Shah, Meera

Purpose: The understanding of cardiac-involved ECD (ECD-CV) and how this affects outcomes and pre-existing diseases remains limited. We aimed to determine the association, burden, and severity of pre-existing and de novo cardiovascular (CV) comorbidities with ECD-CV compared to patients (pts) without cardiac-involved ECD (ECD-noCV). Methods: Records of pts with ECD diagnosed from Jan-1990 to Dec-2021, consecutively seen at Mayo Clinic, AZ, FL, and MN were reviewed. Cardiac involvement was diagnosed by imaging. CV comorbidities were captured before and after ECD diagnosis. CTCAE v3.0 based grading system was used to assess CV comorbidities which graded the conditions as mild (grade 1), moderate (grade 2), severe (grade 3), and life-threatening or disabling (grade 4). Chi-square test was used to determine if having ECD-CV worsens pre-existing CV comorbidities in pts with ECD. As per the established methods, the cumulative CV disease burden was defined after considering 8 CV morbidities that are known to have direct structural and functional implications on the heart, including hypertension (HTN), coronary artery disease (CAD), myocardial infarction (MI), congestive heart failure (CHF), atrial fibrillation (afib), pericardial effusion, valvular disease, and conduction disorders. A composite CV morbidity grade was assigned based on the highest grade in one of the 8 CV comorbidities (Oeffinger, NEJM, 2006). Lastly, a multivariant analysis was conducted using the presence of BRAFV600E mutation, smoking, age ≥ 60 years (yr), conventional vs targeted therapy, and ECD-CV in pts with a composite grade of 4. Results: Among 106 ECD pts, 38% (n=40) had cardiac involvement. The median age at diagnosis of ECD was 57 yr (range: 49-67); 61% were males. The median follow-up was 5 yr (95% CI: 3.5-6.1), and 4.1 yr (95% CI: 3.2-6.5) for ECD-CV and ECD-noCV pts, respectively p=0.92. The median time of CV diagnoses for the entire cohort before and after ECD diagnosis was 4.8 yr (range: 0.06-42.5) and 3.4 yr (range: 0.05-18.7), respectively. BRAFV600E mutation was present in 65% (n=26) of ECD-CV vs 42% (n=28) of ECD-noCV, p=0.02. Eight pts with ECD-CV died; one from sudden cardiac death, while 9 ECD-noCV pts died, p=0.39. Smoking was present in 43% (n=18) of ECD-CV pts compared to 24% with ECD-noCV (n=16), p=0.05. In comparison to ECD-noCV pts, increased prevalence of pre-existing HTN [59% (n=23) vs 35% (n=23), p=0.016] and MI [10% (n=4) vs 2% (n=1), p=0.046] was seen in ECD-CV. There was no difference in hyperlipidemia, diabetes mellitus, CAD, CHF, valvular disease, afib, and conduction disorders among the two cohorts. After ECD diagnosis, de novo diagnosis of hyperlipidemia [67% (n=26) vs 38% (n=25); p=0.004] and CHF [35% (n=14) vs 7% (n=5); p=<0.001] was seen more frequently in ECD-CV pts compared to ECD-noCV. Following the diagnosis of ECD, the severity of pre-existing conditions did not change in pts with or without ECD-CV. ECD-CV pts had a greater cumulative CV disease burden as the total number of CV morbidities was greater in ECD-CV pts both before and after ECD diagnosis (Table 1). Additionally, pts with ECD-CV had greater severity of CV morbidity (composite grade ≥4) than ECD-noCV before (35% v 3%; p=<0.001) and after (42.5% vs 7.6%; p=<0.001) ECD diagnosis. The composite grade of 4 before ECD diagnosis was most attributable to pericardial effusion (42% in pts with ECD-CV vs 0% in pts with ECD-noCV) and CAD (50% in pts with ECD-CV vs 100% in pts with ECD-noCV); after ECD diagnosis pericardial effusion (42% in pts with ECD-CV vs 0% in pts with ECD-noCV) and CAD (41% in pts with ECD-CV vs 100% in pts with ECD-noCV). After considering BRAFV600E mutation, age ≥60, smoking, ECD-CV, and first-line therapy (conventional vs targeted therapy), ECD-CV was the only factor independently associated with a composite grade of ≥4, before (OR 25.6, 95% CI: 4.8-137) and after (OR 9.7, 95% CI: 3.0-30.8) ECD diagnosis. Nonetheless, smoking, age >60, and ECD-CV were independent factors associated when considered a composite grade of ≥3, before and after ECD diagnosis. Conclusion: ECD-CV did not prognosticate pts and did not worsen the pre-existing CV conditions compared to pts with ECD-noCV. However, ECD-CV pts had a higher cumulative CV disease burden and a higher grade of pre-existing and de novo CV morbidities compared to ECD-noCV. The presence of cardiac involvement in ECD was independently associated with a severe cumulative CV disease burden grade.

Introduction While chimeric antigen receptor (CAR)-T cell therapy has transformed the management of hematologic malignancies, it requires frequent monitoring for acute toxicities, almost universally achieved through mandatory hospitalizations, therefore utilizing a significant amount of healthcare resources. At Mayo Clinic in Rochester, MN, our standard practice is administering CAR-T cell therapy in the outpatient setting through a multi-disciplinary hospital-based outpatient (HBO) unit and remote patient monitoring (RPM) program. Our CAR-T RPM program consists of in-home, electronic health record-integrated technology to monitor vital signs and neurologic symptoms for 30 days post CAR-T infusion. Here we report the healthcare utilization outcomes of CAR-T patients managed in the outpatient setting enrolled in the RPM program. Methods Patients undergoing FDA-approved CAR-T cell treatment in the outpatient setting and participating in the CAR-T RPM program at Mayo Clinic Rochester were included in this IRB-approved study. Patient characteristics, healthcare utilization metrics, inpatient interventions, and patient satisfaction data regarding the RPM program were reviewed. Length of stay (LOS) includes any hospitalizations for which the admission date falls within the RPM monitoring period (CAR-T cell infusion to day +30), even if the hospitalization continued past day +30. Results A total of 123 patients participated in the CAR-T RPM program and underwent treatment in the outpatient setting. Median age at CAR-T infusion was 63 years (range 22 - 83), and most patients were male (n=78, 63%). Non-Hodgkin lymphoma was the most common diagnosis (n=74, 60%), followed by multiple myeloma (n=47, 38%), ALL (n=1, 1%) and other (n=1, 1%). During the PRM monitoring period, 19 (16%) patients were managed entirely in the outpatient setting. The remaining 104 (84%) patients required at least one hospitalization, with most patients (n=70, 57%) hospitalized only once [26 (21%) patients were hospitalized twice, and 7 (6%) patients were hospitalized 3 times, and 1 (1%) patient hospitalized 4 times during the monitoring period]. When considering the first hospitalization post CAR-T infusion, the median LOS was 6 days (range 2 - 47). When considering all hospitalizations post CAR-T, the median LOS was 8 days (range 2 - 47). Only 7 (6%) patients required an ICU stay, with a mean ICU stay of 0.2 days (standard deviation 0.9). Cytokine release syndrome (CRS) was noted in 92 (75%) of patients at a median of 3 days (range 0 - 18) post CAR-T infusion. All patients with CRS received tocilizumab, at a median of 4 days post CAR-T infusion. Steroids for CRS were administered in 63 (68%) of these patients. Neurotoxicity was reported in 45 (37%) patients at a median of 7 days (range 1 - 19) post CAR-T infusion. During the RPM monitoring period, patients were seen in our HBO unit with a median of 18 outpatient visits (range 4 - 30). Patient survey data, obtained at the end of the RPM monitoring period, were available from 37 patients. The majority of patients felt that the RPM program helped them feel comfortable managing their health at home, were able to reach a team member, and receive an appropriate response promptly. The majority of patients felt comfortable using the RPM equipment and interacting with providers virtually. Overall, 83% of patients were satisfied with the RPM program and 6% were neither satisfied nor dissatisfied. When asked about the reason for disappointment, technical difficulties/issues with the devices were the themes mentioned by the few patients (11%) who were unsatisfied with the RPM program. Conclusion: Despite the complexities and frequent monitoring associated with CAR-T cell therapy, outpatient CAR-T cell treatment is a feasible model of care. As the standard practice at our institution, the CAR-T RPM program was part of the management of nearly all patients undergoing an FDA-approved CAR-T product in the outpatient setting. RPM in the acute setting post CAR-T infusion is a well-received practice initiative by the majority of patients, including a high level of comfort utilizing the RPM equipment and virtual interaction with the RPM providers.

Background: Upfront autologous stem cell transplant (ASCT) is the standard-of-care for newly diagnosed multiple myeloma (MM) patients. However, relapse and the development of therapy-related myeloid neoplasms (t-MN) post ASCT results in considerable morbidity and mortality. We hypothesized that enrichment of abnormal myeloid progenitors and immune effector cells (IEC) in peripheral blood stem cells (PBSCs) of patients with MM is associated with a higher risk of relapse and development of t-MN. Methods: After institutional review board approval, we screened patients with active MM that were evaluated at Mayo Clinic, Rochester, MN between 01/01/2003 and 12/31/2020. Patients that underwent an ASCT and had cryopreserved PBSC product available for research were included. Using mass cytometry, we performed a comprehensive immunophenotyping of mobilized cryopreserved PBSCs from 54 MM patients undergoing 1 st ASCT, using validated antibody panel of 37 lymphoid- and myeloid-based markers each. Flow cytometry standard files were normalized and concatenated using the Fluidigm acquisition software. Flow cytometry standard files were uploaded to the OMIQ software from Dotmatics (www.omiq.ai, www.dotmatics.com), where transformation and cleaning (doublets, debris) were performed. To correct for between-sample technical variations (“batch effects”), we used fdaNorm within the omiq.ai platform. Clustering and visualization of normalized and concatenaded flowcytometry files were performed within the omiq.ai platform using PhenoGraph and UMAP, respectively. We performed principal component analysis (PCA) to identify major immunomic drivers of variability across patients. We explored the associations between immune subsets and the dichotomized clinical outcomes of interest: progression-free (PFS), myeloid neoplasm-free (MNFS) and overall survival (OS) from ASCT. A 2-sided false discovery rate adjusted P-value of <0.05 was considered significant when multiple comparisons were performed; otherwise, a P-value of <0.05 was considered significant. Results: The most abundant cell populations were T-cells (49% of the CD45 +) and myeloid cells (43% of the CD45 + cells). Clustering on the CD45 + cells revealed 30 unique lymphoid clusters and 27 unique myeloid clusters. Subclustering on NK-cells identified an additional 16 NK-cell clusters. On PCA, principal components-1 (PC-1) and 2 (PC-2) explained 15.2% and 12.5% of the variability in the data, respectively. A high PC-2 score was associated with inferior median PFS (20 vs. 52 months; P=0.005, Figure 1A), MNFS (52 vs. 85 months, P=0.03, Figure 1B) and OS (64 vs. 106 months, P=0.01) from ASCT. Immune subsets enriched in the high PC-2 cohort included the ‘NKT-like’ (CD56 +) T-cell subset which expressed the inhibitory CD159a receptor and TIGIT; a terminally differentiated (CD27/CD28 -, CD57/KLRG1 +) and exhausted (TIGIT/PD-1 +) T cell subset, 2 exhausted (TIGIT/PD-1 +) T cell subsets; an immunosenescent (CD27/CD28 -, CD57/KLRG1 +) T cell subset as well as a terminally exhausted (C27/C28 -, KLRG1/PD-1/TIGIT +) TCRgamma-delta subset. Immune subsets with high loadings within PC-1 (associated with improved clinical outcomes) consisted exclusively of CD4 + cells with early memory phenotypes (CD27/CD28/CD127 +). Abnormal expression of CD7 and HLA-DR expression on myeloid progenitor cell subsets was associated with an inferior PFS, MNFS, and OS. A NK-cell subcluster, characterized by an immature (CD56 high), inhibitory (CD159a +, NKG2A) phenotype, was enriched in patients developing t-MN (0.33% vs. 0.17%, P=0.006). Conclusion: We identify abnormal myeloid and immune effector cells in the PBSCs of patients with MM, even prior to ASCT. Patients with a PBSC product that is enriched in immunosuppressive T and myeloid cell subsets, and with a lower proportion of antigen presenting cells, progenitor populations, and anti-tumor macrophages have a shorter PFS and MNFS. This suggests that the predisposition to t-MN and inferior outcomes for MM are possibly determined early in the disease course. These findings need external validation and have the potential to guide sequencing of future treatment modalities.

Background: Relapsed DLBCL occurs in up to 40% of patients and has a poor prognosis. Current prognostic tools such as the international prognostic index (IPI) and newer forms (NCCN-IPI) only include baseline clinical features. PET/CT features have not yet been incorporated into prognostic risk models despite being essential for staging and response assessment. Deep learning computer algorithms have consistently demonstrated the ability to analyze imaging data and identify features that are not readily apparent to the human eye. Using a deep learning computer vision model that automatically extracted radiomic features from PET/CTs, we examined the association of changes between baseline PET and interim PET (PET2) radiomic features on survival outcomes and compared the prognostic performance with NCCN-IPI. Methods: Adult patients with newly diagnosed diffuse large B-cell lymphoma (DLBCL) between 2005 and 2015 and enrolled in the prospective MER cohort were included. Native DICOM images from FDG PET/CT scans at baseline and interim PET (PET2, after 2-3 cycles of chemotherapy) were preprocessed for regularity and then segmented using the nnU-Net architecture with an external lymphoma-specific model. Feature extraction and standard uptake value (SUV) characterization were accomplished with PyRadiomics and NiBabel. Our computational method included no manual segmentation correction. Individual radiomics features (n=115) and the % change above (Δ High) and below (Δ low) 50 th quartile from baseline to PET2 were evaluated for association with event free survival (EFS) and overall survival (OS) using a univariate Cox regression model and by Kaplan Meier analysis. EFS was defined as time from PET2 to first systemic relapse or death from any cause. OS was defined as time from PET2 to death from any cause. Additionally, Spearman and Pearson correlation matrices were used to guide feature selection while minimizing collinearity. We then assessed the association of the highest scoring radiomics features with EFS in a multivariate cox regression model. Results: A total of 506 patients had baseline and PET2 scans available for radiomic feature extraction. Median age at diagnosis was 61 years (range 18-90), 43% were females. NCCN-IPI composition and outcomes of our cohort was comparable to the previously published NCCN cohort, with patient distribution and 5-year OS as follows: Low (9%, 5-y OS 83%), Low-intermediate (38%, 5-y OS 49%), High-intermediate (41%, 5-y OS 31%), High (11%, 5-y OS 12%). At a median follow up of 81 months,there were 308 events, and272 patients had died.Several radiomics features were associated with EFS including change in tumor surface area (SA Δ) (HR: 1.75, 95% CI 1.36-2.24, p<0.005, median of 26,927 mm 2, Figure 1A) and change in total metabolic tumor volume (TMTV Δ) (HR: 1.64, 95% CI 1.27-2.10, p<0.005, median of 120,473 mm 3, Figure 1B). As a reference, NCCN-IPI univariate Cox regression analysis of EFS produced a Harrell's C-statistic (c) of 0.624. Single radiomics features of SA Δ and TMTV Δ individually produced unadjusted c-statistics of 0.616 and 0.614 respectively. Combinations of radiomics features (SA Δ, TMTV Δ and NCCN-IPI classification) demonstrated cumulative benefit (c=0.652). Conclusion: Automatic PET-CT radiomic feature extraction using deep learning models is feasible among patients with newly diagnosed DLBCL. The change in tumor SA and TMTV appear to be promising radiomic biomarkers. A low decline in either tumor SA or TMTV by PET2 is associated with higher risk of relapse and poor survival outcomes. As individual characteristics, these features are similar prognostic indicators of EFS as the NCCN-IPI. Further investigation into risk stratification when combining radiomics with clinical and genomic variables is warranted. Figure 1. Event free survival for DLBCL patients with A) a change in tumor surface area by PET2 less than 50% percentile (SA_Δ Low) and greater than 50 th percentile (SA_Δ High) and B) a change in TMTV by PET2 less than 50% percentile (TMTV_Δ Low) and greater than 50 th percentile (TMTV_Δ High).

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.

Background: According to the international consensus classification (ICC), diagnosis of myelodysplastic syndromes (MDS) with del(5q) requires presence of i) del5q, alone or associated with no more than one other cytogenetic abnormality, exclusive of -7/del(7q), ii) <5% bone marrow (BM) and <2% circulating blasts, and iii) the absence of “multi-hit” TP53 ( Arber et al. Blood 2022;140: 1200). We have recently reported on TP53 variant allele frequency (VAF) ≥22% being the sole genetic predictor of inferior survival in MDS-del(5q) (Fleti. et al. AJH 2023;98:E76). In the current study, we examined the additional prognostic impact of therapy-related setting, in the context of TP53 VAF and other risk factors. Methods : The current study included patients with MDS-del(5q) who were seen at the Mayo Clinic or Moffitt Cancer Center, USA. Diagnosis of MDS-del(5q) and therapy-related qualification was according to ICC criteria ( Arber et al. Blood 2022;140: 1200), with the exception that patients with biallelic or multi-hit TP53 were included to allow unbiased analysis of impact on prognosis. Patient selection was based on availability of mutation information from next-generation sequencing (NGS). The latter was assessed either at diagnosis or at different time points during the disease course, but at least 3 months before leukemic transformation. Otherwise, all analyses, including estimation of overall and leukemia-free survival, were based on variables collected at time of initial diagnosis of MDS-del(5q). TP53 mutation VAF threshold of 22% was considered based on our previously published report cited above. Age cutoff at 67 years was determined by ROC analysis. Conventional statistical methods were applied using JMP Pro 16.0.0 software (SAS Institute, Cary, NC, USA). Results: A total of 210 patients (median age 72 years; 65% females), meeting the above-stipulated criteria for MDS-del(5q), were considered: 172 primary and 38 therapy-related. Table 1 outlines clinical and laboratory characteristics obtained at the time of diagnosis: 42% were transfusion-dependent, 69% had hemoglobin level <10 g/dL, 30% platelet count <150 x 10(9)/L, and 15% carried additional cytogenetic abnormality. NGS data performed either at diagnosis or at least 3 months prior to leukemic transformation was available in 146 patients; the most frequent mutations were TP53 (21%), SF3B1 (16%), DNMT3A (12%), TET2 (12%), ASXL1 (10%), and JAK2 (7%) (Table 1). At a median follow-up of 3.9 years (range 0.002-18.5) for living patients, 95 (45%) deaths and 34 (16%) leukemic transformations were recorded; treatment undertaken included lenalidomide in 162 (77%) cases. NGS data-exclusive multivariable analysis that included all 210 patients identified therapy-related setting (p<0.01; HR 2.9; Figure 1a), age >67 years (p=0.01; HR 1.8), transfusion-dependency (p=0.04; HR 1.5), and platelet count <150 x 10(9)/L (p=0.03; HR 1.6), as independent predictors of inferior survival. The significant effect of age and platelet count was sustained when analysis was limited to patients with primary MDS-del(5q) but lost when applied to therapy-related disease. NGS data-inclusive multivariable analysis, which was applicable to the subset of 146 patients, identified, as risk factors, therapy-related setting (p<0.01; HR 2.3) and TP53 VAF ≥22% (p<0.01; HR 2.4; Figure 1b) while age >67 years sustained borderline significance (p=0.07; HR 1.8). Figure 1c depicts survival data based on the presence or absence of TP53 VAF ≥22% and therapy-related qualification; low-risk with no risk factors (N=112; median survival 11.7 years; 5/10-year survival 73%/52%) and high-risk with one or both risk factors (N=34; median survival 4 years; 5/10-year survival 42%/14%); leukemia-free survival was affected by TP53 VAF ≥22% (p<0.01) but not therapy-related setting (p=0.13). Conclusions : The current study highlights TP53 VAF and therapy-related setting as the most prominent risk factors for survival in MDS-del(5q) and proposes a simple risk model that distinguishes patients with a projected 10-year survival that exceeds 50%, in the absence of both risk factors, and plummets to 14%, in the presence of one of the two risk factors. Accordingly, allogeneic stem cell transplant might be deferred in the former instance while it should be pursued sooner than later, in the latter instance.

Background: Biallelic TP53 alterations result from either sequence variations or deletions involving TP53 and are not necessarily synonymous with “multi-hit TP53”. According to the 2022 international consensus classification (ICC) for myeloid neoplasms (MN), multi-hit TP53 signifies i) the presence of two or more distinct TP53 mutations, each with variant allele frequency (VAF) ≥10%, ii) a single TP53 mutation with VAF ≥50%, or iii) a single TP53 mutation with VAF ≥10% accompanied by a cytogenetically-apparent del(17p13.1), copy-neutral loss of heterozygosity (LOH) at the 17p TP53 locus, or, in the absence of LOH information, complex karyotype ( Arber et al. Blood 2022;140: 1200). The current study is focused on MN with multi-hit TP53 and examines the additional prognostic impact of morphologic subtype designation, bone marrow (BM) or peripheral blood (PB) blast percentage, TP53 VAF, and MN with diagnostic qualifiers (i.e., therapy-related, or secondary progressing from myelodysplastic/myeloproliferative/overlap syndromes/neoplasms). Methods : The current study was conducted under an institutional review board approved minimum risk protocol that allowed retrospective collection and analysis of data from Mayo Clinic patient records. Multi-hit TP53 was defined as per ICC criteria as outlined above ( Arber et al. Blood 2022;140: 1200). Morphologic subtype designations of MN were according to ICC criteria and assigned at the time of TP53 detection. NGS and cytogenetic information was available in all study patients. Survival analyses were calculated from time of TP53 mutation detection. Conventional statistical methods were applied using JMP Pro 16.0.0 software (SAS Institute, Cary, NC, USA). Results : Initial screening flagged 143 patients with biallelic TP53 abnormalities derived from formal laboratory reports of NGS data and cytogenetic studies; of these, 130 met ICC criteria for multi-hit TP53: pure erythroid leukemia (PEL; N=24), acute myeloid leukemia (AML)-not PEL (N=54), myelodysplastic syndromes (MDS; N=36), MDS/AML (N=11), and other MN (N=5). Of these 130 informative cases, 128 (98%) harbored complex/monosomal karyotype (CK/MK). Further analysis excluded patients with “other” MN (N=5) and those without CK/MK (N=2), the latter to mitigate the confounding effect of CK/MK on survival and the former because of small sample size and disease heterogeneity. Clinical and laboratory characteristics of the 124 study patients are outlined in table 1. Survival analysis stratified by ICC-defined MN subtypes revealed the prognostic relevance of morphological distinction between PEL vs “AML-not PEL” (p<0.01; HR 2.4) and PEL vs “ TP53-muated MDS/AML” (p=0.02; HR 2.3) while survival was similar between “AML-not PEL” and “ TP53-mutated MDS/AML” (p=0.9; Figure 1a). Survival in “ TP53-mutated MDS” was significantly longer, compared to PEL (p<0.01; HR 0.2), “ TP53-mutated AML-not PEL” (p=0.01; HR 0.5), and “ TP53-mutated MDS/AML” (p=0.07; HR 0.5), the latter with borderline significance (Figure 1a). Multivariable analysis confirmed the independent prognostic relevance of ICC subtype designation (p<0.01) and also revealed additional negative prognostic contribution from advanced age (p=0.02), male gender (p=0.02), MN, secondary (p<0.01), MN, therapy-related (p=0.03), and DNMT3A mutation (p=0.02). Significance was retained in all instances, with the exception of MN, therapy-related (p=0.15), when analysis was repeated after excluding PEL cases (Figure 1b). Conclusions : The current study confirms the prognostic validity of the ICC morphologic subtype designation and TP53 VAF classification threshold, in the context of multi-hit TP53. The study also highlights the prognostic distinction between PEL and “ TP53-mutated AML-not PEL” and the prognostic alignment between the latter and “ TP53-muated MDS/AML”, both of which displayed inferior survival, compared to “ TP53-mutated MDS”. The study also suggests additional prognostic contribution from secondary and therapy-related qualification while the observation regarding DNMT3A mutation requires validation with higher number of informative cases.

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: The association between myeloid neoplasm (MN) and autoimmune rheumatological diseases (AIRD) is increasingly recognized. Furthermore, prolonged cytotoxic disease modifying anti-rheumatological drugs (DMARD) for AIRD may potentially induce therapy-related MN (t-MN), a lethal haematological malignancy with dismal outcomes. However, the genetic association and mechanism underpinning immune dysfunction in patients with between MN and AIRD is not fully elucidated. METHODS: In this collaborative study across the two disciplines and continents (Australia and USA), we assessed the burden of well-defined AIRD in a large cohort of MN (n=1702) that included myelodysplastic syndrome (MDS; n=861), acute myeloid leukaemia (AML; n=640), myeloproliferative neoplasm (MPN; n=89) and MDS/MPN overlap (n=112). We characterized the cytogenetic and mutational landscape of MN with AIRD (MN-AIRD) and without AIRD. In order to assess the mechanistic link between AIRD and MN, we investigated the bone marrow microenvironment in MN-AIRD vs. well-curated age matched controls. RESULTS: 132 (7.75%) of MN (n=1702) had concomitant AIRD. Although association of AIRD was observed across all subtypes of MN, enrichment of AIRD was observed in patients with MDS, MDS/MPN and MPN compared to AML (9.5% vs. 9.8% vs 10.1% vs. 4.67%; P <0.001). Inflammatory arthritis (n=65; 49.24%) followed by inflammatory connective tissue diseases (CTD, n=23; 17.42%), polymyalgia rheumatica (PMR, n=18; 13.63%) and vasculitis (n=16; 12.12%) were the most prevalent AIRD in the MN cohort. Notably, 87.7% of inflammatory arthritis were rheumatoid arthritis (RA; n=57). Striking survival differences were observed between t-MN without AIRD and MN-AIRD (14.0 vs. 33.7 months; P <0.0001) and t-MDS no-AIRD and MDS-AIRD (16.1 vs. 33.4 months, P =0.001). Chromosomal abnormalities including complex karyotype, monosomal karyotype, del 5q, del 7q, marker chromosome and TP53 mutations were enriched in t-MN following independent primary cancer (t-MN without AIRD). However striking enrichment of IDH1 and IDH2 (henceforth referred as IDH) mutations in MN with AIRD compared to MN without AIRD (20% vs. 11%, P =0.02), particularly in MDS-AIRD compared to MDS without AIRD (21% vs. 7%; P =0.001). The enrichment of IDH mutations were particularly prevalent in in MN cases with RA compared to other AIRD (34% vs. 11%; P =0.005). While IDH mutations were observed in only 8% and 6% of PMR and inflammatory CTD patients, respectively. Surprisingly higher frequency of IDH mutations was observed in evaluable seronegative compared to seropositive RA patients (62% vs. 0%; P =0.006). We also observed that majority of IDH mutations in seronegative RA were IDH1 (6/8; 75%) while IDH2 mutations were common other AIRD (6/6; 100%) ( P =0.009). We also assessed the impact of DMARDs on genomics of myeloid neoplasm. The majority of MN-AIRD patients treated with methotrexate did not harbor poor risk cytogenetic and mutation features such as complex karyotype, monosomal karyotype, del 5q, del 7q and/or TP53 mutation. In contrast, the majority of patients who had cyclophosphamide harbored high risk cytogenetic and molecular features. To assess pathogenic mechanisms linking MN and AIRD, we evaluated bone marrow microenvironment in myeloid neoplasm patients with and without AIRD. Bone marrow mesenchymal stromal cells (MSC) isolated from MN-AIRD were highly senescent compared to age matched healthy controls, MN without AIRD, with defective proliferating and differentiation capacity of MN-AIRD and secretion of highly proinflammatory cytokines and chemokines. CONCLUSIONS: In summary, our key findings include: 7.8% of MN were associated with AIRD with stronger association of AIRD with MDS and MDS/MPN compared to AML and striking enrichment of IDH mutations in MN with inflammatory arthritis compared to MN with other AIRD (29% vs 11%; P =0.02), especially in seronegative RA compared to seropositive RA (62% vs. 0%; P =0.006). In contrast to cyclophosphamide and azathioprine, majority of cases treated with methotrexate did not harbor poor risk cytogenetic and/or TP53 mutation. As methotrexate is widely used as an anchor drug in many rheumatology treatment regimens, this is a highly significant finding of clinical relevance.