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Hepatology ◽  
2021 ◽  
Author(s):  
Loreta A Kondili ◽  
Monica Monti ◽  
Maria Giovanna Quaranta ◽  
Laura Gragnani ◽  
Valentina Panetta ◽  
...  

Blood ◽  
2021 ◽  
Vol 138 (Supplement 1) ◽  
pp. 213-213
Author(s):  
Aman Wadhwa ◽  
Yanjun Chen ◽  
Lindsey Hageman ◽  
Anna Hoppmann ◽  
Anne L Angiolillo ◽  
...  

Abstract Introduction: Higher BMI at ALL diagnosis is associated with an increased risk of post-induction residual leukemia (Orgel, Blood 2014) and relapse (Butturini, JCO 2007). However, children may experience significant changes in BMI during the pre-maintenance phases of ALL treatment (Withycombe, Pediatr Blood Cancer 2009), necessitating an examination of the association between BMI during maintenance and relapse risk. We hypothesized that higher BMI during maintenance would be associated with a greater risk of relapse. We also explored the association between BMI and red cell thioguanine (TGN) levels to understand whether BMI-associated variations in TGN biodistribution explained the BMI-relapse association. Methods: We used data from COG-AALL03N1 (primary aim was 6MP adherence) to examine the association between BMI during maintenance and relapse risk. Eligibility for enrollment on AALL03N1 included age ≤21y at ALL diagnosis and receiving maintenance therapy in first remission. The current analysis was limited to patients with wild-type thiopurine methyltransferase genotype. BMI (exposure variable) was calculated as sex- and age-based percentile per CDC normative data, and operationalized as normal/underweight [<85%ile], overweight/obese [85-98%ile] and morbidly obese [≥99%ile]). Hazard of relapse (any site) was estimated using multivariable proportional subdistributional hazards regression after adjusting for age at study enrollment, sex, race/ethnicity, NCI risk group, cytogenetics, 6MP dose intensity (6MPDI), and time from initiation of maintenance. We compared fitted means of red cell TGN levels by BMI groups after adjusting for age at enrollment, sex, race/ethnicity, 6MPDI and time from initiation of maintenance using generalized estimated equations. The association between BMI and relapse risk, as well as between BMI and TGN levels, was also examined in a sub-cohort of patients with available 6MP adherence. Results: The sociodemographic and disease characteristics of the 676 study participants are summarized in the Table. Median BMI%ile was 88.5 (range, 0-100); normal/underweight: 43%, overweight/obese: 45%, and morbidly obese: 12%. As shown in the Figure, cumulative incidence of relapse at 2y from start of maintenance therapy was significantly greater among patients with morbid obesity (11.9±4.3%) when compared to those who were overweight/obese (5.4±1.6%) and underweight/normal weight (4.1±1.4%). After adjusting for the variables listed above, we found that patients with morbid obesity had a 3.2-fold greater hazard of relapse (95%CI=1.4-7.5, P=0.008) when compared to patients who were normal/underweight. After adjusting for age at enrollment, sex, race/ethnicity, 6MPDI, and time from initiation of maintenance, patients with morbid obesity had lower mean red cell TGN levels compared to normal/underweight patients (mean difference: -27.2±7.4 pmol/8 x 10 8 erythrocytes, P=0.0002). However, inclusion of TGN in the model did not alter the association between BMI and hazard of relapse (HR=3.8, 95%CI, 1.6-9.0, P=0.003). These findings did not change after adjusting for 6MP adherence in the sub-cohort with available adherence data (n=435). Conclusion: Morbid obesity during maintenance for childhood ALL is associated with relapse as well as lower systemic exposure to 6MP. However, lower TGN levels do not explain the relation between BMI and relapse risk. Therefore, there is a need to understand the mechanism of the relation between morbid obesity during maintenance and relapse risk in children with ALL. Figure 1 Figure 1. Disclosures No relevant conflicts of interest to declare.


Blood ◽  
2021 ◽  
Vol 138 (Supplement 1) ◽  
pp. 1792-1792
Author(s):  
Ruoheng Zhang ◽  
Kevin L Rakszawski ◽  
Myles Nickolich ◽  
W. Christopher Ehmann ◽  
Baldeep Wirk ◽  
...  

Abstract Recipient chimerism increase has been used to predict leukemia relapse in post-hematopoietic cell transplant (HCT) patients with conventional GVHD prophylaxis. However, the value of recipient chimerism increase in patients with post-transplant cyclophosphamide (PTCy) is not clear. We compared PTCy to conventional GVHD prophylaxis (non-PTCy) patients regarding engraftment kinetics and the clinical significance of the 2 chimerism parameters, increasing mixed chimerism (IMC) and degree of recipient chimerism increase at the first event (Δ increase). We studied both total and T-cell-specific chimerism. While leukemia relapse is manifested by an increase in total cell recipient chimerism, an increase in T-cell-specific recipient chimerism may be more impactful in predicting relapse because of the effect of increased T-cell-specific chimerism on the graft-versus-leukemia effect. A total of 220 patients (PTCy: 44, non-PTCy: 176) with AML, MDS, and ALL underwent HCT at our institution from January 2014 to September 2020 and were included in this study (Table). Chimerism was tested at least monthly for the first 3 months, followed by every 3 months up until 1-year post-HCT, and then every 6-12 months thereafter. Short tandem repeat or quantitative PCR were used when percent recipient chimerism was ≥5% and <5%, respectively. Cumulative incidence of competing events and Gray's test were applied for engraftment analysis. Relapse and non-relapse mortality were considered as competing risks for engraftment. Mantel-Byar test and Simon-Makuch plot with landmark analysis were used to visualize disease-free survival (DFS) curves. The Cox proportional hazards model with time-dependent covariates was performed to identify the factors affecting relapse. PTCy patients achieved complete donor chimerism (CC) in total cells earlier at a deeper level (>99%) as compared to non-PTCy patients. Deeper total cell CC (>99%) was achieved in 79.5% of PTCy vs. 51% of non-PTCy patients at day 250, while CC (>95%) was achieved in almost 90% of patients in both groups within 100 days (Figure 1A and B). In comparison, the percentage of PTCy patients achieving T-cell-specific CC was significantly higher at day 250 post-HCT: CC (>95%/>99%) was 79.7%/68.4% in PTCy patients vs. 56.1%/37.5% in non-PTCy patients (Figure 1C and D). To evaluate their impact in predicting relapse, IMC was stratified into no IMC, 1 IMC (≥1 nonconsecutive IMC), and 2 IMC (≥2 consecutive IMC), and degree of recipient chimerism increase at the first event (Δ increase) was stratified into <0.1% (no Δ increase), 0.1-1%, and ≥1%. Two IMC (total), 1 IMC (T-cell), and 2 IMC (T-cell) groups were associated with shorter DFS in non-PTCy patients but not in PTCy patients (Figure 2). One and 2 IMC groups (both total and T-cell) were associated with relapse risk in non-PTCy patients. Furthermore, 1 IMC (T-cell) in non-PTCy patients showed a strong association in relapse risk (HR 7.0 (95%CI 2.83-17.8) p<0.0001). Δ increase ≥1% (total and T-cell) and Δ increase ≥0.1% (T-cell) were associated with shorter DFS in non-PTCy patients, while only Δ increase ≥1% (T-cell) only showed a trend towards shorter DFS in PTCy patients (Figure 3). The Cox regression model showed Δ increase ≥1% in both total, and T-cell chimerism was associated with relapse risk in non-PTCy patients (HR 6.4 (95%CI 2.9-14.2) p<0.0001 and HR 7.2 (95%CI 2.9-18.1) p<0.0001, respectively). Δ increase ≥0.1% (T-cell) in non-PTCy patients was also associated with relapse risk (HR 7.2: 95%CI 2.5-20.4, p<0.0001). In comparison, no association was found between Δ increase and relapse risk in PTCy patients. This is one of the most extensive studies investigating engraftment kinetics and the association of total and T-cell recipient chimerism increase to predict leukemia relapse in PTCy and non-PTCy HCT recipients. We found that PTCy HCT recipients achieved deeper engraftment earlier as compared to non-PTCy recipients. In addition, the two chimerism parameters (IMC and Δ increase) are less reliable in predicting relapse in PTCy than non-PTCy recipients. However, other factors, such as disease type, conditioning regimen, and donor HLA disparity, may have affected engraftment kinetics and the significance of chimerism parameters. Further investigations are warranted to elucidate the impact of the engraftment kinetics and recipient chimerism increase to predict relapse, especially in the PTCy setting. Figure 1 Figure 1. Disclosures Rakszawski: SeaGen: Speakers Bureau. Naik: Takeda: Other: Virtual Advisory Board Member ; Sanofi: Other: Virtual Advisory Board Member ; Kite: Other: Virtual Advisory Board Member. Rybka: Spark Therapeutics: Consultancy; Merck: Consultancy. Claxton: Astellas: Other: Clinical Trial; Novartis: Research Funding; Astex: Research Funding; Cyclacel: Research Funding; Daiichi Sankyo: Research Funding; Incyte: Research Funding.


Blood ◽  
2021 ◽  
Vol 138 (Supplement 1) ◽  
pp. 2924-2924
Author(s):  
Aaron Pruitt ◽  
Feng Gao ◽  
Elisa De Togni ◽  
Aaron Singareddy ◽  
Hunter Cochran ◽  
...  

Abstract Introduction: Haploidentical hematopoietic cell transplantation (haplo-HCT) is an increasingly utilized therapy for a variety of hematologic malignancies. Determining which donor characteristics affect transplant outcomes is of particular interest in haplo-HCT, as there are often multiple donors available for a given patient. A survival benefit with younger donors has been reported in some recent observational studies (DeZern et. al., Blood Advances, March 2021); (Canaani et. al., AJH, Sep. 2017). A decrease in non-relapse mortality (NRM) and increase in relapse with no overall survival difference associated with younger donors has also been observed (Mariotti et. al., Blood Advances, June 2020). These previous studies have utilized populations with bone marrow as the predominant stem cell source. Solomon et al. (BBMT Sep. 2018) observed poorer survival, increased relapse, and worse NRM with parent donors relative to children in a largely peripheral blood population. HLA DR and DP mismatch were noted to be associated with improved survival. Here we describe outcomes in peripheral blood haplo-HCT and their association with potentially selectable donor characteristics including age and relationship to the patient. Patients and Methods: We performed a retrospective review of patients who underwent peripheral blood haplo-HCT with PtCy from July 2009 through May 2021. A total of 323 patients were identified with AML (205), ALL (43), MDS (26), and other (49). Univariate and multivariate analyses (MVA) were conducted examining the effect of donor characteristics on overall survival (OS), NRM, relapse, acute and chronic GVHD. Donor characteristics included age, relationship, ABO status, CMV status, and HLA match grade. We controlled for patient characteristics known to affect outcomes including disease type, DRI, HCT CI, KPS, active disease at transplant, myeloablative conditioning, and prior HCT. Results: Median donor age was 40 (range 15-71) with male predominance (64%). Most were ABO compatible (63%) - 12% had major ABO mismatch, 20% minor, and 4% bidirectional. Donor-recipient CMV status matched in 61% of pairs, 13% were donor positive-recipient negative, 26% donor negative-recipient positive. Most were 5/10 HLA matched (51%) with 20% 6/10 and 13% 7-9/10. Univariate analysis revealed that increasing donor age was associated with higher NRM (HR 2.29, p=0.005 for donors age 30-44; HR 2.06, p=0.012 age > 44) but lower relapse risk (HR 0.56, p=0.012 age 30-44; HR 0.69, p=0.10 age > 44). There were no differences in aGVHD or cGVHD based on donor characteristics in univariate analysis. In MVA, relapse risk was lower in patients with older donors , p=0.046). In contrast, NRM was higher in patients with older donors (HR 1.73 age 30-44, HR 1.69 age > 44, p=0.010). There was no difference in overall survival based on donor age (HR 1.23 age 30-44, HR 1.38 age > 44, p=0.11). We next examined the effect of donor relationship on outcomes while controlling for donor age, patient age, and patient disease risk factors. We found no difference in outcomes between parent, sibling, or child donors. Conclusions: Increasing donor age was associated with lower relapse risk but higher NRM. These competing effects resulted in no difference in OS based on donor age. Other donor factors including relationship (parent / sibling / child), CMV status, ABO mismatch, donor sex, and HLA match grade were not associated with outcomes. Solomon et al. reported better outcomes with child compared to parent donors, a finding not replicated here, however our analysis controlled for donor age which could have been a proxy for relationship in their study. These data suggest that in peripheral blood haplo-HCT, younger donors may be preferred in patients with high risk of transplant related complications. In contrast, older donors may be preferred in patients where relapse risk is high. Data on HLA-DR and DP match is being analyzed and will be presented at the ASH 2021 meeting. Figure 1 Figure 1. Disclosures No relevant conflicts of interest to declare.


Blood ◽  
2021 ◽  
Vol 138 (Supplement 1) ◽  
pp. 2366-2366
Author(s):  
Erika Borlenghi ◽  
Diego Bertoli ◽  
Chiara Cattaneo ◽  
Margherita Sciumé ◽  
Elisa Cerqui ◽  
...  

Abstract Introduction: Acute Myeloid Leukemia (AML) is a heterogeneous disorder characterized by a wide range of cytogenetic and molecular aberrations, that affect prognosis and guide treatment decisions. However there is a still large group of patients (pts) considered at intermediate risk whose outcome needs to be better defined. Next-generation sequencing (NGS) can simultaneously detect various mutations, leading to better define its prognostic profile. The role of some mutations, including isocitrate dehydrogenase (IDH) mutations (IDHm), is still controversial. Aim: We evaluated by NGS monitoring at different time points the prognostic role of IDH1/2m in AML pts with normal karyotype, both in the subgroup with mutations of NPM1 (NPM1m) or FLT3 (FLT3m) and in the subgroup without detectable mutations (wt-AML). Methods: Using Sophia Myeloid Solution kit (SOPHiA Genetics), we performed targeted NGS, covering 30 gene regions, in 104 bone marrow samples collected at diagnosis (53), after first consolidation (30) and at relapse (21), in 53 pts (M/F: 24/29; median age: 56 y, range 22-74), treated according to NILG-AML00 protocol (NCT00400673). Standard PCR to detect NPM1m and FLT3m was performed and we identified 20 NPM1m, 3 NPM1+FLT3-ITDm, 4 FLT3-ITDm and 26 wt-AML. Results: At diagnosis, among 219 pathogenic mutations detected, IDHm represented 10.5% of them (median VAF: 39.1%; range 6.2-49.6%). IDHm was observed in 23/53 pts (43.4%) (IDH1m in 11 and IDH2m in 12). In these pts , more frequently commutated genes were DNMT3A (28%), NPM1 (13%), FLT3-ITD/TKD (14%), ASXL1 (6%), SRSF2 and NRAS (9% each). Complete remission (CR) was achieved in 49/53 (92.5%) pts without difference in response rate according to IDH status (86% in IDHm vs 94% in IDH wild-type, wt). Relapse occurred in 28/49 (57%) pts after a median of 11 months (mo), range 2-61. The frequency of relapse was not significantly different across all types of mutations identified, except for IDH2m which was associated with a higher risk of relapse (10/11 in IDH2m vs 18/38 in IDH2wt; p: 0.014), without differences between R172K and R140Q. On the contrary, IDH1m, present in 18% of relapsed pts, did not impact on relapse (5/10 vs 23/39, p: 0.7). Particularly, in the wt-AML group, the IDH2m was prevalent in pts developing relapse (6/11, 54.5%) and all pts with IDH2m relapsed, with median of 13 mo, range 6-24 (6/6 in IDH2m vs 5/17 in IDH2wt, p:0.0046). Among the co-occurrence mutations, the IDH2/DNMT3A was associated with higher relapse risk (9/9 vs 19/40; p: 0.0063). DNMT3A associated with other mutations did not impact on relapse risk. At a median follow-up of 23 mo, median relapse free survival (RFS) and overall survival (OS) of whole population were 24 and 53 mo, respectively. The IDH2m impacted on OS: 23.5 mo in IDH2m vs 72 in IDH2wt pts (p:0.0093) (Fig 1a), but not in RFS (13 vs 29 mo in IDH2m and IDH2wt, respectively (p:0.1). Considering the subgroups of wt-AML, the RFS (Fig. 1b) and OS were 13 and 23.5 mo in IDH2m vs undefined in IDH2-wt (p:0.0014 and p:0.1), respectively. In pts with NPM1 or FLT3m, RFS and OS were 9 and 53 mo in IDH2m vs 29 and 73 mo in IDH2-wt (p:0.2 and p:0.15), respectively. We did not find the other genomic pattern predicting relapse in this group. After consolidation, NGS monitoring was performed in 30 pts in CR. Of the 13 IDH AML pts evaluated, no mutations was observed in 4 (28.5%); the persistence of IDHm was not associated with a significantly higher relapse (p:0.5). Among other mutations present at diagnosis, NGS clearance after consolidation occurred in pts with NRAS, KRAS, PTPN11 and FLT3-ITD/TKD. Conversely, it was limited for the following mutations: TET2 (8/11), DNMT3A (7/13), SRSF2 (6/6), IDH2 (4/5), ASXL1 (2/2), IDH1 (2/4) and NPM1 (1/12). Overall, the persistence of any type of gene mutations after consolidation was predictive of relapse (2/9 vs 6/7, p:0.04), only in wt-AML subgroup. At relapse, of the 11 IDHm pts analyzed, 7/7 IDH2m and 3/4 IDH1m showed the reappearance of mutations. Conclusion: In this retrospective monocentric study, the presence at diagnosis of IDH2m correlated with relapse risk and with survival, suggesting that additional treatment with targeted agents and or consolidation with allogeneic transplant should be considered. In addition, in AML without NPM1m or FLT3m, the persistence of genes mutation detected by NGS monitoring after consolidation had a significant prognostic value to predict subsequent relapse. Figure 1 Figure 1. Disclosures Borlenghi: Amgen, Janssen: Consultancy. Rossi: Janssen: Membership on an entity's Board of Directors or advisory committees; Celgene: Membership on an entity's Board of Directors or advisory committees; Jazz: Membership on an entity's Board of Directors or advisory committees; Astellas: Membership on an entity's Board of Directors or advisory committees; Daiichi Sankyo: Consultancy, Honoraria; Amgen: Honoraria, Membership on an entity's Board of Directors or advisory committees; Novartis: Membership on an entity's Board of Directors or advisory committees; Alexion: Membership on an entity's Board of Directors or advisory committees; Pfizer: Membership on an entity's Board of Directors or advisory committees; Sanofi: Honoraria; Abbvie: Membership on an entity's Board of Directors or advisory committees; Takeda: Membership on an entity's Board of Directors or advisory committees. Tucci: Gentili: Membership on an entity's Board of Directors or advisory committees; janssen: Membership on an entity's Board of Directors or advisory committees; Takeda: Membership on an entity's Board of Directors or advisory committees.


Blood ◽  
2021 ◽  
Vol 138 (Supplement 1) ◽  
pp. 2306-2306
Author(s):  
Sujith Samarasinghe ◽  
Ajay Vora ◽  
Nicholas John Goulden ◽  
Grace Antony ◽  
Anthony V. Moorman

Abstract After how many years from diagnosis/completing treatment should children and young adults with acute lymphoblastic leukaemia be considered "cured"? Clinical trials generally report 5 year event free survival because the risk of relapse after that time-point is very low and this is assumed to mean cure. Indeed, the risk of relapse after four years from completing therapy was <1% for patients treated on St Jude's trial Total Therapy XV and they proposed that length of follow-up should be considered to be the "time to cure". The group had previously shown that excess deaths beyond ten years in non-irradiated patients was similar to the general population whereas irradiated patients had a greater risk of excess mortality due to the development of secondary neoplasms. However, there was limited information about the time to cure and relative survival in sub-groups with variable relapse risk. We examined the long-term outcome of patients treated on an MRD stratified protocol UKALL 2003 that recruited patients between 2003-2011 to determine time to cure for the overall trial population and prognostic sub-groups defined by age, sex, immunophenotype, cytogenetics and MRD response. There were a total of 3113 patients eligible for analysis with a median follow up of 9.4 years (only 20 patients received cranial irradiation). Relative survival rates were estimated using nationwide population mortality rates as the baseline accounting for age, sex and calendar year (Office of National Statistics 2018 release). The relative survival of UKALL2003 patients compared to equivalent children in the UK population was 96% (95% CI 95.5-96.7). Relative survival did not differ by sex but was significantly worse for patients >10 years compared with younger patients (p<0.001). As expected, patients with good risk cytogenetics ( ETV6-RUNX1, and high hyperdiploidy) and negative bone marrow Ig/TCR MRD at end of induction (EOI) had better relative survival rates and lower excess mortality (all p<0.001) compared with patients who had adverse risk factors (high risk genetics and EOI MRD+). To estimate the time to a cure, we calculated the conditional probability of relapse at 12 years for patients who had survived event to the start of each follow-up year. Although the initial risk of relapse differ significantly by sex, age, MRD and genetics the risk of relapse for all subgroups quickly coalesced at around 5-6 years (Figure 1). Furthermore, the time to cure, defined as a relapse risk <1% (dashed blue line of figure), was similar across all the subgroups at 6-7 years. In conclusion, the relative survival of young patients with good risk cytogenetics and EOI MRD negative status approaches that of their normal peers. However, regardless of prognosis, the time to cure is similar across risk groups. Figure 1 Figure 1. Disclosures No relevant conflicts of interest to declare.


Blood ◽  
2021 ◽  
Vol 138 (Supplement 1) ◽  
pp. 419-419
Author(s):  
Joshua A Fein ◽  
Roni Shouval ◽  
Elizabeth Krieger ◽  
Henning Baldauf ◽  
Katharina Fleischhauer ◽  
...  

Abstract Background: The interaction between donor killer immunoglobulin-like receptor (KIR) and recipient HLA has been postulated to enhance the graft-versus-leukemia effect in allogeneic hematopoietic cell transplantation (HCT) for acute myeloid leukemia (AML). Historically, analyses of individual interactions between single KIR and their respective HLA ligands have yielded conflicting findings, and the clinical importance of these interactions in the matched unrelated donor (MUD) setting remains controversial. Here, we applied a systematic approach, studying both a wide range of KIR and class I HLA interactions at the single-receptor level as well as the most prevalent KIR genotypes in a large cohort of AML patients undergoing MUD transplantation. Methods: We included adult AML patients in complete remission transplanted from an 8/8-HLA MUD between 2010 and 2016 and reported to the Center for International Blood and Marrow Transplant Research (CIBMTR). Donor-KIR and respective recipient-HLA ligand interactions were assessed in multivariable Cox proportional hazard models for standard transplantation outcomes. To account for the compound effect of simultaneous KIR/HLA interactions, we applied a combinatorial approach to identify aggregate KIR genotypes based on combinations of individual KIR genes. The most frequently observed donor-KIR genotypes, in combination with recipient ligands, were evaluated for association with relapse using multivariable regression. Those associated (p < 0.01) with relapse risk were evaluated for differential relapse in a DRST (German stem-cell registry)/Collaborative Biobank cohort of donors/patients with similar inclusion criteria. Results: A total of 2,036 transplantations from the CIBMTR were included. Most patients were treated in first complete remission (78%) and received myeloablative conditioning (59%). We first studied eight known interactions between donor KIR and their respective HLA ligands (Figure A). Only donor-KIR-2DL2+/recipient-HLA-C1+ was associated with reduced relapse (compared to donor-KIR-2DL2-/recipient-HLA-C1+, hazard ratio [HR] 0.80 [95% confidence interval 0.67-0.94], p=0.008). However, no difference was found when comparing HLA-C group pairs among KIR-2DL2+ recipients, suggesting this finding is confounded by co-occurrence of other receptors. There are hundreds of possible KIR gene combinations (i.e. genotypes), which are typically clustered into two primary haplotypes, A and B. To study the cumulative effect of donor KIR, we investigated nine prevalent KIR genotypes (Figure B) and identified three significantly associated with relapse risk. (1) Donor KIR genotype 5 in all recipients irrespective of their HLA (Figure C, n = 138/2,036) and (2) genotype 3 in HLA-Bw4/x recipients (Figure D, n = 51/1,198) had significantly decreased relapse risk (HR 0.53 [0.37-0.78], p=0.002 and 0.34 [0.15-0.75], p=0.008, respectively). (3) KIR genotype 2 was associated with greater relapse in HLA-C1-homozygous recipients (Figure E, n = 87/836, HR 1.62 [1.14-2.30], p=0.007). These findings were not confirmed in the external European dataset (n = 796, Figure 1C-E); however, this cohort differed in ways that might affect the importance of KIRs, such as the higher frequency of reduced intensity conditioning (74% vs. 41%) and in-vivo T-cell depletion (79% vs. 37%). Conclusion: Our systematic investigation in two large AML cohorts receiving MUD allogenic HCT did not validate any association between individual KIR-HLA interactions and clinical outcomes. A combinatorial approach identified combinations potentially protective against relapse, however these could not be confirmed in a second dataset. Overall, our findings do not support KIR-informed donor selection using the approaches outlined here. Figure 1 Figure 1. Disclosures Shouval: Medexus: Consultancy. Kroeger: AOP Pharma: Honoraria; Gilead/Kite: Honoraria; Riemser: Honoraria, Research Funding; Celgene: Honoraria, Research Funding; Jazz: Honoraria, Research Funding; Sanofi: Honoraria; Neovii: Honoraria, Research Funding; Novartis: Honoraria. Horowitz: Daiicho Sankyo: Research Funding; Allovir: Consultancy; Miltenyi Biotech: Research Funding; Medac: Research Funding; Kite/Gilead: Research Funding; Genentech: Research Funding; Jazz Pharmaceuticals: Research Funding; Janssen: Research Funding; Kiadis: Research Funding; CSL Behring: Research Funding; Gamida Cell: Research Funding; bluebird bio: Research Funding; Bristol-Myers Squibb: Research Funding; Amgen: Research Funding; Astellas: Research Funding; Chimerix: Research Funding; GlaxoSmithKline: Research Funding; Novartis: Research Funding; Magenta: Consultancy, Research Funding; Actinium: Research Funding; Mesoblast: Research Funding; Omeros: Research Funding; Orca Biosystems: Research Funding; Pfizer, Inc: Research Funding; Pharmacyclics: Research Funding; Regeneron: Research Funding; Sanofi: Research Funding; Seattle Genetics: Research Funding; Shire: Research Funding; Sobi: Research Funding; Stemcyte: Research Funding; Takeda: Research Funding; Tscan: Research Funding; Vertex: Research Funding; Vor Biopharma: Research Funding; Xenikos: Research Funding. Malmberg: Merck: Research Funding; Vycellix: Consultancy; Fate Therapeutics: Consultancy, Research Funding. Miller: Sanofi: Membership on an entity's Board of Directors or advisory committees; Magenta: Membership on an entity's Board of Directors or advisory committees; ONK Therapeutics: Honoraria, Membership on an entity's Board of Directors or advisory committees; Vycellix: Consultancy; GT Biopharma: Consultancy, Patents & Royalties, Research Funding; Fate Therapeutics, Inc: Consultancy, Patents & Royalties, Research Funding; Wugen: Membership on an entity's Board of Directors or advisory committees. 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. Romee: Crispr Therapeutics: Research Funding; Glycostem: Membership on an entity's Board of Directors or advisory committees. 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 . Weisdorf: Fate Therapeutics: Research Funding; Incyte: Research Funding. Koreth: Biolojic Design: Other: Scientific Advisory Board; Mallinckrodt: Other: Scientific Advisory Board; Cugene: Other: Scientific Advisory Board; Moderna: Consultancy; Amgen: Consultancy; EMD Serono/Merck: Consultancy; Gentibio Inc.: Consultancy; Miltenyi Biotec: Research Funding; BMS: Research Funding; Clinigen Labs: Research Funding; Regeneron: Research Funding; Equillium: Research Funding.


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