‘N-of-1-pathways’ unveils personal deregulated mechanisms from a single pair of RNA-Seq samples: towards precision medicine Vincent Gardeux, 1,2,3,4 Ikbel Achour, 1,2,4 Jianrong Li, 1,2,4 Mark Maienschein-Cline, 4 Haiquan Li, 1,2,4 Lorenzo Pesce, 5 Gurunadh Parinandi, 2,4 Neil Bahroos, 4 Robert Winn, 2,6 Ian Foster, 5,7,8 Joe G N Garcia, 1 Yves A Lussier 1,2,4,5,6,9,10,11,12 ▸ Additional material is published online only. To view please visit the journal online (http://dx.doi.org/10.1136/ amiajnl-2013-002519). For numbered affiliations see end of article. Correspondence to Dr Yves A Lussier, University of Arizona, 1657 E Helen Street, 251 (PO Box 210240), Tucson, AZ 85721, USA; yves@email.arizona.edu VG and IA contributed equally. Received 21 November 2013 Revised 15 May 2014 Accepted 15 May 2014 To cite: Gardeux V, Achour I, Li J, et al. J Am Med Inform Assoc Published Online First: [ please include Day Month Year] doi:10.1136/amiajnl-2013- 002519 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. Software http://lussierlab.org/publications/N-of-1- pathways INTRODUCTION The adoption of precision medicine is regarded as one of the most significant changes in healthcare due to its ability to dramatically improve diagnosis, prognosis, and patient treatment procedures. While DNA polymorphisms can be ascertained as private variants 1 by using a reference genome, individua- lized interpretation of the epigenome, transcriptome, and proteome remains challenging. Since purely DNA sequence-based associations to diseases, such as those found in genome-wide asso- ciation studies (GWAS), 2 are generally insufficient to unveil the biological underpinning mechan- isms, 34 it is necessary for gene expression and tran- scriptomic profiling to bridge this mechanistic gap. 5–7 Further, 99% of individual molecular bio- markers derived from large patient sample predic- tors fail to be reproducible. 8 Even though the simplicity of a single marker is the correct paradigm for Mendelian diseases, it fails in complex pheno- types. Indeed, different proteins jointly participat- ing in a mechanism (eg, pathway) may alternately be deregulated in different individual patients, yet contribute similarly to the disease pathophysiology. A combination of modestly deregulated molecules can lead to similar phenotypes, which suggests that more complex models of bimolecular expression are required than the conventional single gene/ protein marker paradigm. In the absence of individual interpretation of the ’omics scales, clinical trials must be designed over cohort-level features (case and control popula- tions); however, patients with a similar clinical history and environmental background will respond differentially to an identical therapy. We propose that individual transcriptome interpretation will enable stratification of clinical trial populations or better, novel clinical trial designs. Single-subject designs, also known as N-of-1 clin- ical trials, were first introduced by RA Fisher in 1935. 9 This type of studies aim to extract informa- tion from the pattern of variation of one or several observed variables over time, derived from a single sample ( patient, cell, etc). 10 Despite their long existence, N-of-1 trials rely on time series analyses (≥3 patients) and remain underpowered for genom- ics studies. The advent of the increased dynamic range and accuracy of RNA-sequencing over expression arrays 11 12 provides an unparalleled opportunity for studying single subject transcrip- tomes. 13 While molecular biomarker discovery in N-of-1 studies may appear unfeasible, we and others have recently shown that highly reproducible multi-gene signatures can be directly calculated using mechanism-associated genesets leveraged from transcriptomes based on expression array 14–17 or RNA-Seq technologies. 18 Moreover, these geneset classifiers outperform gene-level classifiers and provide biological context, 19 20 in addition to Gardeux V, et al. J Am Med Inform Assoc 2014;0:1–11. doi:10.1136/amiajnl-2013-002519 1 Research and applications Copyright 2014 by American Medical Informatics Association. group.bmj.com on August 25, 2014 - Published by jamia.bmj.com Downloaded from