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Despite the ongoing refinement of current breast cancer staging, clinical outcome is still varied among patients of similar stage. Further exploration of novel prognostic factors is of great clinical value. In our earlier findings, we have demonstrated the added value of fibrotic focus (FF) in the current staging. However the precise composition in different components within FF and underlying mechanism involved remain to be determined. In fact, there is unappreciated heterogeneity in FF. All these have limited its application in clinical practice. In this proposal, a systemic evaluation on the FF features in breast cancers will be performed. We aim to further characterize FF at morphological, cellular and molecular levels as well as its collagen characteristics. Given the multiple lines of evidence showing its relationship with immune components, additional attention will be put on its interaction with immune tumor environment. Finally, we plan to develop prognostic model integrating these different FF features for a more accurate risk stratification. Accumulated evidence suggested that the significance of FF in breast cancer prognosis. Moreover, the impact of fibrotic stroma on tumor immune environment may be implicative in immunotherapy. The more complete understanding of FF is pre-requisite for its development as an applicable prognostic marker in breast cancer.
Background Breast cancers exhibit a wide scope of morphologic features with demonstrated prognostic significance. These features have the advantage of easily evaluated in routine reporting; thus offering great clinical potential. In our earlier analysis, fibrotic focus (FF) was demonstrated to have added value to the current staging. FF refers to the central scar like area in an invasive tumor, and is surrounded by a highly cellular zone of infiltrating tumor cells. It is composed of fibroblasts and collagen fibers forming a radially expanding fibrosclerotic core, with an incidence of around 20%. The precise composition of the different components within FF may affect its prognostic value in breast cancer.
Cancer‐associated fibroblasts (CAF) and collagen components are the most prominent features in FF. FF with proliferative CAF was associated with higher risk of metastasis. Moreover, CAFs identified by fibroblastic markers were correlated with breast cancer recurrence. In breast cancer, different subsets of CAF by differential fibroblastic markers have been characterized recently. They were present in varying proportions in breast cancer subtypes and conferred dissimilar impact on the immune tumor microenvironment (TME). Nonetheless, the relationship of CAF subtypes and FF and whether they will affect the FF prognostic impact remain elusive. Apart from CAF, the potent influence of collagen content in cancer is increasingly appreciated. Collagen stroma can modulate immune TME by inhibiting CD8 T lymphocytes infiltration. The expression of collagen and other extracellular matrix components is prognostic in different types of cancer patients, including breast cancer. For FF, detailed analysis on the collagen content is still lacking. The distinct collagen composition and organization could also impact on the prognostic capabilities of FF. The underlying mechanisms for effects of FF are largely unknown. To date, there are very few reports of molecular analysis of breast cancers with FF. FF has been associated with increased angiogenesis, hypoxia markers expression and activation of hypoxia-inducible factor-1α pathway, and concordantly increased tumor associated macrophage (TAM) infiltration. Exploration of other key biomarkers involved in will provide additional information on FF prognostication.
In this proposal, a systemic evaluation on the FF features in breast cancers will be performed. We aim to further characterize FF at different levels in detail. Finally, we plan to develop prognostic model integrating the different FF features for a more accurate risk stratification.
Study design To characterize FF based on morphologic, structural and molecular features The different morphologic features of FF will be examined and correlated with the clinico-pathologic parameters and breast cancer subtypes. To characterize the collagen stroma, the expression of these different collagens will be determined by IHC analysis. The structural characteristics of the collagen fibers will be determined on SHG signal and/or pico-Sirius red staining. The collagen composition and structural features in cases with different FF morphological features will be compared.
For the characterization of fibroblastic components, CAF populations will be determined by IHC analysis. Based on the reported CAF phenotypes, CAF markers will be examined on the consecutive IHC slides. Co-expression of CAF markers will be evaluated using multiple IHC alignment. The relationship of CAF subpopulations on FF and collagen features as well as clinico-pathologic characteristics will be examined.
The candidates from top differential genes and pathways implicated in FF will be examined. The candidates will be prioritized also based on survey of published literatures and their biological relevance.
Hierarchical clustering analysis will be performed to classify FF based on the different features. The correlation of defined FF subsets and clinico-pathologic features will be investigated.
To determine the impact of FF in immune TME To examine the effects of FF in the immune TME, we will investigate several immune cell populations in situ simultaneously using mIHC. Patients will be clustered based on their immune cell profiles. The immune cells density, their relative ratio, immune profile and spatial relationship with tumor cells will be assessed in relation to different FF features and compare between FF and non-FF cases. The interplay of immune features and FF in prognosis will be evaluated.
To investigate the prognostic value of FF in breast cancer. Having examined the clinico-pathologic correlation of various features of FF and its relationship of tumor immune environment, their prognostic value on overall survival, recurrence and metastasis will be analyszd using the Kaplan-Meier method. Their prognostic impacts will be compared with each other using univariate Cox proportional hazards model. Their independent prognostic value will be determined by multivariate analysis together with other conventional prognostic markers. A novel predictive model will be constructed based on the independent risk factors derived from the multivariate analysis. A receiver operating characteristic (ROC) curve will be used to identify the cutoff value.
Not yet recruiting
Chinese University of Hong Kong
Published on BioPortfolio: 2019-10-16T10:39:35-0400
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