Breast cancer (BCa) is the most common cancer diagnosed in women, with an estimated 1 in 8 women being diagnosed in their lifetime in the US.  Approximately 70% of breast cancers are estrogen receptor (ER) positive (ER+) and human epidermal growth factor receptor 2 negative (HER2-).  Endocrine therapy (ET) reduces recurrence risk and improves survival for many in this group.  However, despite standard of care (SoC) adjuvant ET, over 20% patients with ER+/HER2- BCa experience metastatic recurrence in the years to come, and virtually all patients with metastatic disease eventually experience disease progression on ET due to intrinsic or acquired resistance mechanisms. Progression on ET, however, does not preclude continued responsiveness to alternate forms of ET, including those that combine therapies directed at ER and key signaling pathways that drive ET resistance. However, there are currently no biomarkers that reliably identify which of these advanced breast cancer patients will benefit from these ET-based approaches so that chemotherapy could be avoided or delayed.

The progesterone receptor (PgR) gene is highly regulated by ER at the RNA and protein level, and thus the expression of PgR in ER+ BC would be indicative of the functional status of ER and associated predictive benefit from ET. We will develop co-clinical quantitative PET/CT imaging strategies with genoproteomic discovery to predict response to ET in patients with ER+/HER2- metastatic BCa. We will optimize, validate, and implement FES-PET (as an imaging biomarker for ER expression) and FFNP-PET (as an imaging biomarker for PgR expression) quantitative imaging (QI) strategies to assess the heterogeneity of hormone receptor status as predictors of response to ET in a panel subtype-matched and patient-specific patient-derived xenograft (PDX) to assess and compare the efficacy of FES-PET and FFNP-PET optimized QI metrics to predict response to therapy, and to correlate with mutation status and gene signatures of ER and PgR response to therapy.