Abstract 1557: Developing a predictive cell-based assay for anti-cancer drug selection.
Authors: Balraj Singh, Anna Shamsnia, Milan R. Raythatha, Anthony Lucci
Journal: Cancer Research (2013)

Abstract There is a pressing need of predictive cell-based models for selecting therapies that would have a high likelihood of success in the clinic. Cancer evolves through a clonal selection process, which involves the ability of a cancer cell to survive several rate-limiting steps, and give rise to progeny under favorable conditions. We hypothesized that the cancer cells that are endowed with high metabolic adaptability (ability to survive without critical nutrients) would have significant advantages in facing multiple challenges in the body, including resisting current therapies. To develop a realistic cell-based model of aggressive breast cancer, we reasoned that a robust selection of metabolic adaptability would enrich metastatic cells in cell culture. Investigating an aggressive human triple-negative breast cancer (TNBC) cell line SUM149 that harbors mutant p53 and BRCA1 genes, we recently reported that rare cells (0.01% in population) present in this cell line can be selected based on their ability to survive and proliferate without glutamine. These metabolically adaptable (MA) variants proved to be 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 proved to be more powerful than the biomarker-based selections for isolating the type of rare cells that drive the disease. We are now investigating a variety of experimental therapies to identify those which may eradicate most adaptable breast cancer cells in patients with TNBC. 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. Our studies suggest that the MA cells are resistant to most drugs tested thus far, supporting the validity of our cell-based model. To illustrate with an example of evaluation of a specific drug, we found that a clinically-useful therapy against MET and ALK signaling in lung cancer, crizotinib, failed to eradicate SUM149-MA cells under the conditions it eradicated all parental cells. This result indicates that crizotinib is not suitable for clinical trials if the goal is to eradicate resistant cells in TNBC. However, if innovative clinical studies with crizotinib (such as trial of one) appear promising in a subset of breast cancer (better response than current therapies), our MA variants provide a suitable model to discover therapies that can be combined with crizotinib to increase its efficacy. In conclusion, our approach has a potential of improving therapeutic responses, but this potential can be best realized with close interaction with clinicians treating the disease. Supported by a State of Texas Grant for Rare and Aggressive Cancers. Citation Format: Balraj Singh, Anna Shamsnia, Milan R. Raythatha, Anthony Lucci. Developing a predictive cell-based assay for anti-cancer drug selection. [abstract]. In: Proceedings of the 104th Annual Meeting of the American Association for Cancer Research; 2013 Apr 6-10; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2013;73(8 Suppl):Abstract nr 1557. doi:10.1158/1538-7445.AM2013-1557