No abstract

Background: Despite a tremendous progress in understanding the genome and biology of cancer, there has been only a slow and limited progress in treating advanced disease and metastases. One main reason for this lack of progress is that preclinical testing is performed with little attention to the true nature of the disease. Most therapy-resistant cancers are highly heterogeneous and they constantly evolve. Methods: Here we describe a strategy for getting to the roots of an aggressive cancer and then evaluating therapies (single agent and combination therapies) for their ability to eradicate such roots. To model the roots of often untreatable disease, i.e., triple-negative inflammatory breast cancer, we started with a well-characterized SUM149 cell line (harbors BRCA1 mutation, oncogenic p53 mutation, has defective RB pathway due to p16 deletion, and lacks of PTEN protein expression). The roots can be selected by applying metabolic challenges (e.g., lack of glucose or glutamine in culture medium). The cell-based model based on most adaptable subpopulation of cancer cells is ideally suited for evaluating combination therapies that are required for combating a heterogeneous and evolving disease. Results: The rare metabolically adaptable (MA) variants (0.01% cells in SUM149 cell line), which can survive and grow without glutamine indefinitely, are structurally adaptable as well (highly enriched in mesenchymal phenotype), resistant to chemotherapeutic drugs, and highly metastatic to multiple organs- lungs, liver, brain, and skin from fat pad xenografts in nude mice (Singh et al., PLoS ONE, 2012). Our function-based selection is more powerful than the biomarker-based selections for isolating the type of rare cells that drive the disease. Our recent CGH array and gene expression microarray data provided evidence of widespread chromosomal imbalances associated with the selection and evolution of MA variants. Interestingly, these analyses revealed that the molecular networks that drive obesity (thus important in cancer) are also important in the survival of MA variants. We are now investigating a variety of experimental therapies (safe sensitizers, inhibitors of drivers, and chemotherapies) to identify those which may eradicate most adaptable breast cancer cells in patients with triple-negative breast cancer. It is noteworthy that our assessment of response versus resistance to therapies is based on long-term assays (1-2 months). Short-term assays, which are often used to assess proliferation and apoptosis, are not good at predicting response in the clinic. Conclusions: Our studies suggest that the MA cells are resistant to most drugs tested thus far as single agent, supporting the validity of our cell-based model. The strategy described here to realistically model therapy resistance and to overcome it has a potential of big clinical impact if it can be aligned with clinical trials. Citation Information: Cancer Res 2013;73(24 Suppl): Abstract nr P5-17-03.