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Disease Modeling and Epidemiology Analysis of
aggregated cell-cell statistical distances within pathways unveils
therapeutic-resistance mechanisms in circulating tumor cells A. Grant
Schissler1-4, , Qike Li1-4, , James L. Chen5,6
Colleen Kenost1,3,4, Ikbel
Achour1,3,4, D. Dean Billheimer1,2,4, Haiquan Li1,3,4,
Walter W. Piegorsch2,4,*, and Yves A. Lussier1-4,7,8,* Equal contributors, 1 Center for Biomedical Informatics
and Biostatistics (CB2), The University of Arizona, Tucson, AZ, 85721 USA, 2 Graduate
Interdisciplinary Program in Statistics, The University of Arizona, Tucson,
AZ, 85721, USA, 3 Department of Medicine, The University of
Arizona, Tucson, AZ, 85721, USA, 4 BIO5 Institute, The
University of Arizona, Tucson, AZ, 85721 USA, 5 Departments
of Biomedical Informatics, Division of Bioinformatics, The Ohio State
University, Columbus, OH, 43210, USA, 6 Internal Medicine,
Division of Medical Oncology, The Ohio State University, Columbus, OH, 43210,
USA, 7The University of Arizona Cancer Center, Tucson, AZ, 85719,
USA, 8 Institute for Genomics and Systems Biology, The
University of Chicago, Chicago, IL, 60637, USA Abstract Motivation: As ÔomicsÕ biotechnologies accelerate the capability to contrast a myriad of molecular measurements from a single cell, they also exacerbate current analytical limitations for detecting meaningful single-cell dysregulations. Moreover, mRNA expression alone lacks functional interpretation, limiting opportunities for translation of single-cell transcriptomic insights to precision medicine. Lastly, most single-cell RNA-sequencing analytic approaches are not designed to investigate small populations of cells such as circulating tumor cells shed from solid tumors and isolated from patient blood samples. Results: In response to these characteristics and limitations in current single-cell RNA-sequencing methodology, we introduce an analytic framework that models transcriptome dynamics through the analysis of aggregated cell-cell statistical distances within biomolecular pathways. Cell-cell statistical distances are calculated from pathway mRNA fold changes between two cells. Within an elaborate case study of circulating tumor cells derived from prostate cancer patients, we develop analytic methods of aggregated distances to identify five differentially expressed pathways associated to therapeutic resistance. Our aggregation analyses perform comparably to Gene Set Enrichment Analysis (GSEA) and better than differentially expressed genes followed by gene set enrichment. However, these methods were not designed to inform on differential pathway expression for a single cell. As such, our framework culminates with the novel aggregation method, cell-centric statistics (CCS). CCS quantifies the effect size and significance of differentially expressed pathways for a single cell of interest. Improved rose plots of differentially expressed pathways in each cell highlight the utility of CCS for therapeutic decision-making. Availability: http://www.lussierlab.org/publications/CCS/ Contact: yves@email.arizona.edu; piegorsch@math.arizona.edu |