Hogan, William J.

Abstract Background Clonal cytopenia of undetermined significance (CCUS) is a risk factor for development of myeloid neoplasms (MN) such as myelodysplastic syndrome (MDS) or acute myeloid leukemia (AML). Therapy-related myeloid neoplasm (t-MN) is defined as the development of a MN in the context of prior DNA damaging therapies including chemotherapy, radiation, and immunosuppressive therapies. CCUS diagnosed in the setting of prior DNA damaging therapy (t-CCUS) and its outcomes are not known. The aim of this study was to study clinicopathological features and outcome of t-CCUS and compare it with t-MN. Methods Patients who had received DNA-damaging therapy and subsequently determined to have CCUS or t-MN were identified. The diagnosis of CCUS was made if the patient had non-diagnostic bone marrow biopsy evaluation combined with evidence of pathogenic myeloid somatic genetic alterations using conventional cytogenetics or next generation sequencing (NGS) panel. Pathogenic/likely pathogenic variants (PV) were stratified by DTA (DNMT3A, TET2, and ASXL1) genes vs. others. Event-free survival (EFS) was calculated using interval from t-CCUS to t-MN with death as a competing risk. Overall survival (OS) for t-CCUS and t-MN patients was calculated using interval from t-CCUS or t-MN diagnoses respectively to the last follow up. Statistical analysis was performed using JMP (v14.1, SAS Institute). Results We identified 30 patients with t-CCUS and 259 patients with t-MN. Eleven (37%) t-CCUS patients developed t-MN and were included in both cohorts (Fig. A). Clinical characteristics of t-CCUS patients are shown in Table 1. Briefly, age (median, interquartile range or IQR) at the diagnosis of primary malignancy and t-CCUS was not different between those who developed t-MN and those did not (P=0.09 and 0.378 respectively). There was no difference in hemoglobin, white blood cells, or ANC between the two groups. Patients who developed t-MN had a significantly lower platelet count (68 vs. 130, P<0.01) and higher bone marrow blast percent (2 vs. 0, P=0.04) compared to those who did not. At t-CCUS diagnosis, cytogenetics was abnormal in 11 (37%) patients and none had complex karyotype (CK). Paired cytogenetic analysis at t-CCUS and t-MN was available for 10 patients: 4 had unchanged karyotype at t-MN, whereas 6 had cytogenetic evolution. In contrast, 213 (83%) of 256 t-MN patients with available cytogenetics had abnormalities and 126/256 (49%) had CK. Thus, a significantly higher proportion of patients had CK t-MN compared to t-CCUS (χ 2 26.4, P<0.01). A 42-gene NGS panel identified 36 PV in 21 patients (median 1, range 1-4) at t-CCUS. The most common PV were in TET2 (37.1%), TP53 (11.4%), DNMT3A (8.6%), and ZRSR2 (8.6%) genes. Two patients had paired NGS available at t-CCUS and t-MN: one had BCOR and U2AF1 PV at t-CCUS and acquired an additional NRAS PV at t-MN. The second patient had 2 PV in TP53 at t-CCUS diagnosis, that remained the only PV identified at t-MN. In the t-MN cohort, median number of PV was 2 (range 1-9). The most common PV identified were TP53 (21.5%), TET2 (6.7%), DNMT3A (6.4%), and ASXL1 (6.4%), whereas 4.8% patients had no PV. Three of 22 patients in the t-CCUS cohort had TP53mut, compared to 55 of 157 in the t-MN cohort (χ 2 4, P=0.05). Management strategies for t-CCUS included observation (20), growth factors (4), treatment of primary malignancy (2), immunosuppressives (1), hypomethylating agent followed by allogeneic SCT (1), and others (2). Following t-CCUS, 9 patients progressed to MDS and 2 to AML with a median of 3.5 months (IQR 1.7-43.4). EFS was 43.4 months (IQR 6.8-80.9). The presence of abnormal cytogenetics (Fig. B) and the absence of DTA-mutations (Fig. C) predicted shorter EFS. At last follow up, 12 (40%) deaths were noted. Four (33%) patients died without developing t-MN. Causes of death in the t-CCUS cohort were: t-MN (3), primary malignancy (1), progressive cytopenia without MN (1), infection (1), graft-vs.-host-disease (1), and undetermined (5). t-MN patients had a significantly shorter survival compared to t-CCUS (Fig. D). Conclusion More than 10% of t-MN patients had a precursor t-CCUS. On the other hand, t-CCUS patients are at a high risk of progression to MN. t-MN is characterized by the acquisition of TP53mut, genomic instability, and a significantly shorter survival compared to t-CCUS. The presence of abnormal cytogenetics and the absence of DTA mutations at t-CCUS predict shorter EFS. Figure 1 Figure 1. Disclosures Litzow: Omeros: Other: Advisory Board; AbbVie: Research Funding; Jazz: Other: Advisory Board; Amgen: Research Funding; Astellas: Research Funding; Pluristem: Research Funding; Actinium: Research Funding; Biosight: Other: Data monitoring committee. Patnaik: Kura Oncology: Research Funding; Stemline Therapeutics: Membership on an entity's Board of Directors or advisory committees; Stemline Therapeutics: Membership on an entity's Board of Directors or advisory committees. Al-Kali: Novartis: Research Funding; Astex: Other: Research support to institution.

Abstract BACKGROUND: Our group recently reported on the results of Blood and Marrow Transplant (BMT) Clinical Trials Network (CTN) 1101 a randomized comparison between double umbilical cord blood (dUCB) and haploidentical bone marrow (haplo) with post-transplant cyclophosphamide (ptCy) in the nonmyeloablative setting that showed similar progression free survival (PFS) between the two treatment groups, but lower non-relapse mortality (NRM) and better of overall survival (OS) in the haplo arm. In this secondary analysis we sought to investigate if transplant center experience with haplo and or cord blood HCT had an impact on outcomes. PATIENTS AND METHODS: All patient randomized in BMT CTN 1101 were included. In order to determine the transplant center experience with either haplo or dUCB we queried the Center for International Blood and Marrow Transplant Research (CIBMTR) for number of transplants with each platform in the year prior to initiation of the study. Centers were then grouped as dUCB center (> 10 dUCB, n=117, 10 centers), Haplo center (>10 haplo and ≤10 dUCB, n=110, 2 centers), and ≤10 haplo and ≤10 dUCB HCTs (other center, n=140, 21 centers). Further analysis considered the alternative cut-off for haplo (> 5 vs ≤ 5) experience, and considered the outcomes based on the donor experience vs. others (e.g. dUCB > 10 vs. ≤ 10; haplo > 5 vs. ≤ 5). RESULTS: The effect of center experience on HCT outcomes shown in Figure, below . After adjusting for age, Karnofsky performance score and, disease risk index we found that there was no difference in outcomes between haplo and dUCB for centers that were experienced with dUCB or had limited to no experience with either dUCB or haplo. In contrast, in centers that were primarily experienced with haplo had better outcomes with this donor type, as compared to dUCB. The higher risk of treatment failure (relapse or death) and overall mortality in dUCB in haplo experienced centers was driven by significantly higher risk of relapse. We then considered the transplant experience with each of the donor types separately. In transplant centers that had performed > 10 dUCB, there were similar outcomes for recipients of both dUCB and haplo. Similarly, centers that had ≤ 5 haplo HCTs had no difference in outcomes between donor types suggesting an overlap with centers that had performed > 10 dUCB HCTs. Overall mortality was higher among dUCB recipients in centers that had performed ≤ 10 dUCB. Notably, the hazard ratio of non-relapse mortality favored haplo in all four donor experience type of transplant center, albeit not statistically significant. CONCLUSION: Except for dUCB recipients in centers with < 10 dUCB/year had worse overall mortality, primarily driven by relapse, the transplant center experience in the year prior to the initiation of BMT CTN 1101 had limited impact on the outcomes of this randomized clinical trial. Figure 1 Figure 1. Disclosures Brunstein: NANT: Research Funding; FATE: Research Funding; GamidaCell: Research Funding; BlueRock: Research Funding; AlloVir: Consultancy. Costa: Amgen: Consultancy, Honoraria, Research Funding, Speakers Bureau; Janssen: Consultancy, Honoraria, Research Funding; Sanofi: Consultancy, Honoraria, Speakers Bureau; Pfizer: Consultancy, Honoraria; BMS: Consultancy, Honoraria, Research Funding; Karyopharm: Consultancy, Honoraria. Cutler: Deciphera: Consultancy; Cimeio: Consultancy; Editas: Consultancy; Kadmon: Consultancy; Pfizer: Consultancy; Mallinckrodt: Consultancy; CareDx: Consultancy; Incyte: Consultancy; Omeros: Consultancy; Syndax: Consultancy; Mesoblast: Consultancy; Jazz: Consultancy. Horowitz: Sobi: Research Funding; Regeneron: Research Funding; Orca Biosystems: Research Funding; Tscan: Research Funding; Xenikos: Research Funding; Miltenyi Biotech: Research Funding; Stemcyte: Research Funding; Medac: Research Funding; Vor Biopharma: Research Funding; Kite/Gilead: Research Funding; Janssen: Research Funding; Pharmacyclics: Research Funding; Kiadis: Research Funding; Seattle Genetics: Research Funding; Mesoblast: Research Funding; GlaxoSmithKline: Research Funding; Sanofi: Research Funding; Pfizer, Inc: Research Funding; Omeros: Research Funding; Magenta: Consultancy, Research Funding; Jazz Pharmaceuticals: Research Funding; Vertex: Research Funding; Genentech: Research Funding; Takeda: Research Funding; Novartis: Research Funding; Shire: Research Funding; Gamida Cell: Research Funding; Daiicho Sankyo: Research Funding; CSL Behring: Research Funding; Chimerix: Research Funding; Bristol-Myers Squibb: Research Funding; bluebird bio: Research Funding; Astellas: Research Funding; Amgen: Research Funding; Allovir: Consultancy; Actinium: Research Funding. Horwitz: Gamida Cell: Research Funding. McGuirk: Novartis: Research Funding; Magenta Therapeutics: Consultancy, Honoraria, Research Funding; EcoR1 Capital: Consultancy; Pluristem Therapeutics: Research Funding; Novartis: Research Funding; Bellicum Pharmaceuticals: Research Funding; Astelllas Pharma: Research Funding; Gamida Cell: Research Funding; Fresenius Biotech: Research Funding; Allovir: Consultancy, Honoraria, Research Funding; Juno Therapeutics: Consultancy, Honoraria, Research Funding; Kite/ Gilead: Consultancy, Honoraria, Other: travel accommodations, expense, Kite a Gilead company, Research Funding, Speakers Bureau. Rezvani: US Department of Justice: Consultancy; Kaleido: Other: One-time scientific advisory board; Nohla Therapeutics: Other: One-time scientific advisory board; Pharmacyclics-Abbvie: Research Funding. Rybka: Spark Therapeutics: Consultancy; Merck: Consultancy. Vasu: Kiadis, Inc.: Research Funding; Boehringer Ingelheim: Other: Travel support; Seattle Genetics: Other: travel support; Omeros, Inc.: Membership on an entity's Board of Directors or advisory committees.

Abstract Objective : We describe the Mayo Clinic experience in 797 newly-diagnosed patients with acute myeloid leukemia (AML) serially treated with 7+3 induction chemotherapy that included 3 days of daunorubicin at a daily dose of 60 mg/m2 (dauno-60; n=239) or 90 mg/m2 (dauno-90; n=52), or idarubicin 12 mg/m2 (IDA-12; n=506). Our objective was to compare overall (OS) and relapse-free (RFS) survival outcome. Methods : Newly-diagnosed AML patients seen at our institution and received intensive induction chemotherapy were identified from the Mayo Clinic AML database. Treatment period spanned from January 2004 through May 2021. Follow-up information was updated as of June 2021. Conventional criteria were used to diagnose AML, assign cytogenetic risk category, and classify treatment response. Results : The study group included 797 patients (median age 60 years, range 18-88; 58% males): 506 (63%) patients received IDA-12, 239 (30%) dauno-60, and 52 (7%) dauno-90. The respective median (range) ages were 60 (18-88), 61 (19-82), and 53 (22-72) years (p=0.01) (Table). Primary, secondary, and therapy-related AML accounted for 65%, 25% and 10% of patients treated with IDA-12, 69%, 22%, and 9% of those treated with dauno-60, and 75%, 23% and 2% of patients treated with dauno-90, respectively (p=0.1). The corresponding frequencies of adverse karyotype were 34%, 25% and 25% (p=0.05). CR/CRi was documented in 78% (620/793) of all evaluable patients: IDA-12 80% (400/503), dauno-60 75% (175/238), and dauno-90 87% (45/52) (p=0.1). 210 (34%) patients underwent allogenic hematopoietic stem cell transplant (AHSCT), including 125 (25%), 59 (25%) and 26 (50%) patients, in the three treatment groups, respectively (p=0.0004). After a median (range) follow up 19 (0.2-203) months, 348 (54%) relapses and 518 (65%) deaths were documented. Median (range) survivals for IDA-12, dauno-60 and dauno-90 groups were 21 (0.3-243), 14.5 (0.45-198), and 27.7 (0.2-180) months (p=0.07; figure 1). The respective 1-, 3-, and 5-year OS rates were 67%, 42%, and 34% (IDA-12); 66%, 37%, and 30% (dauno-60); and 78%, 51%, and 49% (dauno-90), respectively; the trend favoring dauno-90 was no longer apparent during age-adjusted analysis (p=0.33). Multivariable analysis that accounted for age, cytogenetic risk category, FLT3-ITD/NPM1 status and AML subtype confirmed the lack of additional contribution from IDA-12 vs dauno-60 vs dauno-90 (p=0.2) while affirming the independent prognostic value of the other four variables; AHSCT carried an additional predictive value for superior survival without altering these results. A total of 348 (54%) relapses were documented: 228 (57%) in the IDA-12; 99 (57%) in the dauno-60; and 21 (47%) in the dauno-90 cohorts (p=0.7); RFS was similar in the three treatment groups (p=0.1; figure 2). Conclusion :In the current large single-institution study of consecutive adult patients with AML, neither the choice of anthracycline (idarubicin vs daunorubicin) or the dose of daunorubicin (60 vs 90 mg/m2) appeared to effect outcome in terms of remission rates or overall or relapse-free survival. The study otherwise confirms the independent favorable effect of younger age, non-adverse karyotype, FLT3-ITD-/NPM1+ status, and AHSCT. Figure 1 Figure 1. Disclosures Patnaik: Kura Oncology: Research Funding; Stemline Therapeutics: Membership on an entity's Board of Directors or advisory committees; Stemline Therapeutics: Membership on an entity's Board of Directors or advisory committees. Al-Kali: Astex: Other: Research support to institution; Novartis: Research Funding. Litzow: Pluristem: Research Funding; Actinium: Research Funding; Amgen: Research Funding; Jazz: Other: Advisory Board; Biosight: Other: Data monitoring committee; Omeros: Other: Advisory Board; AbbVie: Research Funding; Astellas: Research Funding.

Abstract Background: A major cause of mortality in patients receiving hematopoietic stem cell transplantation (HCT) is acute graft-versus-host disease (GVHD), a multiorgan disorder that includes the skin, liver and gastrointestinal tract. We have previously identified elafin, a protease inhibitor overexpressed in inflamed epidermis, as a diagnostic biomarker of GVHD in the skin, the most commonly involved GVHD organ. However, our initial study was limited to a subset of patients with isolated skin GVHD. The main driver of nonrelapse mortality (NRM) in HCT patients is GI GVHD. Two biomarkers, Regenerating islet-derived 3a (REG3α) and Suppressor of tumorigenesis 2 (ST2), have since been validated as biomarkers of GI GVHD that predict long-term outcomes in patients treated for GVHD. We undertook this study to determine the utility of elafin as a prognostic biomarker of acute GVHD in the general population of previously unstudied acute GVHD patients, and to compare it to ST2 and REG3α. Study Design: 526 patients who received systemic corticosteroid treatment for skin GVHD were analyzed from the Mount Sinai Acute GVHD International Consortium (MAGIC), which includes patients from 25 HCT centers. We used ELISA to measure serum concentrations of elafin, ST2 and REG3α. Patients were divided randomly into equal training and validation sets; and we developed a competing risk regression model for 6-month NRM using elafin concentration in the training set. We developed additional models for 6-month NRM using concentrations of ST2 and REG3α, or the combination of all three biomarkers as predictors. We then constructed ROC curves to evaluate the predictive accuracy of each model and to analyze the ability of each model to stratify patients into high- and low-risk groups. We analyzed the cumulative incidence of 6-month NRM and overall survival in each model and compared the accuracy of each model in the validation set. Results: The area under the receiver operating curve (AUROC) for elafin alone was 0.55 whereas it was 0.75 and statistically superior (P = 0.02) for the combination of ST2 and REG3α. The combination of 3 biomarkers produced an AUROC of 0.76 that was not significantly better than the two biomarker model (P = 0.10). Elafin concentrations, either alone or in combination with ST2 and REG3α, did not produce higher hazard ratios of NRM (data not shown). Patients in the low-risk elafin group paradoxically demonstrated a higher incidence of 6-month NRM, although this difference was not statistically significant (17% vs. 11%, P=0.19), and both overall survival at 6 months (68% vs. 68%, P>0.99) and four-week response (78% vs. 78%, P=0.98) were similar in the low- and high-risk elafin groups (Figure 1). As demonstrated in previous data sets, the combination of ST2 and REG3α divided patients into two groups with a nearly five-fold difference in NRM (6.7% vs. 31%, P <0.001). Conclusion: We demonstrated that serum elafin concentrations measured at the initiation of systemic treatment for acute GVHD in a multicenter population of patients treated systemically for acute GVHD do not predict 6-month NRM, overall survival, or treatment response. As seen in previous studies, serum concentrations of the GI GVHD biomarkers ST2 and REG3α were significant predictors of NRM and the addition of elafin levels did not improve their accuracy. These results underscore the importance of GI disease in driving NRM in patients who develop acute GVHD. Figure 1. Cumulative incidence of nonrelapse mortality and overall survival in high and low risk groups Six-month cumulative incidences of nonrelapse mortality (NRM) in high (solid line) and low (dotted line) risk groups defined by optimized biomarker thresholds (upper panels) and six-month overall survival estimated using the Kaplan-Meier method (lower panels). (A) Cumulative incidence of NRM (14%) and overall survival (75%) in the total validation set (N=263). (B) Cumulative incidence of NRM in the low (N=150) and high (N=113) elafin group (17% vs. 11%, P=0.19). Overall survival in the low and high elafin group (68% vs. 68%, P > 0.99). (C) Cumulative incidence of NRM in the low (N=175) and high (N=88) ST2 + REG3a group (6.7 vs. 31%, P < 0.001). Overall survival in the low and high ST2 + REG3a group (77% vs. 51%, P < 0.001). (D) Cumulative incidence of NRM in the low (N=180) and high (N=83) elafin + ST2 + REG3a group (7.0 vs. 30%, P < 0.001). Overall survival in the low and high elafin + ST2 + REG3a group (79% vs. 64%, P < 0.001). Figure 1 Figure 1. Disclosures Ozbek: Viracor: Patents & Royalties: GVHD biomarker patent with royalties from Viracor. DeFilipp: Omeros, Corp.: Consultancy; Incyte Corp.: Research Funding; Regimmune Corp.: Research Funding; Syndax Pharmaceuticals, Inc: Consultancy. Grupp: Novartis, Kite, Vertex, and Servier: Research Funding; Jazz Pharmaceuticals: Consultancy, Other: Steering committee, Research Funding; Novartis, Roche, GSK, Humanigen, CBMG, Eureka, and Janssen/JnJ: Consultancy; Novartis, Adaptimmune, TCR2, Cellectis, Juno, Vertex, Allogene and Cabaletta: Other: Study steering committees or scientific advisory boards. Hexner: Blueprint medicines: Membership on an entity's Board of Directors or advisory committees, Research Funding; Tmunity Therapeutics: Research Funding; PharmaEssentia: Membership on an entity's Board of Directors or advisory committees. Kitko: Co-investigator on two NIH grants as part of the cGVHD consortium: Research Funding; Vanderbilt University Medical Center: Current Employment; PER: Other: PER - CME educational talks about GVHD; Horizon: Membership on an entity's Board of Directors or advisory committees. Qayed: Novartis: Honoraria; Mesoblast: Honoraria; Medexus: Honoraria. Reshef: ilead, BMS, Precision, Immatics, Atara, Takeda, Shire, Pharmacyclics, Incyte: Research Funding; Bayer: Consultancy; Gilead and Novartis: Honoraria; BMS, Regeneron, TScan, Synthekine, Atara, Jasper, Bayer: Consultancy. Levine: Incyte: Consultancy, Research Funding; Viracor: Patents & Royalties: GVHD biomarker patent with royalties from Viracor; Mesoblast: Consultancy, Research Funding; Equillium Bio: Membership on an entity's Board of Directors or advisory committees; X4 Pharmaceuticals: Membership on an entity's Board of Directors or advisory committees; Talaris Therapeutics: Membership on an entity's Board of Directors or advisory committees; Jazz Pharmaceuticals: Membership on an entity's Board of Directors or advisory committees; Omeros: Membership on an entity's Board of Directors or advisory committees; Symbio: Membership on an entity's Board of Directors or advisory committees; Biogen: Research Funding; Kamada: Research Funding. Ferrara: Eurofins Viracor: Consultancy, Other: Royalties. Chen: Incyte: Consultancy; Gamida: Consultancy.

SummaryWe report a comparative analysis of patients with therapy‐related acute lymphoblastic leukaemia (tr‐ALL) vs de novo ALL. We identified 331 patients with B‐ALL; 69 (21%) were classified as tr‐ALL. The most common prior malignancies were breast (23·2%) and plasma cell disorders (20·3%). Patients with tr‐ALL were older (median 63·2 vs. 46·2 years, P < 0.001), more often female (66·7% vs. 43·5%, P < 0·001), and more likely to have hypodiploid cytogenetics (18·8% vs. 5·0%, P < 0·001). In multivariable analysis, patients with tr‐ALL were less likely to achieve complete remission [odds ratio (OR) = 0·16, P < 0·001] and more likely to be minimal residual disease‐positive (OR = 4·86, P = 0·01) but had similar OS after diagnosis and allo‐haematopoietic cell transplantation.

Key Points Post-HSCT hemoglobinuria was associated with graft type (BMT+Cord). Post-HSCT hemoglobinuria was associated with early (48–72 hours) post-HSCT AKI. Graft type (BMT+Cord) was associated with AKI among patients with hemoglobinuria. Background Information on risk factors of hemoglobinuria after hematopoietic stem-cell transplant (HSCT) and its association with AKI, mortality, and engraftment is limited. Methods We conducted a retrospective cohort study on all consecutive adults that underwent HSCT from January 6, 1999, to November 6, 2017. The study included 6039 patients that underwent bone marrow transplantation (BMT), umbilical cord blood, and peripheral blood stem-cell transplantation (PBSCT). Results Early post-HSCT, AKI occurred in 393 (7%) patients, and 52 (0.9%) patients had post-HSCT hemoglobinuria. Post-HSCT hemoglobinuria was associated with graft type (BMT+Cord), underlying disease (lymphoma, acute leukemia), and fludarabine-based conditioning regimen. Post-HSCT hemoglobinuria was associated with early (48–72 hours) post-HSCT AKI. Graft type (BMT+Cord) was associated with AKI among patients with hemoglobinuria. AKI in patients with hemoglobinuria was associated with delayed platelet engraftment and delayed WBC engraftment but not 100-day mortality. Conclusion Close monitoring is recommended in this patient group to facilitate a good engraftment outcome.

AbstractGermline predisposition syndromes (GPS) result from constitutional aberrations in tumor suppressive and homeostatic genes, increasing risk for neoplasia in affected kindred. In this study, we present clinical and genomic data on 144 Mayo Clinic patients with GPS; 59 evaluated prospectively using an algorithm‐based diagnostic approach in the setting of a dedicated GPS/ inherited bone marrow failure syndrome (IBMFS) clinic. Seventy‐two (50%) patients had IBMFS (telomere biology disorders‐32,Fanconi anemia‐18, Diamond Blackfan Anemia – 11, congenital neutropenia–5, Schwachman‐Diamond Syndrome‐5 and Bloom Syndrome‐1), 27 (19%) had GPS with antecedent thrombocytopenia (RUNX1‐FPD‐15, ANKRD26‐6, ETV6‐2, GATA1‐1, MPL‐3), 28 (19%) had GPS without antecedent thrombocytopenia (GATA2 haploinsufficiency‐16, DDX41‐10, CBL‐1 and CEBPA‐1) and 17 (12%) had general cancer predisposition syndromes (ataxia telangiectasia‐7, heterozygous ATM variants‐3, CHEK2‐2, TP53‐2, CDK2NA‐1, NF1‐1 and Nijmegen Breakage Syndrome‐1). Homozygous and heterozygous ATM pathogenic variants were exclusively associated with lymphoproliferative disorders (LPD), while DDX41 GPS was associated with LPD and myeloid neoplasms. The use of somatic NGS‐testing identified clonal evolution in GPS patients, with ASXL1, RAS pathway genes, SRSF2 and TET2 being most frequently mutated. Fifty‐two (91%) of 59 prospectively identified GPS patients had a change in their management approach, including additional GPS‐related screening in 42 (71%), referral for allogenic HSCT workup and screening of related donors in 16 (27%), medication initiation and selection of specific conditioning regimens in 14 (24%), and genetic counseling with specific intent of fertility preservation and preconceptual counseling in 10 (17%) patients; highlighting the importance of dedicated GPS screening, detection and management programs for patients with hematological neoplasms.

SummaryInstitutional database search (1999–2020) for acute myeloid leukaemia (AML) identified 109 cases of myeloid sarcoma (MS), of which 19 were isolated and presented de novo. The latter displayed longer survival (median 78 months), compared to MS with synchronous intramedullary AML (n = 32; median 16 months) and de novo AML without MS (n = 729; median 22 months; P = 0·13). However, the difference in survival was no longer apparent after accounting for bone marrow cytogenetic risk status (P = 0·67). Treatment‐induced MS tumour resolution was not affected by the presence of intramedullary disease (P = 0·61). The current study clarifies the prognosis of de novo isolated MS, in the context of AML.