Background

Mechanisms that increase resistance to apoptosis help promote cellular transformation. Cancer cells have deregulated apoptotic pathways, where increased expression and stability of anti-apoptotic proteins Mcl-1 and Bcl-2 increases resistance to apoptosis. Pathways that increase the stability of proteins in cancer cells remain poorly understood.

Methods

Using human mammary epithelial and established breast cancer cell lines, we assessed the mechanisms that increase the stability of anti-apoptotic proteins in breast cancer cells by caspase assay, western blot, small-inhibitory RNA treatment and immunoprecipitation.

Results

While breast cancer cells were resistant to de novo inhibition of protein synthesis, a rapid proteosome-mediated degradation of Mcl-1 and Bcl-2 induced apoptosis in mammary epithelial cells. Although Mule, an E3 ligase that targets Mcl-1 for degradation was expressed in mammary epithelial and breast cancer cell lines, rapid increase of polyubiquitinated Mcl-1 and Bcl-2 was detected only in mammary epithelial cells. Only transient formation of the Mule-Mcl-1 complex was detected in breast cancer cells. Downregulation of pERK1/2 in breast cancer cells reduced Mcl-1 levels and increased Mcl-1/Mule complex.

Conclusion

Our findings suggest that reduced Mule/Mcl-1 complex has a significant role in increasing the stability of Mcl-1 in breast cancer cells and increased resistance to apoptosis.

Background

Breast cancer, a heterogeneous disease has been broadly classified into oestrogen receptor positive (ER+) or oestrogen receptor negative (ER-) tumour types. Each of these tumours is dependent on specific signalling pathways for their progression. While high levels of survivin, an anti-apoptotic protein, increases aggressive behaviour in ER- breast tumours, oxidative stress (OS) promotes the progression of ER+ breast tumours. Mechanisms and molecular targets by which OS promotes tumourigenesis remain poorly understood.

Results

DETA-NONOate, a nitric oxide (NO)-donor induces OS in breast cancer cell lines by early re-localisation and downregulation of cellular survivin. Using in vivo models of HMLE(HRAS) xenografts and E2-induced breast tumours in ACI rats, we demonstrate that high OS downregulates survivin during initiation of tumourigenesis. Overexpression of survivin in HMLE(HRAS) cells led to a significant delay in tumour initiation and tumour volume in nude mice. This inverse relationship between survivin and OS was also observed in ER+ human breast tumours. We also demonstrate an upregulation of NADPH oxidase-1 (NOX1) and its activating protein p67, which are novel markers of OS in E2-induced tumours in ACI rats and as well as in ER+ human breast tumours.

Conclusion

Our data, therefore, suggest that downregulation of survivin could be an important early event by which OS initiates breast tumour formation.

Computer tomography (CT) imaging plays an important role in cancer detection and quantitative assessment in clinical trials. High-resolution imaging studies on large cohorts of patients generate vast data sets, which are infeasible to analyze through manual interpretation. In this article we describe a comprehensive architecture for computer-aided detection (CAD) and surveillance on lung nodules in CT images. Central to this architecture are the analytic components: an automated nodule detection system, nodule tracking capabilities and volume measurement, which are integrated within a data management system that includes mechanisms for receiving and archiving images, a database for storing quantitative nodule measurements and visualization, and reporting tools. We describe two studies to evaluate CAD technology within this architecture, and the potential application in large clinical trials. The first study involves performance assessment of an automated nodule detection system and its ability to increase radiologist sensitivity when used to provide a second opinion. The second study investigates nodule volume measurements on CT made using a semi-automated technique and shows that volumetric analysis yields significantly different tumor response classifications than a 2D diameter approach. These studies demonstrate the potential of automated CAD tools to assist in quantitative image analysis for clinical trials.