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Measurable residual disease monitoring for patients with acute myeloid leukemia following hematopoietic cell transplantation using error corrected hybrid capture next generation sequencing.

ABSTRACT: Improved systems for detection of measurable residual disease (MRD) in acute myeloid leukemia (AML) are urgently needed, however attempts to utilize broad-scale next-generation sequencing (NGS) panels to perform multi-gene surveillance in AML post-induction have been stymied by persistent premalignant mutation-bearing clones. We hypothesized that this technology may be more suitable for evaluation of fully engrafted patients following hematopoietic cell transplantation (HCT). To address this question, we developed a hybrid-capture NGS panel utilizing unique molecular identifiers (UMIs) to detect variants at 0.1% VAF or below across 22 genes frequently mutated in myeloid disorders and applied it to a retrospective sample set of blood and bone marrow DNA samples previously evaluated as negative for disease via standard-of-care short tandem repeat (STR)-based engraftment testing and hematopathology analysis in our laboratory. Of 30 patients who demonstrated trackable mutations in the 22 genes at eventual relapse by standard NGS analysis, we were able to definitively detect relapse-associated mutations in 18/30 (60%) at previously disease-negative timepoints collected 20-100 days prior to relapse date. MRD was detected in both bone marrow (15/28, 53.6%) and peripheral blood samples (9/18, 50%), while showing excellent technical specificity in our sample set. We also confirmed the disappearance of all MRD signal with increasing time prior to relapse (>100 days), indicating true clinical specificity, even using genes commonly associated with clonal hematopoiesis of indeterminate potential (CHIP). This study highlights the efficacy of a highly sensitive, NGS panel-based approach to early detection of relapse in AML and supports the clinical validity of extending MRD analysis across many genes in the post-transplant setting.

SUBMITTER: Balagopal V

PROVIDER: S-EPMC6816574 | biostudies-literature | 2019

REPOSITORIES: biostudies-literature

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Measurable residual disease monitoring for patients with acute myeloid leukemia following hematopoietic cell transplantation using error corrected hybrid capture next generation sequencing.

Balagopal Vidya V Hantel Andrew A Kadri Sabah S Steinhardt George G Zhen Chao Jie CJ Kang Wenjun W Wanjari Pankhuri P Ritterhouse Lauren L LL Stock Wendy W Segal Jeremy P JP

PloS one 20191028 10

Improved systems for detection of measurable residual disease (MRD) in acute myeloid leukemia (AML) are urgently needed, however attempts to utilize broad-scale next-generation sequencing (NGS) panels to perform multi-gene surveillance in AML post-induction have been stymied by persistent premalignant mutation-bearing clones. We hypothesized that this technology may be more suitable for evaluation of fully engrafted patients following hematopoietic cell transplantation (HCT). To address this que ...[more]

PMID: 31658273

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Project description:We report here the largest study to date of adult patients with acute myeloid leukemia (AML) tested for measurable residual disease (MRD) at the time of autologous hematopoietic cell transplantation (auto-HCT). Seventy-two adult patients who underwent transplantation between 2004 and 2013 at a single academic medical center (University of California San Francisco) were eligible for this retrospective study based on availability of cryopreserved granulocyte colony-stimulating factor (GCSF)-mobilized autologous peripheral blood progenitor cell (PBPC) leukapheresis specimens ("autografts"). Autograft MRD was assessed by molecular methods (real-time quantitative PCR [RQ-PCR] for Wilms tumor 1 (WT1) alone or a multigene panel) and by multiparameter flow cytometry (MPFC). WT1 RQ-PCR testing of the autograft had low sensitivity for relapse prediction (14%) and a negative predictive value of 51%. MPFC failed to identify MRD in any of 34 autografts tested. Combinations of molecular MRD assays, however, improved prediction of post-auto-HCT relapse. In multivariate analysis of clinical variables, including age, gender, race, cytogenetic risk category, and CD34+ cell dose, only autograft multigene MRD as assessed by RQ-PCR was statistically significantly associated with relapse. One year after transplantation, only 28% patients with detectable autograft MRD were relapse free, compared with 67% in the MRD-negative cohort. Multigene MRD, while an improvement on other methods tested, was however suboptimal for relapse prediction in unselected patients, with specificity of 83% and sensitivity of 46%. In patients with known chromosomal abnormalities or mutations, however, better predictive value was observed with no relapses observed in MRD-negative patients in the first year after auto-HCT compared with 83% incidence of relapse in the MRD-positive patients (hazard ratio, 12.45; P?=?.0016). In summary, increased personalization of MRD monitoring by use of a multigene panel improved the ability to risk stratify patients for post-auto-HCT relapse. WT1 RQ-PCR and flow cytometric assessment for AML MRD in autograft samples had limited value for predicting relapse after auto-HCT. We demonstrate that cryopreserved autograft material presents unique challenges for AML MRD testing because of the masking effects of previous GCSF exposure on gene expression and flow cytometry signatures. In the absence of information regarding diagnostic characteristics, sources other than GCSF-stimulated PBSC leukapheresis specimens should be considered as alternatives for MRD testing in AML patients undergoing auto-HCT.

Dataset Information

Measurable residual disease monitoring for patients with acute myeloid leukemia following hematopoietic cell transplantation using error corrected hybrid capture next generation sequencing.

Publications

Measurable residual disease monitoring for patients with acute myeloid leukemia following hematopoietic cell transplantation using error corrected hybrid capture next generation sequencing.

Similar Datasets

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