| Retinoids, analogs of Vitamin A, inhibit breast cancer cell proliferation through receptors in the superfamily of nuclear transcriptional factors. 9- cis retinoic acid (9-cis-RA) is a retinoid pan agonist that activates both RAR and RXR subtypes. N-(4-hydroxyphenyl) retinamide (4-HPR) has unclear receptor selectivity, but shows promising clinical activity. No established in vitro model has been developed to study the problem of acquired retinoid resistance in breast cancer.; We established an in vitro model by generating two stable retinoid resistant cell lines, MCF-7/LCC204-HPR and MCF-7/LCC21 9-cis-RA. These models were generated through selection of an estrogen independent MCF-7 variant (LCC1) against increasing concentrations of 4-HPR (MCF-7/LCC204-HPR ) and 9-cis-RA (MCF-7/LCC219-cis -RA). MCF-7/LCC204-HPR is stably resistant to the drug 4-HPR and shows cross-resistance to 9-cis-RA. In contrast, MCF-7/LCC219-cis -RA is resistant to 9-cis-RA but exhibits no cross-resistance to 4-HPR. RARalpha, RXRbeta, and RARgamma RNA expression in these retinoid resistant cell lines are unaltered with respect to the parental cells, and there is a 50% reduction in the RXRalpha expression of LCC219- cis-RA from the parental cell line. RARbeta is not detected in either the parental or resistant cell lines.; Several genes have been identified with cDNA gene expression microarrays as possible contributors to the retinoid resistant molecular pathways. The data sets from these retinoid resistant/responsive cell line models were visualized with various computational techniques. We trained a neural network Multi-Layer Perceptron (MLP) to predict the retinoid resistant phenotypes of other mammary carcinoma cell lines, i.e., MDA435/LCC6, MCF-7, LCC2, and LCC9. All cell lines, except LCC9, were predicted to respond to 4-HPR and 9-cis-RA. Studies are currently in progress to select genes that contribute to the differences among resistant/responsive phenotypes using computational bioinformatic approaches, to evaluate the putative genes in the resistant pathways, and to cross-validate the MLP predictions. |
