
Faculty, Staff and Student Publications
Publication Date
2-1-2025
Journal
Translational Oncology
Abstract
Bladder cancer (BLCA) genomic profiling has identified molecular subtypes with distinct clinical characteristics and variable sensitivities to frontline therapy. BLCAs can be categorized into luminal or basal subtypes based on their gene expression. We comprehensively characterized nine human BLCA cell lines (UC3, UC6, UC9, UC13, UC14, T24, SCaBER, RT4V6 and RT112) into molecular subtypes using orthotopic xenograft models. Patient-derived, luciferase-tagged BLCA cell lines were cultured in vitro and engrafted into bladders of NSG mice. Tumor growth was monitored using bioluminescence imaging and mRNA-based molecular classification was used to characterize xenografts into molecular subtypes. RNAseq analysis and basal, luminal, and epithelial-mesenchymal transition (EMT) marker expression revealed distinct patterns; certain cell lines expressed predominantly basal or luminal markers while others demonstrated mixed expression. SCaBER expressed high basal and EMT markers and low luminal markers, consistent with a true basal cell. RT4V6 was a true luminal cell line, displaying only high luminal makers. UC13, T24 and UC3 only showed increased expression of EMT markers. RT112, UC6, UC9 and UC14 expressed basal, luminal, and EMT markers. Immunohistochemical analysis validated our findings. Ki67 was assessed as a continuous percentage of positively stained cells. Morphological assessment of xenografts included H&E and α-SMA staining. These findings will allow for the rational use of appropriate models to develop targeted therapies to overcome or manipulate mechanisms of treatment resistance in BLCA.
Keywords
Bladder cancer, Molecular subtypes of bladder cancer, Transcriptomic classifier, Luminal, Basal
DOI
10.1016/j.tranon.2024.102269
PMID
39808845
PMCID
PMC11782912
PubMedCentral® Posted Date
1-13-2025
PubMedCentral® Full Text Version
Post-print
Published Open-Access
yes
Included in
Bioinformatics Commons, Biomedical Informatics Commons, Genetic Phenomena Commons, Medical Genetics Commons, Oncology Commons