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

By Charles J. Vaske, S. Benz, J. Z. Sanborn, Dent Earl, C. Szeto, Jingchun Zhu, David Haussler, Joshua M. Stuart. Bioinform., 2010

781 citations recorded by the source corpus.

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.

Subfields: Cancer Research, Genetics, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/4c707b46a8ca7961461238e26b10be3ae6c51929

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Citations in the corpus

Multi-omics Data Integration, Interpretation, and Its Application (2020, background): “PARADIGM was able to detect the estrogen-and ErbB2-related pathways, 36 which were found to be 2 of the 3 key prognostic signatures in breast cancer in a recent major meta-analysis study.”
Integrative Clinical Genomics of Metastatic Cancer (2017, positive): “Inference of patient-specific activities 27,28 revealed coordinated changes across curated pathways that coalesce into a small number of principal cancer hallmarks (Extended Data Fig.”
Perturbation-response genes reveal signaling footprints in cancer gene expression (2016, methodology): “The following conditions needed to be met in order for us to consider an experiment: (1) the compound or factor used for perturbation was one of our curated list of pathwayperturbing agents (for a list, see table S0); (2) the perturbation lasted for less than 24 hours to capture genes that belong t”
Multi-platform analysis of 12 cancer types reveals molecular classification within and across tissues-of-origin (2014, methodology): “…(i) an algorithm called SuperCluster (Kandoth et al., 2013b) (Figure S2B) and (ii) clustering based on inferred pathway activities from PARADIGM (Vaske et al., 2010), which integrates gene expression and DNA copy number data with a set of predefined pathways to infer the degree of activity of…”
Integrating diverse genomic data using gene sets (2011, background): “Other approaches achieve integration by building a network that exploit existing knowledge on the biological interactions within a pathway [35].”
More Is Better: Recent Progress in Multi-Omics Data Integration Methods (2017, methodology): “Under the unsupervised category, network-based methods are mostly applied for detecting signiﬁcant genes within pathways, discovering sub-clusters, or ﬁnding co-expression network modules (Vaske et al., 2010; Wang et al., 2014; Bonnet et al., 2015).”
Machine Learning and Integrative Analysis of Biomedical Big Data (2019, methodology): “PARADIGM inferred cellular activities helped classify patients into clinically relevant sub-groups.”
Mutual exclusivity analysis identifies oncogenic network modules. (2012, methodology): “Vaske and colleagues have used a factor graph belief propagation algorithm to integrate copy number and expression data to score curated pathways (Vaske et al. 2010).”
Pathway-based personalized analysis of cancer (2013, methodology): “One prominent exception is Pathway Recognition Algorithm using Data Integration on Genomic Models (PARADIGM) (11), a tool that deduces for each pathway and sample a score using the pathway ’ s known connectivity and functional structure.”
Methods for the integration of multi-omics data: mathematical aspects (2016, methodology): “Within the BN framework an important method for multi-omics data integration is Paradigm [51].”
The value of prior knowledge in machine learning of complex network systems (2016, positive): “We speculate that pathway-level data compression has been shown to be effective for real genomic datasets [13, 14, 15, 16, 17] because the pathways chosen are intrinsically selecting the genes known to show errant behavior in cancer, for example.”
Computational solutions for omics data (2013, methodology): “With the recent accumulation of cancer genomic data sets, another important problem is to identify genes and modules in two-way comparisons between tumour cells and normal cells 76,88,97,98 .”