Ashlie Santaliz-Casiano

The majority of breast cancer specific deaths in women with estrogen receptor positive (ER⁺) tumors occur due to metastases that are resistant to therapy. There is a critical need for novel therapeutic approaches to achieve tumor regression and/or maintain therapy responsiveness in metastatic ER⁺ tumors. The objective of this study was to elucidate the role of metabolic pathways that undermine therapy efficacy in ER⁺ breast cancers. Our previous studies identified Exportin 1 (XPO1), a nuclear export protein, as an important player in endocrine resistance progression and showed that combining selinexor (SEL), an FDA-approved XPO1 antagonist, synergized with endocrine agents and provided sustained tumor regression. In the current study, using a combination of transcriptomics, metabolomics and metabolic flux experiments, we identified certain mitochondrial pathways to be upregulated during endocrine resistance. When endocrine resistant cells were treated with single agents in media conditions that mimic a nutrient deprived tumor microenvironment, their glutamine dependence for continuation of mitochondrial respiration increased. The effect of glutamine was dependent on conversion of the glutamine to glutamate, and generation of NAD⁺. PGC1α, a key regulator of metabolism, was the main driver of the rewired metabolic phenotype. Remodeling metabolic pathways to regenerate new vulnerabilities in endocrine resistant breast tumors is novel, and our findings reveal a critical role that ERα-XPO1 crosstalk plays in reducing cancer recurrences. Combining SEL with current therapies used in clinical management of ER⁺ metastatic breast cancer shows promise for treating and keeping these cancers responsive to therapies in already metastasized patients.

Breast cancer is the second leading cause of cancer mortality among women. Mammography and tumor biopsy followed by histopathological analysis are the current methods to diagnose breast cancer. Mammography does not detect all breast tumor subtypes, especially those that arise in younger women or women with dense breast tissue, and are more aggressive. There is an urgent need to find circulating prognostic molecules and liquid biopsy methods for breast cancer diagnosis and reducing the mortality rate. In this study, we systematically evaluated metabolites and proteins in blood to develop a pipeline to identify potential circulating biomarkers for breast cancer risk. Our aim is to identify a group of molecules to be used in the design of portable and low-cost biomarker detection devices. We obtained plasma samples from women who are cancer free (healthy) and women who were cancer free at the time of blood collection but developed breast cancer later (susceptible). We extracted potential prognostic biomarkers for breast cancer risk from plasma metabolomics and proteomics data using statistical and discriminative power analyses. We pre-processed the data to ensure the quality of subsequent analyses, and used two main feature selection methods to determine the importance of each molecule. After further feature elimination based on pairwise dependencies, we measured the performance of logistic regression classifier on the remaining molecules and compared their biological relevance. We identified six signatures that predicted breast cancer risk with different specificity and selectivity. The best performing signature had 13 factors. We validated the difference in level of one of the biomarkers, SCF/KITLG, in plasma from healthy and susceptible individuals. These biomarkers will be used to develop low-cost liquid biopsy methods toward early identification of breast cancer risk and hence decreased mortality. Our findings provide the knowledge basis needed to proceed in this direction.