Microarray technology and its effect on breast cancer (re)classification and prediction of outcome

SCOPUS: ar.jinfo:eu-repo/semantics/publishe

Report on the 4th European Breast Cancer Conference, Hamburg, Germany, 16–20 March 2004

The 4th European Breast Cancer Conference, organized under the auspices of the European Organization for Research and Treatment of Cancer Breast Cancer Group, of the European Breast Cancer Coalition (Europa Donna) and of the European Society of Mastology (EUSOMA), was held in Hamburg, Germany on 16–20 March 2004. The leading theme of the conference was partnership among scientists, clinicians, carers, advocates and patients. The present article provides a brief description of the most important conference presentations on molecular biology, epidemiology, prevention, pathology, diagnosis and treatment at all stages of breast cancer

International Web-based consultation on priorities for translational breast cancer research

Background Large numbers of translational breast cancer research topics have been completed or are underway, but they differ widely in their immediate and/or future importance to clinical management. We therefore conducted an international Web-based consultation of breast cancer professionals to identify the topics most widely considered to be of highest priority. Methods Potential participants were contacted via two large e-mail databases and asked to register, at a Web site, the issues that they felt to be of highest priority. Four hundred nine questions were reduced by a steering committee to 70 unique issues, and registrants were asked to select the 6 questions they considered to be the most important. Results Votes were recorded from 420 voters ( 2,520 votes) from 48 countries, with 48% of voters coming from North America. Half of the voters identified themselves as clinicians, with the remainder being academics, research scientists, or pathologists. The highest priority was to identify molecular signatures to select patients who could be spared chemotherapy, which gained about 50% more votes than the second topic and was consistently voted top by voters in North America, Europe, and the rest of the world. Research scientists voted the determination of the role of stem cells in breast cancer development, progression, and treatment sensitivity as the most important issue, but this was considered the sixth priority for clinicians and fourth overall. Conclusion This exercise may bring a greater focus of research resources onto issues voted as top priorities

The diagnosis and management of pre-invasive breast disease: editor's reply

Introduction: The letter from Badve [1] relating to the series on pre-invasive breast disease, published in the September and November issues of Breast Cancer Research [2-10], is timely and very welcome. It rightly points out that one should be careful in changing classification systems based on limited knowledge and that perhaps discarding the term atypical ductal hyperplasia at the present time may be premature. I completely agree with him; however, there are a few issues I feel obliged to clarify

A simple method for assigning genomic grade to individual breast tumours

<p>Abstract</p> <p>Background</p> <p>The prognostic value of grading in breast cancer can be increased with microarray technology, but proposed strategies are disadvantaged by the use of specific training data or parallel microscopic grading. Here, we investigate the performance of a method that uses no information outside the breast profile of interest.</p> <p>Results</p> <p>In 251 profiled tumours we optimised a method that achieves grading by comparing rank means for genes predictive of high and low grade biology; a simpler method that allows for truly independent estimation of accuracy. Validation was carried out in 594 patients derived from several independent data sets. We found that accuracy was good: for low grade (G1) tumors 83- 94%, for high grade (G3) tumors 74- 100%. In keeping with aim of improved grading, two groups of intermediate grade (G2) cancers with significantly different outcome could be discriminated.</p> <p>Conclusion</p> <p>This validates the concept of microarray-based grading in breast cancer, and provides a more practical method to achieve it. A simple R script for grading is available in an additional file. Clinical implementation could achieve better estimation of recurrence risk for 40 to 50% of breast cancer patients.</p

Neoadjuvant endocrine therapy in primary breast cancer: indications and use as a research tool

Neoadjuvant endocrine therapy has been increasingly employed in clinical practice to improve surgical options for postmenopausal women with bulky hormone receptor-positive breast cancer. Recent studies indicate that tumour response in this setting may predict long-term outcome of patients on adjuvant endocrine therapy, which argues for its broader application in treating hormone receptor-positive disease. From the research perspective, neoadjuvant endocrine therapy provides a unique opportunity for studies of endocrine responsiveness and the development of novel therapeutic agents

Proliferation and estrogen signaling can distinguish patients at risk for early versus late relapse among estrogen receptor positive breast cancers

Introduction: We examined if a combination of proliferation markers and estrogen receptor (ER) activity could predict early versus late relapses in ER-positive breast cancer and inform the choice and length of adjuvant endocrine therapy. Methods: Baseline affymetrix gene-expression profiles from ER-positive patients who received no systemic therapy (n = 559), adjuvant tamoxifen for 5 years (cohort-1: n = 683, cohort-2: n = 282) and from 58 patients treated with neoadjuvant letrozole for 3 months (gene-expression available at baseline, 14 and 90 days) were analyzed. A proliferation score based on the expression of mitotic kinases (MKS) and an ER-related score (ERS) adopted from Oncotype DX® were calculated. The same analysis was performed using the Genomic Grade Index as proliferation marker and the luminal gene score from the PAM50 classifier as measure of estrogen-related genes. Median values were used to define low and high marker groups and four combinations were created. Relapses were grouped into time cohorts of 0-2.5, 0-5, 5-10 years. Results: In the overall 10 years period, the proportional hazards assumption was violated for several biomarker groups indicating time-dependent effects. In tamoxifen-treated patients Low-MKS/Low-ERS cancers had continuously increasing risk of relapse that was higher after 5 years than Low-MKS/High-ERS cancers [0 to 10 year, HR 3.36; p = 0.013]. High-MKS/High-ERS cancers had low risk of early relapse [0-2.5 years HR 0.13; p = 0.0006], but high risk of late relapse which was higher than in the High-MKS/Low-ERS group [after 5 years HR 3.86; p = 0.007]. The High-MKS/Low-ERS subset had most of the early relapses [0 to 2.5 years, HR 6.53; p < 0.0001] especially in node negative tumors and showed minimal response to neoadjuvant letrozole. These findings were qualitatively confirmed in a smaller independent cohort of tamoxifen-treated patients. Using different biomarkers provided similar results. Conclusions: Early relapses are highest in highly proliferative/low-ERS cancers, in particular in node negative tumors. Relapses occurring after 5 years of adjuvant tamoxifen are highest among the highly-proliferative/high-ERS tumors although their risk of recurrence is modest in the first 5 years on tamoxifen. These tumors could be the best candidates for extended endocrine therapy

A critical evaluation of network and pathway based classifiers for outcome prediction in breast cancer

Recently, several classifiers that combine primary tumor data, like gene expression data, and secondary data sources, such as protein-protein interaction networks, have been proposed for predicting outcome in breast cancer. In these approaches, new composite features are typically constructed by aggregating the expression levels of several genes. The secondary data sources are employed to guide this aggregation. Although many studies claim that these approaches improve classification performance over single gene classifiers, the gain in performance is difficult to assess. This stems mainly from the fact that different breast cancer data sets and validation procedures are employed to assess the performance. Here we address these issues by employing a large cohort of six breast cancer data sets as benchmark set and by performing an unbiased evaluation of the classification accuracies of the different approaches. Contrary to previous claims, we find that composite feature classifiers do not outperform simple single gene classifiers. We investigate the effect of (1) the number of selected features; (2) the specific gene set from which features are selected; (3) the size of the training set and (4) the heterogeneity of the data set on the performance of composite feature and single gene classifiers. Strikingly, we find that randomization of secondary data sources, which destroys all biological information in these sources, does not result in a deterioration in performance of composite feature classifiers. Finally, we show that when a proper correction for gene set size is performed, the stability of single gene sets is similar to the stability of composite feature sets. Based on these results there is currently no reason to prefer prognostic classifiers based on composite features over single gene classifiers for predicting outcome in breast cancer