The diagnosis and management of pre-invasive breast disease: Pathological diagnosis – problems with existing classifications

In this review, we comment on the reasons for disagreement in the concepts, diagnosis and classifications of pre-invasive intraductal proliferations. In view of these disagreements, our proposal is to distinguish epithelial hyperplasia, lobular carcinoma in situ and ductal carcinoma in situ, and to abandon the use of poorly reproducible categories, such as atypical ductal hyperplasia or ductal intraepithelial neoplasia, followed by a number to indicate the degree of proliferation and atypia, as these are not practical for clinical decision making, nor for studies aimed at improving the understanding of breast cancer development. If there is doubt about the classification of an intraductal proliferation, a differential diagnosis and the reason for and degree of uncertainty should be given, rather than categorizing a proliferation as atypical

Are triple-negative tumours and basal-like breast cancer synonymous? Authors' response

We read with interest the issues raised by Rakha and colleagues [1] in their response to our recent research article [2], and we are pleased to address them. An important conclusion from our research article is that triple-negative breast cancer can be equated to basal-like breast cancer. In their letter, Rakha and colleagues [1] state that equating triple-negative phenotype (TNP) tumours with basal-like breast cancer is misleading and is not supported by the data we have presented. It is important to realize that, as we have also pointed out in our article [2], the basal-like breast cancer subtype was initially defined based on the gene expression pattern of the so-called ‘intrinsic gene list ’ in only six breast tumours [3]. Since this initial report, the intrinsic gene list that is used to identify basallike breast tumours has been updated multiple times [3-5]

A novel gene expression signature for bone metastasis in breast carcinomas

Metastatic cancer remains the leading cause of death for patients with breast cancer. To understand the mechanisms underlying the development of distant metastases to specific sites is therefore important and of potential clinical value. From 157 primary breast tumours of the patients with known metastatic disease, gene expression profiling data were generated and correlated to metastatic behaviour including site-specific metastasis, metastasis pattern and survival outcomes. We analysed gene expression signatures specifically associated with the development of bone metastases. As a validation cohort, we used a published dataset of 376 breast carcinomas for which gene expression data and site-specific metastasis information were available. 80.5 % of luminal-type tumours developed bone metastasis as opposed to 41.7 % of basal and 55.6 % of HER2-like tumours. A novel 15-gene signature identified 82.4 % of the tumours with bone metastasis, 85.2 % of the tumours which had bone metastasis as first site of metastasis and 100 % of the ones with bone metastasis only (p 9.99e-09), in the training set. In the independent dataset, 81.2 % of the positive tested tumours had known metastatic disease to the bone (p 4.28e-10). This 15-gene signature showed much better correlation with the development of bone metastases than previously identified signatures and was predictive in both ER-positive as well as in ER-negative tumours. Multivariate analyses revealed that together with the molecular subtype, our 15-gene expression signature was significantly correlated to bone metastasis status (p <0.001, 95 % CI 3.86-48.02 in the training set; p 0.001, 95 % CI 1.54-5.00 in the independent set). The 15 genes, APOPEC3B, ATL2, BBS1, C6orf61, C6orf167, MMS22L, KCNS1, MFAP3L, NIP7, NUP155, PALM2, PH-4, PGD5, SFT2D2 and STEAP3, encoded mainly membrane-bound molecules with molecular function of protein binding. The expression levels of the up-regulated genes (NAT1, BBS1 and PH-4) were also found to be correlated to epithelial to mesenchymal transition status of the tumour. We have identified a novel 15-gene expression signature associated with the development of bone metastases in breast cancer patients. This bone metastasis signature is the first to be identified using a supervised classification approach in a large series of patients and will help forward research in this area towards clinical application

Pooling breast cancer datasets has a synergetic effect on classification performance and improves signature stability

Background: Michiels et al. (Lancet 2005; 365: 488-92) employed a resampling strategy to show that the genes identified as predictors of prognosis from resamplings of a single gene expression dataset are highly variable. The genes most frequently identified in the separate resamplings were put forward as a 'gold standard'. On a higher level, breast cancer datasets collected by different institutions can be considered as resamplings from the underlying breast cancer population. The limited overlap between published prognostic signatures confirms the trend of signature instability identified by the resampling strategy. Six breast cancer datasets, totaling 947 samples, all measured on the Affymetrix platform, are currently available. This provides a unique opportunity to employ a substantial dataset to investigate the effects of pooling datasets on classifier accuracy, signature stability and enrichment of functional categories. Results: We show that the resampling strategy produces a suboptimal ranking of genes, which can not be considered to be a 'gold standard'. When pooling breast cancer datasets, we observed a synergetic effect on the classification performance in 73% of the cases. We also observe a significant positive correlation between the number of datasets that is pooled, the validation performance, the number of genes selected, and the enrichment of specific functional categories. In addition, we have evaluated the support for five explanations that have been postulated for the limited overlap of signatures. Conclusion: The limited overlap of current signature genes can be attributed to small sample size. Pooling datasets results in more accurate classification and a convergence of signature genes. We therefore advocate the analysis of new data within the context of a compendium, rather than analysis in isolatio

Expression profiling predicts outcome in breast cancer

Gruvberger et al. postulate, in their commentary [1] published in this issue of Breast Cancer Research, that our “prognostic gene set may not be broadly applicable to other breast tumor cohorts”, and they suggest that “it may be important to define prognostic expression profiles separately in estrogen receptor (ER) positive and negative tumors”. This is based on two observations derived from our gene expression profiling data in breast cancer [2]: the overlap between reporter genes for prognosis and ER status, and Gruvberger et al.’s inability to confirm the prognosis prediction using a nonoptimal selection of 58 of our 231 prognosis reporter genes. The overlap between our prognosis reporter genes and the ER status genes is certainly very large, mainly because ~10 % of all genes on our microarray contain informatio

SIRAC: Supervised Identification of Regions of Aberration in aCGH datasets

<p>Abstract</p> <p>Background</p> <p>Array comparative genome hybridization (aCGH) provides information about genomic aberrations. Alterations in the DNA copy number may cause the cell to malfunction, leading to cancer. Therefore, the identification of DNA amplifications or deletions across tumors may reveal key genes involved in cancer and improve our understanding of the underlying biological processes associated with the disease.</p> <p>Results</p> <p>We propose a supervised algorithm for the analysis of aCGH data and the identification of regions of chromosomal alteration (SIRAC). We first determine the DNA-probes that are important to distinguish the classes of interest, and then evaluate in a systematic and robust scheme if these relevant DNA-probes are closely located, i.e. form a region of amplification/deletion. SIRAC does not need any preprocessing of the aCGH datasets, and requires only few, intuitive parameters.</p> <p>Conclusion</p> <p>We illustrate the features of the algorithm with the use of a simple artificial dataset. The results on two breast cancer datasets show promising outcomes that are in agreement with previous findings, but SIRAC better pinpoints the dissimilarities between the classes of interest.</p

Gene Expression Programs of Human Smooth Muscle Cells: Tissue-Specific Differentiation and Prognostic Significance in Breast Cancers

Smooth muscle is present in a wide variety of anatomical locations, such as blood vessels, various visceral organs, and hair follicles. Contraction of smooth muscle is central to functions as diverse as peristalsis, urination, respiration, and the maintenance of vascular tone. Despite the varied physiological roles of smooth muscle cells (SMCs), we possess only a limited knowledge of the heterogeneity underlying their functional and anatomic specializations. As a step toward understanding the intrinsic differences between SMCs from different anatomical locations, we used DNA microarrays to profile global gene expression patterns in 36 SMC samples from various tissues after propagation under defined conditions in cell culture. Significant variations were found between the cells isolated from blood vessels, bronchi, and visceral organs. Furthermore, pervasive differences were noted within the visceral organ subgroups that appear to reflect the distinct molecular pathways essential for organogenesis as well as those involved in organ-specific contractile and physiological properties. Finally, we sought to understand how this diversity may contribute to SMC-involving pathology. We found that a gene expression signature of the responses of vascular SMCs to serum exposure is associated with a significantly poorer prognosis in human cancers, potentially linking vascular injury response to tumor progression

A comprehensive analysis of prognostic signatures reveals the high predictive capacity of the proliferation, immune response and RNA splicing modules in breast cancer.

INTRODUCTION: Several gene expression signatures have been proposed and demonstrated to be predictive of outcome in breast cancer. In the present article we address the following issues: Do these signatures perform similarly? Are there (common) molecular processes reported by these signatures? Can better prognostic predictors be constructed based on these identified molecular processes? METHODS: We performed a comprehensive analysis of the performance of nine gene expression signatures on seven different breast cancer datasets. To better characterize the functional processes associated with these signatures, we enlarged each signature by including all probes with a significant correlation to at least one of the genes in the original signature. The enrichment of functional groups was assessed using four ontology databases. RESULTS: The classification performance of the nine gene expression signatures is very similar in terms of assigning a sample to either a poor outcome group or a good outcome group. Nevertheless the concordance in classification at the sample level is low, with only 50% of the breast cancer samples classified in the same outcome group by all classifiers. The predictive accuracy decreases with the number of poor outcome assignments given to a sample. The best classification performance was obtained for the group of patients with only good outcome assignments. Enrichment analysis of the enlarged signatures revealed 11 functional modules with prognostic ability. The combination of the RNA-splicing and immune modules resulted in a classifier with high prognostic performance on an independent validation set. CONCLUSIONS: The study revealed that the nine signatures perform similarly but exhibit a large degree of discordance in prognostic group assignment. Functional analyses indicate that proliferation is a common cellular process, but that other functional categories are also enriched and show independent prognostic ability. We provide new evidence of the potentially promising prognostic impact of immunity and RNA-splicing processes in breast cancer.RIGHTS : This article is licensed under the BioMed Central licence at http://www.biomedcentral.com/about/license which is similar to the 'Creative Commons Attribution Licence'. In brief you may : copy, distribute, and display the work; make derivative works; or make commercial use of the work - under the following conditions: the original author must be given credit; for any reuse or distribution, it must be made clear to others what the license terms of this work are

Impact of established prognostic factors and molecular subtype in very young breast cancer patients: pooled analysis of four EORTC randomized controlled trials

Young age at the time of diagnosis of breast cancer is an independent factor of poor prognosis. In many treatment guidelines, the recommendation is to treat young patients with adjuvant chemotherapy regardless of tumor characteristics. However, limited data on prognostic factors are available for young breast cancer patients. The purpose of this study was to determine the prognostic value of established clinical and pathological prognostic factors in young breast cancer patients. Data from four European Organisation for Research and Treatment of Cancer (EORTC) clinical trials were pooled, resulting in a dataset consisting of 9,938 early breast cancer patients with a median follow-up of 11 years. For 549 patients aged less than 40 years at the time of diagnosis, including 341 node negative patients who did not receive chemotherapy, paraffin tumor blocks were processed for immunohistochemistry using a tissue microarray. Cox proportional hazard analysis was applied to assess the association of clinical and pathological factors with overall and distant metastasis free survival. For young patients, tumor size (P = 0.01), nodal status (P = 0.006) and molecular subtype (P = 0.02) were independent prognostic factors for overall survival. In the node negative subgroup, only molecular subtype was a prognostic factor for overall survival (P = 0.02). Young node negative patients bearing luminal A tumors had an overall survival rate of 94% at 10 years' follow-up compared to 72% for patients with basal-type tumors. Molecular subtype is a strong independent prognostic factor in breast cancer patients younger than 40 years of age. These data support the use of established prognostic factors as a diagnostic tool to assess disease outcome and to plan systemic treatment strategies in young breast cancer patient