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Biology subjects

Paiva, A.

Publications and source records attributed to Paiva, A..

3 recordsLinked to original sources

A robust protocol for the systematic collection and expansion of cells from ER+ breast cancertumors and their matching tumor-adjacent tissues

Therapy resistance and tumor recurrence are major challenges in the clinical management of breast cancer. Current data indicates that the breast tumor microenvironment (TME) and the tumor immune microenvironment (TIME) are important modulators of breast cancer cell response to chemotherapies and the development of therapy resistance. To this end, the ability to recreate the tumor microenvironment in the laboratory using autologous primary cells that make up the breast TME has become an indispensable tool for cancer researchers as it allows the study of tumor immunobiology in the context of therapy resistance. Moreover, the clinical relevance of data obtained from single cell transcriptomics and proteomics platforms would be greatly improved if primary autologous tumor cells were used. In this article, we report a robust and efficient workflow to obtain autologous cancer cells, cancer-associated fibroblasts, and tumor-infiltrating immune cells from primary human breast cancer tumors obtained from mastectomy procedures. As well, we show that this protocol can be used to obtain normal-like epithelial cells, fibroblasts, and immune cells from the matching tumor-adjacent breast tissue samples. Also, a robust methodology to expand each of these primary cell types in vitro is presented that allows the maintenance of the primary tumor cell phenotype. The availability of a large number of autologous primary human breast tumor cells and their matching tumor-adjacent tissues will facilitate the study of differential and cancer cell-specific gene expression patterns that will further our understanding of how the TME and TIME influence therapy resistance in the breast tumor context.

cancer biology↗

Balanced Permeability Index: a multi-parameter index for improved in-vitro permeability

The optimization of passive permeability is a key objective for orally available small molecule drug candidates. For drugs targeting the central nervous system (CNS), minimizing P-gp mediated efflux is an additional important target for optimization. The physicochemical properties most strongly associated with high passive permeability and lower P-gp efflux are size, polarity and lipophilicity. In this study, a new metric called the Balanced Permeability Index (BPI) was developed that combines these three properties. The BPI was found to be more effective than any single property in classifying molecules based on their permeability and efflux across a diverse range of chemicals and assays. The BPI can also be used to guide optimization in non-traditional small molecule modalities, such as protein degraders, which often lie outside of traditional small molecule space. BPI is easy to understand, allowing researchers to make decisions about which properties to prioritize during the drug development process.

bioinformatics↗

Unveiling the role of osteosarcoma-derived secretome in premetastatic lung remodeling

Lung metastasis represents the leading cause of osteosarcoma-related death. Progress in preventing lung metastasis is pretty modest due to the inherent complexity of the metastatic process and the lack of suitable models. Herein, we provide mechanistic insights into how osteosarcoma systemically reprograms the lung microenvironment for metastatic outgrowth using metastatic mouse models and a multi-omics approach. We found that osteosarcoma-bearing mice or those preconditioned with cell-secretome harbour profound lung structural alteration with airways damage, inflammation, neutrophil infiltration, and remodelling of the extracellular matrix with deposition of fibronectin and collagen by stromal activated fibroblasts for tumour cell adhesion. These changes, supported by transcriptomic and histological data, promoted and accelerated the development of lung metastasis. Comparative proteome profiling of the cell secretome and mouse plasma identified a large number of proteins engaged in the extracellular-matrix organization, cell-matrix adhesion, neutrophil degranulation, and cytokine-mediated signalling, which were consistent with the observed lung microenvironmental changes. Moreover, we identified EFEMP1, a secreted extracellular matrix glycoprotein, as a potential risk factor for lung metastasis and a poor prognosis factor in osteosarcoma patients.

cancer biology↗