Effects of cell seeding density on real-time monitoring of anti-proliferative effects of transient gene silencing

WOS: 000390112500001PubMed ID: 27981039Background: Real-time cellular analysis systems enable impedance-based label-free and dynamic monitoring of various cellular events such as proliferation. In this study, we describe the effects of initial cell seeding density on the anti-proliferative effects of transient gene silencing monitored via real-time cellular analysis. We monitored the realtime changes in proliferation of Huh7 hepatocellular carcinoma and A7r5 vascular smooth muscle cells with different initial seeding densities following transient receptor potential canonical 1 (TRPC1) silencing using xCELLigence system. Huh7 and A7r5 cells were seeded on E-plate 96 at 10,000, 5000, 1250 and 5000, 2500 cells well(-1), respectively, following silencing vector transfection. The inhibitory effects of transient silencing on cell proliferation monitored every 30 min for 72 h. Results: TRPC1 silencing did not inhibit the proliferation rates of Huh7 cells at 10,000 cells well(-1) seeding density. However, a significant anti-proliferative effect was observed at 1250 cells well(-1) density at each time point throughout 72 h. Furthermore, significant inhibitory effects on A7r5 proliferation were observed at both 5000 and 2500 cells well(-1) for 72 h. Conclusions: Data suggest that the effects of transient silencing on cell proliferation differ depending on the initial cell seeding density. While high seeding densities mask the significant changes in proliferation, the inhibitory effects of silencing become apparent at lower seeding densities as the entry into log phase is delayed. Using the optimal initial seeding density is crucial when studying the effects of transient gene silencing. In addition, the results suggest that TRPC1 may contribute to proliferation and phenotypic switching of vascular smooth muscle cells.Scientific and Technological Research Council of Turkey (TUBITAK Research Project)Turkiye Bilimsel ve Teknolojik Arastirma Kurumu (TUBITAK) [108S072]; Novartis (Turkey)Novartis; Research Infrastructure Project, The State Planning Organization of Turkey (DPT) [2009K120640]This work was supported by The Scientific and Technological Research Council of Turkey (TUBITAK Research Project, 108S072) and Novartis (Turkey) to MT. The xCELLigence system was purchased within the Research Infrastructure Project, The State Planning Organization of Turkey (DPT, 2009K120640)

Accurate prediction of response to endocrine therapy in breast cancer patients: current and future biomarkers

WOS: 000390900700001PubMed ID: 27903276Approximately 70% of patients have breast cancers that are oestrogen receptor alpha positive (ER+) and are therefore candidates for endocrine treatment. Many of these patients relapse in the years during or following completion of adjuvant endocrine therapy. Thus, many ER+ cancers have primary resistance or develop resistance to endocrine therapy during treatment. Recent improvements in our understanding of how tumours evolve during treatment with endocrine agents have identified both changes in gene expression and mutational profiles, in the primary cancer as well as in circulating tumour cells. Analysing these changes has the potential to improve the prediction of which specific patients will respond to endocrine treatment. Serially profiled biopsies during treatment in the neoadjuvant setting offer promise for accurate and early prediction of response to both current and novel drugs and allow investigation of mechanisms of resistance. In addition, recent advances in monitoring tumour evolution through non-invasive (liquid) sampling of circulating tumour cells and cell-free tumour DNA may provide a method to detect resistant clones and allow implementation of personalized treatments for metastatic breast cancer patients. This review summarises current and future biomarkers and signatures for predicting response to endocrine treatment, and discusses the potential for using approved drugs and novel agents to improve outcomes. Increased prediction accuracy is likely to require sequential sampling, utilising preoperative or neoadjuvant treatment and/or liquid biopsies and an improved understanding of both the dynamics and heterogeneity of breast cancer.European CommissionEuropean Commission Joint Research Centre [658170]This work was funded by the European Commission H2020 Marie Sklodowska Curie Action Individual Fellowship (H2020-MSCA-IF, 658170) to CS and Breast Cancer Now to JMD and AHS