Clinical values of gene alterations as marker of minimal residual disease in non-M3 acute myeloid leukemia

Yu, Tingyu; Chi, Jianxiang; Wang, Li

doi:10.1080/16078454.2021.1990503

Cited by 2 publications

(2 citation statements)

References 125 publications

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“…AML with RUNX1::RUNX1T1 fusion represents a favorable subtype, making timely identi cation and diagnosis crucial for optimal treatment selection (10). Common detection methods for this fusion gene include reverse transcription-polymerase chain reaction (RT-PCR), uorescence in situ hybridization (FISH), or next-generation sequencing (NGS) (11,12,13,14). Despite their sensitivity and speci city, these techniques demand extensive resources, time, and specialized laboratory facilities, presenting operational challenges in some laboratories.…”

Section: Introductionmentioning

confidence: 99%

Predicting RUNX1::RUNX1T1 genetic abnormalities in acute myeloid leukemia from bone marrow smears: Can artificial intelligence do better?

Cheng,

Ding,

Wang

et al. 2024

Preprint

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Background: The presence of the RUNX1::RUNX1T1 fusion gene in patients diagnosed with acute myeloid leukemia (AML) subtype is often indicated by distinctive morphological features in myeloblasts from bone marrow (BM) smears. This study aims to evaluate the capacity of artificial intelligence (AI) to identify specific genetic abnormalities based solely on morphological characteristics. The intent is to investigate a non-invasive, cost-effective, and efficient preliminary screening method prior to the application of molecular biological assays. Methods: This multicenter trial included 205 patients diagnosed with AML, of which 75 were AML with RUNX1::RUNX1T1 fusion. A dataset of 65,039 myeloblasts images collected from the BM smears of these patients was compiled for model training, testing, and validation. The study also undertook a comparative analysis of the discrepancies between manual microscopy and AI-based identification. Results: The model demonstrated proficiency in adapting to varied clinical scenarios by applying two different threshold values. Under the threshold of 0.59, the testing and validation cohorts showed sensitivities of 92.86% and 95.65%, with corresponding accuracies of 87.04% and 71.88%. Conversely, by setting the threshold at 0.88, specificities of 92.31% and 92.68% were achieved, along with accuracies of 88.89% and 90.63%. Regardless of the threshold, the AI model consistently outperformed manual microscopy (average accuracy: 50.00%). Conclusion: The model demonstrates a significant capability to discern underlying RUNX1::RUNX1T1 genetic alterations from the morphological attributes of BM nucleated cells with a precision surpassing human observation. This providing a valuable tool highlights its potential for enhancing diagnostic efficiency in clinical practice.

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Section: Introductionmentioning

confidence: 99%

Predicting RUNX1::RUNX1T1 genetic abnormalities in acute myeloid leukemia from bone marrow smears: Can artificial intelligence do better?

Cheng,

Ding,

Wang

et al. 2024

Preprint

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“…For most leukemia-associated genes, such as IDH1 and IDH2, FLT3, WT1, CEBPα, RUNX1, DNMT3A and TET2, qPCR is widely available but lacks standardization. Notably, some of these mutations are not useful for molecular follow-up due to uninformative character [17,18], unstable expression or technical difficulties [3,19]. In other genes, such as RUNX1 and CEBPα with VAF < 50%, individual mutations are very good and stable markers of assessing the persistence of the leukemic clone.…”

Section: Introductionmentioning

confidence: 99%

Standardization of Molecular MRD Levels in AML Using an Integral Vector Bearing ABL and the Mutation of Interest

Nachmias,

Krichevsky,

Gatt

et al. 2023

Cancers

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Quantitative PCR for specific mutation is being increasingly used in Acute Myeloid Leukemia (AML) to assess Measurable Residual Disease (MRD), allowing for more tailored clinical decisions. To date, standardized molecular MRD is limited to typical NPM1 mutations and core binding factor translocations, with clear prognostic and clinical implications. The monitoring of other identified mutations lacks standardization, limiting its use and incorporation in clinical trials. To overcome this problem, we designed a plasmid bearing both the sequence of the mutation of interest and the ABL reference gene. This allows the use of commercial standards for ABL to determine the MRD response in copy number. We provide technical aspects of this approach as well as our experience with 19 patients with atypical NPM1, RUNX1 and IDH1/2 mutations. In all cases, we demonstrate a correlation between response and copy number. We further demonstrate how copy number monitoring can modulate the clinical management. Taken together, we provide proof of concept of a novel yet simple tool, which allows in-house MRD monitoring for identified mutations, with ABL-based commercial standards. This approach would facilitate large multi-center studies assessing the clinical relevance of selected MRD monitoring.

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Clinical values of gene alterations as marker of minimal residual disease in non-M3 acute myeloid leukemia

Cited by 2 publications

References 125 publications

Predicting RUNX1::RUNX1T1 genetic abnormalities in acute myeloid leukemia from bone marrow smears: Can artificial intelligence do better?

Predicting RUNX1::RUNX1T1 genetic abnormalities in acute myeloid leukemia from bone marrow smears: Can artificial intelligence do better?

Standardization of Molecular MRD Levels in AML Using an Integral Vector Bearing ABL and the Mutation of Interest

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