A novel machine-learning-derived genetic score correlates with measurable residual disease and is highly predictive of outcome in acute myeloid leukemia with mutated NPM1

Patkar, Nikhil; Shaikh, Anam Fatima; Kakirde, Chinmayee; Nathany, Shrinidhi; Ramesh, H; Bhanshe, Prasanna; Joshi, Shalik Ram; Kannan, Sadhana; Hasan, Syed Khizer; Chatterjee, Gaurav; Tembhare, Prashant; Shetty, Dhanalaxmi; Gokarn, Anant; Punatkar, Sachin; Bonda, Avinash; Nayak, Lingaraj; Jain, Hasmukh; Khattry, Navin; Bagal, Bhausaheb; Sengar, Manju; Gujral, Sumeet; Subramanian, Papagudi Ganesan

doi:10.1038/s41408-019-0244-2

Cited by 16 publications

(31 citation statements)

References 18 publications

(19 reference statements)

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“…Next-generation sequencing (NGS) measuring immunoglobulin heavy chain (variable, diversity and joining) have been developed for ALL; patients who are NGS negative prior to HCT have been shown to have a reduced risk of relapse (150). NGS is also currently being evaluated for AML (151,152) and may prove useful for risk stratification and perhaps monitoring, but is likely to be complex (153). Highly sensitive MRD evaluation in the pre-HCT period may be used to guide which pediatric patients can undergo HCT with reduced intensity/toxicity conditioning without an excessive risk of relapse, and which patients do have a high risk of relapse and may benefit from the addition of novel relapse prophylaxis, such as minor H antigen-specific T cells.…”

Section: Hematopoietic Cell Transplantation For Pediatric Leukemia Inmentioning

confidence: 99%

Minor Histocompatibility Antigen-Specific T Cells

2020

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Section: Hematopoietic Cell Transplantation For Pediatric Leukemia Inmentioning

confidence: 99%

Minor Histocompatibility Antigen-Specific T Cells

2020

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“…Diagnosis, immunophenotyping and cytogenetic analysis were performed as previously described [3]. [14]. c. Machine learning based genetic score: We developed a supervised ML based approach for identification of prognostic variables most likely to influence outcome in AML with RUNX1-RUNX1T1 as described previously [14].…”

Section: A Patient Detailsmentioning

confidence: 99%

“…[14]. c. Machine learning based genetic score: We developed a supervised ML based approach for identification of prognostic variables most likely to influence outcome in AML with RUNX1-RUNX1T1 as described previously [14]. Additional details pertaining to ML can be seen in the Supplementary Methods that accompanies this manuscript.…”

Section: A Patient Detailsmentioning

confidence: 99%

Machine learning derived genomics driven prognostication for acute myeloid leukemia with RUNX1-RUNX1T1

Shaikh

Kakirde

Dhamne

et al. 2020

Leukemia & Lymphoma

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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.

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“…Treatment of AML and MRD Sampling: All patients were treated with conventional "3+7" induction chemotherapy and further treated with high dose cytarabine (HiDAC) or allogeneic bone marrow transplantation (aBMT), if feasible. 35 (RFS) were calculated as previously described. 9,20,35 The prognostic impact of NGS and FCM-MRD assays on OS and RFS was computed using the Kaplan-Meier method and compared using log-rank test.…”

Section: Patients a Patient Accrual And Initial Work Upmentioning

confidence: 99%

“…FCM-MRD was detected using a previously described 10 colour two tube MRD assay. 9,20,35,38 This approach uses a combination of leukemia associated immunophenotype and difference from normal approaches to detect MRD in AML. is the author/funder, who has granted medRxiv a license to display the preprint in (which was not certified by peer review) preprint…”

Section: Detection Of Mrd In Using Multicolour Flow Cytometry (Fcm-mrd)mentioning

confidence: 99%

Clinical Impact of Panel Based Error Corrected Next Generation Sequencing versus Flow Cytometry to Detect Measurable Residual Disease (MRD) in Acute Myeloid Leukemia (AML)

Patkar

Kakirde

Shaikh

et al. 2020

Preprint

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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.

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A novel machine-learning-derived genetic score correlates with measurable residual disease and is highly predictive of outcome in acute myeloid leukemia with mutated NPM1

Cited by 16 publications

References 18 publications

Minor Histocompatibility Antigen-Specific T Cells

Minor Histocompatibility Antigen-Specific T Cells

Machine learning derived genomics driven prognostication for acute myeloid leukemia with RUNX1-RUNX1T1

Clinical Impact of Panel Based Error Corrected Next Generation Sequencing versus Flow Cytometry to Detect Measurable Residual Disease (MRD) in Acute Myeloid Leukemia (AML)

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