Analyses of BMAL1 and PER2 Oscillations in a Model of Breast Cancer Progression Reveal Changes With Malignancy. (April 2019)

• Record Type: Journal Article
• Authors: Lin, Hui-Hsien; Qraitem, Maan; Lian, Yue; Taylor, Stephanie R.; Farkas, Michelle E.
• Is Part Of: Integrative cancer therapies. Volume 18(2019)
• Publication Date: 2019-04
• Subjects: breast cancer -- isogenic cell lines -- disrupted circadian rhythms -- luciferase reporters -- machine learning -- period-estimation; Cancer -- Alternative treatment -- Periodicals; 616.99406
• DOI: 10.1177/1534735419836494
• Languages: English
• ISSNs: 1534-7354

Abstract

From an epidemiological standpoint, disruptions to circadian rhythms have been shown to contribute to the development of various disease pathologies, including breast cancer. However, it is unclear how altered circadian rhythms are related to malignant transformations at the molecular level. In this article, a series of isogenic breast cancer cells representing disease progression was used to investigate the expression patterns of core circadian clock proteins BMAL1 and PER2. Our model is indicative of 4 stages of breast cancer and includes the following cells: MCF10A (non-malignant), MCF10AT.Cl2 (pre-malignant), MCF10Ca1h (well-differentiated, malignant), and MCF10Ca1a (poorly differentiated, malignant). While studies of circadian rhythms in cancer typically use low-resolution reverse transcription polymerase chain reaction assays, we also employed luciferase reporters BMAL1:Luc and PER2:Luc in real-time luminometry experiments. We found that across all 4 cancer stages, PER2 showed relatively stable oscillations compared with BMAL1. Period estimation using both wavelet-based and damped-sine-fitting methods showed that the periods are distributed over a wide circadian range and there is no clear progression in mean period as cancer severity progresses. Additionally, we used the K-nearest neighbors algorithm to classify the recordings according to cancer line, and found that cancer stages were largely differentiated from one another. Taken together, our data support that there are circadian discrepancies between normal and malignant cells, but it is difficult and insufficient to singularly use period evaluations to differentiate them. Future studies should employ other progressive disease models to determine whether these findings are representative across cancer types or are specific to this series.