Integrating Omics Data for Signaling Pathways, Interactome Reconstruction, and Functional Analysis

Abstract Motivation Drug combinations that simultaneously suppress multiple cancer driver signaling pathways increase therapeutic options and may reduce drug resistance. We have developed a computational systems biology tool, DrugComboExplorer, to identify driver signaling pathways and predict synergistic drug combinations by integrating the knowledge embedded in vast amounts of available pharmacogenomics and omics data. Results This tool generates driver signaling networks by processing DNA sequencing, gene copy number, DNA methylation and RNA-seq data from individual cancer patients using an integrated pipeline of algorithms, including bootstrap aggregating-based Markov random field, weighted co-expression network analysis and supervised regulatory network learning. It uses a systems pharmacology approach to infer the combinatorial drug efficacies and synergy mechanisms through drug functional module-induced regulation of target expression analysis. Application of our tool on diffuse large B-cell lymphoma and prostate cancer demonstrated how synergistic drug combinations can be discovered to inhibit multiple driver signaling pathways. Compared with existing computational approaches, DrugComboExplorer had higher prediction accuracy based on in vitro experimental validation and probability concordance index. These results demonstrate that our network-based drug efficacy screening approach can reliably prioritize synergistic drug combinations for cancer and uncover potential mechanisms of drug synergy, warranting further studies in individual cancer patients to derive personalized treatment plans. Availability and implementation DrugComboExplorer is available at https://github.com/Roosevelt-PKU/drugcombinationprediction. Supplementary information Supplementary data are available at Bioinformatics online.

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FuncTree: Functional Analysis and Visualization for Large-Scale Omics Data

PLoS ONE ◽

10.1371/journal.pone.0126967 ◽

2015 ◽

Vol 10 (5) ◽

pp. e0126967 ◽

Cited By ~ 17

Author(s):

Takeru Uchiyama ◽

Mitsuru Irie ◽

Hiroshi Mori ◽

Ken Kurokawa ◽

Takuji Yamada

Keyword(s):

Functional Analysis ◽

Large Scale ◽

Omics Data

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Functional Analysis of OMICs Data and Small Molecule Compounds in an Integrated “Knowledge-Based” Platform

Methods in Molecular Biology - Protein Networks and Pathway Analysis ◽

10.1007/978-1-60761-175-2_10 ◽

2009 ◽

pp. 177-196 ◽

Cited By ~ 51

Author(s):

Yuri Nikolsky ◽

Eugene Kirillov ◽

Roman Zuev ◽

Eugene Rakhmatulin ◽

Tatiana Nikolskaya

Keyword(s):

Functional Analysis ◽

Small Molecule ◽

Omics Data ◽

Knowledge Based ◽

Integrated Knowledge

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Investigating the relevance of major signaling pathways in cancer survival using a biologically meaningful deep learning model

BMC Bioinformatics ◽

10.1186/s12859-020-03850-6 ◽

2021 ◽

Vol 22 (1) ◽

Author(s):

Jiarui Feng ◽

Heming Zhang ◽

Fuhai Li

Keyword(s):

Neural Network ◽

Survival Analysis ◽

Deep Learning ◽

Survival Time ◽

Signaling Pathways ◽

Cancer Patients ◽

Deep Neural Network ◽

Survival Prediction ◽

Omics Data ◽

Learning Models

Abstract Background Survival analysis is an important part of cancer studies. In addition to the existing Cox proportional hazards model, deep learning models have recently been proposed in survival prediction, which directly integrates multi-omics data of a large number of genes using the fully connected dense deep neural network layers, which are hard to interpret. On the other hand, cancer signaling pathways are important and interpretable concepts that define the signaling cascades regulating cancer development and drug resistance. Thus, it is important to investigate potential associations between patient survival and individual signaling pathways, which can help domain experts to understand deep learning models making specific predictions. Results In this exploratory study, we proposed to investigate the relevance and influence of a set of core cancer signaling pathways in the survival analysis of cancer patients. Specifically, we built a simplified and partially biologically meaningful deep neural network, DeepSigSurvNet, for survival prediction. In the model, the gene expression and copy number data of 1967 genes from 46 major signaling pathways were integrated in the model. We applied the model to four types of cancer and investigated the influence of the 46 signaling pathways in the cancers. Interestingly, the interpretable analysis identified the distinct patterns of these signaling pathways, which are helpful in understanding the relevance of signaling pathways in terms of their application to the prediction of cancer patients’ survival time. These highly relevant signaling pathways, when combined with other essential signaling pathways inhibitors, can be novel targets for drug and drug combination prediction to improve cancer patients’ survival time. Conclusion The proposed DeepSigSurvNet model can facilitate the understanding of the implications of signaling pathways on cancer patients’ survival by integrating multi-omics data and clinical factors.

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Functional analysis of SlERF01 gene in the disease resistance to S.lycopersici

10.21203/rs.2.24362/v4 ◽

2020 ◽

Author(s):

Huanhuan Yang ◽

Fengyi Shen ◽

Hexuan Wang ◽

Tingting Zhao ◽

He Zhang ◽

...

Keyword(s):

Functional Analysis ◽

Signaling Pathways ◽

Gray Leaf Spot ◽

Tomato Production ◽

Ros Signaling ◽

Transgenic Lines ◽

Physiological Indexes ◽

Tomato Gray Leaf Spot ◽

Serious Disease ◽

Resistance To Invasion

Abstract Background: Tomato gray leaf spot caused by Stemphylium lycopersici (S. lycopersici) is a serious disease that can severely hinder tomato production. To date, only Sm has been reported to provide resistance against this disease, and the molecular mechanism underlying resistance to this disease in tomato remains unclear. To better understand the mechanism of tomato resistance to S. lycopersici, real-time quantitative reverse transcription-polymerase chain reaction (qRT-PCR)-based analysis, physiological indexes, microscopy observations and transgenic technology were used in this study.Results: Our results showed that the expression of SlERF01 was strongly induced by S. lycopersici and by exogenous applications of the hormones salicylic acid (SA) and jasmonic acid (JA). Furthermore, overexpression of SlERF01 enhanced the hypersensitive response (HR) to S. lycopersici and elevated the expression of defense genes in tomato. Furthermore, the accumulation of lignin, callose and hydrogen peroxide (H2O2) increased in the transgenic lines after inoculation with S. lycopersici. Taken together, our results showed that SlERF01 played an indispensable role in multiple SA, JA and reactive oxygen species (ROS) signaling pathways to provide resistance to S. lycopersici invasion. Our findings also indicated that SlERF01 could activate the expression of the PR1 gene and enhance resistance to S. lycopersici.Conclusions: We identified the SlERF01 gene, which encodes a novel tomato AP2/ERF transcription factor (TF). Functional analysis revealed that SlERF01 positively regulates tomato resistance to S. lycopersici. Our findings indicate that SlERF01 plays a key role in multiple SA, JA and ROS signaling pathways to provide resistance to invasion by S. lycopersici. The findings of this study not only help to better understand the mechanisms of response to pathogens but also enable targeted breeding strategies for tomato resistance to S. lycopersici.

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A New Approach for Identification of Cancer-related Pathways using Protein Networks and Genomic Data

Cancer Informatics ◽

10.4137/cin.s30800 ◽

2015 ◽

Vol 14s5 ◽

pp. CIN.S30800

Author(s):

André Fonseca ◽

Marco D. Gubitoso ◽

Marcelo S. Reis ◽

Sandro J. De Souza ◽

Junior Barrera

Keyword(s):

Control System ◽

Cancer Cells ◽

Signaling Pathways ◽

Protein Networks ◽

Biological Knowledge ◽

Specific Gene ◽

Omics Data ◽

Cancer Genes ◽

New Information ◽

Cellular Control

Cancer cells have anomalous development and proliferation due to disturbances in their control systems. The study of the behavior of cellular control system requires high-throughput dynamical data. Unfortunately, this type of data is not largely available. This fact motivates the main issue of this article: how to use static omics data and available biological knowledge to get new information about the elements of the control system in cancer cells. Two important measures to access the state of the cellular control system are the gene expression profile and the signaling pathways. This article uses a combination of these two static omics data to gain insights on the states of a cancer cell. To extract information from this kind of data, a statistical computational model was formalized and implemented. In order to exemplify the application of some aspects of the developed conceptual framework, we verified the hypothesis that different types of cancer cells have different disturbed signaling pathways. To this end, we developed a method that recovers small protein networks, called motifs, which are differentially represented in some subtypes of breast cancer. These differentially represented motifs are enriched with specific gene ontologies as well as with new putative cancer genes.

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