The transcriptome‐wide landscape of molecular subtype‐specific <scp>mRNA</scp> expression profiles in acute myeloid leukemia

Mou, Tian; Pawitan, Yudi; Stahl, Matthias; Vesterlund, Mattias; Deng, Wenjiang; Jafari, Rozbeh; Bohlin, Anna; Österroos, Albin; Siavelis, Loannis; Bäckvall, Helena; Erkers, Tom; Kiviluoto, Santeri; Seashore‐Ludlow, Brinton; Östling, Päivi; Orre, Lukas M.; Kallioniemi, Olli; Lehmann, Sören; Lehtiö, Janne; Vu, Trung Nghia

doi:10.1002/ajh.26141

Cited by 9 publications

(11 citation statements)

References 31 publications

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“…We investigate the association between the molecular subtypes and drugs by assessing the functional interaction between drug-target genes and the genes that are specific to the subtypes. The molecular subtype-specific genes are collected from the recent work of Mou et al 14 , and the target genes of each drugs are collected from DrugBank database 15 . For each pair of (subtype, drug), we apply the network enrichment analysis (NEA) 16 to obtain the enrichment score ( z -score) between the two genesets; see further details in the section “AML molecular subtypes in relation to drug response”.…”

Section: Resultsmentioning

confidence: 99%

“…First, the gene expression of each sample is normalized by median centering and unit variance (median = 0, sd = 1). We then focus on AML-context-driven genes, including AML subtype-specific genes: we utilize the findings from Mou et al 14 , which provides a list of genes specific to 11 AML molecular subtypes. For each molecular subtype, we keep 15 top genes.…”

Section: Methodsmentioning

confidence: 99%

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Prediction model for drug response of acute myeloid leukemia patients

et al. 2023

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Despite some encouraging successes, predicting the therapy response of acute myeloid leukemia (AML) patients remains highly challenging due to tumor heterogeneity. Here we aim to develop and validate MDREAM, a robust ensemble-based prediction model for drug response in AML based on an integration of omics data, including mutations and gene expression, and large-scale drug testing. Briefly, MDREAM is first trained in the BeatAML cohort (n = 278), and then validated in the BeatAML (n = 183) and two external cohorts, including a Swedish AML cohort (n = 45) and a relapsed/refractory acute leukemia cohort (n = 12). The final prediction is based on 122 ensemble models, each corresponding to a drug. A confidence score metric is used to convey the uncertainty of predictions; among predictions with a confidence score >0.75, the validated proportion of good responders is 77%. The Spearman correlations between the predicted and the observed drug response are 0.68 (95% CI: [0.64, 0.68]) in the BeatAML validation set, –0.49 (95% CI: [–0.53, –0.44]) in the Swedish cohort and 0.59 (95% CI: [0.51, 0.67]) in the relapsed/refractory cohort. A web-based implementation of MDREAM is publicly available at https://www.meb.ki.se/shiny/truvu/MDREAM/.

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Section: Resultsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Prediction model for drug response of acute myeloid leukemia patients

et al. 2023

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“…refs. [12][13][14][15] ). Only a limited number of studies have investigated differential expression in relapsed and primary resistant (R/PR) AML, and most of these lacked patient-matched longitudinal samples and/or detailed knowledge about underlying genetic alterations in the form of whole genomeor whole exome sequencing (WGS; WES) data (e.g.…”

Section: Introductionmentioning

confidence: 99%

Transcriptomic analysis reveals proinflammatory signatures associated with acute myeloid leukemia progression

et al. 2022

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During the last decade, numerous studies have been carried out to exploit the complexity of genomic and transcriptomic lesions driving acute myeloid leukemia (AML) initiation. These studies have helped improve risk classification and treatment options. Detailed molecular characterization of longitudinal AML samples are, however, sparse, meanwhile relapse and therapy resistance represent the main challenge in AML care. To this end, we performed transcriptome-wide RNA sequencing of longitudinal diagnosis, relapse and/or primary resistant samples from 47 adult and 23 pediatric AML patients with known mutational background. Gene expression analysis revealed the association of short event-free survival with overexpression of GLI2 and IL1R1, as well as downregulation of ST18. Moreover, CR1-downregulation and DPEP1-upregulation were associated with AML relapse both in adults and children. Finally, machine learning and network-based analysis identified overexpressed CD6 and downregulated INSR as highly co-predictive genes depicting important relapse-associated characteristics among adult AML patients. Our findings point towards the importance of a tumor-promoting inflammatory environment in leukemia progression, as indicated by several of the herein identified differentially expressed genes. Together, this knowledge provides the foundation for novel personalized drug targets and has the potential to maximize the benefit of current treatments, to improve cure rates in AML.

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“…It is highly expressed in Cluster 2 and lowly expressed in the rest of the clusters. The cluster-specific genes were selected following two criteria: T1 less than 0.01 and T2 greater than 0.1, which ensures that the expression of cluster-specific genes are higher in one specific cluster than the others, while there is no significant difference in the others [ 56 ]. A total of 141, 359, 740, and 207 genes were considered as the feature-specific genes for each cluster, respectively.…”

Section: Resultsmentioning

confidence: 99%

A Comprehensive Landscape of Imaging Feature-Associated RNA Expression Profiles in Human Breast Tissue

Mou

Liang

et al. 2023

Sensors

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The expression abundance of transcripts in nondiseased breast tissue varies among individuals. The association study of genotypes and imaging phenotypes may help us to understand this individual variation. Since existing reports mainly focus on tumors or lesion areas, the heterogeneity of pathological image features and their correlations with RNA expression profiles for nondiseased tissue are not clear. The aim of this study is to discover the association between the nucleus features and the transcriptome-wide RNAs. We analyzed both microscopic histology images and RNA-sequencing data of 456 breast tissues from the Genotype-Tissue Expression (GTEx) project and constructed an automatic computational framework. We classified all samples into four clusters based on their nucleus morphological features and discovered feature-specific gene sets. The biological pathway analysis was performed on each gene set. The proposed framework evaluates the morphological characteristics of the cell nucleus quantitatively and identifies the associated genes. We found image features that capture population variation in breast tissue associated with RNA expressions, suggesting that the variation in expression pattern affects population variation in the morphological traits of breast tissue. This study provides a comprehensive transcriptome-wide view of imaging-feature-specific RNA expression for healthy breast tissue. Such a framework could also be used for understanding the connection between RNA expression and morphology in other tissues and organs. Pathway analysis indicated that the gene sets we identified were involved in specific biological processes, such as immune processes.

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The transcriptome‐wide landscape of molecular subtype‐specific mRNA expression profiles in acute myeloid leukemia

Cited by 9 publications

References 31 publications

Prediction model for drug response of acute myeloid leukemia patients

Prediction model for drug response of acute myeloid leukemia patients

Transcriptomic analysis reveals proinflammatory signatures associated with acute myeloid leukemia progression

A Comprehensive Landscape of Imaging Feature-Associated RNA Expression Profiles in Human Breast Tissue

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