High-throughput genomic technology in research and clinical management of breast cancer. Exploiting the potential of gene expression profiling: is it ready for the clinic?

The Anti-Cancer Activity of the Naturally Occurring Dipeptide Carnosine: Potential for Breast Cancer

Carnosine is an endogenous dipeptide composed of β-alanine and L-histidine, possessing a multimodal pharmacodynamic profile that includes anti-inflammatory and anti-oxidant activities. Carnosine has also shown its ability to modulate cell proliferation, cell cycle arrest, apoptosis, and even glycolytic energy metabolism, all processes playing a key role in the context of cancer. Cancer is one of the most dreaded diseases of the 20th and 21st centuries. Among the different types of cancer, breast cancer represents the most common non-skin cancer among women, accounting for an estimated 15% of all cancer-related deaths in women. The main aim of the present review was to provide an overview of studies on the anti-cancer activity of carnosine, and in particular its activity against breast cancer. We also highlighted the possible advantages and limitations involved in the use of this dipeptide. The first part of the review entailed a brief description of carnosine's biological activities and the pathophysiology of cancer, with a focus on breast cancer. The second part of the review described the anti-tumoral activity of carnosine, for which numerous studies have been carried out, especially at the preclinical level, showing promising results. However, only a few studies have investigated the therapeutic potential of this dipeptide for breast cancer prevention or treatment. In this context, carnosine has shown to be able to decrease the size of cancer cells and their viability. It also reduces the levels of vascular endothelial growth factor (VEGF), cyclin D1, NAD+, and ATP, as well as cytochrome c oxidase activity in vitro. When tested in mice with induced breast cancer, carnosine proved to be non-toxic to healthy cells and exhibited chemopreventive activity by reducing tumor growth. Some evidence has also been reported at the clinical level. A randomized phase III prospective placebo-controlled trial showed the ability of Zn-carnosine to prevent dysphagia in breast cancer patients undergoing adjuvant radiotherapy. Despite this evidence, more preclinical and clinical studies are needed to better understand carnosine's anti-tumoral activity, especially in the context of breast cancer.

Metastatic heterogeneity of breast cancer: Molecular mechanism and potential therapeutic targets

Breast cancer is one of the most common malignancies among women throughout the world and is the major cause of most cancer-related deaths. Several explanations account for the high rate of mortality of breast cancer, and metastasis to vital organs is identified as the principal cause. Over the past few years, intensive efforts have demonstrated that breast cancer exhibits metastatic heterogeneity with distinct metastatic precedence to various organs, giving rise to differences in prognoses and responses to therapy in breast cancer patients. Bone, lung, liver, and brain are generally accepted as the primary target sites of breast cancer metastasis. However, the underlying molecular mechanism of metastatic heterogeneity of breast cancer remains to be further elucidated. Recently, the advent of novel genomic and pathologic approaches as well as technological breakthroughs in imaging analysis and animal modelling have yielded an unprecedented change in our understanding of the heterogeneity of breast cancer metastasis and provided novel insight for establishing more effective therapeutics. This review summarizes recent molecular mechanisms and emerging concepts on the metastatic heterogeneity of breast cancer and discusses the potential of identifying specific molecules against tumor cells or tumor microenvironments to thwart the development of metastatic disease and improve the prognosis of breast cancer patients.

Economic issues involved in integrating genomic testing into clinical care: the case of genomic testing to guide decision-making about chemotherapy for breast cancer patients

The use of taxanes to treat node-positive (N+) breast cancer patients is associated with heterogeneous benefits as well as with morbidity and financial costs. This study aimed to assess the economic impact of using gene expression profiling to guide decision-making about chemotherapy, and to discuss the coverage/reimbursement issues involved. Retrospective data on 246 patients included in a randomised trial (PACS01) were analyzed. Tumours were genotyped using DNA microarrays (189-gene signature), and patients were classified depending on whether or not they were likely to benefit from chemotherapy regimens without taxanes. Standard anthracyclines plus taxane chemotherapy (strategy AT) was compared with the innovative strategy based on genomic testing (GEN). Statistical analyses involved bootstrap methods and sensitivity analyses. The AT and GEN strategies yielded similar 5-year metastasis-free survival rates. In comparison with AT, GEN was cost-effective when genomic testing costs were less than 2,090€. With genomic testing costs higher than 2,919€, AT was cost-effective. Considering a 30% decrease in the price of docetaxel (the patent rights being about to expire), GEN was cost-effective if the cost of genomic testing was in the 0€-1,139€, range; whereas AT was cost-effective if genomic testing costs were higher than 1,891€. The use of gene expression profiling to guide decision-making about chemotherapy for N+ breast cancer patients is potentially cost-effective. Since genomic testing and the drugs targeted in these tests yield greater well-being than the sum of those resulting from separate use, questions arise about how to deal with extra well-being in decision-making about coverage/reimbursement.

Endocrine Resistance in Breast Cancer

Around 70% of all breast cancers are estrogen receptor alpha positive and hence their development is highly dependent on estradiol. While the invention of endocrine therapies has revolusioned the treatment of the disease, resistance to therapy eventually occurs in a large number of patients. This paper seeks to illustrate and discuss the complexity and heterogeneity of the mechanisms which underlie resistance and the approaches proposed to combat them. It will also focus on the use and development of methods for predicting which patients are likely to develop resistance.

Bioinformatics and Nanotechnologies: Nanomedicine

In this chapter we focus on the bioinformatics strategies for translating genome-wide expression analyses into clinically useful cancer markers with a specific focus on breast cancer with a perspective on new diagnostic device tools coming from the field of nanobiotechnology and the challenges related to high-throughput data integration, analysis, and assessment from multiple sources.

Great progress in the development of molecular biology techniques has been seen since the discovery of the structure of deoxyribonucleic acid (DNA) and the implementation of a polymerase chain reaction (PCR) method. This started a new era of research on the structure of nucleic acids molecules, the development of new analytical tools, and DNA-based analyses that allowed the sequencing of the human genome, the completion of which has led to intensified efforts toward comprehensive analysis of mammalian cell struc ture and metabolism in order to better understand the mechanisms that regulate normal cell behavior and identify the gene alterations responsible for a broad spectrum of human diseases, such as cancer, diabetes, cardiovascular diseases, neurodegenerative disorders, and others.

Integrating contextual miRNA and protein signatures for diagnostic and treatment decisions in cancer

The promise of personalized medicine is highly dependent on the identification of biomarkers that inform diagnostic decisions and treatment options, as well as on the accurate, rapid and cost-effective detection and interpretation of these biomarkers. miRNAs, which are short noncoding regulatory RNAs, are rapidly emerging as a novel class of biomarkers with a unique set of biological and chemical properties that makes them very appealing candidates for theranostic applications in cancer. Since the utility of some protein-encoding gene biomarkers is already exploited in routine clinical practice, it will be important to identify areas in which miRNAs provide complementary or superior information to these existing (and other translational) biomarkers to enhance the diagnostic, prognostic and predictive power of molecular characterization of tumors. In this article, the challenges and opportunities for integration of miRNA-based assays in the clinical toolkit to improve care and management of patients afflicted with solid tumors will be discussed.

Adjuvant therapy in stage I carcinoma of the breast: the influence of multigene analyses and molecular phenotyping

BACKGROUND

Breast Health International and the Kimmel Cancer Center of Thomas Jefferson University cosponsored a consensus conference that included an international group of experts.

METHODS

Since the adoption of adjuvant chemotherapy for stage I, lymph node-negative breast cancers in 1988, investigators have tried to "fine-tune" the treatment criteria. At this consensus conference, the group debated recommendations for adjuvant hormone and cytotoxic chemotherapy in stage I breast cancers.

RESULTS

Discussions during the conference addressed issues of adjuvant therapy for stage I breast cancer and the influence of multigene analyses and molecular phenotyping. The panelists discussed various demographic, morphologic, biologic, and genetic factors expressed by individual tumors and their influence on treatment decisions.

CONCLUSIONS

The panel tried to create guidelines for the consideration of adjuvant treatment of these patients, including both hormone and cytotoxic regimens. They also encouraged further research into the molecular analysis of breast cancers and the introduction of clinical trials based on current data, although they concluded that it is too early to add any of those analyses into the decision-making algorithms of recommendations for the treatment of stage I breast cancer.

Relative impact of key sources of systematic noise in Affymetrix and Illumina gene-expression microarray experiments

BACKGROUND

Systematic processing noise, which includes batch effects, is very common in microarray experiments but is often ignored despite its potential to confound or compromise experimental results. Compromised results are most likely when re-analysing or integrating datasets from public repositories due to the different conditions under which each dataset is generated. To better understand the relative noise-contributions of various factors in experimental-design, we assessed several Illumina and Affymetrix datasets for technical variation between replicate hybridisations of Universal Human Reference (UHRR) and individual or pooled breast-tumour RNA.

RESULTS

A varying degree of systematic noise was observed in each of the datasets, however in all cases the relative amount of variation between standard control RNA replicates was found to be greatest at earlier points in the sample-preparation workflow. For example, 40.6% of the total variation in reported expressions were attributed to replicate extractions, compared to 13.9% due to amplification/labelling and 10.8% between replicate hybridisations. Deliberate probe-wise batch-correction methods were effective in reducing the magnitude of this variation, although the level of improvement was dependent on the sources of noise included in the model. Systematic noise introduced at the chip, run, and experiment levels of a combined Illumina dataset were found to be highly dependent upon the experimental design. Both UHRR and pools of RNA, which were derived from the samples of interest, modelled technical variation well although the pools were significantly better correlated (4% average improvement) and better emulated the effects of systematic noise, over all probes, than the UHRRs. The effect of this noise was not uniform over all probes, with low GC-content probes found to be more vulnerable to batch variation than probes with a higher GC-content.

CONCLUSIONS

The magnitude of systematic processing noise in a microarray experiment is variable across probes and experiments, however it is generally the case that procedures earlier in the sample-preparation workflow are liable to introduce the most noise. Careful experimental design is important to protect against noise, detailed meta-data should always be provided, and diagnostic procedures should be routinely performed prior to downstream analyses for the detection of bias in microarray studies.

Biomarkers and patient selection for PI3K/Akt/mTOR targeted therapies: current status and future directions

The phosphatidylinositol 3-kinase (PI3K)/Akt/ mammalian target of rapamycin (mTOR) pathway regulates a broad spectrum of physiologic and pathologic processes. In breast cancer mutation, amplification, deletion, methylation, and posttranslational modifications lead to significant dysregulation of this pathway leading to more aggressive and potentially drug-resistant disease. Multiple novel agents, targeting different nodes within the pathway are currently under development by both commercial and academic partners. The key to the successful validation of these markers is selection of the appropriate patient groups using biomarkers. This article reviews current progress in this area, highlighting the key molecular alterations described in genes within the PI3K/Akt/mTOR pathway that may have an effect on response to current and future therapeutic interventions. Herein, gaps in current knowledge are highlighted and suggestions for future research directions given that may facilitate biomarker development in partnership with current drug development.

Expression profiling of formalin-fixed paraffin-embedded primary breast tumors using cancer-specific and whole genome gene panels on the DASL® platform

Background

The cDNA-mediated Annealing, extension, Selection and Ligation (DASL) assay has become a suitable gene expression profiling system for degraded RNA from paraffin-embedded tissue. We examined assay characteristics and the performance of the DASL 502-gene Cancer Panel^v1 (1.5K) and 24,526-gene panel (24K) platforms at differentiating nine human epidermal growth factor receptor 2- positive (HER2+) and 11 HER2-negative (HER2-) paraffin-embedded breast tumors.

Methods

Bland-Altman plots and Spearman correlations evaluated intra/inter-panel agreement of normalized expression values. Unequal-variance t-statistics tested for differences in expression levels between HER2 + and HER2 - tumors. Regulatory network analysis was performed using Metacore (GeneGo Inc., St. Joseph, MI).

Results

Technical replicate correlations ranged between 0.815-0.956 and 0.986-0.997 for the 1.5K and 24K panels, respectively. Inter-panel correlations of expression values for the common 498 genes across the two panels ranged between 0.485-0.573. Inter-panel correlations of expression values of 17 probes with base-pair sequence matches between the 1.5K and 24K panels ranged between 0.652-0.899. In both panels, erythroblastic leukemia viral oncogene homolog 2 (ERBB2) was the most differentially expressed gene between the HER2 + and HER2 - tumors and seven additional genes had p-values < 0.05 and log2 -fold changes > |0.5| in expression between HER2 + and HER2 - tumors: topoisomerase II alpha (TOP2A), cyclin a2 (CCNA2), v-fos fbj murine osteosarcoma viral oncogene homolog (FOS), wingless-type mmtv integration site family, member 5a (WNT5A), growth factor receptor-bound protein 7 (GRB7), cell division cycle 2 (CDC2), and baculoviral iap repeat-containing protein 5 (BIRC5). The top 52 discriminating probes from the 24K panel are enriched with genes belonging to the regulatory networks centered around v-myc avian myelocytomatosis viral oncogene homolog (MYC), tumor protein p53 (TP53), and estrogen receptor α (ESR1). Network analysis with a two-step extension also showed that the eight discriminating genes common to the 1.5K and 24K panels are functionally linked together through MYC, TP53, and ESR1.

Conclusions

The relative RNA abundance obtained from two highly differing density gene panels are correlated with eight common genes differentiating HER2 + and HER2 - breast tumors. Network analyses demonstrated biological consistency between the 1.5K and 24K gene panels.

Gene expression profiling of response to mTOR inhibitor everolimus in pre-operatively treated post-menopausal women with oestrogen receptor-positive breast cancer

There is growing evidence that uncontrolled activation of the PI3K/Akt/mTOR pathway contributes to the development and progression of breast cancer. Inhibition of this pathway has antitumour effects in preclinical studies and efficacy in combination with other agents in breast cancer patients. The aim of this study is to characterise the effects of pre-operative everolimus treatment in primary breast cancer patients and to identify potential molecular predictors of response. Twenty-seven patients with oestrogen receptor (ER)-positive breast cancer completed 11-14 days of neoadjuvant treatment with 5-mg everolimus. Core biopsies were taken before and after treatment and analysed using Illumina HumanRef-8 v2 Expression BeadChips. Changes in proliferation (Ki67) and phospho-AKT were measured on diagnostic core biopsies/resection samples embedded in paraffin by immunohistochemistry to determine response to treatment. Patients that responded to everolimus treatment with significant reductions in proliferation (fall in % Ki67 positive cells) also had significant decreases in the expression of genes involved in cell cycle (P = 8.70E-09) and p53 signalling (P = 0.01) pathways. Highly proliferating tumours that have a poor prognosis exhibited dramatic reductions in the expression of cell cycle genes following everolimus treatment. The genes that most clearly separated responding from non-responding pre-treatment tumours were those involved with protein modification and dephosphorylation, including DYNLRB2, ERBB4, PTPN13, ULK2 and DUSP16. The majority of ER-positive breast tumours treated with everolimus showed a significant reduction in genes involved with proliferation, these may serve as markers of response and predict which patients will derive most benefit from mTOR inhibition.

Current and future approach to the pathologist's assessment for targeted therapy in breast cancer

Breast cancer is a common disease in the population. Contrary to public perception, it is a heterogeneous disease with varying morphology, prognosis and response to therapy. The pathological analysis is at the heart of information provided to surgeons and oncologists to plan further management. The pathologist is increasingly asked to test for biomarkers that provide prognostic and predictive information to direct treatment. Staining cancers for ER, PgR and HER2 has become routine and it is likely that addition of other biomarkers including 'basal markers', VEGF and growth factor receptors such as HER1 (EGFR) will soon follow. Microarray based genomic, transcription and proteomic methods are changing our classification systems and identifying novel targets for the development of new therapeutics. It is important for pathologists to appreciate and embrace the new developments as they will impact on daily clinical practice and require accurate assessment of biomarkers to determine treatment options as part of multidisciplinary teams.

Bioinformatics and breast cancer: what can high-throughput genomic approaches actually tell us?

High-throughput genomic technology has rapidly become a major tool for the study of breast cancer. Gene expression profiling has been applied to many areas of research from basic science to translational studies, with the potential to identify new targets for treatment, mechanisms of resistance and to improve on current tools for the analysis of prognosis. However, the sheer scale of the data generated along with the number of different protocols, platforms and analysis methods can make these studies difficult for clinicians to comprehend. Similarly, computational scientists and statisticians that may be called upon to analyse the data generated are often unaware of the processes involved in sample collection or the relevance and impact of genetics and pathological characteristics. There is a pressing need for better understanding of the challenges and limitations of microarray approaches, both in experimental design and data analysis. Holistic, whole-genome approaches are still relatively new and critics have been quick to highlight non-overlapping results from groups testing similar hypotheses. However, it is often subtle differences in the experimental design and technology that underpin the variation between these studies. Rather than indicating that the data are meaningless, this suggests that many findings are real, but highly context dependent. This review explores both the current state and potential of bioinformatics to bring meaning to high-throughput genomic approaches in the understanding of breast cancer.

Approaches towards expression profiling the response to treatment

Over the past 8 years there has been a wealth of breast cancer gene expression studies. The majority of these studies have focused upon characterising a tumour at presentation, before treatment, rather than looking at the effects of treatment on the tumour. More recently, a number of groups have moved from predicting prognosis based upon long-term follow-up to alternative approaches of using expression profiling to measure the effect of treatment on breast tumours and potentially predict response to therapy using either post-treatment samples or both pre-treatment and post-treatment samples. Whilst this provides great potential to further our understanding of the mode of action of treatments and to more accurately select which patients will benefit from a particular treatment, serious issues of experimental design must be considered.

Annotating breast cancer microarray samples using ontologies

As the most common cancer among women, breast cancer results from the accumulation of mutations in essential genes. Recent advance in high-throughput gene expression microarray technology has inspired researchers to use the technology to assist breast cancer diagnosis, prognosis, and treatment prediction. However, the high dimensionality of microarray experiments and public access of data from many experiments have caused inconsistencies which initiated the development of controlled terminologies and ontologies for annotating microarray experiments, such as the standard microarray Gene Expression Data (MGED) ontology(MO). In this paper, we developed BCM-CO, anontology tailored specifically for indexing clinical annotations of breast cancer microarray samples from the NCI Thesaurus. Our research showed that the coverage of NCI Thesaurus is very limited with respect to i) terms used by researchers to describe breast cancer histology (covering 22 out of 48 histology terms); ii) breast cancer cell lines (covering one out of 12 cell lines); and iii) classes corresponding to the breast cancer grading and staging. By incorporating a wider range of those terms into BCM-CO, we were able to indexed breast cancer microarray samples from GEO using BCMCO and MGED ontology and developed a prototype system with web interface that allows the retrieval of microarray data based on the ontology annotations.

The removal of multiplicative, systematic bias allows integration of breast cancer gene expression datasets - improving meta-analysis and prediction of prognosis

Background

The number of gene expression studies in the public domain is rapidly increasing, representing a highly valuable resource. However, dataset-specific bias precludes meta-analysis at the raw transcript level, even when the RNA is from comparable sources and has been processed on the same microarray platform using similar protocols. Here, we demonstrate, using Affymetrix data, that much of this bias can be removed, allowing multiple datasets to be legitimately combined for meaningful meta-analyses.

Results

A series of validation datasets comparing breast cancer and normal breast cell lines (MCF7 and MCF10A) were generated to examine the variability between datasets generated using different amounts of starting RNA, alternative protocols, different generations of Affymetrix GeneChip or scanning hardware. We demonstrate that systematic, multiplicative biases are introduced at the RNA, hybridization and image-capture stages of a microarray experiment. Simple batch mean-centering was found to significantly reduce the level of inter-experimental variation, allowing raw transcript levels to be compared across datasets with confidence. By accounting for dataset-specific bias, we were able to assemble the largest gene expression dataset of primary breast tumours to-date (1107), from six previously published studies. Using this meta-dataset, we demonstrate that combining greater numbers of datasets or tumours leads to a greater overlap in differentially expressed genes and more accurate prognostic predictions. However, this is highly dependent upon the composition of the datasets and patient characteristics.

Conclusion

Multiplicative, systematic biases are introduced at many stages of microarray experiments. When these are reconciled, raw data can be directly integrated from different gene expression datasets leading to new biological findings with increased statistical power.

Evaluation of the current knowledge limitations in breast cancer research: a gap analysis

BACKGROUND

A gap analysis was conducted to determine which areas of breast cancer research, if targeted by researchers and funding bodies, could produce the greatest impact on patients.

METHODS

Fifty-six Breast Cancer Campaign grant holders and prominent UK breast cancer researchers participated in a gap analysis of current breast cancer research. Before, during and following the meeting, groups in seven key research areas participated in cycles of presentation, literature review and discussion. Summary papers were prepared by each group and collated into this position paper highlighting the research gaps, with recommendations for action.

RESULTS

Gaps were identified in all seven themes. General barriers to progress were lack of financial and practical resources, and poor collaboration between disciplines. Critical gaps in each theme included: (1) genetics (knowledge of genetic changes, their effects and interactions); (2) initiation of breast cancer (how developmental signalling pathways cause ductal elongation and branching at the cellular level and influence stem cell dynamics, and how their disruption initiates tumour formation); (3) progression of breast cancer (deciphering the intracellular and extracellular regulators of early progression, tumour growth, angiogenesis and metastasis); (4) therapies and targets (understanding who develops advanced disease); (5) disease markers (incorporating intelligent trial design into all studies to ensure new treatments are tested in patient groups stratified using biomarkers); (6) prevention (strategies to prevent oestrogen-receptor negative tumours and the long-term effects of chemoprevention for oestrogen-receptor positive tumours); (7) psychosocial aspects of cancer (the use of appropriate psychosocial interventions, and the personal impact of all stages of the disease among patients from a range of ethnic and demographic backgrounds).

CONCLUSION

Through recommendations to address these gaps with future research, the long-term benefits to patients will include: better estimation of risk in families with breast cancer and strategies to reduce risk; better prediction of drug response and patient prognosis; improved tailoring of treatments to patient subgroups and development of new therapeutic approaches; earlier initiation of treatment; more effective use of resources for screening populations; and an enhanced experience for people with or at risk of breast cancer and their families. The challenge to funding bodies and researchers in all disciplines is to focus on these gaps and to drive advances in knowledge into improvements in patient care.

Live or let die: oestrogen regulation of survival signalling in endocrine response

The growth of both normal and neoplastic tissues is determined by a balance between cell proliferation and cell death. Thus, understanding how these processes not only drive tumour growth dynamics but also influence therapeutic responsiveness may aid in the development of more effective cancer treatments. Oestrogen is a major aetiological factor in the development and progression of breast cancer, and its effects in driving breast cancer cell proliferation have been extensively studied. What is less well understood is how oestrogen's role as a survival factor influences breast tumour growth and response to therapy. Recent gene expression profiling studies in breast cancer cohorts have suggested that aberrant apoptotic signalling may play a role in responsiveness to endocrine therapies. Thus, further elucidation of this process may lead to identification of clinically relevant end-points to determine and delineate therapeutic response in breast cancer patients.

Innovative genomic-based model for personalized treatment of gastric cancer: integrating current standards and new technologies

In the era of network biology, understanding the complexity of the signaling pathways network in cancer origin, progression and metastasis will dramatically alter and improve treatment strategies. Prognosis of gastric cancer remains poor. Clinical decisions on treatment are based on tumor-node-metastasis (TNM) staging, but are suboptimal. This perspective review, integrating several concepts, including cancer stem cells, provides a novel treatment model for tailoring the best treatment in individual patients with gastric cancer. Biologic metastatic steps (invasion, angiogenesis, intra/extravasation, colonization and microenvironment at distant organs) are orchestrated by mutated genes. Identifying and profiling these key genes and their interactions with environmental factors such as Helicobacter pylori, driver mutations and interacting signaling pathways using high-throughput technologies (including omics, resequencing, genome-wide associations studies and RNAi) in unbiased studies can lead to the development of both novel biomarkers and targeted agents. A comprehensive bench-to-bedside treatment-guided algorithm is provided for optimum preoperative or postoperative combination of cytotoxic and targeted agents. The protocol can be applied with adequate modification for most solid tumors.

Altered MicroRNA expression confined to specific epithelial cell subpopulations in breast cancer

MicroRNAs (miRNAs) are a new class of short noncoding regulatory RNAs (18-25 nucleotides) that are involved in diverse developmental and pathologic processes. Altered miRNA expression has been associated with several types of human cancer. However, most studies did not establish whether miRNA expression changes occurred within cells undergoing malignant transformation. To obtain insight into miRNA deregulation in breast cancer, we implemented an in situ hybridization (ISH) method to reveal the spatial distribution of miRNA expression in archived formalin-fixed, paraffin-embedded specimens representing normal and tumor tissue from >100 patient cases. Here, we report that expression of miR-145 and miR-205 was restricted to the myoepithelial/basal cell compartment of normal mammary ducts and lobules, whereas their accumulation was reduced or completely eliminated in matching tumor specimens. Conversely, expression of other miRNAs was detected at varying levels predominantly within luminal epithelial cells in normal tissue; expression of miR-21 was frequently increased, whereas that of let-7a was decreased in malignant cells. We also analyzed the association of miRNA expression with that of epithelial markers; prognostic indicators such as estrogen receptor, progesterone receptor, and HER2; as well as clinical outcome data. This ISH approach provides a more direct and informative assessment of how altered miRNA expression contributes to breast carcinogenesis compared with miRNA expression profiling in gross tissue biopsies. Most significantly, early manifestation of altered miR-145 expression in atypical hyperplasia and carcinoma in situ lesions suggests that this miRNA may have a potential clinical application as a novel biomarker for early detection.

Phosphohistone H3 expression has much stronger prognostic value than classical prognosticators in invasive lymph node-negative breast cancer patients less than 55 years of age

The proliferation factor mitotic activity index is the strongest prognostic factor in early breast cancer, but it may lack reproducibility. We analyzed the prognostic value of phosphohistone H3, a marker of cells in late G(2) and M phase, measuring highly standardized immunohistochemical nuclear phosphohistone H3 expression by subjective counts and digital image analysis. Expression was compared with classical clinico-pathologic prognostic variables and the mitotic activity index in 119 node-negative invasive breast cancers in patients less than 55 years old treated with adjuvant systemic chemotherapy with long-term follow-up (median 168 months). Nineteen patients (16%) developed distant metastases and 16 (13%) died. Strong phosphohistone H3 expression occurred preferentially in the peripheral growing front; counts were highly reproducible between observers (R=0.92) and highly consistent with digital image analysis (R=0.96). Phosphohistone H3 correlated (P<0.05) with tumor diameter, estrogen receptor, carcinoma grade, and mitotic activity index. Phosphohistone H3 values were systematically (80%) higher than the mitotic activity index. Receiver-operating curve analysis objectively showed that phosphohistone H3 <13 (n=53; 45% of all cases) vs phosphohistone H3> or =13 (n=66; 55% of all cases) was the strongest prognostic threshold, with 20-year recurrence-free survival of distant metastases of 96 and 58%, respectively (P=0.0002, HR=9.6). Mitotic activity index was the second strongest prognostic variable (P=0.003, HR=3.9). In multivariate analysis, phosphohistone H3 <13 vs> or =13 exceeded the prognostic value of the mitotic activity index. None of the other classical prognostic factors examined offered prognostic value additional to phosphohistone H3. Phosphohistone H3 is by far the strongest prognostic variable in early invasive node-negative breast cancer patients less than 55 years old with long-term follow-up.

Are oncoantigens suitable targets for anti-tumour therapy?

When a vaccine-elicited immune response is directed against oncoantigens--proteins required for the neoplastic process--the chance that the tumour will evade the vaccine should be reduced. But how can these causal oncoantigens be identified? One approach is to find tumour-associated and microenvironment-associated oncoantigens required for progression from one tumour stage to the next by comparing gene signatures isolated from the different stages of tumour progression in cancer-prone transgenic mice. Mouse oncoantigens subsequently shown to be involved in human cancer can then be validated in mouse vaccination experiments. This provides the groundwork for the rational design of cancer vaccines for clinical trials.

Origins of breast cancer subtypes and therapeutic implications

This Review summarizes and evaluates the current evidence for the cellular origins of breast cancer subtypes identified by different approaches such as histology, molecular pathology, genetic and gene-expression analysis. Emerging knowledge of the normal breast cell types has led to the hypothesis that the subtypes of breast cancer might arise from mutations or genetic rearrangements occurring in different populations of stem cells and progenitor cells. We describe the common distinguishing features of these breast cancer subtypes and explain how these features relate both to prognosis and to selection of the most appropriate therapy. Recent data indicate that breast tumors may originate from cancer stem cells. Consequently, inhibition of stem-cell self-renewal pathways should be explored because of the likelihood that residual stem cells might be resistant to current therapies.