'N-of-1-pathways' unveils personal deregulated mechanisms from a single pair of RNA-seq samples

Towards precision medicine

Vincent Gardeux, Ikbel Achour, Jianrong Li, Mark Maienschein-Cline, Haiquan Li, Lorenzo Luigi Pesce, Gurunadh Parinandi, Neil Bahroos, Robert Winn, Ian Foster, Joe G.N. Garcia, Yves A. Lussier*

*Corresponding author for this work

Research output: Contribution to journalArticle

21 Citations (Scopus)

Abstract

Background: The emergence of precision medicine allowed the incorporation of individual molecular data into patient care. Indeed, DNA sequencing predicts somatic mutations in individual patients. However, these genetic features overlook dynamic epigenetic and phenotypic response to therapy. Meanwhile, accurate personal transcriptome interpretation remains an unmet challenge. Further, N-of-1 (single-subject) efficacy trials are increasingly pursued, but are underpowered for molecular marker discovery. Method: 'N-of-1-pathways' is a global framework relying on three principles: (i) the statistical universe is a single patient; (ii) significance is derived from geneset/ biomodules powered by paired samples from the same patient; and (iii) similarity between genesets/biomodules assesses commonality and differences, within-study and cross-studies. Thus, patient gene-level profiles are transformed into deregulated pathways. From RNA-Seq of 55 lung adenocarcinoma patients, N-of-1-pathways predicts the deregulated pathways of each patient. Results: 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. Moreover, heatmap and star plots highlight both individual and shared mechanisms ranging from molecular to organ-systems levels (eg, DNA repair, signaling, immune response). 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). Conclusions: 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. It enables mechanism-level classifiers with smaller cohorts as well as N-of-1 studies.

Original languageEnglish (US)
Pages (from-to)1015-1025
Number of pages11
JournalJournal of the American Medical Informatics Association
Volume21
Issue number6
DOIs
StatePublished - Jan 1 2014

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Precision Medicine
RNA
DNA Sequence Analysis
Transcriptome
Epigenomics
DNA Repair
Genes
Disease-Free Survival
Patient Care
Phenotype
Mutation
Survival

ASJC Scopus subject areas

  • Health Informatics

Cite this

Gardeux, Vincent ; Achour, Ikbel ; Li, Jianrong ; Maienschein-Cline, Mark ; Li, Haiquan ; Pesce, Lorenzo Luigi ; Parinandi, Gurunadh ; Bahroos, Neil ; Winn, Robert ; Foster, Ian ; Garcia, Joe G.N. ; Lussier, Yves A. / 'N-of-1-pathways' unveils personal deregulated mechanisms from a single pair of RNA-seq samples : Towards precision medicine. In: Journal of the American Medical Informatics Association. 2014 ; Vol. 21, No. 6. pp. 1015-1025.
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abstract = "Background: The emergence of precision medicine allowed the incorporation of individual molecular data into patient care. Indeed, DNA sequencing predicts somatic mutations in individual patients. However, these genetic features overlook dynamic epigenetic and phenotypic response to therapy. Meanwhile, accurate personal transcriptome interpretation remains an unmet challenge. Further, N-of-1 (single-subject) efficacy trials are increasingly pursued, but are underpowered for molecular marker discovery. Method: 'N-of-1-pathways' is a global framework relying on three principles: (i) the statistical universe is a single patient; (ii) significance is derived from geneset/ biomodules powered by paired samples from the same patient; and (iii) similarity between genesets/biomodules assesses commonality and differences, within-study and cross-studies. Thus, patient gene-level profiles are transformed into deregulated pathways. From RNA-Seq of 55 lung adenocarcinoma patients, N-of-1-pathways predicts the deregulated pathways of each patient. Results: 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. Moreover, heatmap and star plots highlight both individual and shared mechanisms ranging from molecular to organ-systems levels (eg, DNA repair, signaling, immune response). 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). Conclusions: 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. It enables mechanism-level classifiers with smaller cohorts as well as N-of-1 studies.",
author = "Vincent Gardeux and Ikbel Achour and Jianrong Li and Mark Maienschein-Cline and Haiquan Li and Pesce, {Lorenzo Luigi} and Gurunadh Parinandi and Neil Bahroos and Robert Winn and Ian Foster and Garcia, {Joe G.N.} and Lussier, {Yves A.}",
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Gardeux, V, Achour, I, Li, J, Maienschein-Cline, M, Li, H, Pesce, LL, Parinandi, G, Bahroos, N, Winn, R, Foster, I, Garcia, JGN & Lussier, YA 2014, ''N-of-1-pathways' unveils personal deregulated mechanisms from a single pair of RNA-seq samples: Towards precision medicine', Journal of the American Medical Informatics Association, vol. 21, no. 6, pp. 1015-1025. https://doi.org/10.1136/amiajnl-2013-002519

'N-of-1-pathways' unveils personal deregulated mechanisms from a single pair of RNA-seq samples : Towards precision medicine. / Gardeux, Vincent; Achour, Ikbel; Li, Jianrong; Maienschein-Cline, Mark; Li, Haiquan; Pesce, Lorenzo Luigi; Parinandi, Gurunadh; Bahroos, Neil; Winn, Robert; Foster, Ian; Garcia, Joe G.N.; Lussier, Yves A.

In: Journal of the American Medical Informatics Association, Vol. 21, No. 6, 01.01.2014, p. 1015-1025.

Research output: Contribution to journalArticle

TY - JOUR

T1 - 'N-of-1-pathways' unveils personal deregulated mechanisms from a single pair of RNA-seq samples

T2 - Towards precision medicine

AU - Gardeux, Vincent

AU - Achour, Ikbel

AU - Li, Jianrong

AU - Maienschein-Cline, Mark

AU - Li, Haiquan

AU - Pesce, Lorenzo Luigi

AU - Parinandi, Gurunadh

AU - Bahroos, Neil

AU - Winn, Robert

AU - Foster, Ian

AU - Garcia, Joe G.N.

AU - Lussier, Yves A.

PY - 2014/1/1

Y1 - 2014/1/1

N2 - Background: The emergence of precision medicine allowed the incorporation of individual molecular data into patient care. Indeed, DNA sequencing predicts somatic mutations in individual patients. However, these genetic features overlook dynamic epigenetic and phenotypic response to therapy. Meanwhile, accurate personal transcriptome interpretation remains an unmet challenge. Further, N-of-1 (single-subject) efficacy trials are increasingly pursued, but are underpowered for molecular marker discovery. Method: 'N-of-1-pathways' is a global framework relying on three principles: (i) the statistical universe is a single patient; (ii) significance is derived from geneset/ biomodules powered by paired samples from the same patient; and (iii) similarity between genesets/biomodules assesses commonality and differences, within-study and cross-studies. Thus, patient gene-level profiles are transformed into deregulated pathways. From RNA-Seq of 55 lung adenocarcinoma patients, N-of-1-pathways predicts the deregulated pathways of each patient. Results: 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. Moreover, heatmap and star plots highlight both individual and shared mechanisms ranging from molecular to organ-systems levels (eg, DNA repair, signaling, immune response). 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). Conclusions: 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. It enables mechanism-level classifiers with smaller cohorts as well as N-of-1 studies.

AB - Background: The emergence of precision medicine allowed the incorporation of individual molecular data into patient care. Indeed, DNA sequencing predicts somatic mutations in individual patients. However, these genetic features overlook dynamic epigenetic and phenotypic response to therapy. Meanwhile, accurate personal transcriptome interpretation remains an unmet challenge. Further, N-of-1 (single-subject) efficacy trials are increasingly pursued, but are underpowered for molecular marker discovery. Method: 'N-of-1-pathways' is a global framework relying on three principles: (i) the statistical universe is a single patient; (ii) significance is derived from geneset/ biomodules powered by paired samples from the same patient; and (iii) similarity between genesets/biomodules assesses commonality and differences, within-study and cross-studies. Thus, patient gene-level profiles are transformed into deregulated pathways. From RNA-Seq of 55 lung adenocarcinoma patients, N-of-1-pathways predicts the deregulated pathways of each patient. Results: 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. Moreover, heatmap and star plots highlight both individual and shared mechanisms ranging from molecular to organ-systems levels (eg, DNA repair, signaling, immune response). 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). Conclusions: 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. It enables mechanism-level classifiers with smaller cohorts as well as N-of-1 studies.

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