Alternative targeted therapy for early HER2 positive breast cancer

Her2 (erbB2) receptor is overexpressed in around twenty percent of early breast cancer and historically conferred a poorer prognosis (1). Targeted anti-Her2 therapy has significantly improved outcomes for these women.

Is chemotherapy dose intensity adequate in breast cancer management in the Australian healthcare setting: a retrospective analysis

Aim
To determine the adequacy of chemotherapy received dose intensity (RDI) in breast cancer treatment in a general population and to identify factors that influence RDI.

Methods
A retrospective analysis of breast cancer patients who commenced a course of i.v. chemotherapy in 2008 was undertaken. Data were collected on patient and tumor characteristics, chemotherapy regimen, dose (including delays, reductions and the reasons for these), granulocyte colony-stimulating factor (G-CSF) use and febrile neutropenia incidence. RDI was calculated using the planned and actual dose received and time taken. A level of ≥85% RDI was considered acceptable for treatment given with curative intent.

Results
In all, 131 patients (aged 28 to 77 years) received chemotherapy in adjuvant (n = 76, 58%), neoadjuvant (n = 11, 8%) and metastatic settings (n = 44, 34%). RDI did not reach 85% for 12% adjuvant, 36% neoadjuvant and 34% metastatic cases (χ2 = 10.55, P = 0.005). Overall, 43% of patients received G-CSF.

Conclusion
Acceptable chemotherapy RDI was delivered for most patients in the adjuvant setting but not in the neoadjuvant setting. G-CSF treatment contributed to the optimization of dose intensity in the adjuvant setting only. Dose intensity in the metastatic setting was considered satisfactory where quality of life is the primary focus. Other factors can be modified to improve RDI.

The use of sensitive chemical antibodies for diagnosis: detection of low levels of Epcam in breast cancer

EpCAM is expressed at low levels in a variety of normal human epithelial tissues, but is overexpressed in 70–90% of carcinomas. From a clinico-pathological point of view, this has both prognostic and therapeutic significance. EpCAM was first suggested as a therapeutic target for the treatment of epithelial cancers in the 1990s. However, following several immunotherapy trials, the results have been mixed. It has been suggested that this is due, at least in part, to an unknown level of EpCAM expression in the tumors being targeted. Thus, selection of patients who would benefit from EpCAM immunotherapy by determining EpCAM status in the tumor biopsies is currently undergoing vigorous evaluation. However, current EpCAM antibodies are not robust enough to be able to detect EpCAM expression in all pathological tissues.

Here we report a newly developed EpCAM RNA aptamer, also known as a chemical antibody, which is not only specific but also more sensitive than current antibodies for the detection of EpCAM in formalin-fixed paraffin-embedded primary breast cancers. This new aptamer, together with our previously described aptamer, showed no non- specific staining or cross-reactivity with tissues that do not express EpCAM. They were able to reliably detect target proteins in breast cancer xenograft where an anti-EpCAM antibody (323/A3) showed limited or no reactivity. Our results demonstrated a more robust detection of EpCAM using RNA aptamers over antibodies in clinical samples with chromogenic staining. This shows the potential of aptamers in the future of histopathological diagnosis and as a tool to guide targeted immunotherapy.

Hub-based reliable gene expression algorithm to classify ER+ and ER- breast cancer subtypes

Identifying gene signatures that are associatedwith the estrogen receptor based breast cancer samples is achallenging problem that has significant implications in breastcancer diagnosis and treatment. Various existing approaches foridentifying gene signatures have been developed but are not ableto achieve the satisfactory results because of their severallimitations. Subnetwork-based approaches have shown to be arobust classification method that uses interaction datasets suchas protein-protein interaction datasets. It has been reported thatthese interaction datasets contain many irrelevant interactionsthat have no biological meaning associated with them, and thusit is essential to filter out those interactions which can improvethe classification results. In this paper, we therefore, proposed ahub-based reliable gene expression algorithm (HRGE) thateffectively extracts the significant biologically-relevantinteractions and uses hub-gene topology to generate thesubnetwork based gene signatures for ER+ and ER- breastcancer subtypes. The proposed approach shows the superiorclassification accuracy amongst the other existing classifiers, inthe validation dataset.

Brain metastases in breast cancer

Despite therapeutic advances, the development of breast cancer brain metastases (BCBM) is still the harbinger of a dismal prognosis. Patient outcomes vary depending on factors, including tumor phenotype, extent of disease within and outside the brain, as well as patient performance status. Treatment includes surgery, radiation therapy and systemic therapy determined by patient and tumor characteristics. Despite these approaches, novel treatments are needed and there is growing interest in systemic therapies. However, the efficacy of pharmacologic agents is hampered by poor penetration of drugs across the blood–brain barrier. Therefore, there is a pressing need for a greater understanding of the natural history of BCBM to guide the development of further therapies. This review analyzes prognosis and treatment of BCBM by tumor phenotype and discusses ongoing research into new therapies.

Correlation between extent of osteolytic damage and metastatic burden of human breast cancer metastasis in nude mice: Real-time PCR quantitation

Orthotopic or intracardiac injection of human breast cancer cell lines into immunocompromised mice allows study of the molecular basis of breast cancer metastasis. We have established a quantitative real-time PCR approach to analyze metastatic spread of human breast cancer cells inoculated into nude mice via these routes. We employed MDA-MB-231 human breast cancer cells genetically tagged with a bacterial β-galactosidase (Lac-Z) retroviral vector, enabling their detection by TaqMan® real-time PCR. PCR detection was linear, specific, more sensitive than conventional PCR, and could be used to directly quantitate metastatic burden in bone and soft organs. Attesting to the sensitivity and specificity of the PCR detection strategy, as few as several hundred metastatic MDA-MB-231 cells were detectable in 100 μm segments of paraffin-embedded lung tissue, and only in samples adjacent to sections that scored positive by histological detection. Moreover, the measured real-time PCR metastatic burden in the bone environment (mouse hind-limbs, n=48) displayed a high correlation to the degree of osteolytic damage observed by high resolution X-ray analysis (r 2=0.972). Such a direct linear relationship to tumor burden and bone damage substantiates the so-called `vicious cycle' hypothesis in which metastatic tumor cells promote the release of factors from the bone which continue to stimulate the tumor cells. The technique provides a useful tool for molecular and cellular analysis of human breast cancer metastasis to bone and soft organs, can easily be extended to other cell/marker/organ systems, and should also find application in preclinical assessment of anti-metastatic modalities.

Doxycycline-inducible expression of SPARC/osteonectin/BM40 in MDA-MB-231 human breast cancer cells results in growth inhibition

SPARC (secreted protein acidic and rich in cysteine)/BM40/Osteonectin is a matricellular protein with multiple effects on cell behaviour. In vitro, its major known functions are anti-adhesive and anti-proliferative, and it is associated with tissue remodelling and cancer in vivo. SPARC is overexpressed in many cancers, including breast cancer, and the effects of SPARC seem to be cell type-specific. To study the effects of SPARC on breast cancer, we transfected SPARC into the MDA-MB-231 BAG, human breast cancer cell line using the Tet-On inducible system. By western analysis, we found low background levels in the MDA-MB-231 BAG and clone X parental cells, and prominent induction of SPARC protein expression after doxycycline treatment in SPARC transfected clones X5, X21, X24 and X75. Induction of SPARC expression did not affect cell morphology or adhesiveness to collagens type I and IV, but it slowed the rate of proliferation in adherent cultures. Cell cycle analysis showed that SPARC slowed the progression to S phase. Doxycycline induction of SPARC also slowed the rate of monolayer wound closure in the cultured wound healing assay. Thymidine inhibition of proliferation abrogated this effect, confirming that it was due to anti-proliferation rather than inhibition of migration. Consistent with this, we were unable to detect any differences in migration and Matrigel outgrowth analysis of doxycycline-stimulated cells. We conclude that SPARC is inhibitory to human breast cancer cell proliferation, and does not stimulate migration, in contrast to its stimulatory effects reported for melanoma (proliferation and migration) and glioma (migration) cells. Similar growth repression by SPARC has been reported for ovarian cancer cells, and this may be a common feature among carcinoma.

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.