Qike Li's Abstracts

Qike Li's Abstracts

     Qike Li
     Ph.D. Student
     Statistics

     Conference Summary
     
The 6th Annual Translational Bioinformatics Conference
     Jeju Island, Korea

Lay Abstract

I have submitted a manuscript and will be presenting the work entitled “N-of-1-pathways
MixEnrich: advancing precision medicine via single-subject analysis in discovering dynamic
changes of transcriptomes”. This work describes a novel statistical method for personalized
(precision) medicine with an application in cancer diagnosis. Our method identifies
functionally relevant gene sets (e.g. pathways) that are responsible for disease progression
for a single patient. Our goal is to advance personalized medicine, with an emphasis on
cancer diagnosis, through the development of novel analytics.
No two cancers are alike, just as no two people are alike. Personalized medicine is
treatment that tailed to individual’s disease mechanisms. This treatment strategy centered
on the ability to identify the genetic differences between patients and to predict the best
therapy based on patient’s genetic profiles. It is in contrast to the conventional one-sizefits-
all treatment, in which disease treatment and prevention are designed for the average
patients, with less consideration of the between-patient differences.
The statistical method, which I will be presenting at the Translational Bioinformatics
Conference, addresses some challenges in personalized medicine. Personalized medicine
necessitates making discoveries using the data collected from a single patient, and data of a
single patient are often noisy and with a small sample size (a small number of samples).
Our work demonstrated that the proposed method has high statistical power even when
sample size is small and also is accurate with the presence of noisy data.

Abstract

N-of-1-pathways MixEnrich: advancing precision medicine via single-subject
analysis in discovering dynamic changes of transcriptomes

Qike Li1-4,§, A. Grant Schissler1-4,§, Vincent Gardeux1-3, Ikbel Achour1-3, Colleen Kenost1-3,
Joanne Berghout 1-3, Haiquan Li1-3,*, Hao Helen Zhang4,5,*, Yves A. Lussier1-4, 6-7,*

1Center for Biomedical Informatics and Biostatistics, The University of Arizona, Tucson, AZ, 85721, USA 2Bio5 Institute, The University of Arizona, Tucson, AZ, 85721, USA 3Department of Medicine, The University of Arizona, Tucson, AZ, 85721, USA 4Graduate Interdisciplinary Program in Statistics, The University of Arizona, Tucson, AZ, 85721, USA 5Department of Mathematics, The University of Arizona, Tucson, AZ, 85721, USA 6University of Arizona Cancer Center, The University of Arizona, Tucson, AZ, 85721, USA, 7Institute for Genomics and Systems Biology, The University of Chicago, IL 60637, USA
§These authors contributed equally to the work
*To whom correspondence should be addressed

Background: Transcriptome analytic tools are commonly used across patient cohorts to develop drugs and predict
clinical outcomes. However, as precision medicine pursues more accurate and individualized treatment decisions,
these methods are not designed to address single-patient transcriptome analyses. We previously developed and
validated the N-of-1-pathways framework using two methods, Wilcoxon and Mahalanobis Distance (MD), for
personal transcriptome analysis derived from a pair of samples of a single patient. Although, both methods uncover
concordantly dysregulated pathways, they are not designed to detect dysregulated pathways with up- and downregulated
genes (bidirectional dysregulation) that are ubiquitous in biological systems.

Results: We developed N-of-1-pathways MixEnrich, a mixture model followed by a gene set enrichment test, to
uncover bidirectional and concordantly dysregulated pathways one patient at a time. We assess its accuracy in a
comprehensive simulation study and in a RNA-Seq data analysis of head and neck squamous cell carcinomas
(HNSCCs). In presence of bidirectionally dysregulated genes in the pathway or in presence of high background
noise, MixEnrich substantially outperforms previous single-subject transcriptome analysis methods, both in the
simulation study and the HNSCCs data analysis (ROC Curves; higher true positive rates; lower false positive rates).
Bidirectional and concordant dysregulated pathways uncovered by MixEnrich in each patient largely overlapped
with the quasi-gold standard compared to other single-subject and cohort-based transcriptome analyses.

Conclusion: The greater performance of MixEnrich presents an advantage over previous methods to meet the
promise of providing accurate personal transcriptome analysis to support precision medicine at point of care.

Availabilityhttp://lussierlab.org/publications/MixEnrich
Contact: yves@arizona.eduhzhang@math.arizona.edu
Keywords: Precision Medicine, Single-Subject Analysis, N-of-1-pathways, Mixture Model, RNA-Seq, head and
neck squamous cell carcinomas (HNSCCs)