Alfred Schissler Abstract
Alfred Schissler
Ph.D. Candidate
Statistics GIDP
23rd Annual International Conference on Intelligent Systems
for Molecular Biology
Dublin, Ireland
July 10-14, 2015
Dynamic changes of RNA-sequencing expression for precision medicine: N-of-1 pathways Mahalanobis distance within pathways of single subjects predicts breast cancer survivalz’
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 a 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 to quantify personal pathway-level deregulation. We demonstrate that his approach, N-of-1-pathways Paired Samples Mahalanobis Distance (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 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 this expand the “interpretable ‘omics” of single subjects (e.g. personalome). Availability: http://www.lussierlab.net/publications/N-of-1pathways(link is external)
Abstract (for Lay Audience)
This work describes a novel, significant, and pragmatic statistical informatics method for precision medicine in the treatment of cancer. Statistical informatics is an emerging field that informs formal statistical methods with knowledge accumulated from basic sciences (such as biology and chemistry) and applied sciences (for example clinical practice, computer science, epidemiology.
Precision medicine, often referred to as personalized medicine, seeks to use measurements of the molecules of life, including messenger ribonucleic acid (mRNA), to individualize treatment for patients. The state-of-the art in the field supplies little more than analysis of specific biomarker or un-interpretable statistics.
In the present study, we devise clinically relevant metrics based on mRNA sequencing from a single subject. We do not rely on population-based statistics to predict cancer survival. We incorporate biological databases to improve or statistics with mechanistic (known cellular processes) knowledge compiled over decades of biological work. We also provide novel visualizations to allow clinicians to make appropriate decisions quickly. Our methods shows promise to realize the path to precision medicine, creating individualized treatment plans.