Analysis of aggregated cell-cell statistical distances within pathways unveils therapeutic-resistance mechanisms in circulating tumor cells.
As 'omics' 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.



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 with Gene Set Enrichment Analysis 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.



http://www.lussierlab.org/publications/CCS/ CONTACT: yves@email.arizona.edu or piegorsch@math.arizona.edu



Supplementary data are available at Bioinformatics online.
Center for Biomedical Informatics and Biostatistics (CB2) Graduate Interdisciplinary Program in Statistics Department of Medicine BIO5 Institute, The University of Arizona, Tucson, AZ 85721, USA.
A Grant
AG
Schissler
Center for Biomedical Informatics and Biostatistics (CB2) Graduate Interdisciplinary Program in Statistics Department of Medicine BIO5 Institute, The University of Arizona, Tucson, AZ 85721, USA.
Qike
Q
Li
Division of Bioinformatics, Departments of Biomedical Informatics Division of Medical Oncology, Department of Internal Medicine, The Ohio State University, Columbus, OH 43210, USA.
James L
JL
Chen
Center for Biomedical Informatics and Biostatistics (CB2) Department of Medicine BIO5 Institute, The University of Arizona, Tucson, AZ 85721, USA.
Colleen
C
Kenost
Center for Biomedical Informatics and Biostatistics (CB2) Department of Medicine BIO5 Institute, The University of Arizona, Tucson, AZ 85721, USA.
Ikbel
I
Achour
Center for Biomedical Informatics and Biostatistics (CB2) Graduate Interdisciplinary Program in Statistics BIO5 Institute, The University of Arizona, Tucson, AZ 85721, USA.
D Dean
DD
Billheimer
Center for Biomedical Informatics and Biostatistics (CB2) Department of Medicine BIO5 Institute, The University of Arizona, Tucson, AZ 85721, USA.
Haiquan
H
Li
Graduate Interdisciplinary Program in Statistics BIO5 Institute, The University of Arizona, Tucson, AZ 85721, USA.
Walter W
WW
Piegorsch
Center for Biomedical Informatics and Biostatistics (CB2) Graduate Interdisciplinary Program in Statistics Department of Medicine BIO5 Institute, The University of Arizona, Tucson, AZ 85721, USA The University of Arizona Cancer Center, Tucson, AZ 85719, USA Institute for Genomics and Systems Biology, The University of Chicago, Chicago, IL, 60637, USA.
Yves A
YA
Lussier
D019008:Drug Resistance, Neoplasm; D020869:Gene Expression Profiling; D006801:Humans; D008297:Male; D009360:Neoplastic Cells, Circulating / Q000187:drug effects*; D011471:Prostatic Neoplasms / Q000188:drug therapy; D012313:RNA; D017423:Sequence Analysis, RNA; D059467:Transcriptome
D012313:RNA