We accrued 201 patients of adult AML treated with conventional therapy, in morphological remission, and evaluated MRD using sensitive error-corrected next generation sequencing (NGS-MRD) and multiparameter flow cytometry (FCM-MRD) at the end of induction (PI) and consolidation (PC). Nearly 71% of patients were PI NGS-MRD+ and 40.9% PC NGS-MRD+ (median VAF 0.76%). NGS-MRD+ patients had a significantly higher cumulative incidence of relapse (p = 0.003), inferior overall survival (p = 0.001) and relapse free survival (p < 0.001) as compared to NGS-MRD− patients. NGS-MRD was predictive of inferior outcome in intermediate cytogenetic risk and demonstrated potential in favorable cytogenetic risk AML. PI NGS-MRD− patients had a significantly improved survival as compared to patients who became NGS-MRD− subsequently indicating that kinetics of NGS-MRD clearance was of paramount importance. NGS-MRD identified over 80% of cases identified by flow cytometry at PI time point whereas FCM identified 49.3% identified by NGS. Only a fraction of cases were NGS-MRD− but FCM-MRD+. NGS-MRD provided additional information of the risk of relapse when compared to FCM-MRD. We demonstrate a widely applicable, scalable NGS-MRD approach that is clinically informative and synergistic to FCM-MRD in AML treated with conventional therapies. Maximum clinical utility may be leveraged by combining FCM and NGS-MRD modalities.

Detection of measurable residual disease (MRD) by mutation specific techniques has prognostic relevance in NPM1 mutated AML (NPM1mut AML). However, the clinical utility of next generation sequencing (NGS) to detect MRD in AML remains unproven. We analysed the clinical significance of monitoring MRD using ultradeep NGS (NGS-MRD) and flow cytometry (FCM-MRD) in 137 samples obtained from 83 patients of NPM1mut AML at the end of induction (PI) and consolidation (PC). We could monitor 12 different types of NPM1 mutations at a sensitivity of 0.001% using NGS-MRD. We demonstrated a significant correlation between NGS-MRD and real time quantitative PCR (RQ-PCR). Based upon a one log reduction between PI and PC time points we could classify patients as NGS-MRD positive (<1log reduction) or negative (>1log reduction). NGS-MRD, FCM-MRD as well as DNMT3A mutations were predictive of inferior overall survival (OS) and relapse free survival (RFS). On a multivariate analysis NGS-MRD emerged as an independent, most important prognostic factor predictive of inferior OS (hazard ratio, 3.64; 95% confidence interval [CI] 1.58 to 8.37) and RFS (hazard ratio, 4.8; 95% CI:2.24 to 10.28). We establish that DNA based NPM1 NGS MRD is a highly useful test for prediction of relapse and survival in NPM1mut AML.

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Introduction: One of the mainstays of chemotherapy in acute myeloid leukemia (AML) is induction with a goal to achieve morphological complete remission (CR). However, not all patients by this remission criterion achieve long-term remission and a subset relapse. This relapse is explained by the presence of measurable residual disease (MRD). Methods: We accrued 451 consecutive patients of adult AML (from March 2012 to December 2017) after informed consent. All patients received standard chemotherapy. MRD testing was done at post-induction and, if feasible, post-consolidation using 8- and later 10-color FCM. Analysis of MRD was done using a combination of difference from normal and leukemia-associated immunophenotype approaches. Conventional karyotyping and FISH were done as per standard recommendations, and patients were classified into favorable, intermediate, and poor cytogenetic risk groups. The presence of FLT3 -ITD, NPM1 , and CEBPA mutations was detected by a fragment length analysis-based assay. Results: As compared to Western data, our cohort of patients was younger with a median age of 35 years. There were 62 induction deaths in this cohort (13.7%), and 77 patients (17.1%) were not in morphological remission. The median follow-up was 26.0 months. Poor-risk cytogenetics and the presence of FLT3 -ITD were significantly associated with inferior outcome. The presence of post-induction MRD assessment was significantly associated with adverse outcome with respect to OS ( p = 0.01) as well as RFS ( p = 0.004). Among established genetic subgroups, detection of MRD in intermediate cytogenetic and NPM1 mutated groups was also highly predictive of inferior outcome. On multivariate analysis, immunophenotypic MRD at the end of induction and FLT3 -ITD emerged as independent prognostic factors predictive for outcome. Conclusion: This is the first data from a resource-constrained real-world setting demonstrating the utility of AML MRD as well as long-term outcome of AML. Our data is in agreement with other studies that determination of MRD is extremely important in predicting outcome. AML MRD is a very useful guide for guiding post-remission strategies in AML and should be incorporated into routine treatment algorithms.

Panel based next generation sequencing was performed on a discovery cohort of AML with RUNX1-RUNX1T1. Supervised machine learning identified NRAS mutation and absence of mutations in ASXL2, RAD21, KIT and FLT3 genes as well as a low mutation to be associated with favorable outcome. Based on this data patients were classified into favorable and poor genetic risk classes. Patients classified as poor genetic risk had a significantly lower overall survival (OS) and relapse free survival (RFS). We could validate these findings independently on a validation cohort (n ¼ 61). Patients in the poor genetic risk group were more likely to harbor measurable residual disease. Poor genetic risk emerged as an independent risk factor predictive of inferior outcome. Using an unbiased computational approach based we provide evidence for gene panel-based testing in AML with RUNX1-RUNX1T1 and a framework for integration of genomic markers toward clinical decision making in this heterogeneous disease entity.

A 35 gene error corrected next generation sequencing (NGS) panel was created using single molecule molecular inversion probes with applicability to 83% of acute myeloid leukemia (AML). We accrued 201 patients of adult AML treated with conventional therapy, in morphological remission and evaluated measurable residual disease using NGS (NGS-MRD) as well as multiparameter flow cytometry (FCM-MRD) at post induction (PI) and consolidation (PC) time points. A total of 344 mutations were detected [median VAF of 0.95% (0.76% after excluding mutations in DNMT3A, TET2, ASXL1)] during assessment of MRD. Nearly 71% of patients harbored PI NGS-MRD (and 40.9% harbored PC-MRD). Patients harboring NGS-MRD had a significantly higher cumulative incidence of relapse (CIR), inferior overall survival (OS) and relapse free survival (RFS) as compared to NGS-MRD negative patients at PI and PC time points. NGS-MRD was predictive of inferior outcome in intermediate cytogenetic risk and demonstrated potential in favorable cytogenetic risk AML. Patients who cleared PI NGS-MRD (and stayed negative) had a significantly improved survival as compared to patients who became negative subsequently indicating that kinetics of NGS-MRD clearance was of paramount importance. NGS-MRD identified over 80% of cases identified by flow cytometry at PI time point whereas FCM identified 49.3% identified by NGS. Both FCM and NGS MRD were important in predicting outcome however, PI NGS-MRD emerged as the most important independent prognostic factor predictive of inferior outcome. We demonstrate that panel based NGS-MRD is highly predictive of outcome and advantageous when compared to FCM-MRD in AML treated with conventional therapies.

Introduction: Core binding factor acute myeloid leukemia (CBF-AML) is one of the commonest subtypes of AML characterized presence of t(8;21)(q22;q22) or inv(16)(p13q22)/t(16;16)(p13;q22). It is characterised by a high frequency of somatic mutations especially in RAS and tyrosine kinase signalling pathways. Here we investigated the feasibility of improving risk prediction of CBF-AML using machine learning algorithms. Methods: We developed a next generation sequencing panel that targeted 50 genes implicated in the pathogenesis of myeloid malignancies using single molecule molecular inversion probes. This panel was used to sequence 106 patients of CBF-AML accrued over a six year period (March 2012 - December 2018) treated with conventional "3 + 7" chemotherapy. Post data analysis, we devised a supervised machine learning (ML) approach for identification of mutations most likely to predict for favorable outcome in CBF-AML. We included somatic mutations in genes occurring in CBF-AML at a frequency of >5%. A total of 11 variables were included for feature selection to predict for favorable outcome (including mutations in ASXL2, CSF3R,FLT3, KIT, NF1, NRAS, RAD21, TET2 and WT1 genes as well as mutation burden). Approaches for supervised ML were naïve bayes, generalized linear model, logistic regression, deep learning and random forest methods. Based on the ML results top 6 selected variables were allotted an individual score. A final score for that case was devised as a sum total of the individual scores. These sum were used to generate a genetic risk for a patient. Overall survival (OS) was calculated from date of diagnosis to time of last follow up or death. Relapse free survival (RFS) was calculated from date of CR till time to relapse or death or last follow up if in CR. Results of the genetic risk were analyzed for their impact on OS and RFS using log rank test. Multivariate analysis was performed using cox proportional hazards regression model. Results: The median follow up of the cohort was 27.6 months. A total of 181 somatic mutations were identified in this subset of AML with 86.7% harbouring at least one somatic mutation (median = 2). Based on ML data, a genetic score was formulated that incorporated mutations in RAD21, FLT3, KIT D816, ASXL2, NRAS genes as well as high mutation burden (≥2) into two genetic risk classes (favorable risk and poor ML derived genetic genetic risk). Patients classified as poor genetic risk had a significantly lower OS [median OS: 34.8 months; 95% confidence interval (CI) (14.2-34.8); p=0.0086] and RFS [median RFS: 17.9 months; 95%CI (12.7-33.6); p=0.0043] as compared to patients with favorable genetic risk (median OS and RFS not reached). These results can be seen in Figure 1. On multivariate analysis poor genetic risk was the most important independent risk factor that predicted for inferior OS [hazard ratio(HR), 2.7; 95% CI 1.3 to 5.7] and RFS (HR, 2.6; 95% CI:1.3 to 5.1). Conclusions In a proof of concept, we describe a novel ML derived genomics scoring model that provides a mechanism to risk stratify CBF-AML, a seemingly homogeneous disease entity. This study, to the best of our knowledge represents a novel application of ML to CBF mutated AML. Our data indicates that this scoring system will be useful in identifying CBF mutated AML patients who are at higher risk of relapse and distinguishes them from patients who are truly good risk. Figure 1 Disclosures No relevant conflicts of interest to declare.

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