Hogan, William J.

[Background] The current standard first-line treatment for acute graft-versus-host disease (GVHD), systemic high-dose steroids, induces clinical responses in a majority of patients. Flares of GVHD after initial improvement often occur during tapering or discontinuation of immunosuppression. There is no consensus regarding the definition of GVHD flare and little has been published regarding its natural history. [Methods] We retrospectively evaluated clinical data and blood samples from 968 allogeneic hematopoietic cell transplantation (HCT) recipients from 23 Mount Sinai Acute GVHD International Consortium (MAGIC) transplant centers who achieved complete response (CR) or very good partial response (VGPR) within 4 weeks of acute GVHD treatment between 2014 and 2021. VGPR was defined as the complete resolution of acute GVHD manifestations except residual stage 1 skin disease. Flares were defined as a recurrence of acute GVHD after achievement of CR/VGPR if (1) patients had an increased symptom severity of at least 1 organ stage; (2) received intensified treatment (increase in steroid dose ≥ 0.25 mg/kg or initiation of additional systemic immunosuppression); and (3) had no prior history of primary disease relapse or donor lymphocyte infusion preceding the flare. Serum samples obtained at the time of CR/VGPR and at the time of flares were analyzed for ST2 and REG3α concentrations that generated MAGIC algorithm probabilities (MAPs) resulting in previously validated Ann Arbor (AA) scores (1, 2, and 3). [Results] The median recipient age at HCT was 55 years (range: 0 to 79); 18.6% of patients had grades III-IV acute GVHD before CR/VGPR. The median maximum daily dose of corticosteroids before CR/VGPR was 1 mg/kg methylprednisolone equivalent (range, 0.1 to 3.2 mg/kg). Flares developed within 6 months following CR/VGPR in 210/968 (21.7%) patients with a median onset of 28 days (range: 2 to 448). Symptom severity at the onset of flare symptoms varied widely (grade I: 22%; grade II: 38%; grade III-IV: 41%) and the development of a flare was associated with a five-fold increase in NRM (hazard ratio [HR], 4.84 [95% CI, 3.19-7.36], P < 0.001) when considered as a time-dependent covariate in a multivariate regression model. The large majority of non-relapse deaths after flares (54/71, 76%) were due to acute GVHD or complications from its treatment. The AA scores at the time of first CR/VGPR successfully stratified patients for risk of six-month NRM (AA1: 5%, AA2: 11%, AA3: 34%, P < 0.001) (Figure A). Increasing AA score at CR/VGPR was also associated with significantly increased risk of GVHD flares (Figure B). Not only did the risk of flare increase with each rising AA score, but the number of patients with severe (grade III/IV) symptoms at flare onset also increased (AA1: 34%, AA2: 43%, AA3: 54%). We next generated a multivariate regression model of risk factors for the development of flares. High AA scores at the time of CR/VGPR (AA2: HR, 1.81 [95% CI, 1.32-2.48], P = 0.001; AA3: HR, 3.14 [95% CI, 1.98-4.98], P < 0.001), HCT from HLA mismatched unrelated donor (HR, 1.74 [95% CI, 1.00-3.02], P = 0.049), and interestingly, rapid achievement of CR/VGPR from the time of initial treatment (≤14 days) (HR, 1.84 [95% CI, 1.21-2.80], P = 0.004) were all identified as significant risk factors. Meanwhile, no other transplant, GVHD, or treatment characteristics associated with the development of flares including maximum GVHD severity, maximum steroid dose, and use of additional immunosuppressive agents other than steroids before CR/VGPR. AA scores measured at the onset of flare symptoms in 98 patients also predicted six-month NRM (AA1, 6%; AA2, 19%; AA3 42%, P = 0.01). [Conclusion] Despite advances in treatment, flares of GVHD symptoms following excellent (CR/VGPR) responses still occur in over one fifth of patients in current practice and are associated with a five-fold increase in six-month NRM. AA scores from serum biomarkers predict outcomes despite the absence of symptoms, suggesting that MAGIC biomarkers detect subclinical damage to GI crypts that can drive later recrudescence of clinical GVHD. Measurement of the MAP at the time of response to GVHD treatment may thus help to risk-stratify tapering strategies of immunosuppressive therapy.

The overall response rate (ORR) 28 days (D28) after treatment has been adopted as the primary endpoint for clinical trials of acute graft versus host disease (GVHD). ORR combines complete response (CR) and partial response (PR) because CR and PR have very similar non relapse mortality (NRM). D28 ORR has some disadvantages: first, physicians usually decide to modify immunosuppression much earlier than D28, and second, NRM does not always correlate well with ORR at D28, particularly for patients who present with intermediate risk or low risk disease. MAGIC serum biomarkers at day 14 (D14) of GVHD treatment have been shown to correlate better with NRM than clinical symptom response (Srinagesh et al, Blood Advances, 2019). We therefore hypothesized that a combination of clinical symptom severity and biomarkers at D14 of treatment would predict NRM at least as well and possibly better than clinical response at D28. We studied 1144 patients from 23 MAGIC sites who were systemically treated with at least 0.25 mg/kg/day of systemic steroids (prednisone equivalents) and had clinical data and serum samples available in the MAGIC database and biorepository. We divided the patients into a training set (n=764) and a validation set (n=380). We used 12-month NRM from the time of acute GVHD onset as the primary outcome of interest. We first trained a recursive partitioning algorithm in the training set to create a response model (MAGIC D14 clinical response) according to overall GVHD grade at both onset and D14 that predicted 12-month NRM and then applied it to the validation set. Patients that either progressed to grade III/IV GVHD, continued having grade III/IV GVHD, or only improved to grade II at D14 from grade III/IV at D0 had high NRM and were classified as non-responders. Patients with grade I/II GVHD at day 0 who had grade 0-II GVHD at day 14 and patients who improved to grade 0/I at day 14 from grade III/IV at day 0 had low NRM and were classified as responders. Although counterintuitive, responders included patients whose GVHD remained at grade II because the 12 month NRM of this group was only 20%. The new clinical model predicted NRM as well as the D28 standard response model in the validation set. When we integrated the D14 MAP biomarker score (low: ≤ 0.290 vs high: >0.290) with the new D14 clinical response (D14 MAGIC integrated response), we observed three response groups of integrated response with strikingly different 12 month NRM (8%, 35%, 76%, p<0.001), in contrast to the D28 standard response (Figure 1A, 1B). D14 complete response required MAGIC clinical response with low MAP; D14 partial response required either MAGIC clinical response with high MAP or MAGIC clinical non-response with low MAP; and D14 non-response required MAGIC clinical non-response with high MAP. The D14 response groups also displayed significant differences in 12 month OS (82%, 58%, 14% respectively, p<0.001) We then compared the D14 integrated response with the D28 standard response for 12 month NRM prediction. This D14 integrated response model was superior for time-dependent AUC (0.78 vs 0.71, P=0.03), sensitivity (0.71 vs 0.54), positive predictive value (0.52 vs 0.48) and negative predictive value (0.92 vs 0.89), with minimal loss in specificity (0.85 vs 0.87) in the prediction of 12-month NRM compared to the D28 standard response model. Using decision curve analysis, the D14 integrated response displayed higher net benefit (correct identification of patients that will experience NRM by 12 months) over the whole range of threshold probabilities/preferences for changing immunosuppression when 12-month NRM was used as the outcome. We conclude first, that the definition of response that uses onset and D14 grades is as accurate as D28 ORR model; and second, that a model using clinical grades and biomarkers on D14 more accurately predicts long term NRM than the standard D28 definition and may be useful as a clinical trial endpoint.

Introduction: Allogeneic hematopoietic cell transplantation (HCT) remains the only therapy for long-term disease control for many high-risk hematologic malignancies (HM). HCT use in older HM patients (pts) continues to increase. However, concerns of excessive transplant-related morbidity and non-relapse mortality (NRM) limit referrals and broader application of curative intent HCT. The HCT-comorbidity index (HCT-CI) was initially developed to improve risk-stratification of NRM. More recently, geriatric assessment (GA) and biomarkers have emerged as promising additional tools that may refine estimates of these risks. We hypothesized that a combination of health assessments by GA and biomarkers would constitute a robust and valid model, CHARM, for accurate personalized estimation of one-year (1-yr) NRM. Here, we report the results of the largest, first of its kind, prospective study of older recipients of HCT through the Blood and Marrow Transplant Clinical Trials Network (BMT CTN) 1704 study (NCT03992352). Patients and Methods: Adults aged ≥60 years (yrs) with HM institutionally eligible for HCT were enrolled (n=1226) from 49 centers in the US between 2019 - 2021. Within 21 days of conditioning, 13 prospectively identified older pt.-specific health variables, informed from prior studies, were collected: age, HCT-CI, % of weight loss over the past yr, pt reported Karnofsky performance status, PROMIS physical function scale, instrumental activities of daily living (IADL), number of falls, PROMIS depression scale, number of prescribed medications, 4-meter walk speed, cognition by the Montreal Cognitive Assessment (MoCA), C-reactive protein (CRP) and albumin. The CHARM score to predict NRM within 1 yr was built using a multivariable (MVA) Fine-Gray model, with multiple imputation to handle modest missingness in covariates and grouped penalized variable selection using SCAD to identify variables to retain in the model. Final variables were used as continuous. The model further considered adjustment for conditioning intensity, CMV serostatus, and donor type. Area under the ROC curve (AUC) was calculated to validate the CHARM and compare to HCT-CI alone, with bootstrap sampling to correct for optimism in the within-sample AUC. MVA of overall survival (OS) within 1 yr used the CHARM score and adjusted for other significant variables derived from stepwise regression. Results: The primary analysis included 1105 pts who were confirmed eligible and proceeded to transplant. Median age was 67 (range 60-82) and 32% were ≥ 70 yrs. AML (45%) and MDS (30%) were the most common indications and matched donors the most frequent donor type at 72%. Reduced intensity conditioning with fludarabine (flu)-melphalan (38%) and flu-busulfan (20%) were the most common regimens. Baseline geriatric vulnerabilities among evaluable pts were frequent including slow walk speed (<0.8 meter/second) in 22%, any IADL limitation in 37%, and cognitive impairment (MoCA < 23) in 12%. 1-yr NRM was lower than expected at 14.4% with 1-yr OS of 72%. In the MVA model, albumin, CRP, HCT-CI, and weight loss were independently associated with NRM (Table) and comprised the CHARM. Of note, increasing age did not significantly affect NRM. The CHARM had within sample and bootstrap corrected AUC of 0.641 and 0.607 whereas the HCT-CI alone achieved corresponding AUC values of 0.601 and 0.598. NRM by tertiles is shown in Figure. Each point increase in the CHARM (median -2.2504, range -3.6327 to 0.1381), was associated with a hazard ratio (HR) of 2.83 (95% CI 2.12 - 3.78, p < 0.0001) for NRM. The CHARM also predicted overall mortality within 1-yr with a HR of 2.04 (1.65- 2.53, p <0.0001) per one point increase, independent of the disease risk index, which had a HR of 1.5 (95% CI 1.12 - 1.99, p =0.0059) for high/very high vs low/intermediate categories. Conclusions: We report on the first prospectively established tool, CHARM, to risk-stratify older adult HCT comprised of simple and readily available parameters in transplant and oncology clinics. Adopting CHARM in practice may promote HCT referrals and access for older pts, given low risks of NRM among CHARM lower 2/3 rd tertiles. Identifying high-risk CHARM pts pre-HCT will promote developing novel strategies to reduce NRM. Further analysis of the impacts of the 13 health assessment variables on geriatric morbidity such as disability trajectories is in-process.

Background: Venetoclax (Ven) in combination with hypomethylating agent (HMA) is the preferred treatment for elderly/unfit patients with newly-diagnosed acute myeloid leukemia (AML). Current prognostication for AML is based on European LeukemiaNet (ELN) genetic risk stratification which has limited applicability in patients treated with Ven-HMA ( Blood, 2022). Accordingly, our primary objective was to determine predictors of treatment response and survival outcomes in treatment-naïve AML patients receiving Ven-HMA. Methods: Our study population was recruited from Mayo Clinic (MN, FL, AZ), after institutional review board approval and based on documentation of newly-diagnosed AML treated with Ven-HMA outside of clinical trials between November 2018 and April 2023. Cytogenetic and molecular studies were performed by conventional karyotype, and next-generation sequencing, respectively. Response was assessed according to the 2022 ELN criteria Results: Patient characteristics A total of 301 newly-diagnosed AML patients (median age 73 years, 66% male, 62% de novo) received a median of 3 cycles (range 1-48) of azacitidine 75 mg/m2 days 1-7 ( n=100) or decitabine 20 mg/m2 days 1-5 ( n=201) with Ven. ELN 2022 cytogenetic risk included favorable (1%, n=4), intermediate (61%, n=184) or adverse (38%, n=113). Mutations involved TP53 in 77/301 (25%), ASXL1 in 54/279 (19%), RUNX1 in 54/296 (18%), NPM1 in 44/299 (15%), DNMT3A in 40/296 (13%), K/NRAS in 41/296 (14%), IDH1 in 20/301 (6%), IDH2 in 30/301 (10%), FLT3-ITD in 25/301 (8%) and DDX41 in 9/247 (4%) of informative cases. Predictors of response Complete remission, with (CR) or without (CRi), count recovery, was documented in 182 (60%) patients, including CR in 36% and measurable residual disease (MRD) negative by flow cytometry in 77% of 105 informative cases that achieved CR/CRi. Median time to CR/CRi was 1.4 months. In univariate analysis, CR/CRi was more likely to occur in the presence of NPM1 mutation, (86% vs. 56%; p < .01), IDH2 mutation, (80% vs. 58%; p = .02), DNMT3A mutation, (78% vs. 57%; p = .01), or DDX41 mutation, (100% vs. 58%; p <.01), and in the absence of adverse karyotype, (71% vs. 42%; p < .01), TP53 mutation, (67% vs. 40%; p <.01), FLT3-ITD mutation, (63% vs. 36%; p = .01), or RUNX1 mutation, (64% vs. 44%; p = .01); in multivariable analysis, adverse karyotype, ( p < .01), FLT3-ITD ( p < .01), RUNX1 ( p < .01), NPM1 ( p = .03), IDH2 ( p = .04), and DDX41, ( p = .01) mutations remained significant. Predictors of survival After a median follow-up of 8.5 months (range 0.5-56), 13.2 months for living patients, 174 (58%) deaths, 73 relapses (40% of those achieving CR/CRi), and 41 (14%; including 35 in CR/CRi) allogeneic hematopoietic stem cell tranplants (AHSCT) were documented. In univariate analysis of pre-treatment variables, thrombocytopenia < 100 x 10 9/l, (HR 1.5, 95% CI 1.03-2.2; p = .03), adverse karyotype, (HR 2.8, 95% CI 2.1-3.8; p < .01) TP53 mutation, (HR 2.5, 95% CI 1.8-3.5; p < .01), or absence of IDH2 mutation, (HR 3.5, 95% CI 1.7-7.6; p < .01) predicted inferior survival. Failure to achieve CR/CRi (HR 4.5, 95% CI 3.3-6.1; p < .01) was also associated with inferior survival and in multivariable analysis, failure to achieve CR/CRi, (HR 3.4, 95% CI 2.5-4.8; p< .01), presence of adverse karyotype, (HR 1.6, 95% CI 1.1-2.6; p = .02), TP53 mutation, (HR 1.6, 95% CI 1.002-2.4; p = .04), and absence of IDH2 mutation (HR 2.2, 95% CI 1.004-4.7; p = .04) were identified as risk factors for survival. Subsequent HR-weighted scoring resulted in three-tiered risk stratification: low (0-1 point; n= 130), intermediate (2-3 points; n= 105), and high (4-5 points; n= 66), with respective median survival (3-year rate) of 28.9 (48%), 9.6 (6%), and 3.1 (0%) months ( p < .01) (Figure). AHSCT had an independent favorable impact on survival (HR 0.3, 95% CI 0.1-0.6; p < .01), most apparent in low ( p =0.04), and intermediate ( p <.01), as opposed to high ( p = .06) risk. Conclusions: In the current single institutional series of Ven-HMA treated newly-diagnosed AML, response to Ven-HMA was the foremost predictor of survival. Additional risk factors for survival included adverse karyotype, presence of TP53 and absence of IDH2 mutations. These observations allowed for a three-tiered genetics-enhanced survival model with CR/CRi as a backbone, and also confirmed survival advantage from AHSCT, regardless of risk category.

Introduction Atrial fibrillation/flutter (Afib) contributes independently to the hematopoietic cell transplant comorbidity index (HCTCI) and is a known complication of allogeneic hematopoietic transplantation (AlloHCT) which has been linked to inferior outcomes, particularly in older patients. An electrocardiogram (ECG)-based artificial intelligence (AI) algorithm to predict Afib and cardiac contractile dysfunction risk has been validated (Lancet 394:861, 2019). We hypothesized that assessment of pre-transplant Afib risk using the AI algorithm may predict future development of Afib and help identify patients at higher risk for this complication. Methods 191 consecutive MDS patients undergoing AlloHCT with curative intent using myeloablative (28.3%) or reduced intensity conditioning (62.3%) in the Mayo Clinic Comprehensive Cancer Center (Rochester, Jacksonville, Phoenix) between November 2015 and January 2022 were included in this retrospective analysis. Primary outcome was Afib within 100 days and 365 days of transplantation, and we also assessed the outcomes of Death and Relapse. Revised IPSS (IPSS-R) and HCTCI scores were included for all patients. Elevated Afib risk score using the AI algorithm was calculated from pre-transplant ECG, performed during vital organ testing at AlloHCT clearance. In addition to left ventricular (LV) ejection fraction (EF) we evaluated detailed echocardiographic (Echo) parameters including LV volume and mass indices, left atrial volume index (LAVI), maximum trans mitral flow velocities during LV filling (E velocity) and during the diastolic phase due to atrial contraction (A velocity), the velocity of the annulus away from the apex during the protodiastolic phase (e' velocity), E/A and E/e' ratios, the presence of valvular disease, and right ventricular systolic pressure (RVSP). We performed an analysis using a Cox proportional hazards regression models (adjusting for age and sex) to evaluate associations with Afib risk, Relapse, and Overall Survival. Results The median age was 64 years (18-75), 58.6% were male, 6.9% non-white race, and 3.7% Hispanic/Latino ethnicity. Most patients had an HCTCI score of 0-3 (76.3%). GVHD prophylaxis was predominantly with calcineurin inhibitor-based regimes plus/minus methotrexate or mycophenolate mofetil (83.8%). 5.2% of patients had a history of Afib before transplant. Non-relapse mortality was 7.9% at 100 days, and 18.3% at 365 days. N=25 patients (13.1%) developed Afib within 100 days, and n=28 (14.7%) within 1-year after AlloHCT. On multivariable analysis, Age [Hazard Ratio (HR) 2.83, 95% Confidence Interval (CI) 1.36-5.89, p=0.0055] and Echo parameters [including LAVI (HR 2.10, CI 1.23-3.58, p=0.0067), E velocity (HR 3.28, CI 1.29-8.80, p=0.013), E/A ratio (HR 3.74, CI 1.75-7.97, p=0.0006), E/e ' ratio (HR 1.81, CI 1.07-3.07, p=0.026) and RVSP (HR 1.68, CI 1.04-2.70, p=0.032)] were significantly associated with Afib risk; elevated AI risk score was significant in the univariate model (HR 2.74, CI 1.15-6.53, p=0.023) but was borderline in the multivariate model (HR 2.34, CI 0.98-5.60, p=0.056) ( Table 1). Based on previous observations we evaluated the association of AI and Echo parameters with Relapse and Death after AlloHCT ( Table 1). Age (HR 1.32, CI 1.01-1.71, p=0.041) and IPSS-R (HR 1.20, CI 1.08-1.33, p=0.0009) were significantly associated with Death after AlloHCT, while HCTCI (HR 1.10, CI 1.00-1.22, p=0.052) and higher LV EF (HR 0.63, CI 0.38-1.06, p=0.084) were borderline. For Relapse, only IPSS-R (HR 1.52, CI 1.31-1.75, p=1.22E-08) was significant, while increase Echo E velocity was of borderline significance (HR 0.45, CI 0.20-1.02, p=0.057). Conclusions Afib is a common post-AlloHCT complication and is more frequent with increasing Age. Echo parameters (specifically E/A ratio) predict significantly higher Afib risk, while the baseline ECG AI algorithm was of borderline significance. The selection of patients with lower HCTCI scores, and the low background prevalence of Afib in this cohort (i.e., selection of patients with lower baseline cardiac morbidity) are possible explanations for non-significance of the AI algorithm. As anticipated Age and IPSS-R are the greatest predictors of Death after AlloHCT, although the suggestion of an association with baseline Echo parameters highlights the importance of identifying those who may benefit from preemptive Cardiology intervention.

Introduction: Fludarabine combined with melphalan 140 mg/m2 (FluMel140) is a commonly used reduced-intensity conditioning (RIC) regimen in patients undergoing matched related (MRD) and unrelated donor (MUD) allogeneic stem cell transplant (alloSCT). Ciurea et al Blood 2020, described outcomes of Flu 160 mg/m2 and Mel 100 mg/m2. In this study, we describe our experience of attenuated melphalan dosing 100-120 mg/m2 versus 140 mg/m2 with fludarabine 125 mg/m2 (attenuated FluMel versus regular dose FluMel). Methods: We retrospectively reviewed patients from November 2016 to April 2023 with AML, ALL or MDS undergoing alloSCT who received fludarabine and melphalan. Patients who received the lower dose of melphalan were considered to be the attenuated FluMel cohort, whereas patients receiving the melphalan 140 mg/m2 were considered to be the control cohort. Kaplan-Meier and log-rank tests were used to estimate OS. NRM and relapse incidence (RI) was calculated using competing risk analysis. R 4.2.0 (R Foundation for Statistical Computing) was used for statistical analyses. Results: A total of 134 patients [92 (68.7%) males] were evaluated. Thirty-seven (27.6%) patients received attenuated FluMel and 97 (72.4%) patients received regular dose FluMel (controls). Among the patients in the attenuated FluMel cohort, 5 (13.5%) patients also received fludarabine at doses less than 125 mg/m 2 [median 20 mg/m 2 (range 87.5-112.5 mg/m 2]. Median age at transplant was 65 years (range 30 - 76 years). The most common disease was AML [75 (56.0%) patients] ( Table 1). Patients in the attenuated FluMel group had comparable iothalamate clearance compared to control cohort (clearance <65 ml/min/1.73 m2: 18.9% vs. 12.4%, P = 0.52). Patients in the attenuated FluMel group had comparable rates of high/very high DRI to those in control cohort (51.4% vs. 43.3%, P = 0.91). Compared to the control cohort, patients in the attenuated FluMel group were less likely to have a matched related donor (21.6% vs. 45.4%, P = 0.02) and were more likely to receive PTCy for GVHD prophylaxis (37.8% vs. 16.5%, P = 0.02). Among the patients receiving attenuated FluMel, the median melphalan dose administered was 100 mg/m2. The median time to neutrophil engraftment was 19 days (IQR 16-21 days), and median time to platelet engraftment was 18 days (range 12-25 days). Eleven (29.7%) patients developed post-alloSCT mucositis, and 3 (8.1%) patients developed grade 3-4 mucositis. All the patients in the attenuated FluMel group achieved 100% CD33 donor chimerism at day +30 (n=35) and day +100 (n=28) after alloSCT. Similarly, 26 (74.3%, n = 35) patients achieved ≥ 95% CD3 donor chimerism at day +30 after alloSCT and 23 (82.1%, n = 28) patients achieved ≥ 95% CD3 donor chimerism at day +100 after alloSCT. Median follow-up of the entire cohort after alloSCT was 2.3 years (95% CI 2.1-2.5 years). Median follow-up was 1.5 years in the attenuated FluMel cohort and 2.5 years for the control cohort ( P<0.001) . Survival was similar among patients receiving attenuated FluMel vs. control cohort (2-year OS 74.7% vs. 65.0%, P = 0.28). At 2 years post-alloSCT, the attenuated FluMel and control cohort had similar non-relapse mortality (19.9% vs. 24.4%, P=0.58) and relapse (22% vs. 15.2%, P=0.51). The attenuated FluMel was not associated with an increased incidence of relapse (HR 1.36, 95% CI 0.50-3.73, P=0.55) or NRM (HR 0.74, 95% CI 0.28-1.95, P = 0.55). GVHD-free relapse free (GRFS) survival was also similar among the attenuated Flu/Mel and control cohort (1-year GRFS rate 41.1% vs. 45.6%, P = 0.8). Among the patients receiving PTCy for GVHD prophylaxis, the 2-year OS in patients receiving attenuated FluMel was higher than the control group, but it was not statistically significant (2-year OS rate: 90% vs. 55%, P=0.37, Figure 1). Compared to the control group, the attenuated FluMel group had similar rates of NRM at 100 days (10% vs. 12.5%), 1 year (NA vs. 25%) and at 2 years after alloSCT (NA vs. 25%, P=0.54). GRFS at 1 year was also similar between the attenuated FluMel and control group (68.6% vs. 43.8%, P = 0.69). Conclusion: The attenuated dosed FluMel conditioning is a safe and effective regimen for elderly patients with acute leukemia and MDS, and is non-inferior to regular dose FluMel. Among patients receiving PTCy for GVHD prophylaxis, the lower dose of melphalan was non-inferior to regular dose FluMel for survival outcomes. Larger studies are needed to confirm our findings.

BACKGROUND: Recently proposed World Health Organization (WHO, 5 th edition) and International Consensus Classification (ICC) recognized myeloid neoplasms (MN) harboring TP53-mutated ( TP53mut) as a separate entity. However, there are critical differences between the two classifications regarding allelic-status, variance allele frequency (VAF) cut-off and blast categories, that may lead to under- or overestimation of the risk. METHODS: We analyzed MN harboring TP53mut (VAF ≥2%, n=536) managed at Mayo Clinic (USA) and South Australia Health Network (Australia). ICC (Arber et al, Blood 2022) and WHO (Bluebook, Beta v2) definitions were used to stratify MN harboring TP53mut. RESULTS: Of 536 cases, 379 (71%) had myelodysplastic syndromes (MDS) (<20% blasts) and 157 (29%) had acute myeloid leukemia (AML). Using WHO, 160 (42%) of 379 were classified as MDS-bi TP53 as they harbored 2 TP53mut (n=115; 30%) or 1 TP53mut plus confirmed loss of heterozygosity (LOH) or copy neutral LOH (cnLOH) of TP53 locus (n=45; 12%). Sixty-two (16%) TP53mut MDS with VAF >49% were classified as presumptive MDS-bi TP53. Of the 157 cases with 1 TP53mut VAF ≤49%, 17 confirmed to have an intact wild-type TP53 copy and were classified as monoallelic. While 140 (89%) cases did not have copy number variation (CNV) analysis. We next evaluated the number of TP53mut cases classified differently by WHO and ICC. As WHO does not recognize TP53mut AML as a separate entity, 157 (29%) TP53mut AML were not classifiable. 46% (174/379) of the TP53mut MDS including 48 (30%), 12 (19%) and 79 (65%) of bi TP53, presumptive bi TP53 and monoallelic TP53mut MDS according to WHO were classified differently by ICC ( Figure 1A). Discordance between the two classifications were predominantly driven by: (i) The difference in VAF cut-off. Sixty patients (11%) with TP53mut VAF <10% were excluded from the ICC classification of TP53mut MN as it mandates TP53mut VAF cut-off ≥10%. 11 (18%) and 41 (68%) of these had bi TP53, monoallelic TP53mut MDS by WHO criteria while 8 (13%) had AML. Importantly, within the VAF <10% cohort, median overall survival (OS) of 2 TP53mut or 1 TP53mut with 17p13.1 (hereafter 17p) deletion or complex karyotype (CK, n=30) was significantly poor compared to 1 TP53mut VAF <10% (11.6 vs 32.4 months; P =0.002, Figure 1B), suggesting that ICC may underestimate the poor prognosis of multi-hit or multi-hit equivalent TP53mut MN with VAF <10%. (ii) The difference in bone marrow blast cut-off. The underlying assumption by WHO is that prognosis of bi TP53mut MDS is poor irrespective of blast percentage category (0-9% vs 10-19%). In contrast, ICC considers that TP53mut MDS/AML (blasts 10-19%) is associated with poor prognosis irrespective of TP53 allelic status. Median OS of TP53mut MDS with 10-19% blasts was significantly poor compared to blast 0-9% (6.4 vs 11.6 months, P <0.0001). Second, median OS of bi TP53mut was significantly poor than monoallelic TP53mut in MDS- blast 0-9% (10.1 vs 15.9 months; P =0.0001) but not in blasts 10-19% (6.8 vs 6.2 months; P =0.19). Hence, exclusion of monoallelic TP53mut MDS with blast 10-19% as TP53mut MN from the WHO classification may underestimate the poor prognosis. (iii) The difference in the definition of biallelic and multi-hit TP53 loss. 35 cases with 1 TP53mut VAF ≤49%, had 17p deletion but did not have CNV analysis to confirm the loss of TP53 locus and could not be classified further using WHO. ICC classified 18 (50%) of these cases as multi-hit. Median OS of 1 TP53mut VAF ≤49%, and 17p deletion on karyotype was significantly poor than 1 TP53mut (VAF ≤49%without 17p deletion on karyotype (6.4 vs 14.6 months, P =0.001). (iv) The difference in the definition of multi-hit equivalent. 32% (33/104) WHO defined monoallelic cases without available CNV data, were classified as multi-hit-equivalent by ICC as they harbored CK. However, WHO does not consider CK as presumptive evidence of bi TP53. Median OS of 1 TP53mut VAF ≤49% with CK was significantly poor compared to 1 TP53mut without CK (8.2 vs 26.9 months, P =0.0008). CONCLUSION: Our study provides evidence that poor prognosis of ‘multi-hit’ TP53 with VAF <10% is underestimated by ICC. On the other hand, poor prognosis of monoallelic TP53 MDS with 10-19% blasts as well as 1 TP53mut VAF ≤49% in the presence of CK is underestimated by WHO. These data suggest that both classifications can be further refined.

AbstractThe World Health Organization (WHO) classification system categorizes advanced systemic mastocytosis (SM‐Adv) into aggressive SM (ASM), mast cell leukemia (MCL), and SM with associated hematological neoplasm (SM‐AHN). By contrast, the International Consensus Classification (ICC) requires “immature” MC cytomorphology for the diagnosis of MCL and limits SM‐AHN to myeloid neoplasms (SM‐AMN). The current study includes 329 patients with SM‐Adv (median age 65 years, range 18–88; males 58%): WHO subcategories SM‐AHN (N = 212; 64%), ASM (N = 99; 30%), and MCL (N = 18; 6%); ICC subcategories SM‐AMN (N = 190; 64%), ASM (N = 99; 33%), and MCL (N = 9; 3%); WHO‐defined MCL with “mature” MC cytomorphology and SM‐AHN associated with lymphoid neoplasms were operationally labeled as “MCL‐mature” (N = 9) and SM‐ALN (N = 22), respectively, and distinguished from ICC‐defined MCL and SM‐AMN. Multivariable analysis that included the Mayo alliance risk factors for survival in SM (age >60 years, anemia, thrombocytopenia, increased alkaline phosphatase) revealed more accurate survival prediction with the ICC versus WHO classification order: (i) survival was significantly worse with MCL‐immature versus MCL‐mature (hazard ratio [HR] 15; p < .01), (ii) prognostic distinction between MCL and SM‐AHN/AMN was confirmed in the context of ICC (HR 9.3; p < .01) but not WHO classification order (p = .99), (iii) survival was similar between MCL‐mature and SM‐AMN (p = .18), and (iv) SM‐AMN (HR 1.7; p < .01) but not SM‐ALN (p = .37) was prognostically distinct from ASM. The current study provides evidence for the independent prognostic contribution of both the ICC system for SM‐Adv and the Mayo alliance risk factors for survival in SM.