Gene expression profile associated with response to doxorubicin-based therapy in breast cancer

Reciprocal changes in gene expression profiles of cocultured breast epithelial cells and primary fibroblasts

The importance of epithelial-stroma interaction in normal breast development and tumor progression has been recognized. To identify genes that were regulated by these reciprocal interactions, we cocultured a nonmalignant (MCF10A) and a breast cancer derived (MDA-MB231) basal cell lines, with fibroblasts isolated from breast benign-disease adjacent tissues (NAF) or with carcinoma-associated fibroblasts (CAF), in a transwell system. Gene expression profiles of each coculture pair were compared with the correspondent monocultures, using a customized microarray. Contrariwise to large alterations in epithelial cells genomic profiles, fibroblasts were less affected. In MDA-MB231 highly represented genes downregulated by CAF derived factors coded for proteins important for the specificity of vectorial transport between ER and golgi, possibly affecting cell polarity whereas the response of MCF10A comprised an induction of genes coding for stress responsive proteins, representing a prosurvival effect. While NAF downregulated genes encoding proteins associated to glycolipid and fatty acid biosynthesis in MDA-MB231, potentially affecting membrane biogenesis, in MCF10A, genes critical for growth control and adhesion were altered. NAFs responded to coculture with MDA-MB231 by a decrease in the expression of genes induced by TGFbeta1 and associated to motility. However, there was little change in NAFs gene expression profile influenced by MCF10A. CAFs responded to the presence of both epithelial cells inducing genes implicated in cell proliferation. Our data indicate that interactions between breast fibroblasts and basal epithelial cells resulted in alterations in the genomic profiles of both cell types which may help to clarify some aspects of this heterotypic signaling.

Subsets of very low risk Wilms tumor show distinctive gene expression, histologic, and clinical features

PURPOSE

Recent studies suggest that children <24 months with stage I favorable histology Wilms tumors <550 g [very low risk Wilms tumors (VLRWT)] have an excellent prognosis when treated with nephrectomy only, without adjuvant chemotherapy. The identification of risk categories within VLRWT may enable refinement of their definition and optimization of their therapy.

EXPERIMENTAL DESIGN

To define biologically distinct subsets, global gene expression analysis was done on 39 VLRWT that passed all quality-control parameters and the clusters identified were validated in an independent set of 11 VLRWT. Validation of select differentially expressed genes was done with immunohistochemistry on a tissue microarray from 20 of 39 tumors. Loss of heterozygosity (LOH) for 11p15, 1p, and 16q was analyzed in 52 tumors using PCR.

RESULTS

Two distinctive clusters were identified. One cluster included 9 tumors with epithelial differentiated tubular histology, paucity of nephrogenic rests, lack of LOH for 1p, 16q, and 11p, absence of relapse, and a unique gene expression profile consistent with arrest following mesenchymal-to-epithelial transition. The second cluster included 13 tumors with mixed histology, intralobar nephrogenic rests, and decreased expression of WT1. Three of 6 relapses occurred in this cluster. Of 43 informative tumors, 11p LOH was present in 5 of 5 relapses and 11 of 38 nonrelapses.

CONCLUSIONS

Two subsets comprising a total of 56% of VLRWT are identified that have pathogenetic and molecular differences and apparent differences in risk for relapse. If these predictors can be prospectively validated, this would enable the refinement of clinical stratification and less arbitrary definition of VLRWT.

Mucin 1 (MUC1) is a novel partner for MAL2 in breast carcinoma cells

BACKGROUND

The MAL2 gene, encoding a four-transmembrane protein of the MAL family, is amplified and overexpressed in breast and other cancers, yet the significance of this is unknown. MAL-like proteins have trafficking functions, but their molecular roles are largely obscure, partly due to a lack of known binding partners.

METHODS

Yeast two-hybrid screening of a breast carcinoma cDNA expression library was performed using a full-length MAL2 bait, and subsequent deletion mapping experiments were performed. MAL2 interactions were confirmed by co-immunoprecipitation analyses and confocal microscopy was employed to compare protein sub-cellular distributions. Sucrose density gradient centrifugation of membranes extracted in cold Triton X-100 was employed to compare protein distributions between Triton X-100-soluble and -insoluble fractions.

RESULTS

The tumor-associated protein mucin 1 (MUC1) was identified as a potential MAL2 partner, with MAL2/MUC1 interactions being confirmed in myc-tagged MAL2-expressing MCF-10A cells using co-immunoprecipitation assays. Deletion mapping experiments demonstrated a requirement for the first MAL2 transmembrane domain for MUC1 binding, whereas the MAL2 N-terminal domain was required to bind D52-like proteins. Confocal microscopy identified cytoplasmic co-localisation of MUC1 and MAL2 in breast cell lines, and centrifugation of cell lysates to equilibrium in sucrose density gradients demonstrated that MAL2 and MUC1 proteins were co-distributed between Triton X-100-soluble and -insoluble fractions. However co-immunoprecipitation analyses detected MAL2/MUC1 interactions in Triton X-100-soluble fractions only. Myc-MAL2 expression in MCF-10A cells was associated with both increased MUC1 detection within Triton X-100-soluble and -insoluble fractions, and increased MUC1 detection at the cell surface.

CONCLUSION

These results identify MUC1 as a novel MAL2 partner, and suggest a role for MAL2 in regulating MUC1 expression and/or localisation.

Microarray-based Gene Expression Profiling as a Clinical Tool for Breast Cancer Management: Are We There Yet?

Breast cancer is a heterogeneous disease, encompassing several histological types and clinical behaviors. Current histopathological classification systems are based on descriptive entities with prognostic significance. Few prognostic and predictive markers beyond those offered by histopathological analysis are available. High-throughput molecular technologies are reshaping our understanding of breast cancer, of which microarray-based gene expression has received most attention. This method has been used to derive a molecular taxonomy for breast cancer, which has provided interesting insights into the biology of the disease. Class prediction studies have generated a multitude of prognostic/predictive signatures, which herald the promise for an improvement in treatment decision making. However, most of the signatures developed to date seem to have discriminatory power almost restricted to estrogen receptor—positive disease. This review addresses the contribution of gene expression profiling to our understanding of breast cancer and its clinical management.

Functional genomic analysis of drug sensitivity pathways to guide adjuvant strategies in breast cancer

The widespread introduction of high throughput RNA interference screening technology has revealed tumour drug sensitivity pathways to common cytotoxics such as paclitaxel, doxorubicin and 5-fluorouracil, targeted agents such as trastuzumab and inhibitors of AKT and Poly(ADP-ribose) polymerase (PARP) as well as endocrine therapies such as tamoxifen. Given the limited power of microarray signatures to predict therapeutic response in associative studies of small clinical trial cohorts, the use of functional genomic data combined with expression or sequence analysis of genes and microRNAs implicated in drug response in human tumours may provide a more robust method to guide adjuvant treatment strategies in breast cancer that are transferable across different expression platforms and patient cohorts.

Intracellular localization of the tumor suppressor HtrA1/Prss11 and its association with HPV16 E6 and E7 proteins

We have a long-standing interest in a nuclear protease which appears to be involved in carcinogenesis. We recently identified the protease as high temperature requirement factor A 1 (HtrA1), also known as Prss11, which is member of an oxidative stress-response family of proteases. HtrA1 has been classified as a secreted protease involved in TGFbeta signaling, but recent work has shown HtrA1 to be a tumor suppressor. Here we show that processed forms of HtrA1 are found intracellularly and intranuclearly, and the active intranuclear form of HtrA1 shows an approximately Mr 29,000. Further, expression of HPV E6/E7 proteins is associated with a post-transcriptional up-regulation of HtrA1 (most notably the nuclear form), and HtrA1 is found associated with both HPV E6 and E7 proteins.

Tumor slices as a model to evaluate doxorubicin in vitro treatment and expression of trios of genes PRSS11, MTSS1, CLPTM1 and PRSS11, MTSS1, SMYD2 in canine mammary gland cancer

Background

In women with breast cancer submitted to neoadjuvant chemotherapy based in doxorubicin, tumor expression of groups of three genes (PRSS11, MTSS1, CLPTM1 and PRSS11, MTSS1, SMYD2) have classified them as responsive or resistant. We have investigated whether expression of these trios of genes could predict mammary carcinoma response in dogs and whether tumor slices, which maintain epithelial-mesenchymal interactions, could be used to evaluate drug response in vitro.

Methods

Tumors from 38 dogs were sliced and cultured with or without doxorubicin 1 μM for 24 h. Tumor cells were counted by two observers to establish a percentage variation in cell number, between slices. Based on these results, a reduction in cell number between treated and control samples ≥ 21.7%, arbitrarily classified samples, as drug responsive. Tumor expression of PRSS11, MTSS1, CLPTM1 and SMYD2, was evaluated by real time PCR. Relative expression results were then transformed to their natural logarithm values, which were spatially disposed according to the expression of trios of genes, comprising PRSS11, MTSS1, CLPTM1 and PRSS11, MTSS1, SMYD2. Fisher linear discrimination test was used to generate a separation plane between responsive and non-responsive tumors.

Results

Culture of tumor slices for 24 h was feasible. Nine samples were considered responsive and 29 non-responsive to doxorubicin, considering the pre-established cut-off value of cell number reduction ≥ 21.7%, between doxorubicin treated and control samples. Relative gene expression was evaluated and tumor samples were then spatially distributed according to the expression of the trios of genes: PRSS11, MTSS1, CLPTM1 and PRSS11, MTSS1, SMYD2. A separation plane was generated. However, no clear separation between responsive and non-responsive samples could be observed.

Conclusion

Three-dimensional distribution of samples according to the expression of the trios of genes PRSS11, MTSS1, CLPTM1 and PRSS11, MTSS1, SMYD2 could not predict doxorubicin in vitro responsiveness. Short term culture of mammary gland cancer slices may be an interesting model to evaluate chemotherapy activity.

Implications of applied research for prognosis and therapy of breast cancer

Breast cancer is the one of leading causes of cancer-related deaths in women within economically developed regions of the world. The heterogeneity of the natural history of breast cancer complicates patient management in that there is tremendous variability in response to treatment and for survival. More recently, several biomarkers (hormone receptor status and HER2 expression) have been added to the risk evaluation and therapeutic assessments. Evolving knowledge of molecular biology and newer techniques, such as genomics and proteomics, offer the potential to better define the biologic nature of the disease process, both for risk and therapy. This review discusses classical as well as new prognostic and predictive techniques. These are leading to a paradigm shift from empirical treatment to an individually tailored approach, which may soon become a realistic option for patients, based on specific molecular profiles.

Randomized phase II adjuvant trial of dose-dense docetaxel before or after doxorubicin plus cyclophosphamide in axillary node-positive breast cancer

PURPOSE

An anthracycline-based combination followed by, or combined with, a taxane is the sequence used in most adjuvant chemotherapy regimens. We hypothesized that administering the taxane before the anthracycline combination would be associated with fewer dose reductions and delays than the reverse sequence. To test this hypothesis, a randomized phase II multicenter adjuvant chemotherapy trial was performed.

PATIENTS AND METHODS

Fifty-six patients with axillary node-positive, nonmetastatic breast cancer were randomly assigned either to group A (docetaxel [DOC] 75 mg/m(2) intravenously [IV] every 14 days for four cycles followed by doxorubicin 60 mg/m(2) and cyclophosphamide 600 mg/m(2) [AC] IV every 14 days for four cycles); or to group B (AC followed by DOC) at the identical doses and schedule. Pegfilgrastim 6 mg subcutaneous injection was administered 1 day after the chemotherapy in all treatment cycles. The primary objective was to administer DOC without dose reductions or delays before or after AC and calculate the relative dose intensity (RDI) of DOC and AC.

RESULTS

The majority of toxicities were grade 0 to 2 irrespective of sequence. The RDI for DOC was 0.96 and 0.82, respectively, in groups A (DOC followed by AC) and B (AC followed by DOC), with more frequent dose reductions occurring in group B (46% v 18%). The RDI for AC was 0.95 and 0.98 in groups A and B, respectively.

CONCLUSION

The administration of DOC before AC results in fewer DOC dose reductions and a higher RDI than the reverse sequence. Larger trials evaluating the sequence of DOC before anthracyclines are justified.

DLC-1:a Rho GTPase-activating protein and tumour suppressor

The deleted in liver cancer 1 (DLC-1) gene encodes a GTPase activating protein that acts as a negative regulator of the Rho family of small GTPases. Rho proteins transduce signals that influence cell morphology and physiology, and their aberrant up-regulation is a key factor in the neoplastic process, including metastasis. Since its discovery, compelling evidence has accumulated that demonstrates a role for DLC-1 as a bona fide tumour suppressor gene in different types of human cancer. Loss of DLC-1 expression mediated by genetic and epigenetic mechanisms has been associated with the development of many human cancers, and restoration of DLC-1 expression inhibited the growth of tumour cells in vivo and in vitro. Two closely related genes, DLC-2 and DLC-3, may also be tumour suppressors. This review presents the current status of progress in understanding the biological functions of DLC-1 and its relatives and their roles in neoplasia.

Gene expression profiling in breast cancer

PURPOSE OF REVIEW

Gene expression profiling has highlighted the biologic heterogeneity of breast cancer and has begun to influence the ability of the medical community to individualize patient therapy. The review is intended to highlight the most important advances in the field over recent years with an emphasis on those most relevant to the practicing oncologist.

RECENT FINDINGS

Two prognostic profiling assays, the Mammaprint and Oncotype Dx, are in phase III clinical trials designed to evaluate their contribution to therapeutic decision making. Predictive profiles for both chemotherapy and targeted therapy are also in development. In addition, application of genetic profiling techniques to a variety of tumor types is starting to identify those processes, like proliferation, that are integral to carcinogenesis as a whole.

SUMMARY

The biologic heterogeneity of breast cancer has become clearer through genome-wide profiling technologies. Validation of the clinical utility of prognostic profiles may enable oncologists to better identify those patients whose prognosis justifies more intensive therapy, while predictive profiles may soon be able to determine which type of chemotherapy a patient should receive. In addition, profiling is starting to identify new therapeutic targets which will point the field of breast cancer oncology in new directions.

Limitations of pharmacogenomic predictor discovery in Phase II clinical trials

Pharmacogenomic marker discovery projects are increasingly incorporated into Phase II clinical trials in the hope of identifying molecular predictors of response to therapy. At the same time, some of the inherent technical and conceptual limitations of this approach are also increasingly recognized. This manuscript will review some of these limitations. We suggest that it may be more productive to use pharmacogenomic, or other molecular data, from small Phase II clinical trials to assess the clinical utility of previously defined putative markers rather than use the data primarily for discovery. We also describe a simple clinical trial design that could accomplish this. The content of this article is based on a presentation during a symposium on 'Novel Trial Designs for Early Drug Development’ at the Annual Meeting of the American Association of Cancer Research in Los Angeles, CA, USA, on April 17, 2007 [101] .

Thirty-gene pharmacogenomic test correlates with residual cancer burden after preoperative chemotherapy for breast cancer

PURPOSE

We examined whether the response predicted by a 30-gene pharmacogenomic test correlated with the residual cancer burden (RCB) after preoperative chemotherapy with paclitaxel, 5-fluorouracil, doxorubicin, and cyclophosphamide (T/FAC).

EXPERIMENTAL DESIGN

Gene expression profiling was done at diagnosis in 74 patients with stages I to III breast cancer and was used to calculate a pharmacogenomic score and predict response to chemotherapy [pathologic complete response (pCR) or residual disease (RD)]. All patients received 6 months of preoperative T/FAC. Following pathologic review, a RCB score was calculated based on residual tumor and lymph node features. Four RCB classes were assigned; RCB-0 (pCR), RCB-I (near-PCR), RCB-II (moderate RD), and RCB-III (extensive RD). The correlations between the pharmacogenomic score, predicted pathologic response, RCB score, and RCB class were examined.

RESULTS

Thirty-three patients were predicted to have pCR, and 40 were predicted to have RD. Observed responses were RCB-0: n=20 (27%); RCB-I: n=5 (7%); RCB-II: n=36 (49%); and RCB-III: n=13 (16%) patients. Pharmacogenomic and RCB scores were correlated (Pearson's R=-0.501, P<0.0001). There was no difference between the mean genomic predictor scores for RCB-0/I groups (P=0.94), but these were different from the mean scores of the RCB-II/III groups (P<0.001). Among the 25 patients with RCB-0/I response, 19 (76%) were predicted to achieve pCR. The pharmacogenomic test correctly predicted RD in 92% of the patients with RCB-III, which corresponds to chemotherapy-resistant disease.

CONCLUSIONS

The 30-gene pharmacogenomic test showed good correlation with the extent of residual invasive cancer burden measured as both continuous and categorical variables.

Molecular profiling in breast cancer

Molecular profiling has provided biological evidence for the heterogeneity of breast cancer through the identification of intrinsic subtypes like Luminal A, Luminal B, HER2+/ER- and basal-like. It has also led to the development of clinically applicable gene expression-based prognostic panels like the Mammaprint and Oncotype Dx. The increasingly sophisticated understanding allowed by this and similar technology promises future individualized therapy.

The evolution of treatment strategies: aiming at the target

Historically, the selection of adjuvant systemic therapy in early breast cancer has relied on risk assessment embodied by the TNM classification. Since the 2005 International St. Gallen Consensus, treatment selection now involves firstly identifying critical targets and then using risk to assess the trade-off between anticipated toxicity and efficacy. This evolution in treatment strategies began with the identification of the estrogen receptor, and culminated with the HER2 receptor, with recent astounding success in several adjuvant trials. Newer technologies including gene expression profiles and micrometastases tracking bear exciting potential in refining the treatment strategies further. Alongside the progress in developing agents that target different molecules across the whole breast cancer population, these newer technologies aim to tailor adjuvant treatment further by identifying breast cancer subgroups that may benefit most from being targeted with specific therapy, by defining molecular subtypes, recognizing chemo-sensitivity and resistance, identifying at-risk gene signatures, and by detecting stem cells capable of generating metastases. This paper will review this evolution of treatment strategies, from the lessons learnt from the past, to the exciting promise of tailored therapy of the future.

Taking gene-expression profiling to the clinic: when will molecular signatures become relevant to patient care?

The advent of microarray technology has enabled scientists to simultaneously investigate the expression of thousands of genes. Gene-expression profiling studies have provided a molecular classification of breast cancer into clinically relevant subtypes, new tools to predict disease recurrence and response to different treatments, and new insights into various oncogenic pathways and the process of metastatic progression. Here we describe the state of the art of gene-expression studies in breast cancer, and consider both their current limitations and future promises. We also discuss the potential of molecular signatures to have an impact on individual breast cancer patient management, and ultimately to accelerate the transition between empirical and tailored medicine.

Expression genomics in breast cancer research: microarrays at the crossroads of biology and medicine

Genome-wide expression microarray studies have revealed that the biological and clinical heterogeneity of breast cancer can be partly explained by information embedded within a complex but ordered transcriptional architecture. Comprising this architecture are gene expression networks, or signatures, reflecting biochemical and behavioral properties of tumors that might be harnessed to improve disease subtyping, patient prognosis and prediction of therapeutic response. Emerging 'hypothesis-driven' strategies that incorporate knowledge of pathways and other biological phenomena in the signature discovery process are linking prognosis and therapy prediction with transcriptional readouts of tumorigenic mechanisms that better inform therapeutic options.

Clinical and therapeutic perspectives of gene expression profiling for breast cancer

The standard method for defining prognosis for patients with breast cancer is an integrated model including clinicopathological features, such as tumour size, histological grade, nodal involvement, hormone receptor status and HER-2 overexpression. Nowadays, two multigene prognostic models can stratify patients in new categories of risk. Notably, clinicopathological prognostic prediction and genomic signatures are discordant in at least 30% of cases. For this reason, two trials are going on, aiming to validate clinical utility of gene profiling. As regards the predictive value of genomic assays, many models have been carried out, demonstrating the capacity to identify with high sensitivity and specificity resistant and non-resistant tumours, differently from the traditional markers. These predictors, however, need to be validated by prospective clinical trials.

Inhibition of cell proliferation, induction of apoptosis, reactivation of DLC1, and modulation of other gene expression by dietary flavone in breast cancer cell lines

BACKGROUND

Dietary flavone was previously shown to increase the expression of deleted in liver cancer-1 gene (DLC-1) in HT-29 colon carcinoma cell line [Herzog A, Kindermann B, Doring F, Daniel H, Wenzel U. Pleiotropic molecular effects of the pro-apoptotic dietary constituent flavone in human colon cancer cells identified by protein and mRNA expression profiling. Proteomics 2004;4:2455-64]. DLC-1 that encodes a Rho GTPase-activating protein, functions as a tumor suppressor gene and is frequently inactivated or down-regulated in several common cancers. Restoration of DLC-1 expression suppresses in vitro tumor cells proliferation and tumorigenicity in vivo.

METHODS

Here, the effect of flavone was examined in several DLC-1-deficient cell lines derived from different types human cancer using assays for cell proliferation, gene expression and transfer.

RESULTS

We show that exposure to 150 microM flavone increased DLC1 expression in breast but not in liver or prostate carcinoma cells or a nonmalignant breast epithelial cell line. Flavone restored the expression of DLC1 in the breast carcinoma cell lines MDA-MB-468, MDA-MB-361, and BT20 as well as in the colon carcinoma cell line HT-29 all of which are DLC-1-negative due to promoter hypermethylation. We further show that flavone inhibited cell proliferation, induced cell cycle arrest at G(2)-M, increased p21(Waf1) gene expression, and caused apoptosis. Microarray analysis of these aggressive and metastatic breast carcinoma cells revealed 29 flavone-responsive genes, among which the DNA damage-inducible GADD genes were up-regulated and the proto-oncogene STMN1 and IGFBP3 were down-regulated.

CONCLUSIONS

Flavone-mediated alterations of genes that regulate tumor cell proliferation, cell cycle, and apoptosis contribute to chemopreventive and antitumoral effects of flavone. Alone or in combination with demethylating agents, flavone may be an effective adjunct to chemotherapy in preventing breast cancer metastasis.

Clinicians' guide to microarrays

Cancer is a heterogeneous disease with wide-ranging subsets of patients who have different prognoses and who respond differently to treatments. Accordingly, deciding the best treatment strategy has become a priority in cancer care. The development of microarray technology over the last decade has caused great excitement, with the promise that these new tools may provide molecular signatures to help predict patient outcome and direct therapeutic approaches. Here, we describe the different types of microarrays available and discuss their pros and cons from a clinical perspective, with respect to breast cancer.

Visfatin/pre-B-cell colony-enhancing factor: a protein with various suggested functions

Pre-B-cell colony-enhancing factor (PBEF) was recently found in high levels in visceral fat, and was therefore renamed visfatin. This new adipocytokine exerts insulin-mimetic effects in mice and in cultured cells by binding to and activating the insulin receptor. Despite some recent studies on this topic, the proposed role of visfatin in metabolism remains largely unknown. Initially, PBEF/visfatin was discovered as a cytokine for the differentiation of B-cells. Pre-B-cell colony-enhancing factor was also shown to inhibit apoptosis of neutrophils in sepsis and was discussed as a novel biomarker for acute lung injury (ALI). Although PBEF is missing a signal sequence, its secretion and function as a molecule involved in the regulation of inflammatory processes was reported in several studies. Investigations of PBEF/visfatin in gestational membranes suggest a function in the physiologic and pathologic pathways leading to labor. Furthermore, it was found upregulated in colorectal cancer and was brought into connection with the regulation of the cell cycle. Intra-cellular, PBEF/visfatin acts as a cytosolic enzyme involved in nicotinamide adenine dinucleotide (NAD) synthesis. This activity was shown to be important for vascular smooth muscle cell (SMC) maturation, indicating a possible involvement in vascular pathology. The important physiologic role of PBEF/visfatin is also underlined by its evolutionary highly conserved gene in different species. This review summarizes the current knowledge of the various functions of PBEF/visfatin towards involvements in pathophysiology of several diseases.

Developing gene expression signatures of pathway deregulation in tumors

Recent advances in our understanding of cancer biology have led to the development of therapies targeting specific signaling pathways. Molecular targeting promises to improve our ability to predict who will respond by assessing the state of these targeted pathways in patients. However, a single pathway can be deregulated by multiple mechanisms, and for some pathways it may be difficult to assess activation state by analyzing a single oncogene or tumor suppressor. Therefore, developing gene expression signatures of pathway activation status using model systems or human tumor samples may enable a more reliable measurement of pathway activity. This review discusses recent advances in the identification of gene expression-based signatures of pathway deregulation and how this information may lead to improved therapeutic response prediction.

Down-regulation of PHLDA1 gene expression is associated with breast cancer progression

In a previous study, using differential display reverse transcriptase-PCR (DDRT-PCR) we showed that down-regulation of the PHLDA1 (pleckstrin homology-like domain, family A, member 1; also named TDAG51) mRNA was down-regulated in breast tumors compared with normal breast tissue. The present study was conducted to determine the expression pattern and predictive prognostic value of PHLDA1 in breast cancer. A series of 720 primary invasive breast tumors were examined for PHLDA1 expression. PHLDA1 mRNA expression was determined in 74 breast tumors using quantitative Real Time PCR analysis (qPCR). PHLDA1 protein expression was evaluated by immunohistochemistry (IHC) using Tissue Microarrays (TMA) containing 699 primary invasive breast tumors. Reduced PHLDA1 mRNA expression was identified in 72% (53/74) of the primary breast tumors analyzed. Seventy-three percent (512/699) of cases analyzed showed negative PHLDA1 protein expression. Down-regulation of PHLDA1 protein was a strong predictor of poor prognosis for breast cancer patients. Breast cancer patients with tumors that were negative for PHLDA1 protein expression had shorter disease free survival (P < 0.001) and overall survival (P < 0.001) than patients with tumors that were positive for PHLDA1 protein expression. In addition patients with tumors exhibiting reduced PHLDA1 expression and paucity for ER had the worse outcome (P < 0.001). Multivariate analysis indicated that PHLDA1 protein expression is an independent prognostic factor of patient survival. To our knowledge, the expression pattern of PHLDA1 in breast cancer has not previously been investigated. Our results provide strong evidence that reduced PHLDA1 expression is important in breast cancer progression and could serve as useful prognostic marker of disease outcome.

Current Trends and Controversies over Pre-operative Chemotherapy for Women with Operable Breast Cancer

The multi-disciplinary approach, including surgery, chemotherapy, endocrine therapy and radiation therapy, has become the standard treatment for primary breast cancer patients. The indication of pre-operative chemotherapy has been extended to women with potentially operable breast cancer based on the results of large randomized studies and has become an attractive option that extends the chance of breast conservation. The clinical and pathological responses to pre-operative chemotherapy correlates with long-term outcome. The anthracycline-containing regimen is now considered the standard. Sequential administration of non-cross-resistant drugs, namely taxanes, improves local tumor response but its long-term benefit has been controversial. Prediction of response to pre-operative chemotherapy still remains a challenge. Identification of useful predictive markers and development of molecular-targeted drugs is the key to individualized therapy in the future.

Gene Expression Profiling and Clinical Outcome in Breast Cancer

Pathologic and clinical heterogeneity of breast cancer reflects the poorly documented, complex, and combinatory molecular basis of the disease and is in part responsible for therapeutic failures. The DNA microarray technique allows the analysis of RNA expression of several thousands of genes simultaneously in a sample. There are multiple potential applications of the technique in cancer research. A number of recent studies have shown the promising role of gene expression profiling in breast cancer by identifying new prognostic subclasses unidentifiable by conventional parameters and new prognostic and/or predictive gene signatures, whose predictive impact is superior to conventional histoclinical prognostic factors. In this review we describe current use of DNA microarrays in the prognosis of breast cancer. We also discuss issues that need to be addressed in the near future to allow the method to reach its full potential.

The 17q12-q21 amplicon: Her2 and topoisomerase-IIalpha and their importance to the biology of solid tumours

Her2 and topoisomerase-IIalpha (T2A) gene amplification are separate events, although the latter is more frequently seen in Her2 amplified (34-90%) than in Her2 non-amplified (5-10%) tumours. There is a better correlation between Her2 amplification and protein overexpression in breast cancer (BC) than in other tumour types. This marker is also considered a powerful prognostic factor in BC, with similar data emerging in other solid tumours such as bladder, ovarian, endometrial, gastro-oesophageal and non-small cell lung cancer. Her2 amplification and/or overexpression are highly predictive of response to HER2-targeted compounds such as trastuzumab and lapatinib but have been inconsistent predictors of response to cytotoxic chemotherapy. There is also evidence that these tumours are relatively resistant to anti-oestrogen therapy (tamoxifen) but not to oestrogen deprivation (e.g. with aromatase inhibitors). T2A aberrations are uncommon events in solid tumours, with an overall prevalence of approximately 10%. T2A amplification has shown inconsistent correlation with T2A protein expression in preclinical and clinical studies, mainly because non-genetic events such as proliferation rate can also affect protein expression. Expression of T2A protein has not been shown to reliably predict response to T2A inhibitors, despite the fact that this enzyme is the direct target for these compounds. In BC, T2A amplification appears to be a good predictor of response to anthracyclines, but these data are still in the process of validation. The significance of T2A deletions is currently under investigation, but contrary to what was previously thought, it may also predict benefit from treatment with T2A inhibitors. The prognostic significance of T2A aberrations is currently unknown.

Visfatin is present in bovine mammary epithelial cells, lactating mammary gland and milk, and its expression is regulated by cAMP pathway

Visfatin was originally identified as a growth factor for immature B cells, and recently demonstrated to bind insulin receptor. Visfatin mRNA and protein were detected by RT-PCR and Western blot analysis in cloned bovine mammary epithelial cells, lactating bovine mammary gland and human breast cancer cell line, MCF-7. Immunocytochemical staining localized the visfatin protein in the cytosol and nucleus of both cells. Quantitative-RT-PCR analysis revealed that the expression of the visfatin mRNA was significantly elevated when treated with forskolin (500 microM), isopreterenol (1-10 microM) and dibutyric cyclic AMP (1 mM) for 24 h, and significantly reduced when treated with insulin (5-50 ng/ml) and dexsamethasone (0.5-250 nM) for 24 h. These results indicate that mammary epithelial cells express the visfatin protein and secrete them into the milk.

Molecular classification of breast cancer: implications for selection of adjuvant chemotherapy

Adjuvant chemotherapy improves survival of patients with stage I-III breast cancer but it is being increasingly recognized that the benefit is not equal for all patients. Molecular characteristics of the cancer affect sensitivity to chemotherapy. In general, estrogen-receptor-negative disease is more sensitive to chemotherapy than estrogren-receptor-positive disease. Large-scale genomic analyses of breast cancer suggest that further molecular subsets may exist within the categories defined by hormone receptor status. It is hoped that the new molecular classification schemes might improve patient selection for therapy. Before any new molecular classification (or predictive test) is adopted for routine clinical use, however, several criteria need to be met. There must be an agreed and reproducible method by which to assign molecular class to a new case. Cancers that belong to different molecular classes must show differences in disease outcome and treatment efficacy that affect management and treatment selection. Also desirable are results from prospective clinical trials that demonstrate improved patient outcome when the new test is used in decision-making, compared with the current standard of care. This Review describes the current limitations and future promises of gene-expression-based molecular classification of breast cancer and how it might impact on selection of adjuvant therapy for individual patients.

Breast cancer expression profiling: the impact of microarray testing on clinical decision making

The available clinical prognostic tools show an obvious limitation in predicting the outcome of breast cancer patients, and pathological features cannot classify tumours accurately. Microarray-based molecular classification of breast tumours or selection of gene expression panels to improve risk prediction or treatment outcomes are thought to be theoretically superior to established clinical and pathological criteria, based on guidelines such as the St Gallen and National Institute of Health consensus, or which use specific prognostic tools, such as the Nottingham Prognostic Index or Adjuvant-Online algorithm. Although two diagnostic tests based on gene expression profiling of breast cancer are commercially available, a new molecular classification and molecular forecasting of breast cancer based on expression profiling cannot outperform the standard tumour diagnostic at present. This review focuses on some important problems in the practical application of molecular profiling of breast cancer for clinical purposes.

DNA arrays as predictors of efficacy of adjuvant/neoadjuvant chemotherapy in breast cancer patients: current data and issues on study design

Chemotherapy provides variable benefit to patients with breast cancer, with usually modest but occasionally severe side effects. Hence, there is a need to identify predictive biomarkers for its efficacy. DNA arrays have been used in this setting as potential novel predictive diagnostic tools. Several gene signatures and single gene markers were proposed to predict response to chemotherapy. Although this technology offers interesting perspectives through large-scale analysis of the transcriptome, its ability to identify clinically relevant predictors is highly dependent on study design. In the present manuscript, we will review currently available results of breast cancer pharmacogenomics and focus on aspects of study design that are critical to reliably identify predictive biomarkers using DNA array technology. We will discuss whether studies should be done in the overall, unselected breast cancer population or in specific homogeneous molecular subclasses. Next, we will compare advantages and limitations of cohort-based and case-control studies. The choice of end-point to discriminate between sensitive and resistant patients will also be examined.

Gene expression profiling of clinical stages II and III breast cancer

Clinical stage (CS) is an established indicator of breast cancer outcome. In the present study, a cDNA microarray platform containing 692 genes was used to identify molecular differences between CSII and CSIII disease. Tumor samples were collected from patients with CSII or CSIII breast cancer, and normal breast tissue was collected from women without invasive cancer. Seventy-eight genes were deregulated in CSIII tumors and 22 in CSII tumors when compared to normal tissue, and 20 of them were differentially expressed in both CSII and CSIII tumors. In addition, 58 genes were specifically altered in CSIII and expression of 6 of them was tested by real time RT-PCR in another cohort of patients with CSII or CSIII breast cancer and in women without cancer. Among these genes, MAX, KRT15 and S100A14, but not APOBEC3G or KRT19, were differentially expressed on both CSIII and CSII tumors as compared to normal tissue. Increased HMOX1 levels were detected only in CSIII tumors and may represent a molecular marker of this stage. A clear difference in gene expression pattern occurs at the normal-to-cancer transition; however, most of the differentially expressed genes are deregulated in tumors of both CS (II and III) compared to normal breast tissue.

Hypoxic induction of human visfatin gene is directly mediated by hypoxia-inducible factor-1

Visfatin has been originally identified as a growth factor for early stage B cells and recently known as an adipokine. Here, we report that hypoxia induces the visfatin mRNA and protein levels in MCF7 breast cancer cells. We also demonstrate that induction of visfatin gene is regulated by hypoxia-inducible factor-1alpha (HIF-1alpha). Moreover, 5'-flanking promoter region of human visfatin gene contains two functional HIF responsive elements (HREs), activating the expression of visfatin. Mutation of these HREs in the visfatin promoter abrogates activation of a luciferase reporter gene driven by visfatin promoter under hypoxia. Taken together, our results demonstrate that visfatin is a new hypoxia-inducible gene of which expression is stimulated through the interaction of HIF-1 with HRE sites in its promoter region.

Translating genomic biomarkers into clinically useful diagnostics

The landmark sequencing of the human genome has ushered in a new field of large-scale research. Advances in understanding the molecular basis of disease have opened up new opportunities to develop genomics-based tools to diagnose, predict disease onset or recurrence, tailor treatment options, and assess treatment response. Although still in the early stages of research and development, genomic biomarker research has the capability of providing a comprehensive insight into pathophysiological processes as well as more precise predictors of outcome not previously attainable with traditional biomarkers. Before genomic biomarkers are incorporated into clinical practice, several issues will need to be addressed in order to generate the necessary levels of evidence to demonstrate analytical and clinical validity and utility. In addition, efforts will be needed to educate health professionals and the public about genomics-based tools, revise regulatory oversight mechanisms, and ensure privacy safeguards of the information generated from these new tests.

The effect of the stromal component of breast tumours on prediction of clinical outcome using gene expression microarray analysis

Introduction

The aim of this study was to examine the effect of the cellular composition of biopsies on the error rates of multigene predictors of response of breast tumours to neoadjuvant adriamycin and cyclophosphamide (AC) chemotherapy.

Materials and methods

Core biopsies were taken from primary breast tumours of 43 patients prior to AC, and subsequent clinical response was recorded. Post-chemotherapy (day 21) samples were available for 16 of these samples. Frozen sections of each core were used to estimate the proportion of invasive cancer and other tissue components at three levels. Transcriptional profiling was performed using a cDNA array containing 4,600 elements.

Results

Twenty-three (53%) patients demonstrated a 'good' and 20 (47%) a 'poor' clinical response. The percentage invasive tumour in core biopsies collected from these patients varied markedly. Despite this, agglomerative clustering of sample expression profiles showed that almost all biopsies from the same tumour aggregated as nearest neighbours. SAM (significance analysis of microarrays) regression analysis identified 144 genes which distinguished high- and low-percentage invasive tumour biopsies at a false discovery rate of not more than 5%. The misclassification error of prediction of clinical response using microarray data from pre-treatment biopsies (on leave-one-out cross-validation) was 28%. When prediction was performed on subsets of samples which were more homogeneous in their proportions of malignant and stromal cells, the misclassification error was considerably lower (8%–13%, p < 0.05 on permutation).

Conclusion

The non-tumour content of breast cancer samples has a significant effect on gene expression profiles. Consideration of this factor improves accuracy of response prediction by expression array profiling. Future gene expression array prediction studies should be planned taking this into account.