Abstract P3-01-07: Early detection of metastatic breast cancer through gene expression and cell heterogeneity at an engineered metastatic niche
Authors: Orbach Sophia M , Oakes Robert S , Brooks Michael D , Bushnell Grace G , Kandagatla Pridvi , Wicha Max S , Jeruss Jacqueline S , Shea Lonnie D 
Published: March 2020

Abstract
Background: Current technology limits the detection of metastatic lesions until the patient becomes symptomatic or radiological evidence is identified, and thus invasive cancer cells are not detectable prior to their colonization in distant organs. We have developed a biomaterial implant that serves as a synthetic metastatic niche that recruits tumor cells prior to their accumulation in the lung, a natural metastatic niche. We aimed to dissect the heterogeneity of stromal and immune cells at the synthetic and lung metastatic niches over time, thereby identifying changes in cell populations, phenotypes, and gene expression that could be used for the molecular staging of metastatic disease.
Methods: Porous polycaprolactone scaffolds (5 mm diameter x 2 mm height) were implanted subcutaneously into Balb/c mice two weeks before triple negative murine breast cancer cells (4T1) were inoculated into the mammary fat pad. We tracked disease progression in the lung from a pre-metastatic niche (7 days post-inoculation) to micrometastases (14 days) to overt metastatic disease (21 days). Ten genes were selected from the TaqManTM Open Array Mouse Inflammation Panel to develop a PCR-based signature and single value decomposition (SVD) score in the scaffold that successfully delineated metastatic staging. Furthermore, the scaffold and lung were analyzed through single cell RNA-sequencing (Drop-seq) to identify heterogeneity and contributions of each cell type.
Results: Ten genes were identified from Open Array of the scaffolds that directly correlated to disease stage in the lungs – S100a8, S100a9, Pglyrp1, Ltf, Camp, Ela2, and Chi3l3 increased with metastatic progression, while Bmp15, Ccl22 and Ccr7 decreased. SVD was applied to establish a single score from the scaffold that could be used for accurate molecular staging. Scaffolds were scored on a scale from 0 to 1, where a value of 0.2 – 0.3 generally correlated to pre-metastatic disease, 0.4 - 0.8 indicated micrometastases and 0.8 – 1.0 demarked metastasis. This model successfully predicted disease stage with an area under the curve of 0.878. When the scaffold-derived gene signature was applied to the lung metastases (21 days), the output was comparable to the scaffold, further demonstrating the physiological relevance of the implants. Single cell analysis indicated that the genes in the signature were primarily expressed by macrophages and neutrophils. After 7 days, 22% of macrophages and 83% of neutrophils were classified as tumor associated cells in the scaffold. In contrast, only 16% of macrophages and 44% of neutrophils in the lung were tumor associated at this time point. These findings support the hypothesis that the scaffolds could be used as early surrogates for the pre-metastatic niche, becoming responsive prior to their biological metastatic counterpart. Moreover, this finding was demonstrated through changes in T cell phenotype, specifically loss of cytotoxic Cd8+ T cells. After 7 days, Cd8+ T cells in the scaffolds decreased by 35%. Whereas Cd8+ T cells in the lungs decreased by 35% after 14 days.
Conclusions: A biomaterial scaffold was employed as a synthetic niche that recruits metastatic tumor cells. These scaffolds established a molecular signature of metastatic breast cancer using a 10-gene panel. Additionally, single cell analysis identified the cell types that induce this signature and the disease-associated changes that occur in the scaffold earlier than the lung. In summary, we have shown the potential of a biomaterial scaffold to act as a surrogate metastatic niche that can be used to delineate disease progression and understand the role of specific cell types in the formation of this niche. Mechanistic investigation into the synthetic niche may identify novel therapeutic targets that could be activated to inhibit the advent of metastasis.
Citation Format: Sophia M Orbach, Robert S Oakes, Michael D Brooks, Grace G Bushnell, Pridvi Kandagatla, Max S Wicha, Jacqueline S Jeruss, Lonnie D Shea. Early detection of metastatic breast cancer through gene expression and cell heterogeneity at an engineered metastatic niche [abstract]. In: Proceedings of the 2019 San Antonio Breast Cancer Symposium; 2019 Dec 10-14; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2020;80(4 Suppl):Abstract nr P3-01-07.