Moreno, Yenny Vanegas

Introduction: The development of increasingly sensitive tests for the detection of measurable residual disease (MRD), such as multiparameter flow cytometry (MFC) and next-generation sequencing (NGS) assays, has changed clinical practice in acute lymphoblastic leukemia (ALL). IGH V(D)J-based NGS MRD is the most sensitive assay to predict relapse in patients (pts) with B-ALL (Short, Blood Adv 2022). However, data regarding the use of T-cell receptor (TR) NGS MRD in T-ALL are limited and recent reports from pediatric cohorts suggest that persistent TR MRD may not predict relapse in T-ALL (Roy, ASH 2023, abstract 2976). With limited options for the treatment of relapsed/refractory T-ALL, accurate prediction of high relapse risk may provide an opportunity for early intervention. In this study, we evaluated the prognostic utility of TRB/TRG NGS MRD and its correlation with MFC MRD in adult patients with T-ALL. Methods: We retrospectively evaluated the MRD data and clinical outcomes of precursor T (pre-T) and early T precursor (ETP) ALL patients treated at four academic centers in the US. MRD was evaluated using MFC in CLIA-certified labs at individual sites with a sensitivity of 10-4, and the clonoSEQ (Adaptive Technologies, lnc) NGS assay, with a sensitivity of 10-6. Pts were included if they had a trackable TR clone and NGS MRD remission assessment in at least one time point. The correlation between the percentage of MRD by MFC vs NGS was calculated with the Pearson correlation coefficient, using every available MRD assessment for T-ALL pts in complete remission. Relapse-free survival was stratified based on NGS MRD level at the time of post-induction and post-consolidation assays, and comparisons were made with the log-rank test. The positive predictive value (PPV) for relapse of NGS MRD was calculated for pts with MRD > 10-6 at the post-induction and post-consolidation period. Results: A total of 19 pts who met our inclusion criteria, with a median age of 30 years (range, 18-65). 9 pts (47%) had ETP-ALL, 10 pts had pre-T ALL. Pts had a trackable TRB/TRG clone in 76% of cases, with 66% having a trackable TRG clone and 38% having a trackable TRB clone. 10 pts (53%) were treated with a modified AALL0434 regimen as first line therapy, 6 pts (32%) were treated with CALGB10403, 1 pt (5%) was treated with hyperCVAD, 1 pt (5%) was treated with a BFM regimen, and 1 pt (5%) was treated with CALGB9511. Out of 11 T-ALL pts with MRD data available post-induction, 6 pts (55%) had detectable MRD by NGS and no detectable MRD by MFC. Out of 13 pts with available post-consolidation MRD data, 2 pts (15%) had undetectable MRD by both NGS and MFC and 9 pts (69%) had detectable MRD by NGS only. There were no pts with detectable MRD by MFC and undetectable MRD by NGS at either timepoint. A total of 64 MRD data points from 19 pts were available to evaluate MRD correlation by MFC vs NGS, for which the Pearson correlation coefficient showed a statistically significant linear correlation (p < 0.01). At post-induction, 9 pts had NGS MRD > 10-4 and 2 pts had NGS MRD < 10-4. With a median follow-up time of 19.8 months, pts with NGS MRD > 10-4 had a median RFS of 19.8 months and pts with NGS MRD < 10-4 had a median RFS of 14.8 months. The log-rank test comparing RFS between these two groups was not statistically significant (p= 0.18). Persistent NGS MRD at post-induction had a PPV for relapse of only 18%. At post-consolidation, 7 pts had NGS MRD > 10-4, 4 pts had NGS MRD < 10-4, and 2 pts had undetectable MRD by NGS. With a median follow-up time of 17.3 months, pts with NGS MRD > 10-4 had a median RFS of 15.3 months and pts with NGS MRD < 10-4 or undetectable had a median RFS of 20.6 months. The log-rank test comparing RFS between these two groups was not statistically significant (p= 0.6). Persistent NGS MRD at post-consolidation had a PPV for relapse of only 27%. Conclusion: Despite statistically significant linear correlation between MFC and NGS MRD testing, there were no RFS differences between pts with NGS MRD > 10-4 vs NGS MRD < 10-4. All patients had persistent NGS MRD post-induction, which is markedly lower than the reported 20-25% NGS undetectable MRD rate in B-ALL post-induction. Persistent NGS MRD has low PPV for relapse in T-ALL, which limits its utility in clinical decision making for this population.

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.