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

Abstract

BackgroundThe 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.ResultsCross-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).ConclusionsThe 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.Softwarehttp://lussierlab.org/publications/N-of-1-pathways

Towards a PBMC “virogram assay” for precision medicine: Concordance between ex vivo and in vivo viral infection transcriptomes

“…This led to a total of 3234 GO-BP gene sets and 205 KEGG pathway gene sets. This filtering protocol follows the default one used in GSEA and a protocol we have previously identified as optimal for these studies [7, 8, 18–21]. …”

“…N-of-1- pathways is a framework dedicated to the personalized medicine field that we initially proposed in the context of cancer analyses [7, 8]. It was successfully applied to lung adenocarcinoma visualization of single patient survival and proved to unveil biologically significant dysregulated pathways by using only one pair of samples taken from the same patient in two different conditions [7] (such as before and after treatment or uninvolved vs tumoral cells).…”

“…It was successfully applied to lung adenocarcinoma visualization of single patient survival and proved to unveil biologically significant dysregulated pathways by using only one pair of samples taken from the same patient in two different conditions [7] (such as before and after treatment or uninvolved vs tumoral cells). It was also applied in ovarian and breast cancer cell lines to confirm the unsupervised identification of dysregulated pathways after a knockdown of PTBP1 and PTBP2 genes that control alternative splicing [8].…”

Evolution of Translational Bioinformatics: lessons learned from TBC 2016

“…Li et al (The University of Arizona, USA) propose advanced methods for analyzing personal transcriptomes derived from a pair of samples of a single patient [7]. To complement previously published methods [8], those authors developed the N-of-1-pathways MixEnrich mixture model, which combined with a gene set enrichment test could be used to uncover bidirectional and concordantly dysregulated pathways in individual patients. Bidirectional and concordant dysregulated pathways uncovered by MixEnrich in each patient largely overlapped with the quasi-gold standard, in contrast to other single-subject and cohort-based transcriptome analyses confirmed in both the simulation study and data on head and neck squamous cell carcinoma.…”

The genomics of micronutrient requirements

“…The ultimate benefits of N-of-1 trials may derive from the reality that interventions of whatever type rarely work in everyone (March et al 1994;Nikles et al 2006;Elobeid et al 2009). N-of-1 trials explore this variability in an objective way while simultaneously leading to an informed decision about the best way to treat an individual patient with their own data (Bacchetti et al 2011;Lillie et al 2012;Gardeux et al 2014;Nikles et al 2014;Morine et al 2014). From a global economic and healthcare perspective, one cannot perform one trial per each subject or patient in order to intervene and treat in a personalized fashion.…”