Early detection of breast cancer based on gene-expression patterns in peripheral blood cells

Praveen Sharma, Narinder S Sahni, Robert Tibshirani, Per Skaane, Petter Urdal, Hege Berghagen, Marianne Jensen, Lena Kristiansen, Cecilie Moen, Pradeep Sharma, Alia Zaka, Jarle Arnes, Torill Sauer, Lars A Akslen, Ellen Schlichting, Anne-Lise Børresen-Dale, Anders Lönneborg, Praveen Sharma, Narinder S Sahni, Robert Tibshirani, Per Skaane, Petter Urdal, Hege Berghagen, Marianne Jensen, Lena Kristiansen, Cecilie Moen, Pradeep Sharma, Alia Zaka, Jarle Arnes, Torill Sauer, Lars A Akslen, Ellen Schlichting, Anne-Lise Børresen-Dale, Anders Lönneborg

Abstract

Introduction: Existing methods to detect breast cancer in asymptomatic patients have limitations, and there is a need to develop more accurate and convenient methods. In this study, we investigated whether early detection of breast cancer is possible by analyzing gene-expression patterns in peripheral blood cells.

Methods: Using macroarrays and nearest-shrunken-centroid method, we analyzed the expression pattern of 1,368 genes in peripheral blood cells of 24 women with breast cancer and 32 women with no signs of this disease. The results were validated using a standard leave-one-out cross-validation approach.

Results: We identified a set of 37 genes that correctly predicted the diagnostic class in at least 82% of the samples. The majority of these genes had a decreased expression in samples from breast cancer patients, and predominantly encoded proteins implicated in ribosome production and translation control. In contrast, the expression of some defense-related genes was increased in samples from breast cancer patients.

Conclusion: The results show that a blood-based gene-expression test can be developed to detect breast cancer early in asymptomatic patients. Additional studies with a large sample size, from women both with and without the disease, are warranted to confirm or refute this finding.

Figures

Figure 1
Figure 1
Misclassification rate as a function of threshold value and the number of genes involved. The error was calculated using the majority rule. A nondecision was counted as an error. The upper graph shows that the minimum overall misclassification error was observed at a threshold value of 2.28. The lower graph shows the profile for misclassification error for breast-cancer (C) and non-breast-cancer (N) samples as a function of threshold value and the number of genes involved.
Figure 2
Figure 2
Relative expression of 13 predictive genes with the highest scores in breast-cancer and non-breast-cancer samples. Red circles represent samples from women with breast cancer and green circles represent samples from women with no signs of breast cancer. The number on the upper axis represents the position ID of predictive genes in the array (Table 3).

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