Breast cancer prognosis risk estimation using integrated gene expression and clinical data

Novel prognostic markers are needed so newly diagnosed breast cancer patients do not undergo any unnecessary therapy. Various microarray gene expression datasets based studies have generated gene signatures to predict the prognosis outcomes, while ignoring the large amount of information contained in established clinical markers. Nevertheless, small sample sizes in individual microarray datasets remain a bottleneck in generating robust gene signatures that show limited predictive power. The aim of this study is to achieve high classification accuracy for the good prognosis group and then achieve high classification accuracy for the poor prognosis group.

RRHGE: a novel approach to classify the estrogen receptor based breast cancer subtypes

Breast cancer is the most common type of cancer among females with a high mortality rate. It is essential to classify the estrogen receptor based breast cancer subtypes into correct subclasses, so that the right treatments can be applied to lower the mortality rate. Using gene signatures derived from gene interaction networks to classify breast cancers has proven to be more reproducible and can achieve higher classification performance. However, the interactions in the gene interaction network usually contain many false-positive interactions that do not have any biological meanings. Therefore, it is a challenge to incorporate the reliability assessment of interactions when deriving gene signatures from gene interaction networks. How to effectively extract gene signatures from available resources is critical to the success of cancer classification.

A prospective cohort study of the effects of adjuvant breast cancer chemotherapy on taste function, food liking, appetite and associated nutritional outcomes

'Taste' changes are commonly reported during chemotherapy. It is unclear to what extent this relates to actual changes in taste function or to changes in appetite and food liking and how these changes affect dietary intake and nutritional status.

IPA: Integrated predictive gene signature from gene expression based breast cancer patient samples

Background: Novel predictive markers are needed to accurately diagnose the breast cancer patients so they do not need to undergo any unnecessary aggressive therapies. Various gene expression studies based predictive gene signatureshave generated in the recent past to predict the binary estrogen-receptor subclass or to predict the therapy response subclass. However, the existing algorithms comes with many limitations, including low predictive performances over multiple cohorts of patients and non-significant or limited biological roles associated with thepredictive gene signatures. Therefore, the aim of this study is to develop novel predictive markers with improved performances.Methods: We propose a novel prediction algorithm called IPA to construct a predictive gene signature for performing multiple prediction tasks of predicting estrogen-receptor based binary subclass and predicting chemotherapy response (neoadjuvantly) based binary subclass. The constructed gene signature with considering multiple classification techniques was used to evaluate the algorithm performance on multiple cohorts of breast cancer patients.Results: The evaluation on multiple validation cohorts demonstrated that proposed algorithm achieved stable and high performance to perform prediction tasks, with consideration given to any classification techniques. We show that the predictive gene signature of our proposed algorithm reflects the mechanisms underlying the estrogen-receptors or response to therapy with significant greater biological interpretations, compared with the other existing algorithm.

Tumour but not stromal expression of β3 integrin is essential, and is required early, for spontaneous dissemination of bone-metastatic breast cancer.

Although many preclinical studies have implicated β3 integrin receptors (αvβ3 and αIIbβ3) in cancer progression, β3 inhibitors have shown only modest efficacy in patients with advanced solid tumours. The limited efficacy of β3 inhibitors in patients could arise from our incomplete understanding of the precise function of β3 integrin and, consequently, inappropriate clinical application. Data from animal studies are conflicting and indicate heterogeneity with respect to the relative contributions of β3-expressing tumour and stromal cell populations in different cancers. Here we aimed to clarify the function and relative contributions to metastasis of tumour versus stromal β3 integrin in clinically relevant models of spontaneous breast cancer metastasis, with particular emphasis on bone metastasis. We show that stable down-regulation of tumour β3 integrin dramatically impairs spontaneous (but not experimental) metastasis to bone and lung without affecting primary tumour growth in the mammary gland. Unexpectedly, and in contrast to subcutaneous tumours, orthotopic tumour vascularity, growth and spontaneous metastasis were not altered in mice null for β3 integrin. Tumour β3 integrin promoted migration, protease expression and trans-endothelial migration in vitro and increased vascular dissemination in vivo, but was not necessary for bone colonization in experimental metastasis assays. We conclude that tumour, rather than stromal, β3 expression is essential and is required early for efficient spontaneous breast cancer metastasis to bone and soft tissues. Accordingly, differential gene expression analysis in cohorts of breast cancer patients showed a strong association between high β3 expression, early metastasis and shorter disease-free survival in patients with oestrogen receptor-negative tumours. We propose that β3 inhibitors may be more efficacious if used in a neoadjuvant setting, rather than after metastases are established. Copyright © 2014 Pathological Society of Great Britain and Ireland. Published by John Wiley & Sons, Ltd.

YM155 down-regulates survivin and XIAP, modulates autophagy and induces autophagy-dependent DNA damage in breast cancer cells

The aim of this study was to determine the potency and molecular mechanism of action of YM155, a first-in-class survivin inhibitor that is currently under phase I/II clinical investigations, in various drug-resistant breast cancers including the oestrogen receptor positive (ER(+) ) tamoxifen-resistant breast cancer and the caspase-3-deficient breast cancer.

Bone-derived soluble factors and laminin-511 cooperate to promote migration, invasion and survival of bone-metastatic breast tumor cells

Tumor intrinsic and extrinsic factors are thought to contribute to bone metastasis but little is known about how they cooperate to promote breast cancer spread to bone. We used the bone-metastatic 4T1BM2 mammary carcinoma model to investigate the cooperative interactions between tumor LM-511 and bone-derived soluble factors in vitro. We show that bone conditioned medium cooperates with LM-511 to enhance 4T1BM2 cell migration and invasion and is sufficient alone to promote survival in the absence of serum. These responses were associated with increased secretion of MMP-9 and activation of ERK and AKT signaling pathways and were partially blocked by pharmacological inhibitors of MMP-9, AKT-1/2 or MEK. Importantly, pre-treatment of 4T1BM2 cells with an AKT-1/2 inhibitor significantly reduced experimental metastasis to bone in vivo. Promotion of survival and invasive responses by bone-derived soluble factors and tumor-derived LM-511 are likely to contribute to the metastatic spread of breast tumors to bone.

Assessing and managing breast cancer risk: Clinicians' current practice and future needs

Decision support tools for the assessment and management of breast cancer risk may improve uptake of prevention strategies. End-user input in the design of such tools is critical to increase clinical use. Before developing such a computerized tool, we examined clinicians' practice and future needs. Twelve breast surgeons, 12 primary care physicians and 5 practice nurses participated in 4 focus groups. These were recorded, coded, and analyzed to identify key themes. Participants identified difficulties assessing risk, including a lack of available tools to standardize practice. Most expressed confidence identifying women at potentially high risk, but not moderate risk. Participants felt a tool could especially reassure young women at average risk. Desirable features included: evidence-based, accessible (e.g. web-based), and displaying absolute (not relative) risks in multiple formats. The potential to create anxiety was a concern. Development of future tools should address these issues to optimize translation of knowledge into clinical practice.

Transitioning to routine breast cancer risk assessment and management in primary care: what can we learn from cardiovascular disease?

To capitalise on advances in breast cancer prevention, all women would need to have their breast cancer risk formally assessed. With ~85% of Australians attending primary care clinics at least once a year, primary care is an opportune location for formal breast cancer risk assessment and management. This study assessed the current practice and needs of primary care clinicians regarding assessment and management of breast cancer risk. Two facilitated focus group discussions were held with 17 primary care clinicians (12 GPs and 5 practice nurses (PNs)) as part of a larger needs assessment. Primary care clinicians viewed assessment and management of cardiovascular risk as an intrinsic, expected part of their role, often triggered by practice software prompts and facilitated by use of an online tool. Conversely, assessment of breast cancer risk was not routine and was generally patient- (not clinician-) initiated, and risk management (apart from routine screening) was considered outside the primary care domain. Clinicians suggested that routine assessment and management of breast cancer risk might be achieved if it were widely endorsed as within the remit of primary care and supported by an online risk-assessment and decision aid tool that was integrated into primary care software. This study identified several key issues that would need to be addressed to facilitate the transition to routine assessment and management of breast cancer risk in primary care, based largely on the model used for cardiovascular disease.

Smoothened activates breast cancer stem-like cell and promotes tumorigenesis and metastasis of breast cancer

Smoothened (Smo) is a G protein-coupled receptor protein encoded by the Smo gene of the hedgehog signalling pathway, which is thought to play an important role in maintaining organ patterning, cell differentiation and self-renewal. The possible role of Smo in the process of tumorigenesis and metastasis of breast cancer still remains unclear. The present experiments were to investigate the effect of Smo on activating breast cancer stem-like CD44(+)CD24(-) cells and the tumorigenesis and metastasis of breast cancer. By injected CD44(+)CD24(-) cells (1×10(4)) into the cleared fat pad of NOD/SCID mice, it was observed that CD44(+)CD24(-) cells possess higher tumor-initiating capacity and metastasis properties than equal numbers of non-CD44(+)CD24(-) cells. The mRNA and protein expressions of Smo in CD44(+)CD24(-) cells were higher than those in non-CD44(+)CD24(-) cells, indicating that Smo may play a role in maintaining breast cancer stem cell features. qRT-PCR results revealed that expressions of STAT3, Bcl-2 and cyclinD1 mRNA in MCF-7 cells were decreased after transfected by Smo siRNA. In addition, the expressions of MMP-2 and MMP-9 were downregulated in MCF-7 cells after Smo expression was inhibited. Smo inhibition may be a possible therapeutic target that potentially suppresses breast tumor formation and development.

An aptamer-mediated siRNA delivery system targeting breast tumourigenic cells

In this study, through developing a RNA interference based gene-knockdown system, the expression of Survivin - a critical gene involved in breast cancer initiation and relapse was silenced. This approach reversed chemo-resistance, transforming Doxorubicin - a classical chemotherapy drug to one able to act on drug-resistant breast cancer stem cells.

Computational methods for breast cancer diagnosis, prognosis, and treatment prediction

The research presented here develops a robust reliability algorithm for the identification of reliable protein interactions that can be incorporated with a gene expression dataset to improve the algorithm performance, and novel breast cancer based diagnostic, prognostic and treatment prediction algorithms, respectively, which take into account the existing issues in order to provide a fair estimation of their performance.