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. 2006 Oct 30:7:278.
doi: 10.1186/1471-2164-7-278.

Converting a breast cancer microarray signature into a high-throughput diagnostic test

Annuska M Glas  1 , Arno Floore, Leonie J M J Delahaye, Anke T Witteveen, Rob C F Pover, Niels Bakx, Jaana S T Lahti-Domenici, Tako J Bruinsma, Marc O Warmoes, René Bernards, Lodewyk F A Wessels, Laura J Van't Veer
Affiliations

Converting a breast cancer microarray signature into a high-throughput diagnostic test

Annuska M Glas et al. BMC Genomics. .

Abstract

Background: A 70-gene tumor expression profile was established as a powerful predictor of disease outcome in young breast cancer patients. This profile, however, was generated on microarrays containing 25,000 60-mer oligonucleotides that are not designed for processing of many samples on a routine basis.

Results: To facilitate its use in a diagnostic setting, the 70-gene prognosis profile was translated into a customized microarray (MammaPrint) containing a reduced set of 1,900 probes suitable for high throughput processing. RNA of 162 patient samples from two previous studies was subjected to hybridization to this custom array to validate the prognostic value. Classification results obtained from the original analysis were then compared to those generated using the algorithms based on the custom microarray and showed an extremely high correlation of prognosis prediction between the original data and those generated using the custom mini-array (p < 0.0001).

Conclusion: In this report we demonstrate for the first time that microarray technology can be used as a reliable diagnostic tool. The data clearly demonstrate the reproducibility and robustness of the small custom-made microarray. The array is therefore an excellent tool to predict outcome of disease in breast cancer patients.

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Figures

Figure 1
Figure 1
MammaPrint 8-pack, a single 1" × 3" slide containing 8 mini-arrays with 1,900 60-mer oligonucleotide probes, allowing for eight individual hybridizations simultaneously. The samples are hybridized against a common breast cancer reference pool.
Figure 2
Figure 2
Expression data matrix of 70 prognostic markers genes from tumors of 78 breast cancer patients hybridized using the custom microarray. Each row represents a tumor and each column a gene. Genes are ordered according to their original ordering. Tumors are ordered by their correlation to the average profile of the good prognosis group (middle panel). The metastases status for each patient is shown in the right panel. White indicates patients who developed metastases within 5 years after the initial diagnosis, black indicates patients who continued to be metastasis free for at least 5 years.
Figure 3
Figure 3
Comparison of current data to published values [6]. Correlation of the 70 genes from each tumor to the average expression profile of the good outcome patients is plotted. On the Y axis results from the customized 8-pack test is plotted and on the X axis results are plotted using published data from the original paper [6] using Xdev values (see text)
Figure 4
Figure 4
Custom array outcome of replicate experiments. Cosine correlation to the good prognosis template is plotted, and is highly similar between duplicate experiments.
Figure 5
Figure 5
Custom diagnostic microarray outcome of two samples over time. The correlation to the good prognosis profile of three samples (HRC, LRC, and BLS) of >100 measurements over a period of 12 months shows constant outcome.
Figure 6
Figure 6
A. Kaplan-Meier Analysis of the probability that patients would remain free of distant metastases among 145 patients with lymph-node-negative breast cancer. Blue: Current Good prognosis profile group; green dashed: Good prognosis profile group; Red: Current Poor prognosis profile group; Magenta dashed: previous published data [13] Poor prognosis profile group. B. Kaplan-Meier Analysis of the probability of overall survival among 145 patients with lymph-node-negative breast cancer.
See this image and copyright information in PMC

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