Why do pathway methods work better than they should?

Szalai, Bence; Sáez-Rodríguez, Julio

doi:10.1002/1873-3468.14011

Cited by 29 publications

(24 citation statements)

References 35 publications

(56 reference statements)

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“…While our feature genes exhibited certain correlations with known driver genes, this itself does not provide direct evidence of their functional significance. Feature genes could also be the result of the downstream effects of mutational activities or be co-opted as a representation of an aberrant region where the pathogenetic activity is provided by different genomic elements (Szalai and Saez-Rodriguez, 2020 ). In the analysis, we were prudent to limit propositions of the impact of individual feature genes.…”

Section: Discussionmentioning

confidence: 99%

Signatures of Discriminative Copy Number Aberrations in 31 Cancer Subtypes

Gao

Baudis

2021

Front. Genet.

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Copy number aberrations (CNA) are one of the most important classes of genomic mutations related to oncogenetic effects. In the past three decades, a vast amount of CNA data has been generated by molecular-cytogenetic and genome sequencing based methods. While this data has been instrumental in the identification of cancer-related genes and promoted research into the relation between CNA and histo-pathologically defined cancer types, the heterogeneity of source data and derived CNV profiles pose great challenges for data integration and comparative analysis. Furthermore, a majority of existing studies have been focused on the association of CNA to pre-selected “driver” genes with limited application to rare drivers and other genomic elements. In this study, we developed a bioinformatics pipeline to integrate a collection of 44,988 high-quality CNA profiles of high diversity. Using a hybrid model of neural networks and attention algorithm, we generated the CNA signatures of 31 cancer subtypes, depicting the uniqueness of their respective CNA landscapes. Finally, we constructed a multi-label classifier to identify the cancer type and the organ of origin from copy number profiling data. The investigation of the signatures suggested common patterns, not only of physiologically related cancer types but also of clinico-pathologically distant cancer types such as different cancers originating from the neural crest. Further experiments of classification models confirmed the effectiveness of the signatures in distinguishing different cancer types and demonstrated their potential in tumor classification.

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Section: Discussionmentioning

confidence: 99%

Signatures of Discriminative Copy Number Aberrations in 31 Cancer Subtypes

Gao

Baudis

2021

Front. Genet.

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“…In contrast to existing methods that use gene expression as a direct proxy for signaling [82][83][84][85], our approach infers transcription factor activities from the expression data and uses these as an anchor to infer upstream signaling networks relevant to the regulation of our phenotypes. Transcription factor activities can represent the outcome of a signal transduction process compared to the expression profiles and are thus a better proxy for cell signaling activities of the cell [86]. Such an approach has been previously used, for example by the tool CARNIVAL [87].…”

Section: Discussionmentioning

confidence: 99%

Identification of phenotype-specific networks from paired gene expression-cell shape imaging data

Barker

Petsalaki

Giudice

et al. 2021

Preprint

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The morphology of breast cancer cells is often used as an indicator of tumour severity and prognosis. Additionally, morphology can be used to identify more fine-grained, molecular developments within a cancer cell, such as transcriptomic changes and signaling pathway activity. Delineating the interface between morphology and signaling is important to understand the mechanical cues that a cell processes in order to undergo epithelial-to-mesenchymal transition and consequently metastase. However, the exact regulatory systems that define these changes remain poorly characterised. In this study, we employ a network-systems approach to integrate imaging data and RNA-seq expression data. By constructing a cell-shape signaling network from shape correlated gene expression modules and their upstream regulators, we found central roles for development pathways such as Wnt and Notch as well as evidence for the fine control of NFkB signaling by numerous kinase and transcriptional regulators. Further analysis of our network implicates the small GTPase, Rap1 as a potential mediator between the sensing of mechanical stimuli and regulation of NFkB activity. Overall, our analysis provides mechanistic information on the interplay between cell signalling, gene regulation and cell morphology and our approach is generalisable to other cell phenotypes.

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“…phosphorylation, acetylation) of signaling proteins constituting the pathway. Thus, mapping transcriptomic signatures of pathway genes to compute pathway activities can produce misleading target pathway hypotheses ( Szalai and Saez-Rodriguez, 2020 ).…”

Section: Introductionmentioning

confidence: 99%

Predicting mechanism of action of novel compounds using compound structure and transcriptomic signature coembedding

et al. 2021

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Motivation Identifying mechanism of actions (MoA) of novel compounds is crucial in drug discovery. Careful understanding of MoA can avoid potential side effects of drug candidates. Efforts have been made to identify MoA using the transcriptomic signatures induced by compounds. However, these approaches fail to reveal MoAs in the absence of actual compound signatures. Results We present MoAble, which predicts MoAs without requiring compound signatures. We train a deep learning-based coembedding model to map compound signatures and compound structure into the same embedding space. The model generates low-dimensional compound signature representation from the compound structures. To predict MoAs, pathway enrichment analysis is performed based on the connectivity between embedding vectors of compounds and those of genetic perturbation. Results show that MoAble is comparable to the methods that use actual compound signatures. We demonstrate that MoAble can be used to reveal MoAs of novel compounds without measuring compound signatures with the same prediction accuracy as that with measuring them. Availability and implementation MoAble is available at https://github.com/dmis-lab/moable Supplementary information Supplementary data are available at Bioinformatics online.

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Why do pathway methods work better than they should?

Cited by 29 publications

References 35 publications

Signatures of Discriminative Copy Number Aberrations in 31 Cancer Subtypes

Signatures of Discriminative Copy Number Aberrations in 31 Cancer Subtypes

Identification of phenotype-specific networks from paired gene expression-cell shape imaging data

Predicting mechanism of action of novel compounds using compound structure and transcriptomic signature coembedding

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