Quantitative gene expression assessment identifies appropriate cell line models for individual cervical cancer pathways.
BACKGROUND
Cell lines have been used to study cancer for decades, but truly quantitative assessment of their performance as models is often lacking. We used gene expression profiling to quantitatively assess the gene expression of nine cell line models of cervical cancer.


RESULTS
We find a wide variation in the extent to which different cell culture models mimic late-stage invasive cervical cancer biopsies. The lowest agreement was from monolayer HeLa cells, a common cervical cancer model; the highest agreement was from primary epithelial cells, C4-I, and C4-II cell lines. In addition, HeLa and SiHa cell lines cultured in an organotypic environment increased their correlation to cervical cancer significantly. We also find wide variation in agreement when we considered how well individual biological pathways model cervical cancer. Cell lines with an anti-correlation to cervical cancer were also identified and should be avoided.


CONCLUSIONS
Using gene expression profiling and quantitative analysis, we have characterized nine cell lines with respect to how well they serve as models of cervical cancer. Applying this method to individual pathways, we identified the appropriateness of particular cell lines for studying specific pathways in cervical cancer. This study will allow researchers to choose a cell line with the highest correlation to cervical cancer at a pathway level. This method is applicable to other cancers and could be used to identify the appropriate cell line and growth condition to employ when studying other cancers.
Carlson
Mark W
MW
Department of Biomedical Engineering, The University of Texas at Austin, Austin, Texas 78712, USA. mark.carlson@tufts.edu <mark.carlson@tufts.edu>
Iyer
Vishwanath R
VR
Marcotte
Edward M
EM
D045744:Cell Line, Tumor / Q000378:metabolism*; D005260:Female; D020869:Gene Expression Profiling; D006801:Humans; D020411:Oligonucleotide Array Sequence Analysis; D012333:RNA, Messenger / Q000235:genetics / Q000378:metabolism*; D002583:Uterine Cervical Neoplasms / Q000235:genetics* / Q000378:metabolism
D012333:RNA, Messenger