Abstract
BackgroundThe 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.ResultsIn 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.ConclusionThis 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.
Highlights
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
Decisions regarding medical treatment in early breast cancer are guided by disease stage, morphological characteristics, and selected biological factors - notably expression of oestrogen and progesterone receptors and the HER2 oncogene [1]
Retrospective data reveal a high degree of concordance between microarray-technology and established methods for determining oestrogen and progesterone receptor status [2,3,4,5], as well as expression of the HER2 oncogene [6]
Summary
We tested the rank-based algorithm in a data set with 251 histopathologically graded breast cancers, here referred to as the Uppsala data set [9]. An assumption of equal average rank for G3-probe sets compared to G1-probe sets was not met: G1-probe sets had higher average expression than G3-probe sets. This resulted in bias towards low genomic grade classification calls; we removed 37 lowly expressed genes from the larger group (G3-probe sets). With this optimization performed in the Uppsala cohort, we went on and validated the method in independent data (Table 1). The improvement in prognostic accuracy is illustrated in Figure 1: with array-based grading histologic G2 tumours
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