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'N-of-1-pathways' unveils personal deregulated mechanisms from a single pair of RNA-Seq samples: towards precision medicine.

Gardeux V, Achour I, Li J, Maienschein-Cline M, Li H, Pesce L, Parinandi G, Bahroos N, Winn R, Foster I, Garcia JG, Lussier YA - J Am Med Inform Assoc (2014)

Bottom Line: Cross-patient N-of-1-pathways obtains comparable results with conventional genesets enrichment analysis (GSEA) and differentially expressed gene (DEG) enrichment, validated in three external evaluations.Patients were ranked based on the similarity of their deregulated mechanisms to those of an independent gold standard, generating unsupervised clusters of diametric extreme survival phenotypes (p=0.03).The N-of-1-pathways framework provides a robust statistical and relevant biological interpretation of individual disease-free survival that is often overlooked in conventional cross-patient studies.

View Article: PubMed Central - PubMed

Affiliation: Department of Medicine, Bio5 Institute, UA Cancer Center, University of Arizona, Tucson, Arizona, USA Department of Medicine, University of Illinois at Chicago, Chicago, Illinois, USA Department of Informatics, School of Engineering, EISTI (École Internationale des Sciences du Traitement de l'Information), Cergy-Pontoise, France Institute for Translational Health Informatics, University of Illinois at Chicago, Chicago, Illinois, USA.

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Unveiling the individuality and commonality of the deregulated mechanisms (exploration dataset). (A) Heatmap generated by the list of z scores for each patient (in rows) and each Gene Ontology annotation of Biological Process (GO-BP) term found significantly deregulated in at least one of the 55 patients (in columns). Below the heatmap, three subpanels show additional details for each GO-BP term: (i) the number of patient sharing the given pathway (patient count), (ii) the curated categorization of the GO-BP terms into 10 classes, and (iii) the similarity with the external gold standard (GS) (see the ‘Information theoretic similarity’ section in the Methods). (B) Kaplan–Meier survival curve of the three Partitioning Around Medoids (PAM) clusters derived from the GO-BP z score without clinical information (see ‘The Kaplan–Meier survival curve’ section in the Methods). When only the two most extreme clusters are considered, there is a statistically significant difference in survival (p=0.03), while the difference is just a trend (p=0.09) when the three clusters are considered together. (C, D) Clustering of distinct patients according to the two principal components (see the ‘Principal component analysis of individual GO-BPs’ section in the Methods) of individual GO-BP terms performed on the exploration set. Two diametric extreme survival phenotypes are annotated: ‘death of disease <1 yr’ (red) and ‘disease-free survival >5 yr’ (blue). PCA, principal component analysis. *t test, p<0.05.
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AMIAJNL2013002519F4: Unveiling the individuality and commonality of the deregulated mechanisms (exploration dataset). (A) Heatmap generated by the list of z scores for each patient (in rows) and each Gene Ontology annotation of Biological Process (GO-BP) term found significantly deregulated in at least one of the 55 patients (in columns). Below the heatmap, three subpanels show additional details for each GO-BP term: (i) the number of patient sharing the given pathway (patient count), (ii) the curated categorization of the GO-BP terms into 10 classes, and (iii) the similarity with the external gold standard (GS) (see the ‘Information theoretic similarity’ section in the Methods). (B) Kaplan–Meier survival curve of the three Partitioning Around Medoids (PAM) clusters derived from the GO-BP z score without clinical information (see ‘The Kaplan–Meier survival curve’ section in the Methods). When only the two most extreme clusters are considered, there is a statistically significant difference in survival (p=0.03), while the difference is just a trend (p=0.09) when the three clusters are considered together. (C, D) Clustering of distinct patients according to the two principal components (see the ‘Principal component analysis of individual GO-BPs’ section in the Methods) of individual GO-BP terms performed on the exploration set. Two diametric extreme survival phenotypes are annotated: ‘death of disease <1 yr’ (red) and ‘disease-free survival >5 yr’ (blue). PCA, principal component analysis. *t test, p<0.05.

Mentions: Since a gold standard (GS) for lung adenocarcinoma does not exist, we generated proxy GSs151718 in order to objectively assess the accuracy of the significantly deregulated mechanisms identified by N-of-1-pathways (see online supplementary methods and figures 2–5).


'N-of-1-pathways' unveils personal deregulated mechanisms from a single pair of RNA-Seq samples: towards precision medicine.

Gardeux V, Achour I, Li J, Maienschein-Cline M, Li H, Pesce L, Parinandi G, Bahroos N, Winn R, Foster I, Garcia JG, Lussier YA - J Am Med Inform Assoc (2014)

Unveiling the individuality and commonality of the deregulated mechanisms (exploration dataset). (A) Heatmap generated by the list of z scores for each patient (in rows) and each Gene Ontology annotation of Biological Process (GO-BP) term found significantly deregulated in at least one of the 55 patients (in columns). Below the heatmap, three subpanels show additional details for each GO-BP term: (i) the number of patient sharing the given pathway (patient count), (ii) the curated categorization of the GO-BP terms into 10 classes, and (iii) the similarity with the external gold standard (GS) (see the ‘Information theoretic similarity’ section in the Methods). (B) Kaplan–Meier survival curve of the three Partitioning Around Medoids (PAM) clusters derived from the GO-BP z score without clinical information (see ‘The Kaplan–Meier survival curve’ section in the Methods). When only the two most extreme clusters are considered, there is a statistically significant difference in survival (p=0.03), while the difference is just a trend (p=0.09) when the three clusters are considered together. (C, D) Clustering of distinct patients according to the two principal components (see the ‘Principal component analysis of individual GO-BPs’ section in the Methods) of individual GO-BP terms performed on the exploration set. Two diametric extreme survival phenotypes are annotated: ‘death of disease <1 yr’ (red) and ‘disease-free survival >5 yr’ (blue). PCA, principal component analysis. *t test, p<0.05.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4215042&req=5

AMIAJNL2013002519F4: Unveiling the individuality and commonality of the deregulated mechanisms (exploration dataset). (A) Heatmap generated by the list of z scores for each patient (in rows) and each Gene Ontology annotation of Biological Process (GO-BP) term found significantly deregulated in at least one of the 55 patients (in columns). Below the heatmap, three subpanels show additional details for each GO-BP term: (i) the number of patient sharing the given pathway (patient count), (ii) the curated categorization of the GO-BP terms into 10 classes, and (iii) the similarity with the external gold standard (GS) (see the ‘Information theoretic similarity’ section in the Methods). (B) Kaplan–Meier survival curve of the three Partitioning Around Medoids (PAM) clusters derived from the GO-BP z score without clinical information (see ‘The Kaplan–Meier survival curve’ section in the Methods). When only the two most extreme clusters are considered, there is a statistically significant difference in survival (p=0.03), while the difference is just a trend (p=0.09) when the three clusters are considered together. (C, D) Clustering of distinct patients according to the two principal components (see the ‘Principal component analysis of individual GO-BPs’ section in the Methods) of individual GO-BP terms performed on the exploration set. Two diametric extreme survival phenotypes are annotated: ‘death of disease <1 yr’ (red) and ‘disease-free survival >5 yr’ (blue). PCA, principal component analysis. *t test, p<0.05.
Mentions: Since a gold standard (GS) for lung adenocarcinoma does not exist, we generated proxy GSs151718 in order to objectively assess the accuracy of the significantly deregulated mechanisms identified by N-of-1-pathways (see online supplementary methods and figures 2–5).

Bottom Line: Cross-patient N-of-1-pathways obtains comparable results with conventional genesets enrichment analysis (GSEA) and differentially expressed gene (DEG) enrichment, validated in three external evaluations.Patients were ranked based on the similarity of their deregulated mechanisms to those of an independent gold standard, generating unsupervised clusters of diametric extreme survival phenotypes (p=0.03).The N-of-1-pathways framework provides a robust statistical and relevant biological interpretation of individual disease-free survival that is often overlooked in conventional cross-patient studies.

View Article: PubMed Central - PubMed

Affiliation: Department of Medicine, Bio5 Institute, UA Cancer Center, University of Arizona, Tucson, Arizona, USA Department of Medicine, University of Illinois at Chicago, Chicago, Illinois, USA Department of Informatics, School of Engineering, EISTI (École Internationale des Sciences du Traitement de l'Information), Cergy-Pontoise, France Institute for Translational Health Informatics, University of Illinois at Chicago, Chicago, Illinois, USA.

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Related in: MedlinePlus