The pan-cancer pathological regulatory landscape

Falco, Matías M.; Bleda, Marta; Carbonell-Caballero, José; Dopazo, Joaquín

doi:10.1038/srep39709

Cited by 33 publications

(36 citation statements)

References 81 publications

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“…Inference of TF activities from the expression levels of their putative targets is becoming a widespread tool to extract functional insight from transcriptomic data [2][3][4][5][6][7]69 . Although several strategies exist to define the TF' targets (i.e.…”

Section: Discussionmentioning

confidence: 99%

Benchmark and integration of resources for the estimation of human transcription factor activities

García-Alonso

Ibrahim

Türei

et al. 2018

Preprint

132

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Prediction of transcription factor (TF) activities from the gene expression of their targets (i.e. TF regulon) is becoming a widely-used approach to characterize the functional status of transcriptional regulatory circuits. Several strategies and datasets have been proposed to link the target genes likely regulated by a TF, each one providing a different level of evidence. The most established ones are: (i) manually curated repositories, (ii) interactions derived from ChIP-seq binding data, (iii) in silico prediction of TF binding on gene promoters, and (iv) reverse-engineered regulons from large gene expression datasets. However, it is not known how these different sources of regulons affect the TF activity estimations, and thereby downstream analysis and interpretation. Here we compared the accuracy and biases of these strategies to define human TF regulons by means of their ability to predict changes in TF activities in three reference benchmark datasets. We assembled a collection of TF-target interactions among 1,541 TFs, and evaluated how the different molecular and regulatory properties of the TFs, such as the DNA-binding domain, specificities or mode of interaction with the chromatin, affect the predictions of TF activity changes. We assessed their coverage and found little overlap on the regulons derived from each strategy and better performance by literature-curated information followed by ChIP-seq data. We provide an integrated resource of all TF-target interactions derived through these strategies with a confidence score, as a resource for enhanced prediction of TF activities.

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Section: Discussionmentioning

confidence: 99%

Benchmark and integration of resources for the estimation of human transcription factor activities

García-Alonso

Ibrahim

Türei

et al. 2018

Preprint

132

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“…TFs activities derived from gene expression data have attracted much attention in cancer research during the last few years. Recent studies have applied DNA-binding networks derived from ENCODE ChIP-Seq data to compare TF activity profiles across different cancers and evaluate their potential as prognostic markers [11,12] . Alternative approaches have estimated protein activities using tumor-specific inferred gene networks and applied them to characterise the impact of somatic alterations [8,9] , proposing new hypotheses on how specific driver mutations may alter transcriptional regulators.…”

Section: Discussionmentioning

confidence: 99%

“…Different from driver alterations in intracellular kinase-mediated signalling cascades, where redundancy may bypass the driver or provide compensatory mechanisms, aberrant transcriptional regulators have been argued to be harder to circumvent by secondary genomic alterations [7] . Consequently, TFs have been proposed as key nodal oncogenic drivers and their activity patterns used to characterise genomic aberrations in cancer [8][9][10] or their influence in a patient's prognosis [11,12] .…”

Section: Introductionmentioning

confidence: 99%

Transcription factor activities enhance markers of drug response in cancer

Garcia‐Alonso

Iorio

Matchan

et al. 2017

Preprint

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Transcriptional dysregulation is a key feature of cancer. Transcription factors (TFs) are the main link between signalling pathways and the transcriptional regulatory machinery of the cell, positioning them as key oncogenic inductors and therefore potential targets of therapeutic intervention. We implemented a computational pipeline to infer TF regulatory activities from basal gene expression and applied it to publicly available and newly generated RNA-seq data from a collection of 1,010 cancer cell lines and 9,250 primary tumors. We show that the predicted TF activities recapitulate known mechanisms of transcriptional dysregulation in cancer and dissect mutant-specific effects in driver genes. Importantly, we show the potential for predicted TF activities to be used as markers of sensitivity to the inhibition of their upstream regulators. Furthermore, combining these inferred activities with existing pharmacogenomic markers significantly improves the stratification of sensitive and resistant cell lines for several compounds. Our approach provides a framework to link driver genomic alterations with transcriptional dysregulation that helps to predict drug sensitivity in cancer and to dissect its mechanistic determinants.

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“…The variant overlapped a MYC:MAX heterodimer motif, the MYC:MAC dimer is crucial for binding of MYC to the TF binding site (41). Both MYC and MYC:MAX are known as oncogenes in many cancer types, including breast cancer (42)(43)(44). Genes: ATP6AP1L, ATG10 and RPS23 were found affected by the candidate SNPs.…”

Section: Enrichment Of Genetic Variantsmentioning

confidence: 99%

funMotifs: Tissue-specific transcription factor motifs

Smolinska-Garbulowska

Marzouka

et al. 2019

Preprint

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Transcription factors (TF) regulate gene expression by binding to specific sequences known as motifs. A bottleneck in our knowledge of gene regulation is the lack of functional characterization of TF motifs, which is mainly due to the large number of predicted TF motifs, and tissue specificity of TF binding. We built a framework to identify tissue-specific functional motifs (funMotifs) across the genome based on thousands of annotation tracks obtained from large-scale genomics projects including ENCODE, RoadMap Epigenomics and FANTOM. The annotations were weighted using a logistic regression model trained on regulatory elements obtained from massively parallel reporter assays. Overall, genome-wide predicted motifs of 519 TFs were characterized across fifteen tissue types. funMotifs summarizes the weighted annotations into a functional activity score for each of the predicted motifs. funMotifs enabled us to measure tissue specificity of different TFs and to identify candidate functional variants in TF motifs from the 1000 genomes project, the GTEx project, the GWAS catalogue, and in 2,515 cancer samples from the Pan-cancer analysis of whole genome sequences (PCAWG) cohort. To enable researchers annotate genomic variants or regions of interest, we have implemented a command-line pipeline and a web-based interface that can publicly be accessed on: http://bioinf.icm.uu.se/funmotifs.

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The pan-cancer pathological regulatory landscape

Cited by 33 publications

References 81 publications

Benchmark and integration of resources for the estimation of human transcription factor activities

Benchmark and integration of resources for the estimation of human transcription factor activities

Transcription factor activities enhance markers of drug response in cancer

funMotifs: Tissue-specific transcription factor motifs

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