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Molecular natural history of breast cancer: Leveraging transcriptomics to predict breast cancer progression and aggressiveness.

ABSTRACT: BACKGROUND:Characterizing breast cancer progression and aggressiveness relies on categorical descriptions of tumor stage and grade. Interpreting these categorical descriptions is challenging because stage convolutes the size and spread of the tumor and no consensus exists to define high/low grade tumors. METHODS:We address this challenge of heterogeneity in patient-specific cancer samples by adapting and applying several tools originally created for understanding heterogeneity and phenotype development in single cells (specifically, single-cell topological data analysis and Wanderlust) to create a continuous metric describing breast cancer progression using bulk RNA-seq samples from individual patient tumors. We also created a linear regression-based method to predict tumor aggressiveness in vivo from bulk RNA-seq data. RESULTS:We found that breast cancer proceeds along three convergent phenotype trajectories: luminal, HER2-enriched, and basal-like. Furthermore, 31 296 genes (for luminal cancers), 17 827 genes (for HER2-enriched), and 18 505 genes (for basal-like) are dynamically differentially expressed during breast cancer progression. Across progression trajectories, our results show that expression of genes related to ADP-ribosylation decreased as tumors progressed (while PARP1 and PARP2 increased or remained stable), suggesting the potential for a differential response to PARP inhibitors based on cancer progression. Additionally, we developed a 132-gene expression regression equation to predict mitotic index and a 23-gene expression regression equation to predict growth rate from a single breast cancer biopsy. CONCLUSION:Our results suggest that breast cancer dynamically changes during disease progression, and growth rate of the cancer cells is associated with distinct transcriptional profiles.

SUBMITTER: Cook DJ

PROVIDER: S-EPMC7221450 | biostudies-literature | 2020 May

REPOSITORIES: biostudies-literature

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Molecular natural history of breast cancer: Leveraging transcriptomics to predict breast cancer progression and aggressiveness.

Cook Daniel J DJ Kallus Jonatan J Jörnsten Rebecka R Nielsen Jens J

Cancer medicine 20200323 10

<h4>Background</h4>Characterizing breast cancer progression and aggressiveness relies on categorical descriptions of tumor stage and grade. Interpreting these categorical descriptions is challenging because stage convolutes the size and spread of the tumor and no consensus exists to define high/low grade tumors.<h4>Methods</h4>We address this challenge of heterogeneity in patient-specific cancer samples by adapting and applying several tools originally created for understanding heterogeneity and ...[more]

PMID: 32207233

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Project description:Aromatase inhibitors (AIs), such as letrozole, are considered as first-line treatment for estrogen receptor-positive breast cancer in postmenopausal women. Despite the successful use of letrozole, resistance to therapy, tumor relapse and metastasis remain principal causes of patient mortality. Although there is no therapy currently available for AI-resistant breast cancer, previous reports have demonstrated that AI resistance is associated with hormone independence, increased growth factor signaling, enhanced cellular motility and epithelial to mesenchymal transition (EMT). This suggests a convergence of EMT and cancer stem cells (CSCs) in endocrine resistance. The present study evaluated the contribution of mammospheres in letrozole-resistant breast cancer by characterizing mammospheres and their potential impact on cellular motility. Ovariectomized immunocompromised female mice were inoculated in the mammary fat pad with either letrozole-resistant MCF-7 cells (LTLT-Ca) or letrozole-sensitive MCF-7 cells (AC-1). Subsequently, intratumoral CSC marker expression was assessed by immunohistochemistry. The results indicated that LTLT-Ca tumors were CD44+/CD24+, while AC-1 tumors presented low CD44/CD24 expression. Since mammosphere formation depends on CSCs, both cell lines were cultured either adherently (2D) or as mammospheres (3D) to assess the CD44/CD24 protein expression profile. When 3D culturing both cell lines, higher expression levels of CD44 and CD24 were observed when compared with their adherent counterparts, with the most robust change observed in the LTLT-Ca cell line. To quantitate the breast cancer stem cell activity, mammosphere formation assays were performed, and the LTLT-Ca cells formed mammospheres at a 3.4-fold higher index compared with AC-1 cells. Additionally, targeted gene expression arrays were conducted to compare the LTLT-Ca 3D and 2D cells, revealing that LTLT-Ca 3D cells displayed decreased expression levels of genes involved in cell adhesion and tumor suppression (e. g., E-cadherin, caveolin 1 and β-catenin). To validate this finding, wound healing assays were performed, and LTLT-Ca mammospheres exhibited a 70% wound closure, whereas AC-1 mammospheres exhibited a 39% wound closure. Collectively, the present findings demonstrated a strong association between AI-resistant mammospheres and an increased propensity for migration, which may be indicative of a poor prognosis.

Dataset Information

Molecular natural history of breast cancer: Leveraging transcriptomics to predict breast cancer progression and aggressiveness.

Publications

Molecular natural history of breast cancer: Leveraging transcriptomics to predict breast cancer progression and aggressiveness.

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