Inference of patient-specific pathway activities from multi-dimensional cancer genomics data using PARADIGM
Open accessAbstract
Motivation: High-throughput data is providing a comprehensive view of the molecular changes in cancer tissues. New technologies allow for the simultaneous genome-wide assay of the state of genome copy number variation, gene expression, DNA methylation and epigenetics of tumor samples and cancer cell lines. Analyses of current data sets find that genetic alterations between patients can differ but often involve common pathways. It is therefore critical to identify relevant pathways involved in cancer progression and detect how they are altered in different patients. Results: We present a novel method for inferring patient-specific genetic activities incorporating curated pathway interactions among genes. A gene is modeled by a factor graph as a set of interconnected variables encoding the expression and known activity of a gene and its products, allowing the incorporation of many types of omic data as evidence. The method predicts the degree to which a pathway's activities (e.g. internal gene states, interactions or high-level ‘outputs’) are altered in the patient using probabilistic inference. Compared with a competing pathway activity inference approach called SPIA, our method identifies altered activities in cancer-related pathways with fewer false-positives in both a glioblastoma multiform (GBM) and a breast cancer dataset. PARADIGM identified consistent pathway-level activities for subsets of the GBM patients that are overlooked when genes are considered in isolation. Further, grouping GBM patients based on their significant pathway perturbations divides them into clinically-relevant subgroups having significantly different survival outcomes. These findings suggest that therapeutics might be chosen that target genes at critical points in the commonly perturbed pathway(s) of a group of patients. Availability:Source code available at http://sbenz.github.com/Paradigm Contact: jstuart@soe.ucsc.edu Supplementary information: Supplementary data are available at Bioinformatics online.
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A large set of multi-omics data integrative tools and methods have been reported for the discovery of biomarkers.
“Along with the introduction of high-throughput technologies and the availability of multi-omics data derived from a number of samples, a large set of multi-omics data integrative tools and methods have been reported for the discovery of biomarkers.”
From Bioinformatics Analysis and Validation of Potential Markers Associated with Prediction and Prognosis of Gastric Cancer (2024) · cites this paper as [97]
The Introduction (page not available in our copy) says that few computational approaches had been available and that new approaches were needed to connect multiple types of genomic alteration. It describes an existing pathway method as limited to a single data type, contrary to the claim that a large set of multi-omics integrative tools had been reported for biomarker discovery. (LLM, conf=0.84)
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Experimental — accuracy not yet measuredThese methods can be divided into categories, such as Bayesian integration, network and pathway integration, fusion-based integration, similarity-based integration, and other multivariate approaches.
“These methods can be divided into categories, such as Bayesian integration (e.g., Pathway Recognition Algorithm using Data Integration on Genomic Models (PARADIGM) [97], iClusterPlus [98], Patient-specific data fusion (PSDF) [99]), network and pathway integration (e.g., Similarity network fusion (SNF) [100], PaintOmics [101], MultiGSEA [102], multi-omics gene-set analysis (MOGSA) [103], Cytoscape”
From Bioinformatics Analysis and Validation of Potential Markers Associated with Prediction and Prognosis of Gastric Cancer (2024) · cites this paper as [97]
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- Authors
- Vaske, C. J., Benz, S. C., Sanborn, J. Z., Earl, D., Szeto, C., Zhu, J., Haussler, D., & Stuart, J. M.
- Affiliations
- Howard Hughes Medical Institute (US)
- 1 Howard Hughes Medical Institute and 2 Department of Biomolecular Engineering and Center for Biomolecular Science and Engineering, UC Santa Cruz, CA, USA
- Corresponding author
- Stuart, Joshua M.
- ORCID
- Venue
- Bioinformatics
- Publisher
- Oxford University Press (OUP)
- Published
- 2010-06-01
- Volume
- 26
- Issue
- 12
- Pages
- i237-i245
- ISSN
- 1367-4803 (ISSN-L)
- 1367-4811
- PMC
- PMC2881367
- OpenAlex
- W2100714130
- Cited by
- 786
- References
- 6
- Citations by year
- 2026: 8 · 2025: 31 · 2024: 39 · 2023: 32 · 2022: 48 · 2021: 52 · 2020: 66 · 2019: 59 · 2018: 58 · 2017: 58 · 2016: 65 · 2015: 68 · 2014: 74 · 2013: 56 · 2012: 44
- FWCI
- 16.30
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Bioinformatics (publishedVersion, cc-by-nc)
- PubMed Central (publishedVersion, cc-by-nc)
- PubMed (publishedVersion)
- Funders
- Howard Hughes Medical Institute
- Cancer Research Coordinating Committee
- National Institutes of Health (5r21ca135937-02)
- National Cancer Institute (R21 CA135937, U24CA143858)
- National Institute of General Medical Sciences (T32GM070386)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Bioinformatics and Genomic Networks
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Cancer Research › Cancer Genomics and Diagnostics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene expression and cancer classification
- Keywords
- pathway activity inference, paradigm, factor graphs, probabilistic inference, multi-omics integration, cancer genomics, pathway perturbation, glioblastoma, breast cancer, patient subgroups, survival outcomes, targeted therapy, gene expression, copy number variation, DNA methylation, epigenetics
- MeSH terms
- Breast Neoplasms, Female, Glioblastoma, Humans, Neoplasms, Software, Gene Expression Profiling, Genomics, DNA Copy Number Variations
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