Molecular characterization of breast cancer cell lines through multiple omic approaches

Breast cancer cell lines are frequently used as model systems to study the cellular properties and biology of breast cancer. Our

These two new kinase or "Kin-OMIC" analyses add another dimension of important data about these frequently used breast cancer cell lines. This will assist researchers in selecting the most appropriate cell lines to use for breast cancer studies and provide context for the interpretation of the emerging results.

We determined key protein expression data using immunoblot analyses. In addition, two analyses on serum-starved cells were carried out to identify cellular proteins and pathways that are activated in these cells. These analyses were performed using a commercial PathScan array and a novel and more extensive phosphopeptide-based kinome analysis that queries 1290 phosphorylation events in major signaling pathways. Data about this panel of breast cancer cell lines was also accessed from several online sources, compiled and summarized for the following areas: molecular classification, mRNA expression, mutational status of key proteins and other possible cancer-associated mutations, and the tumorigenic and metastatic capacity in mouse xenograft models of breast cancer.

The cell lines that were characterized included 10 estrogen receptor (ER)-positive, 12 human epidermal growth factor receptor 2 (HER2)-amplified and 18 triple negative breast cancer cell lines, in addition to 4 non-tumorigenic breast cell lines. Within each subtype, there was significant genetic heterogeneity that could impact both the selection of model cell lines and the interpretation of the results obtained. To capture the net activation of key signaling pathways as a result of these mutational combinations, profiled pathway activation status was examined. This provided further clarity for which cell lines were particularly deregulated in common or unique ways. Conclusions: These two new kinase or "Kin-OMIC" analyses add another dimension of important data about these frequently used breast cancer cell lines. This will assist researchers in selecting the most appropriate cell lines to use for breast cancer studies and provide context for the interpretation of the emerging results.