Abstract Background: Core binding factorfactor (CBF) leukemias are defined by presence of unique translocation events; t(8;21)(RUNX1::RUNX1T1) or inv(16)/t(16;16) (CBFB:: MYH11) and are thus amenable for quantitative polymerase chain reaction (qPCR) monitoring during treatment and follow up. Despite being considered good-risk, significant number of patients relapse and early predictors to identify at risk patients is critically important. We defined optimal molecular response (OMR) as transcript reduction in bone marrow to ≤0.1 at end of induction (EOI) and ≤0.01 at end of cycle 2 or 3 (EOC2/3) and at end of treatment (EOT) (Borthakur et al. Am J Hematol 2022;97:1427-34). Achievements of these responses define patient subsets that have excellent release (RFS) and overall survival (OS). We asked the question whether serial monitoring from peripheral blood (PB) during induction cycle can help define patients who will achieve OMR in bone marrow (BM) and thereby, can be used to identify patients at risk of poorer outcomes. Methods: We included patients with newly diagnosed CBF-AML treated with gemtuzumab ozogamicin in combination with fludarabine, cytarabine, and granulocyte colony-stimulating factor (FLAG-GO). Weekly qPCR were measured prospectively during induction cycle for the relevant CBF transcript. A linear mixed model analysis was used to evaluate the association between early molecular kinetics from PB during induction and achievement of OMR at the end of induction (EOC1) and after one or two cycles of consolidation (EOC2/3). OMR response was defined as a transcript ratio of ≤0.1%, corresponding to a ≥3-log reduction from baseline at EOC1, and ≤0.01% after EOC2/3 from bone marrow sample (Borthakur et al. Am J Hematol 2022;97:1427-34). Results: Nineteen patients were included in this analysis. The median age at diagnosis was 50 years (range, 21–77), and 11 (58%) were male. Thirteen patients (68%) had variant type A inv(16), and six (32%) had t(8;21). Extramedullary disease (EMD) was observed in two patients (11%), including one with isolated EMD involvement. Seven patients (37%) had additional cytogenetic abnormalities beyond the CBF-defining abnormality. The most common co-mutations were NRAS (n=13, 68%), KRAS (n=2, 11%), FLT3-TKD (n=2, 11%), and WT1 (n=2, 11%). The median fusion transcript ratio at presentation was 100 (range, 100-100) for patients with t(8;21), and 92 (range, 30-100) for those with inv(16). The patient with isolated EMD had a bone marrow transcript ratio of 1.83 at diagnosis. At the EOC1, 9 patients (47%) had qPCR MRD levels ≤0.1% (n=7/13 - 54% for patients with inv(16); and n=2/6 - 33% for those with t(8;21)). Following EOC2/3, 12 of 16 (75%) evaluable patients achieved MRD ≤0.01% (n=9/11 – 82% for patients with inv(16); and n=3/5 - 60% for those with t(8;21)). Patients who achieved OMR at EOC1 trended toward being younger compared with those who do not achieve OMR (47 vs. 61 years, p=0.065). In contrast, baseline qPCR level did not differ between the two groups. The aggregate curves separated nicely among optimal molecular responders at EOC1 versus non-responders, but the differences in serial qPCR values among these two groups despite a trend (p=0.083), did not achieve statistical significance. When comparing patients over time, a statistically significant interaction was observed between changes in PB qPCR and OMR at EOC2/3 (p=0.015), indicating that the trend of change in PB qPCR levels from baseline to EOC1 differed significantly between EOC2/3 responders and non-responders. Although, not statistically significant, the largest differences in PB qPCR scores between responders and non-responders were observed between days 7 and 14 (EOC1: −18.6, p=0.239; EOC2/3: 21.2, p=0.141), suggesting a potential early divergence in molecular response. By days 21 and 28, the groups showed smaller differences (EOC1: −8.8, p=0.629; EOC2/3: −9.5, p=0.61), indicating a trend toward convergence in qPCR levels over time. Conclusion: Serial PB qPCR measurement during induction for CBF transcripts has the potential to predict achievement of OMR at EOC1 and EOC2/3 to help very early risk stratification of future relapses but comprehensive data from a larger cohort is needed as we continue to collect data in prospective manner.
Ohanian et al. (Mon,) studied this question.