Abstract CN07-04: Patient-specific pathway analysis using PARADIGM identifies key activities in multiple cancers.

ABSTRACTPathway level understanding of cancer plays a key role in precision oncology. In this study, we developed a novel data-driven model, called the OR-gate Network (ORN), to simultaneously infer functional relationships among mutations, patient-specific pathway activities, and gene co-expression. In principle, logical OR gates agree with mutual exclusivity patterns in somatic mutations and bicluster patterns in transcriptomic profiles. In a trained ORN, the differential expression profiles of tumours can be explained by somatic mutations perturbing signalling pathways. We applied ORN to lower grade glioma (LLG) samples in TCGA and breast cancer samples from METABRIC. Both datasets have shown pathway patterns related to immune response and cell cycles. In LLG samples, ORN identified multiple metabolic pathways closely related to glioma development and revealed two pathways closely related to patient survival. Additional results from the METABRIC datasets showed that ORN could characterize key mechanisms of cancer and connect them to less studied somatic mutations (e.g., BAP1, MIR604, MICAL3, and telomere activities), which may generate novel hypothesis for targeted therapy.

Download Full-text

Inference of patient-specific pathway activities from multi-dimensional cancer genomics data using PARADIGM

Bioinformatics ◽

10.1093/bioinformatics/btq182 ◽

2010 ◽

Vol 26 (12) ◽

pp. i237-i245 ◽

Cited By ~ 479

Author(s):

Charles J. Vaske ◽

Stephen C. Benz ◽

J. Zachary Sanborn ◽

Dent Earl ◽

Christopher Szeto ◽

...

Keyword(s):

Cancer Genomics ◽

Patient Specific ◽

Specific Pathway

Download Full-text

KeyPathwayMiner 4.0: condition-specific pathway analysis by combining multiple omics studies and networks with Cytoscape

BMC Systems Biology ◽

10.1186/s12918-014-0099-x ◽

2014 ◽

Vol 8 (1) ◽

Cited By ~ 38

Author(s):

Nicolas Alcaraz ◽

Josch Pauling ◽

Richa Batra ◽

Eudes Barbosa ◽

Alexander Junge ◽

...

Keyword(s):

Pathway Analysis ◽

Specific Pathway

Download Full-text

Using predictive machine learning models for drug response simulation by calibrating patient-specific pathway signatures

npj Systems Biology and Applications ◽

10.1038/s41540-021-00199-1 ◽

2021 ◽

Vol 7 (1) ◽

Author(s):

Sepehr Golriz Khatami ◽

Sarah Mubeen ◽

Vinay Srinivas Bharadhwaj ◽

Alpha Tom Kodamullil ◽

Martin Hofmann-Apitius ◽

...

Keyword(s):

Machine Learning ◽

Drug Response ◽

Clinical Decision Making ◽

Biological Process ◽

Drug Repurposing ◽

Clinical Decision ◽

Patient Specific ◽

Drug Candidates ◽

Scoring Algorithm ◽

Specific Pathway

AbstractThe utility of pathway signatures lies in their capability to determine whether a specific pathway or biological process is dysregulated in a given patient. These signatures have been widely used in machine learning (ML) methods for a variety of applications including precision medicine, drug repurposing, and drug discovery. In this work, we leverage highly predictive ML models for drug response simulation in individual patients by calibrating the pathway activity scores of disease samples. Using these ML models and an intuitive scoring algorithm to modify the signatures of patients, we evaluate whether a given sample that was formerly classified as diseased, could be predicted as normal following drug treatment simulation. We then use this technique as a proxy for the identification of potential drug candidates. Furthermore, we demonstrate the ability of our methodology to successfully identify approved and clinically investigated drugs for four different cancers, outperforming six comparable state-of-the-art methods. We also show how this approach can deconvolute a drugs’ mechanism of action and propose combination therapies. Taken together, our methodology could be promising to support clinical decision-making in personalized medicine by simulating a drugs’ effect on a given patient.

Download Full-text

pathwayPCA: an R package for integrative pathway analysis with modern PCA methodology and gene selection

10.1101/615435 ◽

2019 ◽

Cited By ~ 4

Author(s):

Gabriel J. Odom ◽

Yuguang Ban ◽

Lizhong Liu ◽

Xiaodian Sun ◽

Alexander R. Pico ◽

...

Keyword(s):

Pathway Analysis ◽

Gene Selection ◽

Principal Component ◽

R Package ◽

Statistical Methodology ◽

Driver Genes ◽

High Throughput Technology ◽

Adaptive Elastic Net ◽

Specific Pathway ◽

Wide Scientific Community

ABSTRACTWith the advance in high-throughput technology for molecular assays, multi-omics datasets have become increasingly available. However, most currently available pathway analysis software provide little or no functionalities for analyzing multiple types of -omics data simultaneously. In addition, most tools do not provide sample-specific estimates of pathway activities, which are important for precision medicine. To address these challenges, we present pathwayPCA, a unique R package for integrative pathway analysis that utilizes modern statistical methodology including supervised PCA and adaptive elastic-net PCA for principal component analysis. pathwayPCA can analyze continuous, binary, and survival outcomes in studies with multiple covariate and/or interaction effects. We provide three case studies to illustrate pathway analysis with gene selection, integrative analysis of multi-omics datasets to identify driver genes, estimating and visualizing sample-specific pathway activities in ovarian cancer, and identifying sex-specific pathway effects in kidney cancer. pathwayPCA is an open source R package, freely available to the research community. We expect pathwayPCA to be a useful tool for empowering the wide scientific community on the analyses and interpretation of the wealth of multiomics data recently made available by TCGA, CPTAC and other large consortiums.

Download Full-text