skip to main content

DOE PAGESDOE PAGES

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

Background: The emergence of precision medicine allowed the incorporation of individual molecular data into patient care. This research entails, 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 diametricmore » 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.« less
Authors:
 [1] ;  [2] ;  [2] ;  [3] ;  [2] ;  [4] ;  [3] ;  [3] ;  [5] ;  [6] ;  [4] ;  [7]
  1. Univ. of Arizona, Tucson, AZ (United States); Univ. of Illinois at Chicago, Chicago, IL (United States); EISTI (Ecole Internationale des Sciences du Traitement de l'Information), Cergy-Pontoise (France)
  2. Univ. of Arizona, Tucson, AZ (United States); Univ. of Illinois at Chicago, Chicago, IL (United States)
  3. Univ. of Illinois at Chicago, Chicago, IL (United States)
  4. Argonne National Lab. (ANL), Argonne, IL (United States); Univ. of Chicago, Chicago, IL (United States)
  5. Univ. of Illinois at Chicago, Chicago, IL (United States); Univ. of Illinois Cancer Center, Chicago, IL (United States)
  6. Univ. of Arizona, Tucson, AZ (United States)
  7. Univ. of Arizona, Tucson, AZ (United States); Univ. of Illinois at Chicago, Chicago, IL (United States); Argonne National Lab. (ANL), Argonne, IL (United States)
Publication Date:
OSTI Identifier:
1225365
Grant/Contract Number:
AC02-06CH11357
Type:
Accepted Manuscript
Journal Name:
Journal of the American Medical Informatics Association
Additional Journal Information:
Journal Volume: 21; Journal Issue: 6; Journal ID: ISSN 1067-5027
Research Org:
Argonne National Laboratory (ANL), Argonne, IL (United States)
Sponsoring Org:
USDOE
Country of Publication:
United States
Language:
English
Subject:
N-of-1; single subject design; precision medicine; personalized medicine; personal transcriptome; geneset