had a related donor and 33% had an unrelated donor available. In 46% of the patients, donor search was initiated at the time of enrollment. For 7% of patients, donor search was unsuccessful prior to enrollment and reinitiated. The OS at 2 years for patients with a related or an unrelated donor available was 75% (95% CI, 54% to 100%) and 47% (95% CI, 31% to 71%), respectively, while it was 29% (95% CI, 18% to 48%) for patients for whom donor search was initiated at time of enrollment (p = .09). Conclusions: Salvage therapy with CLARA, and subsequent conditioning with clofarabine and melphalan prior to allogeneic HSCT, provides good anti-leukemic activity in patients with relapsed or refractory AML. Fast unrelated donor search and work up, with conditioning in aplasia allowed a high rate of successful HSCTs. The leukemia-free survival for this group of elderly, high risk AML patients is very promising. Figure 1 Figure 1. Overall survival for all patients, n=84 Figure 2 Figure 2. Leukemia-free survival for all patients with primary treatment success, n=51 Disclosures Middeke: Genzyme: Speakers Bureau. Off Label Use: Clofarabine for AML. Schetelig:Genzyme: Research Funding; DKMS German Bone Marrow Donor Center: Employment.