Format

Send to

Choose Destination
Bioinformatics. 2015 Jun 15;31(12):i293-302. doi: 10.1093/bioinformatics/btv253.

Dynamic changes of RNA-sequencing expression for precision medicine: N-of-1-pathways Mahalanobis distance within pathways of single subjects predicts breast cancer survival.

Author information

1
University of Arizona Center for Biomedical Informatics and Biostatistics (CB2), Tucson, AZ, USA, Graduate Interdisciplinary Program in Statistics, Department of Medicine and BIO5 Institute, University of Arizona, Tucson, AZ, USA University of Arizona Center for Biomedical Informatics and Biostatistics (CB2), Tucson, AZ, USA, Graduate Interdisciplinary Program in Statistics, Department of Medicine and BIO5 Institute, University of Arizona, Tucson, AZ, USA University of Arizona Center for Biomedical Informatics and Biostatistics (CB2), Tucson, AZ, USA, Graduate Interdisciplinary Program in Statistics, Department of Medicine and BIO5 Institute, University of Arizona, Tucson, AZ, USA University of Arizona Center for Biomedical Informatics and Biostatistics (CB2), Tucson, AZ, USA, Graduate Interdisciplinary Program in Statistics, Department of Medicine and BIO5 Institute, University of Arizona, Tucson, AZ, USA.
2
University of Arizona Center for Biomedical Informatics and Biostatistics (CB2), Tucson, AZ, USA, Graduate Interdisciplinary Program in Statistics, Department of Medicine and BIO5 Institute, University of Arizona, Tucson, AZ, USA University of Arizona Center for Biomedical Informatics and Biostatistics (CB2), Tucson, AZ, USA, Graduate Interdisciplinary Program in Statistics, Department of Medicine and BIO5 Institute, University of Arizona, Tucson, AZ, USA University of Arizona Center for Biomedical Informatics and Biostatistics (CB2), Tucson, AZ, USA, Graduate Interdisciplinary Program in Statistics, Department of Medicine and BIO5 Institute, University of Arizona, Tucson, AZ, USA University of Arizona Center for Biomedical Informatics and Biostatistics (CB2), Tucson, AZ, USA, Graduate Interdisciplinary Program in Statistics, Department of Medicine and BIO5 Institute, University of Arizona, Tucson, AZ, USA University of Arizona Center for Biomedical Informatics and Biostatistics (CB2), Tucson, AZ, USA, Graduate Interdisciplinary Program in Statistics, Department of Medicine and BIO5 Institute, University of Arizona, Tucson, AZ, USA.
3
University of Arizona Center for Biomedical Informatics and Biostatistics (CB2), Tucson, AZ, USA, Graduate Interdisciplinary Program in Statistics, Department of Medicine and BIO5 Institute, University of Arizona, Tucson, AZ, USA University of Arizona Center for Biomedical Informatics and Biostatistics (CB2), Tucson, AZ, USA, Graduate Interdisciplinary Program in Statistics, Department of Medicine and BIO5 Institute, University of Arizona, Tucson, AZ, USA University of Arizona Center for Biomedical Informatics and Biostatistics (CB2), Tucson, AZ, USA, Graduate Interdisciplinary Program in Statistics, Department of Medicine and BIO5 Institute, University of Arizona, Tucson, AZ, USA.

Abstract

MOTIVATION:

The conventional approach to personalized medicine relies on molecular data analytics across multiple patients. The path to precision medicine lies with molecular data analytics that can discover interpretable single-subject signals (N-of-1). We developed a global framework, N-of-1-pathways, for a mechanistic-anchored approach to single-subject gene expression data analysis. We previously employed a metric that could prioritize the statistical significance of a deregulated pathway in single subjects, however, it lacked in quantitative interpretability (e.g. the equivalent to a gene expression fold-change).

RESULTS:

In this study, we extend our previous approach with the application of statistical Mahalanobis distance (MD) to quantify personal pathway-level deregulation. We demonstrate that this approach, N-of-1-pathways Paired Samples MD (N-OF-1-PATHWAYS-MD), detects deregulated pathways (empirical simulations), while not inflating false-positive rate using a study with biological replicates. Finally, we establish that N-OF-1-PATHWAYS-MD scores are, biologically significant, clinically relevant and are predictive of breast cancer survival (P < 0.05, n = 80 invasive carcinoma; TCGA RNA-sequences).

CONCLUSION:

N-of-1-pathways MD provides a practical approach towards precision medicine. The method generates the magnitude and the biological significance of personal deregulated pathways results derived solely from the patient's transcriptome. These pathways offer the opportunities for deriving clinically actionable decisions that have the potential to complement the clinical interpretability of personal polymorphisms obtained from DNA acquired or inherited polymorphisms and mutations. In addition, it offers an opportunity for applicability to diseases in which DNA changes may not be relevant, and thus expand the 'interpretable 'omics' of single subjects (e.g. personalome).

AVAILABILITY AND IMPLEMENTATION:

http://www.lussierlab.net/publications/N-of-1-pathways.

PMID:
26072495
PMCID:
PMC4765863
DOI:
10.1093/bioinformatics/btv253
[Indexed for MEDLINE]
Free PMC Article

Supplemental Content

Full text links

Icon for Silverchair Information Systems Icon for PubMed Central
Loading ...
Support Center