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Projection of Expression Profiles to Transcription Factor Activity Space Provides Added Information.

ABSTRACT: Acute myeloid leukemia (AML) is an aggressive type of leukemia, characterized by the accumulation of highly proliferative blasts with a disrupted myeloid differentiation program. Current treatments are ineffective for most patients, partly due to the genetic heterogeneity of AML. This is driven by genetically distinct leukemia stem cells, resulting in relapse even after most of the tumor cells are destroyed. Thus, personalized treatment approaches addressing cellular heterogeneity are urgently required. Reconstruction of Transcriptional regulatory Networks (RTN) is a tool for inferring transcriptional activity in patients with various diseases. In this study, we applied this method to transcriptome profiles of AML patients to test if it provided additional information for the interpretation of transcriptome data. We showed that when RTN results were added to RNA-seq results, superior clusters were formed, which were more homogenous and allowed the better separation of patients with low and high survival rates. We concluded that the external knowledge used for RTN analysis improved the ability of unsupervised machine learning to find meaningful patterns in the data.

SUBMITTER: Bornshten R

PROVIDER: S-EPMC9601664 | biostudies-literature | 2022 Oct

REPOSITORIES: biostudies-literature

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Projection of Expression Profiles to Transcription Factor Activity Space Provides Added Information.

Bornshten Rut R Danilenko Michael M Rubin Eitan E

Genes 20221008 10

Acute myeloid leukemia (AML) is an aggressive type of leukemia, characterized by the accumulation of highly proliferative blasts with a disrupted myeloid differentiation program. Current treatments are ineffective for most patients, partly due to the genetic heterogeneity of AML. This is driven by genetically distinct leukemia stem cells, resulting in relapse even after most of the tumor cells are destroyed. Thus, personalized treatment approaches addressing cellular heterogeneity are urgently r ...[more]

PMID: 36292704

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Project description:BackgroundThe identification of transcription factors (TFs) associated with a biological process is fundamental to understanding its regulatory mechanisms. From microarray data, however, the activity changes of TFs often cannot be directly observed due to their relatively low expression levels, post-transcriptional modifications, and other complications. Several approaches have been proposed to infer TF activity changes from microarray data. In some models, a linear relationship between gene expression and TF-gene binding strength is assumed. In some other models, the target genes of a TF are first determined by a significance cutoff to binding affinity scores, and then expression differentiation is checked between the target and other genes.ResultsWe propose a novel method, referred to as BASE (binding association with sorted expression), to infer TF activity changes from microarray expression profiles with the help of binding affinity data. It searches the maximum association between bind affinity profile of a TF and expression change profile along the direction of sorted differentiation. The method does not make hard target gene selection, rather, the significances of TF activity changes are evaluated by permutation tests of binding association at the end. To show the effectiveness of this method, we apply it to three typical examples using different kinds of binding affinity data, namely, ChIP-chip data, motif discovery data, and positional weighted matrix scanning data, respectively. The implications obtained from all three examples are consistent with established biological results. Moreover, the inferences suggest new and biological meaningful hypotheses for further investigation.ConclusionThe proposed method makes transcription inference from profiles of expression and binding affinity. The same machinery can be used to deal with various kinds of binding affinity data. The method does not require a linear assumption, and has the desirable property of scale-invariance with respect to TF-specific binding affinity. This method is easy to implement and can be routinely applied for transcriptional inferences in microarray studies.

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Projection of Expression Profiles to Transcription Factor Activity Space Provides Added Information.

Publications

Projection of Expression Profiles to Transcription Factor Activity Space Provides Added Information.

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