Sex differences in idiopathic pulmonary fibrosis (IPF) suggest a protective role for estrogen (E2); however, mechanistic studies in animal models have produced mixed results. Reports using cell lines have investigated molecular interactions between transforming growth factor beta1 (TGF-β1) and estrogen receptor (ESR) pathways in breast, prostate, and skin cells, but no such interactions have been described in human lung cells. To address this gap in the literature, we investigated a role for E2 in modulating TGF-β1-induced signaling mechanisms and identified novel pathways impacted by estrogen in bronchial epithelial cells. We investigated a role for E2 in modulating TGF-β1-induced epithelial to mesenchymal transition (EMT) in bronchial epithelial cells (BEAS-2Bs) and characterized the effect of TGF-β1 on ESR mRNA and protein expression in BEAS-2Bs. We also quantified mRNA expression of ESRs in lung tissue from individuals with IPF and identified potential downstream targets of E2 signaling in BEAS-2Bs using RNA-Seq and gene set enrichment analysis. E2 negligibly modulated TGF-β1-induced EMT; however, we report the novel observation that TGF-β1 repressed ESR expression, most notably estrogen receptor alpha (ESR1). Results of the RNA-Seq analysis showed that TGF-β1 and E2 inversely modulated the expression of several genes involved in processes such as extracellular matrix (ECM) turnover, airway smooth muscle cell contraction, and calcium flux regulation. We also report that E2 specifically modulated the expression of genes involved in chromatin remodeling pathways and that this regulation was absent in the presence of TGF-β1. Collectively, these results suggest that E2 influences unexplored pathways that may be relevant to pulmonary disease and highlights potential roles for E2 in the lung that may contribute to sex-specific differences.

The majority of high-grade serous ovarian cancers originate in the fallopian tubes, however, the corresponding structural changes in the extracellular matrix (ECM) have not been well-characterized. This information could provide new insight into the carcinogenesis and provide the basis for new diagnostic tools. We have previously used the collagen-specific Second Harmonic Generation (SHG) microscopy to probe collagen fiber alterations in high-grade serous ovarian cancer and in other ovarian tumors, and showed they could be uniquely identified by machine learning approaches. Here we couple SHG imaging of serous tubal intra-epithelial carcinomas (STICs), high-grade cancers, and normal regions of the fallopian tubes, using three distinct image analysis approaches to form a classification scheme based on the respective collagen fiber morphology. Using a linear discriminant analysis, we achieved near 100% classification accuracy between high-grade disease and the other tissues, where the STICs and normal regions were differentiated with ~75% accuracy. Importantly, the collagen in high-grade disease in both the fallopian tube and the ovary itself have a similar collagen morphology, further substantiating the metastasis between these sites. This analysis provides a new method of classification, but also quantifies the structural changes in the disease, which may provide new insight into metastasis.

Background: The collagen architecture in high grade serous ovarian cancer (HGSOC) is highly remodeled compared to the normal ovary and the fallopian tubes (FT). We previously used Second Harmonic Generation (SHG) microscopy and machine learning to classify the changes in collagen fiber morphology occurring in serous tubal intraepithelial carcinoma (STIC) lesions that are concurrent with HGSOC. We now extend these studies to examine collagen remodeling in pure p53 signatures, STICs and normal regions in tissues that have no concurrent HGSOC. This is an important distinction as high-grade disease can result in distant collagen changes through a field effect mechanism. Methods: We trained a linear discriminant model based on SHG texture and image features as a classifier to discriminate the tissue groups. We additionally performed mass spectrometry analysis of normal and HGSOC tissues to associate the differential expression of collagen isoforms with collagen fiber morphology alterations. Results: We quantified the differences in the collagen architecture between normal tissue and the precursors with good classification accuracy. Through proteomic analysis, we identified the downregulation of single α-chains including those for Col I and III, where these results are consistent with our previous SHG-based supramolecular analyses. Conclusion: This work provides new insights into ECM remodeling in early ovarian cancer and suggests the combined use of SHG microscopy and mass spectrometry as a new diagnostic/prognostic approach.

High-grade serous ovarian cancer (HGSOC) is the predominant subtype of ovarian cancer (OC), occurring in more than 80% of patients diagnosed with this malignancy. Histological and genetic analysis have confirmed the secretory epithelial of the fallopian tube (FT) as a major site of origin of HGSOC. Although there have been significant strides in our understanding of this disease, early stage detection and diagnosis are still rare. Current clinical imaging modalities lack the ability to detect early stage pathogenesis in the fallopian tubes and the ovaries. However, there are several microscopic imaging techniques used to analyze the structural modifications in the extracellular matrix (ECM) protein collagen in ex vivo FT and ovarian tissues that potentially can be modified to fit the clinical setting. In this perspective, we evaluate and compare the myriad of optical tools available to visualize these alterations and the invaluable insights these data provide on HGSOC initiation. We also discuss the clinical implications of these findings and how these data may help novel tools for early diagnosis of HGSOC.