Mutational profiling of myeloid neoplasms associated genes may aid the diagnosis of acute myeloid leukemia with myelodysplasia-related changes

Yu, James; Du, Yi; Jalil, Anum; Ahmed, Zohaib; Mori, Shahram; Patel, Rushang D.; Varela, Juan Carlos; Chang, Chung‐Che

doi:10.1016/j.leukres.2021.106701

Cited by 3 publications

(2 citation statements)

References 23 publications

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“…Several recent studies on AML-related diseases have also demonstrated the effectiveness of applying state-of-the-art ML techniques in pattern recognition, risk prediction, and survival prediction. These diseases include acute lymphoblastic leukemia (Fitter et al, 2021 ), myelodysplastic syndrome (Radhachandran et al, 2021 ), breast cancer (Kate and Nadig, 2017 ), prostate cancer (Zolbanin et al, 2015 ; Rabaan et al, 2022 ), rectal cancer (Wang et al, 2022 ), skin cancer (Ahmed et al, 2022 ), nasopharynx cancer (Jing et al, 2020 ), pancreatic cancer (Walczak and Velanovich, 2018 ; Muhammad et al, 2019 ; Wang et al, 2020 ), infective endocarditis (Ris et al, 2019 ), AML in pediatric patients (Hoch et al, 2021 ), and AML with myelodysplasia-related changes (Yu et al, 2021 ). The success observed indicates that contemporary ML techniques can automatically uncover meaningful patterns within vast datasets.…”

Section: Related Workmentioning

confidence: 99%

A decision support system to recommend appropriate therapy protocol for AML patients

Castro,

Almeida,

Machado-Neto

et al. 2024

Front. Artif. Intell.

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IntroductionAcute Myeloid Leukemia (AML) is one of the most aggressive hematological neoplasms, emphasizing the critical need for early detection and strategic treatment planning. The association between prompt intervention and enhanced patient survival rates underscores the pivotal role of therapy decisions. To determine the treatment protocol, specialists heavily rely on prognostic predictions that consider the response to treatment and clinical outcomes. The existing risk classification system categorizes patients into favorable, intermediate, and adverse groups, forming the basis for personalized therapeutic choices. However, accurately assessing the intermediate-risk group poses significant challenges, potentially resulting in treatment delays and deterioration of patient conditions.MethodsThis study introduces a decision support system leveraging cutting-edge machine learning techniques to address these issues. The system automatically recommends tailored oncology therapy protocols based on outcome predictions.ResultsThe proposed approach achieved a high performance close to 0.9 in F1-Score and AUC. The model generated with gene expression data exhibited superior performance.DiscussionOur system can effectively support specialists in making well-informed decisions regarding the most suitable and safe therapy for individual patients. The proposed decision support system has the potential to not only streamline treatment initiation but also contribute to prolonged survival and improved quality of life for individuals diagnosed with AML. This marks a significant stride toward optimizing therapeutic interventions and patient outcomes.

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Section: Related Workmentioning

confidence: 99%

A decision support system to recommend appropriate therapy protocol for AML patients

Castro,

Almeida,

Machado-Neto

et al. 2024

Front. Artif. Intell.

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“…One notable study by Smith et al [44] investigated the role of gene mutation profiles in differentiating AML with myelodysplasia-related changes (AML-MRC) from non-MRC AML. They utilized a Next-Generation Sequencing (NGS) panel covering 53 AML-related genes and applied supervised tree-based classification models, including decision tree, random forest, and XGBoost, as well as logistic regression.…”

Section: State-of-the-art Comparisonmentioning

confidence: 99%

Extreme Gradient Boosting Tuned with Metaheuristic Algorithms for Predicting Myeloid NGS Onco-Somatic Variant Pathogenicity

et al. 2023

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The advent of next-generation sequencing (NGS) technologies has revolutionized the field of bioinformatics and genomics, particularly in the area of onco-somatic genetics. NGS has provided a wealth of information about the genetic changes that underlie cancer and has considerably improved our ability to diagnose and treat cancer. However, the large amount of data generated by NGS makes it difficult to interpret the variants. To address this, machine learning algorithms such as Extreme Gradient Boosting (XGBoost) have become increasingly important tools in the analysis of NGS data. In this paper, we present a machine learning tool that uses XGBoost to predict the pathogenicity of a mutation in the myeloid panel. We optimized the performance of XGBoost using metaheuristic algorithms and compared our predictions with the decisions of biologists and other prediction tools. The myeloid panel is a critical component in the diagnosis and treatment of myeloid neoplasms, and the sequencing of this panel allows for the identification of specific genetic mutations, enabling more accurate diagnoses and tailored treatment plans. We used datasets collected from our myeloid panel NGS analysis to train the XGBoost algorithm. It represents a data collection of 15,977 mutations variants composed of a collection of 13,221 Single Nucleotide Variants (SNVs), 73 Multiple Nucleoid Variants (MNVs), and 2683 insertion deletions (INDELs). The optimal XGBoost hyperparameters were found with Differential Evolution (DE), with an accuracy of 99.35%, precision of 98.70%, specificity of 98.71%, and sensitivity of 1.

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Genetic Characteristics According to Subgroup of Acute Myeloid Leukemia with Myelodysplasia-Related Changes

Kang

Jung

Park

et al. 2022

JCM

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Acute myeloid leukemia with myelodysplasia-related changes (AML-MRC) includes heterogeneous conditions such as previous history and specific cytogenetic and morphological properties. In this study, we analyze genetic aberrations using an RNA-based next-generation sequencing (NGS) panel assay in 45 patients with AML-MRC and detect 4 gene fusions of KMT2A-SEPT9, KMT2A-ELL, NUP98-NSD1, and RUNX1-USP42 and 81 somatic mutations. Overall, all patients had genetic aberrations comprising of not only cytogenetic changes, but also gene fusions and mutations. We also demonstrated several characteristic genetic mutations according to the AML-MRC subgroup. TP53 was the most commonly mutated gene (n = 11, 24%) and all were found in the AML-MRC subgroup with myelodysplastic syndrome-defining cytogenetic abnormalities (AML-MRC-C) (p = 0.002). These patients showed extremely poor overall survival not only in AML-MRC, but also within the AML-MRC-C subgroup. The ASXL1 (n = 9, 20%) and SRSF2 (n = 7, 16%) mutations were associated with the AML-MRC subgroup with >50% dysplasia in at least two lineages (AML-MRC-M) and were frequently co-mutated (55%, 6/11, p < 0.001). Both mutations could be used as surrogate markers to diagnose AML-MRC, especially when the assessment of multilineage dysplasia was difficult. IDH1/IDH2 (n = 13, 29%) were most commonly mutated in AML-MRC, followed by CEBPA (n = 5, 11%), PTPN11 (n = 5, 11%), FLT3 (n = 4, 9%), IDH1 (n = 4, 9%), and RUNX1 (n = 4, 9%). These mutations were not limited in any AML-MRC subgroup and could have more significance as a risk factor or susceptibility marker for target therapy in not only AML-MRC, but also other AML categories.

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Mutational profiling of myeloid neoplasms associated genes may aid the diagnosis of acute myeloid leukemia with myelodysplasia-related changes

Cited by 3 publications

References 23 publications

A decision support system to recommend appropriate therapy protocol for AML patients

A decision support system to recommend appropriate therapy protocol for AML patients

Extreme Gradient Boosting Tuned with Metaheuristic Algorithms for Predicting Myeloid NGS Onco-Somatic Variant Pathogenicity

Genetic Characteristics According to Subgroup of Acute Myeloid Leukemia with Myelodysplasia-Related Changes

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