Abstract: Introduction Although the labeled CD19 targeting CAR-T cell constructs axi-cel and tisa-cel are generally associated with an acceptable safety profile, non-relapse deaths can occur. Little is known about timing, causes and predictors of NRM following SOC CAR-T cell therapy for LBCL. Here, we analyzed frequency, causes, and risk factors of non-relapse deaths with focus on late NRM (beyond 4 weeks after dosing) using registry data provided by the DRST, the national partner of the EBMT. Methods Patients were selected from 356 consecutive patients who received SOC CAR-T treatment of LBCL between November 2018 and April 2021 at 21 German centers and were registered with the DRST/EBMT. Baseline patient, disease, and transplant data were collected from MED-A cellular therapy forms. Centers were contacted to provide additional treatment and follow-up information. Patients with late NRM (defined as NRM occurring beyond 4 weeks after dosing without prior LBCL relapse or progression) were compared with all patients surviving progression-free the 4-week landmark after dosing without subsequent NRM. Cumulative incidences of NRM were calculated considering relapse/progression as competing event. Results The analysis set consisted of 312 patients surviving progression-free at least 28 days after CAR-T treatment and remained alive until the end of follow-up or had a documented cause of death. Median age was 61 years (19-83), 66% were male, 52% had an IPI ≥3, 13 had an ECOG score & gt;1, 70% had received ≥3 treatment lines, 33% had failed a prior HCT, and 78% were refractory at lymphodepletion. 50% had been treated at a center contributing ≥20 cases with axi-cel (52%) or tisa-cel (48%). Grade ≥3 CRS and grade ≥3 neurotoxicity (NT) had occurred in 11% each, and 7% had no neutrophil recovery at day 100 post dosing or at last follow-up, whatever was earlier. With a median follow-up of 11.2 months, 124 patients (40%) had died, 109 (35%) LBCL-related, and 15 (5%) because of NRM. The cumulative incidence of late NRM at 12 months post dosing was 4.3% (95%CI 2.0-6.6). Causes of NRM were infections in 10 patients (bacterial or fungal sepsis/pneumonia 6; viral/atypical pneumonia/encephalitis 4); late NT 2; hyperinflammatory syndrome 1; 2 nd malignancy 1; unknown 1). Of note, 5 of the 6 lethal fungal/bacterial infections occurred subsequent to high grade NT. There was no significant difference between patients experiencing and not experiencing NRM in terms of age, gender, IPI, ECOG, pretreatment lines, prior HCT, disease status at lymphodepletion, and grade ≥3 CRS frequency. However, a significantly larger proportion of patients with late NRM had failed neutrophil recovery (27% vs 5%, p 0.011), had experienced grade ≥3 NT (40% vs 10%, p 0.0031), and/or had received axi-cel (93% vs 51%, p 0.001). Patients having neutrophil non-recovery and/or grade ≥3 NT had a 12-month NRM incidence of 16% (95%CI 5.1-26.9) vs 2.5% (95%CI 0.3-4.7) in patients with none of these 2 factors. Conclusions Late NRM in patients receiving SOC CAR-T treatment for LBCL is largely driven by infections. Risk factors for late NRM appear to be protracted neutropenia and higher grade NT, suggesting that intensified anti-bacterial/anti-fungal prophylaxis may be considered in patients with persisting critical neutropenia or exposed to high-dose steroids for NT treatment. Figure 1 Figure 1. Disclosures Dreger: BMS: Consultancy; AstraZeneca: Consultancy, Speakers Bureau; Bluebird Bio: Consultancy; AbbVie: Consultancy, Speakers Bureau; Gilead Sciences: Consultancy, Speakers Bureau; Janssen: Consultancy; Novartis: Consultancy, Speakers Bureau; Riemser: Consultancy, Research Funding, Speakers Bureau; Roche: Consultancy, Speakers Bureau. Schubert: Gilead: Consultancy. Holtick: Sanofi: Honoraria; Celgene: Honoraria. Subklewe: Miltenyi: Research Funding; Takeda: Speakers Bureau; Gilead: Consultancy, Research Funding, Speakers Bureau; Klinikum der Universität München: Current Employment; MorphoSys: Research Funding; Novartis: Consultancy, Research Funding, Speakers Bureau; Roche: Research Funding; Seattle Genetics: Consultancy, Research Funding; Pfizer: Consultancy, Speakers Bureau; Janssen: Consultancy; BMS/Celgene: Consultancy, Research Funding, Speakers Bureau; Amgen: Consultancy, Research Funding, Speakers Bureau. Bastian: Abbvie: Other; Amgen: Consultancy, Honoraria; Astra Zeneca: Honoraria, Other; BMS and Celgene: Consultancy, Honoraria, Other; Kite-Gilead: Consultancy, Honoraria; MSD: Consultancy, Honoraria, Other, Research Funding; Novartis: Consultancy, Honoraria, Other, Research Funding; Pentixafarm: Consultancy, Honoraria; Pfizer: Consultancy, Honoraria; Roche: Consultancy, Honoraria; Takeda: Consultancy, Honoraria, Other, Research Funding. Ayuk: Gilead: Honoraria; Celgene/BMS: Honoraria; Janssen: Honoraria; Takeda: Honoraria; Miltenyi Biomedicine: Honoraria; Mallinckrodt/Therakos: Honoraria, Research Funding; Novartis: Honoraria. Marks: Kite/Gilead: Membership on an entity's Board of Directors or advisory committees; Kite/Gilead: Honoraria; Merck: Consultancy; AbbVie: Other: Meeting attendance. Penack: Priothera: Consultancy; Takeda: Research Funding; Incyte: Research Funding; Neovii: Honoraria; Pfizer: Honoraria; Therakos: Honoraria; Novartis: Honoraria; MSD: Honoraria; Jazz: Honoraria; Gilead: Honoraria; Astellas: Honoraria; Shionogi: Consultancy; Omeros: Consultancy. Koenecke: EUSA Pharm: Consultancy; Kite/Gilead: Consultancy; BMS/Celgene: Consultancy; Janssen: Consultancy; Novartis: Consultancy. Von Bonin: Daiichi Sankyo: Other: traveling support and advisory fees; Novartis: Other: traveling support and advisory fees; Kite/Gilead: Other: traveling support and advisory fees. Stelljes: Novartis: Consultancy, Speakers Bureau; MSD: Consultancy, Speakers Bureau; Pfizer: Consultancy, Research Funding, Speakers Bureau; Amgen: Consultancy, Speakers Bureau; Medac: Speakers Bureau; Celgene/BMS: Consultancy, Speakers Bureau; Kite/Gilead: Consultancy, Speakers Bureau. Glass: BMS: Consultancy; Helios Klinik Berlin-Buch: Current Employment; Kite: Consultancy; Novartis: Consultancy; Riemser: Research Funding; Roche: Consultancy, Research Funding, Speakers Bureau. Baldus: Novartis: Honoraria; Amgen: Honoraria; Celgene/BMS: Honoraria; Jazz: Honoraria. Vucinic: MSD: Honoraria; Novartis: Honoraria; Gilead: Honoraria, Other: Travel Sponsoring; Janssen: Honoraria, Other: Travel Sponsoring; Abbvie: Honoraria, Other: Travel Sponsoring. Topp: Universitatklinikum Wurzburg: Current Employment; Celgene: Consultancy, Research Funding; Janssen: Consultancy; Kite, a Gilead Company: Consultancy, Research Funding; Novartis: Consultancy; Roche: Consultancy, Research Funding; Gilead: Research Funding; Regeneron: Consultancy, Research Funding; Macrogeniecs: Research Funding; Amgen: Consultancy, Research Funding. Schroers: BMS/Celgene: Consultancy, Honoraria; Janssen: Consultancy, Honoraria; GSK: Consultancy, Honoraria; Takeda: Honoraria. Hanoun: AstraZeneca: Honoraria; Abbvie: Other: travel expenses; Novartis: Research Funding. Thomas: AbbVie: Honoraria, Speakers Bureau; Art tempi: Honoraria, Speakers Bureau; BMS/Celgene: Consultancy, Honoraria, Other, Research Funding, Speakers Bureau; EUSA Pharma: Consultancy, Honoraria; Janssen: Consultancy, Honoraria, Other; Kite/Gilead: Honoraria, Other, Research Funding, Speakers Bureau; Medigene: Consultancy, Honoraria, Other; Novartis: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Other; Pfizer: Consultancy, Honoraria, Other, Speakers Bureau. Kröger: Novartis: Research Funding; Riemser: Honoraria, Research Funding; Sanofi: Honoraria; Neovii: Honoraria, Research Funding; Jazz: Honoraria, Research Funding; Gilead/Kite: Honoraria; Celgene: Honoraria, Research Funding; AOP Pharma: Honoraria. Bethge: Novartis: Consultancy, Honoraria, Speakers Bureau; Miltenyi Biotec: Consultancy, Honoraria, Research Funding, Speakers Bureau; Kite-Gilead: Consultancy, Honoraria, Speakers Bureau; Janssen: Consultancy, Honoraria, Speakers Bureau; Celgene: Consultancy, Honoraria, Speakers Bureau.

Abstract: To develop a prognostic scoring system tailored for therapy-related myelodysplastic syndromes (tMDS), we put together a database containing 1933 patients (pts) with tMDS from Spanish, German, Swiss, Austrian, US, Italian, and Dutch centers diagnosed between 1975-2015. Complete data to calculate the IPSS and IPSS-R were available in 1603 pts. Examining different scoring systems, we found that IPSS and IPSS-R do not risk stratify tMDS as well as they do primary MDS (pMDS), thereby supporting the need for a tMDS-specific score (Kuendgen et al., ASH 2015). The current analysis focuses on cytogenetic information as a potential component of a refined tMDS score, based on this large, unique patient cohort. Of the 1933 pts, 477 had normal karyotype (KT), 197 had missing cytogenetics, while 467 had a karyotype not readily interpretable. Incomplete karyotype descriptions will be reedited for the final evaluation. Of the remaining 1269 pts the most frequent cytogenetic abnormalities (abn) were: -7, del(5q), +mar, +8, del(7q), -5, del(20q), -17, -18, -Y, del(12p), -20, and +1 with 〉 30 cases each. Frequencies are shown in Table 1. Some abn were observed mostly or solely within complex KTs, such as monosomies, except -7. Others, like del(20q) or -Y, are mainly seen as single or double abn, while del(5q), -7, or del(7q) are seen in complex as well as non-complex KTs. The cytogenetic profile overlapped with that of pMDS (most frequent abn: del(5q), -7/del(7q), +8, -18/del(18q), del(20q), -5, -Y, -17/del(17p), +21, and inv(3)/t(3q) (Schanz et al, JCO 2011)), with notable differences including overrepresentation of complete monosomies, a higher frequency of -7 or t(11q23), and a more frequent occurrence of cytogenetic subtypes in complex KTs, which was especially evident in del(5q) occurring as a single abn in 16%, compared to 70% within a complex KT. IPSS-R cytogenetic groups were distributed as follows: Very Good (2%), Good (35%), Int (17%), Poor (15%), Very Poor (32%). Regarding the number of abn (including incomplete KT descriptions) roughly 30% had a normal KT, 20% 1, 10% 2, and 40% ≥3 abn, compared to pMDS: 55% normal KT, 29% 1, 10% 2, and 6% ≥3 abn. To be evaluable for prognostic information, abn should occur in a minimum of 10 pts. As a single aberration this was the case for -7, +8, del(5q), del(20q), del(7q), -Y, and t(11;varia) (q23;varia). Of particular interest, there was no apparent prognostic difference between -7 and del(7q); del(5q) as a single abn was associated with a relatively good survival, while the prognosis was poor with the first additional abn; t(11q23) occurred primarily as a single abn and was associated with an extremely poor prognosis, and prognosis of pts with ≥4 abn was dismal independent of composition (Table 1). To develop a more biologically meaningful scoring system containing homogeneous and prognostically stable groups, we will further combine subgroups with different abn leading to the same cytogenetic consequences. For example, deletions, unbalanced translocations, derivative chromosomes, dicentric chromosomes of 17p, and possibly -17 all lead to a loss of genetic material at the short arm of this respective chromosome affecting TP53. Further information might be derived from analyses of the minimal common deleted regions. For some abn, like del(11q), del(3p), and del(9q), this can be refined to one chromosome band only (table 1). Conclusion: Development of a robust scoring system for all subtypes of tMDS is challenging using existing variables. This focused analysis on the cytogenetic score component shows that favorable KTs are evident in a substantial proportion of pts, in contrast to historic data describing unfavorable cytogenetics in the majority of pts. Although complex and monosomal KTs are overrepresented, this suggests the existence of distinct tMDS-subtypes, although some of these cases might not be truly therapy-induced despite a history of cytotoxic treatment. The next steps will be to analyze the prognosis of the different groups, develop a tMDS cytogenetic score, and examine minimal deleted regions to identify candidate genes for development of tMDS, as well as to describe the possible influence of different primary diseases and treatments (radio- vs chemotherapy, different drugs) on induction of cytogenetic subtypes. Our detailed analysis of tMDS cytogenetics should reveal important prognostic information and is likely to help understand mechanisms of MDS development. Disclosures Komrokji: Novartis: Consultancy, Speakers Bureau; Celgene: Membership on an entity's Board of Directors or advisory committees, Research Funding. Sole:Celgene: Membership on an entity's Board of Directors or advisory committees. Sekeres:Celgene: Membership on an entity's Board of Directors or advisory committees; Millenium/Takeda: Membership on an entity's Board of Directors or advisory committees. Roboz:Cellectis: Research Funding; Agios, Amgen, Amphivena, Astex, AstraZeneca, Boehringer Ingelheim, Celator, Celgene, Genoptix, Janssen, Juno, MEI Pharma, MedImmune, Novartis, Onconova, Pfizer, Roche/Genentech, Sunesis, Teva: Consultancy. Steensma:Amgen: Consultancy; Genoptix: Consultancy; Janssen: Consultancy; Celgene: Consultancy; Millenium/Takeda: Consultancy; Ariad: Equity Ownership. Schlenk:Pfizer: Honoraria, Research Funding; Amgen: Research Funding. Valent:Amgen: Honoraria; Deciphera Pharmaceuticals: Research Funding; Celgene: Honoraria, Research Funding; Novartis: Honoraria, Research Funding; Ariad: Honoraria, Research Funding; Deciphera Pharmaceuticals: Research Funding. Giagounidis:Celgene Corporation: Consultancy. Giagounidis:Celgene Corporation: Consultancy. Platzbecker:Celgene Corporation: Honoraria, Research Funding; TEVA Pharmaceutical Industries: Honoraria, Research Funding; Novartis: Honoraria, Research Funding; Janssen-Cilag: Honoraria, Research Funding; Amgen: Honoraria, Research Funding. Lübbert:Janssen-Cilag: Other: Travel Funding, Research Funding; Celgene: Other: Travel Funding; Ratiopharm: Other: Study drug valproic acid.

Abstract: Background: The International Prognostic Scoring System (IPSS) for MDS has recently been revised (IPSS-R). However both scoring systems were developed after exclusion of therapy-related cases and data on its usefulness in treatment-related MDS (tMDS) is limited. Aims and Methods: We analyzed 1837 pts from Spanish, German, Swiss, Austrian, US, Italian, and Dutch centers diagnosed 1975-2015. Complete data to calculate the IPSS/-R was available in 1511 pts. The impact of prognostic features was analyzed by uni- and multivariable models and estimated by a measure of concordance for censored data (Dxy). Results: Median age was 68 years. 1% of pts had 5q-syndrome, 13% RCUD, 4% RARS, 27% RCMD/-RS, 18% RAEB 1, 18% RAEB 2, 4% CMML 1, 2% CMML 2, 3% MDS-U, and 7% AML (RAEB-T) according to WHO-classification. Regarding cytogenetics 38% exhibited good, 14% intermediate, and 48% poor-risk according to IPSS, and 2% very good, 36% good, 17% intermediate, 15% poor, and 31% very poor according to IPSS-R. Prognostic risk groups were 12% IPSS low, 34% int 1, 36% int 2, and 18% high, while the IPSS-R was very low in 8%, low in 20%, intermediate in 17%, high in 23%, and very high in 32%. The most frequent primary diseases were NHL 28%, breast cancer 16%, myeloma 6%, prostate cancer 6%, Hodgkins disease 5%, and 4% gastrointestinal tumors. Patients received chemotherapy in 75% and radiotherapy in 47%. Regarding chemotherapeutic drugs, most pts received combination regimens containing alkylating agents in 65%, topoisomerase inhibitors in 44%, antitubulin agents in 26%, and antimetabolites in 26%. Median follow-up from MDS diagnosis was 59 months, median survival 16 months. Since a disease altering treatment is, at least in higher risk disease, which is overrepresented in tMDS, standard of care, we decided to analyze treated as well as untreated pts to avoid a selection bias. This included stem cell transplantation in 16% with a median survival of 24 months. Features with influence on survival and time to AML in univariable analysis included FAB, WHO, IPSS, IPSS-R, cytogenetics, hb, platelets, marrow and peripheral blasts, ferritin, LDH, fibrosis, ß2-microglobulin, and use of alkylating agents for the treatment of primary disease. For hemoglobin, platelets, LDH, fibrosis, and ß2-microglobulin the influence was stronger on survival. Year of diagnosis, age, gender, neutrophil count, WBC, use of chemo or radiotherapy as well as other chemotherapeutic agents had no marked influence on both outcomes. According to our results, both the IPSS (Dxy 0.29 for survival, 0.32 for AML) and IPSS-R (Dxy 0.34, 0.32 for AML) perform moderately in tMDS, but not as well as in primary MDS (pMDS). Therefore, existing prognostic models need to be adjusted to tMDS. However, this appears to be not without difficulties. The scores tested, as well as most prognostic variables themselves perform inferior compared to pMDS. It becomes even more complicated since tMDS in itself is even more heterogeneous than pMDS. Scores and variables perform differently depending on the primary disease or therapy. The IPSS/-R and its variables perform for example better in pts with solid tumors compared to hematologic diseases or in pts who have received radio- instead of chemotherapy, but also in pts after prostate compared to breast cancer. In addition to the integration of further variables, new cutoffs, or the weighting of existing variables, we are currently testing the possibility of separate score versions for different tMDS subgroups. Separate score versions for survival and time to AML would also give differing weights to most features. Hemoglobin, platelets and cytogenetics would get more weight for survival, while marrow blasts would be more important regarding AML. Conclusion: In contrast to early descriptions of tMDS, with poor risk cytogenetics in the vast majority of pts and a uniformly poor prognosis, surprisingly we find good risk karyotypes in a relatively large number of pts. Although, poor risk cytogenetics are still overrepresented, this indicates, different types of tMDS exist. Our analysis shows that many variables exhibit prognostic influence in tMDS and the IPSS or preferably IPSS-R can be applied in these pts. However, the prognostic power of both scores is inferior compared to pMDS, making an optimized tMDS score reasonable. Currently data from further IWG centers is integrated in our database and further analyses are performed to propose a tMDS specific score. Figure 1. Figure 1. Disclosures Komrokji: Novartis: Research Funding, Speakers Bureau; Pharmacylics: Speakers Bureau; Incyte: Consultancy; Celgene: Consultancy, Research Funding. Sekeres:TetraLogic: Membership on an entity's Board of Directors or advisory committees; Celgene Corporation: Membership on an entity's Board of Directors or advisory committees; Amgen: Membership on an entity's Board of Directors or advisory committees. Steensma:Celgene: Consultancy; Incyte: Consultancy; Amgen: Consultancy; Onconova: Consultancy. Valent:Novartis: Consultancy, Honoraria, Research Funding; Ariad: Honoraria, Research Funding; Bristol-Myers Squibb: Honoraria; Pfizer: Honoraria; Celgene: Honoraria. Platzbecker:Boehringer: Research Funding; Celgene: Honoraria, Research Funding; Novartis: Honoraria, Research Funding. Esteve:Celgene: Consultancy, Honoraria; Janssen: Consultancy, Honoraria.

Abstract: Introduction The CD19 targeting CAR-T cell constructs axicabtagene ciloleucel (axi-cel) and tisagenlecleucel (tisa-cel) have become an accepted standard salvage treatment of LBCL beyond the second line. Patients scheduled for approved CAR-T cell therapies usually have 4-8 weeks wait time for CAR-T cell infusion, thus often requiring bridging strategies in rapidly progressing patients to achieve disease control until start of lymphodepletion. It is still unclear, however, if the adverse impact of active progressive lymphoma can be overcome by successful bridging. We have addressed this question using registry data provided by the German Registry for Stem Cell Transplantation (DRST), the national partner of the EBMT. Methods We analyzed 356 consecutive patients who received standard of care axi-cel (n=173) or tisa-cel (n=183) treatment of LBCL between November 2018 and April 2021 at 21 German centers and were registered with the DRST/EBMT. Baseline patient, disease, and transplant data were collected from MED-A cellular therapy forms. Centers were contacted to provide additional treatment and follow-up information. Predictors of progression-free survival (PFS) were analyzed by uni- and multivariate comparisons. Results Compared to the approval trials, patients were of poor risk with 58% presenting with elevated LDH at lymphodepletion and 71% having received ≥3 pretreatment lines, resulting in ineligibility for the ZUMA-1 study in 87% of cases. Kaplan-Maier estimates of overall survival, PFS and non-relapse mortality (NRM) 12 months after dosing were 52%, 30% and 7%, respectively. Information on bridging was available for 355 patients (99%). Of these, 279 patients (78%) underwent at least one line of bridging attempt, whereas bridging was deemed unnecessary in 76 patients (22%). A wide variety of modalities were employed for bridging, with the most frequent being chemoimmunotherapy (n=188), chemotherapy (n=41), radiation (n=30), immunotherapy (n=12) and steroids (n=6). Bridging resulted in disease control (CR/PR) in 58 of 270 patients evaluable for response (21%). With a median follow-up of 11 months, 12-month PFS rates for patients without bridging, successful bridging, and bridging failure were 41%, 52%, and 20%, respectively, p= & lt;0.001 (Figure). Of note, an increased LDH at lymphodepletion did not impair PFS within the bridging responders, but affected the outcome of those patients who did not respond or not undergo bridging (p & lt;0.0001). The adverse impact of bridging failure on PFS was confirmed after multivariable adjustment for confounders (p=0.001, HR 2.083; 95% CI 1.358-3.195). Other significant risk factors for PFS on multivariate analysis were elevated LDH (p=0.012, HR 1.46; 95% CI 1.08-1.96), tisa-cel (p=0.0109, HR 1.41; 95% CI 1.06-1.88) and ECOG (p=0.021, HR 1.22; 95% CI 1.03-1.45). Conclusion The results of this large German GLA/DRST analysis suggest that effective bridging can overcome the adverse impact of active disease on the outcome of standard-of-case CD19 CAR-T therapy. With current treatment strategies, however, bridging is often unsuccessful, highlighting the need for exploring innovative tools for inducing temporary LBCL control for CAR-T therapy preparation. Figure 1 Figure 1. Disclosures Bethge: Novartis: Consultancy, Honoraria, Speakers Bureau; Kite-Gilead: Consultancy, Honoraria, Speakers Bureau; Miltenyi Biotec: Consultancy, Honoraria, Research Funding, Speakers Bureau; Janssen: Consultancy, Honoraria, Speakers Bureau; Celgene: Consultancy, Honoraria, Speakers Bureau. Schmitt: TolerogenixX: Current holder of individual stocks in a privately-held company; Novartis: Other: Travel grants, Research Funding; Kite Gilead: Other: Travel grants; Apogenix: Research Funding; MSD: Membership on an entity's Board of Directors or advisory committees; Bluebird Bio: Other: Travel grants; Hexal: Other: Travel grants, Research Funding. Holtick: Celgene: Honoraria; Sanofi: Honoraria. Borchmann: Gilead Sciences: Honoraria; BMS/Celgene: Honoraria; Janssen: Honoraria; Miltenyi Biotech: Honoraria; Novartis: Honoraria. Subklewe: Klinikum der Universität München: Current Employment; Pfizer: Consultancy, Speakers Bureau; Roche: Research Funding; Novartis: Consultancy, Research Funding, Speakers Bureau; MorphoSys: Research Funding; Janssen: Consultancy; Seattle Genetics: Consultancy, Research Funding; Takeda: Speakers Bureau; Miltenyi: Research Funding; Gilead: Consultancy, Research Funding, Speakers Bureau; Amgen: Consultancy, Research Funding, Speakers Bureau; BMS/Celgene: Consultancy, Research Funding, Speakers Bureau. von Tresckow: Roche: Consultancy, Honoraria; Kite-Gilead: Consultancy, Honoraria; Pfizer: Consultancy, Honoraria; Pentixafarm: Consultancy, Honoraria; Novartis: Consultancy, Honoraria, Other: congress and travel support, Research Funding; MSD: Consultancy, Honoraria, Other: congress and travel support, Research Funding; BMS-Celgene: Consultancy, Honoraria, Other: congress and travel support; AstraZeneca: Honoraria, Other: congress and travel support; Amgen: Consultancy, Honoraria; AbbVie: Other: congress and travel support; Takeda: Consultancy, Honoraria, Other, Research Funding. Ayuk: Gilead: Honoraria; Mallinckrodt/Therakos: Honoraria, Research Funding; Janssen: Honoraria; Takeda: Honoraria; Miltenyi Biomedicine: Honoraria; Celgene/BMS: Honoraria; Novartis: Honoraria. Kroeger: Novartis: Honoraria; AOP Pharma: Honoraria; Gilead/Kite: Honoraria; Riemser: Honoraria, Research Funding; Celgene: Honoraria, Research Funding; Jazz: Honoraria, Research Funding; Sanofi: Honoraria; Neovii: Honoraria, Research Funding. Wulf: Takeda: Consultancy, Honoraria; Novartis: Consultancy, Honoraria; Gilead: Consultancy, Honoraria; Clinigen: Consultancy, Honoraria. Marks: Merck: Consultancy; Kite/Gilead: Honoraria; AbbVie: Other: Meeting attendance; Kite/Gilead: Membership on an entity's Board of Directors or advisory committees. Penack: Astellas: Honoraria; Gilead: Honoraria; Jazz: Honoraria; Omeros: Consultancy; Shionogi: Consultancy; Priothera: Consultancy; Incyte: Research Funding; Takeda: Research Funding; Therakos: Honoraria; Pfizer: Honoraria; Neovii: Honoraria; Novartis: Honoraria; MSD: Honoraria. Koenecke: Kite/Gilead: Consultancy; BMS/Celgene: Consultancy; Janssen: Consultancy; Novartis: Consultancy; EUSA Pharm: Consultancy. Von Bonin: Kite/Gilead: Other: traveling support and advisory fees; Novartis: Other: traveling support and advisory fees; Daiichi Sankyo: Other: traveling support and advisory fees. Stelljes: Amgen: Consultancy, Speakers Bureau; Celgene/BMS: Consultancy, Speakers Bureau; Medac: Speakers Bureau; Novartis: Consultancy, Speakers Bureau; Pfizer: Consultancy, Research Funding, Speakers Bureau; MSD: Consultancy, Speakers Bureau; Kite/Gilead: Consultancy, Speakers Bureau. Glass: BMS: Consultancy; Helios Klinik Berlin-Buch: Current Employment; Kite: Consultancy; Novartis: Consultancy; Riemser: Research Funding; Roche: Consultancy, Research Funding, Speakers Bureau. Baldus: Novartis: Honoraria; Amgen: Honoraria; Celgene/BMS: Honoraria; Jazz: Honoraria. Vucinic: Janssen: Honoraria, Other: Travel Sponsoring; Novartis: Honoraria; Abbvie: Honoraria, Other: Travel Sponsoring; Gilead: Honoraria, Other: Travel Sponsoring; MSD: Honoraria. Topp: Celgene: Consultancy, Research Funding; Kite, a Gilead Company: Consultancy, Research Funding; Roche: Consultancy, Research Funding; Novartis: Consultancy; Janssen: Consultancy; Amgen: Consultancy, Research Funding; Gilead: Research Funding; Regeneron: Consultancy, Research Funding; Macrogeniecs: Research Funding; Universitatklinikum Wurzburg: Current Employment. Schroers: BMS/Celgene: Consultancy, Honoraria; Janssen: Consultancy, Honoraria; GSK: Consultancy, Honoraria; Takeda: Honoraria. Thomas: Abbvie: Honoraria, Speakers Bureau; Art tempi: Honoraria, Speakers Bureau; BMS-Celgene: Consultancy, Honoraria, Other: travel support, Research Funding, Speakers Bureau; EUSA Pharma: Consultancy, Honoraria; Janssen: Consultancy, Honoraria, Other: travel support; Kite-Gilead: Honoraria, Other: travel support, Research Funding, Speakers Bureau; Medigene: Consultancy, Honoraria, Other: Travel support; Novartis: Consultancy, Honoraria, Other: travel support, Speakers Bureau; Pfizer: Consultancy, Honoraria, Other: Travel support, Speakers Bureau. Dreger: Bluebird Bio: Consultancy; BMS: Consultancy; AbbVie: Consultancy, Speakers Bureau; Riemser: Consultancy, Research Funding, Speakers Bureau; Novartis: Consultancy, Speakers Bureau; Roche: Consultancy, Speakers Bureau; Gilead Sciences: Consultancy, Speakers Bureau; Janssen: Consultancy; AstraZeneca: Consultancy, Speakers Bureau.

Abstract: Current classifications (World Health Organization-HAEM5/ICC) define up to 26 molecular B-cell precursor acute lymphoblastic leukemia (BCP-ALL) disease subtypes by genomic driver aberrations and corresponding gene expression signatures. Identification of driver aberrations by transcriptome sequencing (RNA-Seq) is well established, while systematic approaches for gene expression analysis are less advanced. Therefore, we developed ALLCatchR, a machine learning-based classifier using RNA-Seq gene expression data to allocate BCP-ALL samples to all 21 gene expression-defined molecular subtypes. Trained on n = 1869 transcriptome profiles with established subtype definitions (4 cohorts; 55% pediatric / 45% adult), ALLCatchR allowed subtype allocation in 3 independent hold-out cohorts (n = 1018; 75% pediatric / 25% adult) with 95.7% accuracy (averaged sensitivity across subtypes: 91.1% / specificity: 99.8%). High-confidence predictions were achieved in 83.7% of samples with 98.9% accuracy. Only 1.2% of samples remained unclassified. ALLCatchR outperformed existing tools and identified novel driver candidates in previously unassigned samples. Additional modules provided predictions of samples blast counts, patient’s sex, and immunophenotype, allowing the imputation in cases where these information are missing. We established a novel RNA-Seq reference of human B-lymphopoiesis using 7 FACS-sorted progenitor stages from healthy bone marrow donors. Implementation in ALLCatchR enabled projection of BCP-ALL samples to this trajectory. This identified shared proximity patterns of BCP-ALL subtypes to normal lymphopoiesis stages, extending immunophenotypic classifications with a novel framework for developmental comparisons of BCP-ALL. ALLCatchR enables RNA-Seq routine application for BCP-ALL diagnostics with systematic gene expression analysis for accurate subtype allocation and novel insights into underlying developmental trajectories.

Abstract: Abstract 3551 Introduction: Early thymic progenitor acute lymphoblastic leukemia (ETP-ALL) has been characterized as new T-ALL subgroup characterized by a specific gene expression profile reflecting the signature of normal early thymic progenitors (ETP). ETP-ALL is also defined by a certain immunophenotype (CD1a−, CD8−, CD5weak with expression of stem cell and/or myeloid markers). Recently, we described a high rate of FLT3 mutations in T-ALL, which were almost exclusively found in the subgroup of ETP-ALL. To further unravel additional molecular alterations we now investigated in a large cohort of adult ETP-ALL patients the NOTCH1 /FBXW7 mutation status, as NOTCH1 activation, frequently detected in T-ALL patients (50–70%), represents an attractive target for γ-secretase inhibitors. In addition, we analyzed the T-cell receptor (TCR) rearrangement status in these ETP-ALL patients. An absence of TCR rearrangement was linked to an immature T-ALL subgroup and associated with a poor prognosis in paediatric patients. Patients and methods: We explored a cohort of 68 ETP-ALL adult patients (55 male, 13 female, median age 38 years). These samples were selected based on flow cytometry data from a total of 1241 T-ALL samples that had been sent to the diagnostic GMALL reference laboratory between 1997 and 2007. DNA was extracted from these diagnostic specimens and the NOTCH1 mutation status was determined by direct sequencing of the N-terminal and the C-terminal regions of the HD domain, the TAD region, the N-terminal and the C-terminal region of the PEST domain. The FBXW7 mutation status was analyzed by direct sequencing of the amplified PCR products of exons 8 and 9. TCR rearrangement status was assessed by the IdentiCloneTM TCRG Gene Clonality Assay (InVivoScribe Technologies). FLT3 mutations were assessed using the FLT3 mutation assay (InVivoScribe Technologies). Results: We found NOTCH1 mutations in 11 of the 68 ETP-ALL patients. This rate of only 16% is significantly lower compared to a cohort of 128 non-ETP T-ALL adult patients with a NOTCH1 mutation frequency of 61%. Of the identified NOTCH1 mutations, 6 were in the HD domain, 2 in the TAD region and 3 in the PEST domain. No FBXW7 mutations were found in the 68 ETP-ALL patients compared to 12% in the cohort of non-ETP T-ALL. The analysis of the TCR rearrangement revealed that 38 ETP-ALL patients (56%) lacked clonal TCR rearrangement, while 30 patients (44%) had a monoclonal TCR rearrangement. No correlation was found between TCR status and NOTCH1 mutation status. As previously reported, ETP-ALL patients showed a high frequency of FLT3 mutations (n=23/68, 34%) and these FLT3 mutated ETP-ALL cases had a distinct immunophenotype characterized by the positivity for CD2, CD117 and CD13. Interestingly, ETP-ALL patients with FLT3 mutations predominantly lacked clonal TCR rearrangements (46%), whereas patients without FLT3 mutations showed more frequently TCR rearrangements (79%; p=0.007). FLT3 and NOTCH1 mutations were mutually exclusive. Interestingly, NOTCH1 mutations were indicative for an ETP-ALL phenotype distinct to the FLT3 mutated cases: NOTCH1 mutated patients were characterized by a lower rate of positivity for CD2 (18% vs 56%, p=0.02), CD13 (28% vs 65%, p=0.02), and CD117 (18% vs 56%, p=0.02) compared to NOTCH1 wild type patients. Likewise, patients lacking of clonal TCR rearrangement also displayed a specific immunophenotype with a higher rate of positivity for CD2 (68% vs. 27%, P 〈 0.001) and CD13 (70% vs. 43%, P=0.023) compared to patients with a clonal TCR status. Conclusion: ETP-ALL patients represent a distinct molecular subgroup of adult T-ALL patients with a yet unreported low frequency of NOTCH1 mutations and a high rate of FLT3 mutations. ETP-ALL is further characterized by a lack of clonal TCR rearrangements indicating a prothymocyte arrest of this very immature T-ALL subgroup. Moreover, within the ETP-ALL subgroup, different genetic alterations drive distinct phenotypes likely representing separate molecular entities; consequently these high risk ETP-ALL patients should be differentially treated with respect to targeted therapies. '-secretase inhibitors might likely be not effective due to the lack of activating NOTCH1 mutations. In contrast, ETP-ALL patients with a FLT3 mutation could benefit from a specific treatment with FLT3 inhibitors. Disclosures: Hofmann: Celgene: Honoraria, Membership on an entity's Board of Directors or advisory committees.

Abstract: Abnormal epigenetic regulation has been implicated in oncogenesis. Mutations in the DNA methyltransferase 3A (DNMT3A) gene were recently demonstrated in acute myeloid leukemia (AML) as a candidate for the initiating of lesions in AML with adverse clinical outcome. Using direct sequencing, identification of the DNMT3A mutations was done in 320 AML patients. Additionally, we analyzed NPM1, FLT3-ITD, FLT3-D835, IDH1 and IDH2 mutations by PCR and DNA sequencing. The retrospective analysis of diagnostic bone marrow samples was performed in AML patients treated in our institution. In all AML patients double induction therapy containing of “7+3” therapy followed by 4 cycles of high dose AraC consolidation was implicated in accordance with therapeutic standards of our institution. We identified DNMT3A mutations in 22% of AML patients. The most common of mutations affect amino acid R822 in exon 23. The DNMT3A mutation was highly enriched in the group of patients with intermediate-risk profile (35%, P 〈 0.01). Unlikely FLT3, DNMT3A mutations were absent in all AML patients with favorable-risk group (P 〈 0.001). AML patients with DNMT3A mutations were older (P=0.05), had higher WBC and platelet counts (P=0.02 for both comparison) and higher relapse rate (P=0.01). The median overall survival among AML patients with DNMT3A mutations was significantly shorter than among patients without such mutation (13.3 months versus 31.3 months, P 〈 0.01). Occurrence of DNMT3A mutations was associated with presence of other common mutations, such as NPM1, FLT3-ITD, FLT3-D835, IDH1 and IDH2. Correlations of the DNMT3A mutations with NPM1 and FLT3-ITD mutations were significant (P 〈 0.001). Our results indicate that DNMT3A mutations are highly recurrent in AML patients with intermediate-risk profile. DNMT3A mutations are highly correlated with NPM1/FLT3-ITD mutations and are associated with an unfavorable prognosis. The discovery of recurrent mutations in DNMT3A gene may provide a new prognostic marker for the risk stratification for AML patients. Disclosures: No relevant conflicts of interest to declare.

Abstract: Background: Approximately 5% of adult acute myeloid leukemia (AML) cases are associated with balanced translocations of chromosome 11q23, and AML with t(9;11)(p22;q23) is recognized as a distinct entity by the WHO Classification. Similarly, the presence of t(4;11)(q21;q23), which accounts for 8-10% of B-cell precursor acute lymphoblastic leukemia (ALL) in patients over the age of 20 years, defines a distinct entity termed "B-lymphoblastic leukemia with t(v;11q23)" according to the WHO Classification. On the molecular level, t(11q23) result in fusion of the KMT2A (also called MLL) gene, which encodes a histone 3 lysine 4 methyltransferase, to a broad spectrum of more than 70 partner genes. The prognosis of patients with relapsed/refractory KMT2A-rearranged leukemia is very poor, and new treatment approaches are needed. Using in vitro and in vivo experimental models, we previously identified cyclin dependent kinase 6 (CDK6) as a potential therapeutic target in KMT2A-rearranged leukemias (Placke et al. Blood. 2014;124:13-23). Aims: To evaluate the tolerability and efficacy of the small-molecule CDK4/6 inhibitor palbociclib in KMT2A-rearranged AML and ALL within a genotype-guided clinical trial (AMLSG 23-14; ClinicalTrials gov. Identifier NCT02310243). Methods: Patients with KMT2A-rearranged leukemia, either relapsed/refractory or newly diagnosed but ineligible for intensive chemotherapy, are enrolled. The study is a phase Ib/IIa trial with a safety/tolerability part in the phase Ib using the standard palbociclib dose of 125 mg once daily for 21 days in a 28-day cycle. Based on a 3+3 modified Fibonacci design, a dose deescalation to 100 mg and 75 mg in case of toxicity is possible in sequential cohorts. If no or only one limiting toxicity is observed among 6 patients at one dose level, this dose level will be taken forward to the phase IIa expansion part of the study. Limiting toxicities are defined as toxicities attributable to palbociclib, expected or unexpected. The expansion part of the study is based on Simon's optimal 2-stage design with 18 patients and 43 patients in the 2 stages. Results: The phase Ib of the study has been completed with recruitment of 6 patients with relapsed/refractory leukemia (AML, n=3; treatment-related AML, n=2; ALL, n=1; refractory to intensive chemotherapy, n=2; relapse, n=4 [following allogeneic stem cell transplantation, n=3; following chemotherapy, n=1]). Cytogenetic results were as follows: t(9;11), n=3; t(6;11), n=1; t(11;19), n=1; t(4;11), n=1. The median white blood cell count (WBC) at study inclusion was 7.05 G/l (range, 0.9-61.0). To control hyperleukocytosis, 3 patients were treated with hydroxyurea during the first week of palbociclib and one patient with corticosteroids. No limiting toxicity occurred during the first 28-day cycle, the limiting-toxicity assessment period. White blood cell counts rapidly decreased after one week of palbociclib at a dose of 125 mg/day and remained low until week 3 (median, 1.6 G/l; range, 0.6-1.9). The median WBC after one week of drug holiday was 1.9 G/l (range, 1.3-7.3). Response assessment revealed one partial remission, 3 disease stabilizations, and 2 cases of progressive disease. Four patients completed further treatment cycles (median, 2; range 2-6), with one patient achieving a complete remission with incomplete hematologic recovery after cycle 2. This patient, a 76-year-old man with t(11;19)-positive de novo AML refractory to chemotherapy with daunorubicin and cytarabine, relapsed after cycle 6, and correlative laboratory studies are underway to determine potential resistance mechanisms. Conclusions: Palbociclib is well tolerated in patients with refractory/relapsed KMT2A-rearranged leukemia with no occurrence of limiting toxicities and has clinical activity in this prognostically unfavorable subset of AML/ALL. Therefore, the study will be taken forward to the efficacy part with accrual of further patients. In addition, the protocol is currently amended as a basket trial with inclusion of patients with locally advanced/metastatic chordoma based on preclinical evidence that CDK4/6 dependence represents a specific liability of chordoma cells that could be exploited for therapeutic benefit. Disclosures Lübbert: Celgene: Other: Travel Funding; Ratiopharm: Other: Study drug valproic acid; Janssen-Cilag: Other: Travel Funding, Research Funding. Schlenk:Amgen: Research Funding; Pfizer: Honoraria, Research Funding.