Conference Program

CAMO: A molecular congruence analysis framework for evaluating model organisms (310157)

Adrian V. Lee, Departments of Pharmacology and Chemical Biology, UPMC Hillman Cancer Center 
Jingyi Jessica Li, Department of Statistics, University of California, 
Song Liu, Department of Computer Science and Technology, Qilu University of Technology 
Tianzhou Ma, Department of Epidemiology and Biostatistics, School of Public Health, University of Maryland 
Steffi Oesterreich, Departments of Pharmacology and Chemical Biology, UPMC Hillman Cancer Center 
Tanbin Rahman, Department of Biostatistics, University of Texas MD Anderson Cancer Center 
Zhao Ren, Department of Statistics, University of Pittsburgh 
Etienne Sibille, Campbell Family Mental Health Research Institute at the Centre for Addiction and Mental Health 
George C. Tseng, Department of Biostatistics, School of Public Health, University of Pittsburgh 
Xiangrui Zeng, Computational Biology Department, Carnegie Mellon University 
Yingjin Zhang, Department of Biostatistics, University of Pittsburgh 
Li Zhu, Department of Biostatistics, University of Pittsburgh 
*Wei Zong, Department of Biostatistics, University of Pittsburgh 

Keywords: Model organism, Molecular congruence analysis, Transcriptome, Translational research

As human clinical studies are often expensive, lengthy and with many constraints, model organisms, such as mouse and rat, play an indispensable role in almost all disease domains. Although instrumental and popular, application of model organisms has raised caution. Two previous PNAS reports presented controversial conclusions of mouse model’s resemblance to human in inflammatory transcriptomic responses, which triggered debates on its usefulness. However, little effort has been made for an objective quantification and mechanistic exploration of a model organism’s resemblance to human in terms of molecular response under disease or drug treatment. We hereby propose a framework, namely Congruence Analysis for Model Organisms (CAMO), for transcriptomic response analysis by developing threshold-free differential expression analysis, quantitative concordance/discordance scores incorporating data variabilities, pathway-centric downstream investigation, knowledge retrieval by text mining, and topological gene module detection for hypothesis generation. Instead of a genome-wide vague and dichotomous answer of “poorly” or “greatly” mimicking humans, CAMO assists researchers to numerically quantify and to visually identify molecular mechanisms and pathway subnetworks that are best or least mimicked by model organisms, providing foundations for hypothesis generation and subsequent translational decisions.