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

• Published in: Journal of the American Medical Informatics Association : JAMIA Vol. 21; no. 6; pp. 1015 - 1025
• Main Authors: Gardeux, Vincent, Achour, Ikbel, Li, Jianrong, Maienschein-Cline, Mark, Li, Haiquan, Pesce, Lorenzo, Parinandi, Gurunadh, Bahroos, Neil, Winn, Robert, Foster, Ian, Garcia, Joe G N, Lussier, Yves A
• Format: Journal Article
• Language: English
• Published: England Oxford University Press 01.11.2014; BMJ Publishing Group
• Subjects: Adenocarcinoma - genetics; Adenocarcinoma of Lung; Adult; Aged; Aged, 80 and over; BASIC BIOLOGICAL SCIENCES; Biomarkers, Tumor; Computational Biology; Disease-Free Survival; Female; Gene Expression Profiling; geneset; Genetic Markers; Humans; Lung Neoplasms - genetics; Male; Middle Aged; Mutation; N-of-1; Oligonucleotide Array Sequence Analysis; Patient-Centered Care; personal transcriptome; personalized medicine; precision medicine; Research and Applications; RNA - analysis; RNA, Neoplasm; Sequence Analysis, RNA; single subject design; Transcriptome; N-of-1; Single Subject Design; Personal Transcriptome; Personalized Medicine; Geneset; Precision Medicine
• ISSN: 1067-5027; 1527-974X
• DOI: 10.1136/amiajnl-2013-002519

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. '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. 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). 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. http://lussierlab.org/publications/N-of-1-pathways.