Integrative Analysis of Gene Expression Data by RNA Sequencing for Differential Diagnosis of Acute Leukemia: Potential Application of Machine Learning

Lee, Jaewoong; Cho, Sungmin; Hong, Su Young; Kang, Dain; Choi, Hayoung; Lee, Jong-Mi; Yoon, Jihyun; Cho, Byung-Sik; Lee, Seok; Kim, Hee-Je; Kim, Myungshin; Kim, Yonggoo

doi:10.3389/fonc.2021.717616

Cited by 6 publications

(4 citation statements)

References 33 publications

(37 reference statements)

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“…Bioinformatics analysis was carried out using both customized and manufacturer-provided pipelines. Variants were selected and annotated using analytics algorithms and public databases [ 22 ]. All mutations were manually verified using the Integrative Genomic Viewer [ 23 ].…”

Section: Methodsmentioning

confidence: 99%

Spectrum of Genetic Mutations in Korean Pediatric Acute Lymphoblastic Leukemia

Yoo

Ahn

Lee

et al. 2022

JCM

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The wide application of next-generation sequencing (NGS) technologies has led to the discovery of multiple genetic alterations in pediatric acute lymphoblastic leukemia (ALL). In this work, we aimed to investigate the mutational spectrum in pediatric ALL. We employed a St. Mary’s customized NGS panel comprising 67 leukemia-related genes. Samples were collected from 139 pediatric ALL patients. Eighty-five patients (61.2%) harbored at least one mutation. In B-cell ALL, the RAS pathway is the most involved pathway, and the three most frequently mutated genes were NRAS (22.4%), KRAS (19.6%), and PTPN11 (8.4%). NRAS and PTPN11 were significantly associated with a high hyperdiploidy karyotype (p = 0.018 and p < 0.001, respectively). In T-cell ALL, the three most frequently mutated genes were NOTCH1 (37.5%), FBXW7 (16.6%), and PTEN (6.2%). Several pairs of co-occurring mutations were found: NRAS with SETD, NRAS with PTPN11 in B-cell ALL (p = 0.024 and p = 0.020, respectively), and NOTCH1 with FBXW7 in T-cell ALL (p < 0.001). The most frequent newly emerged mutation in relapsed ALL was NT5C2. We procured comprehensive genetic information regarding Korean pediatric ALL using NGS technology. Our findings strengthen the current knowledge of recurrent somatic mutations in pediatric ALL.

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Section: Methodsmentioning

confidence: 99%

Spectrum of Genetic Mutations in Korean Pediatric Acute Lymphoblastic Leukemia

Yoo

Ahn

Lee

et al. 2022

JCM

Self Cite

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“…Gao et al predicted a significant association between Luminal and HER2 breast cancer subtypes and estrogen/progesterone and HER2 receptor status, using the DeepCC method (Gao et al, 2019). Lee et al comprehensively analyzed RNA-seq data and identified a potential role for machine learning in identifying categories of acute leukemia (Lee et al, 2021). Based on traditional statistical analysis, we found that Circ_0059706 level are closely related with survival, suggesting its potential as a biomarker in patients with AML.…”

Section: Discussionmentioning

confidence: 80%

Predicting the influence of Circ_0059706 expression on prognosis in patients with acute myeloid leukemia using classical statistics and machine learning

Wen

et al. 2022

Front. Genet.

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Background: Various circular RNA (circRNA) molecules are abnormally expressed in acute myeloid leukemia (AML), and associated with disease occurrence and development, as well as patient prognosis. The roles of circ_0059706, a circRNA derived from ID1, in AML remain largely unclear.Results: Here, we reported circ_0059706 expression in de novo AML and its association with prognosis. We found that circ_0059706 expression was significantly lower in AML patients than in controls (p < 0.001). Survival analysis of patients with AML divided into two groups according to high and low circ_0059706 expression showed that overall survival (OS) of patients with high circ_0059706 expression was significantly longer than that of those with low expression (p < 0.05). Further, female patients with AML and those aged >60 years old in the high circ_0059706 expression group had longer OS than male patients and those younger than 60 years. Multiple regression analysis showed that circ_0059706 was an independent factor-affecting prognosis of all patients with AML. To evaluate the prospects for application of circ_0059706 in machine learning predictions, we developed seven types of algorithm. The gradient boosting (GB) model exhibited higher performance in prediction of 1-year prognosis and 3-year prognosis, with AUROC 0.796 and 0.847. We analyzed the importance of variables and found that circ_0059706 expression level was the first important variables among all 26 factors included in the GB algorithm, suggesting the importance of circ_0059706 in prediction model. Further, overexpression of circ_0059706 inhibited cell growth and increased apoptosis of leukemia cells in vitro.Conclusion: These results provide evidence that high expression of circ_0059706 is propitious for patient prognosis and suggest circ_0059706 as a potential new biomarker for diagnosis and prognosis evaluation in AML, with high predictive value and good prospects for application in machine learning algorithms.

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“…The high volume of RNA sequencing data per individual makes it necessary to employ machine learning techniques to process the raw information for the identification of subtypes [7,11,12,[18][19][20]. In one such study, Gu et al used clustering algorithms to characterize the transcriptional landscape of ALL [12]; the clusters were further investigated against identifiable genomic lesions to classify individual patients and to refine the taxonomy of ALL.…”

Section: Introductionmentioning

confidence: 99%

Multi-cohort transcriptomic subtyping of B-cell acute lymphoblastic leukemia

Mäkinen

Rehn

Breen

et al. 2022

Preprint

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Acute lymphoblastic leukemia (ALL) is the most common childhood cancer and comprises multiple genetically distinguishable subtypes. To detect subtypes, current pipelines include fusion calling, polymorphisms, candidate gene copy numbers and cytogenetics but these approaches have limitations. RNA-seq provides a functional genome-wide snapshot that enables classification of ALL subtypes, however, typical mRNA-seq clustering analyses lack the rigor of quantitative modelling. Furthermore, high-dimensional gene expression data across cohorts and countries come with biases that previous transcriptomics studies have not addressed. Our aim was to integrate easy-to-interpret reliable transcriptome-wide biomarkers into subtyping pipelines. We analyzed 2,046 samples from two continents, carefully adjusted for biases, and applied a rigorous machine learning design with independent replication. Six ALL subtypes that covered 32% of patients were robustly detected by mRNA-seq (PPV ≥ 87%). Five other frequent subtypes were distinguishable in 40% of patients, although overlapping transcriptional profiles led to lower accuracy (52% ≤ PPV ≤ 73%). Based on these findings, we developed the Allspice tool that predicts ALL subtypes and driver genes from unadjusted mRNA-seq read counts as encountered in real-world settings. Allspice also includes quantitative classification and safety metrics to help determine the most plausible genetic drivers for cases where other findings are inconclusive.

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Integrative Analysis of Gene Expression Data by RNA Sequencing for Differential Diagnosis of Acute Leukemia: Potential Application of Machine Learning

Cited by 6 publications

References 33 publications

Spectrum of Genetic Mutations in Korean Pediatric Acute Lymphoblastic Leukemia

Spectrum of Genetic Mutations in Korean Pediatric Acute Lymphoblastic Leukemia

Predicting the influence of Circ_0059706 expression on prognosis in patients with acute myeloid leukemia using classical statistics and machine learning

Multi-cohort transcriptomic subtyping of B-cell acute lymphoblastic leukemia

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