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High-sensitivity array analysis of gene expression for the early detection of disseminated breast tumor cells in peripheral blood.

ABSTRACT: Early detection is an effective means of reducing cancer mortality. Here, we describe a highly sensitive high-throughput screen that can identify panels of markers for the early detection of solid tumor cells disseminated in peripheral blood. The method is a two-step combination of differential display and high-sensitivity cDNA arrays. In a primary screen, differential display identified 170 candidate marker genes differentially expressed between breast tumor cells and normal breast epithelial cells. In a secondary screen, high-sensitivity arrays assessed expression levels of these genes in 48 blood samples, 22 from healthy volunteers and 26 from breast cancer patients. Cluster analysis identified a group of 12 genes that were elevated in the blood of cancer patients. Permutation analysis of individual genes defined five core genes (P < or = 0.05, permax test). As a group, the 12 genes generally distinguished accurately between healthy volunteers and patients with breast cancer. Mean expression levels of the 12 genes were elevated in 77% (10 of 13) untreated invasive cancer patients, whereas cluster analysis correctly classified volunteers and patients (P = 0.0022, Fisher's exact test). Quantitative real-time PCR confirmed array results and indicated that the sensitivity of the assay (1:2 x 10(8) transcripts) was sufficient to detect disseminated solid tumor cells in blood. Expression-based blood assays developed with the screening approach described here have the potential to detect and classify solid tumor cells originating from virtually any primary site in the body.

SUBMITTER: Martin KJ

PROVIDER: S-EPMC30192 | biostudies-literature | 2001 Feb

REPOSITORIES: biostudies-literature

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High-sensitivity array analysis of gene expression for the early detection of disseminated breast tumor cells in peripheral blood.

Martin K J KJ Graner E E Li Y Y Price L M LM Kritzman B M BM Fournier M V MV Rhei E E Pardee A B AB

Proceedings of the National Academy of Sciences of the United States of America 20010220 5

Early detection is an effective means of reducing cancer mortality. Here, we describe a highly sensitive high-throughput screen that can identify panels of markers for the early detection of solid tumor cells disseminated in peripheral blood. The method is a two-step combination of differential display and high-sensitivity cDNA arrays. In a primary screen, differential display identified 170 candidate marker genes differentially expressed between breast tumor cells and normal breast epithelial c ...[more]

PMID: 11226293

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Project description:BACKGROUND: Breast cancer is worldwide the second most common type of cancer after lung cancer. Traditional mammography and Tissue Microarray has been studied for early cancer detection and cancer prediction. However, there is a need for more reliable diagnostic tools for early detection of breast cancer. This can be a challenge due to a number of factors and logistics. First, obtaining tissue biopsies can be difficult. Second, mammography may not detect small tumors, and is often unsatisfactory for younger women who typically have dense breast tissue. Lastly, breast cancer is not a single homogeneous disease but consists of multiple disease states, each arising from a distinct molecular mechanism and having a distinct clinical progression path which makes the disease difficult to detect and predict in early stages. RESULTS: In the paper, we present a Support Vector Machine based on Recursive Feature Elimination and Cross Validation (SVM-RFE-CV) algorithm for early detection of breast cancer in peripheral blood and show how to use SVM-RFE-CV to model the classification and prediction problem of early detection of breast cancer in peripheral blood.The training set which consists of 32 health and 33 cancer samples and the testing set consisting of 31 health and 34 cancer samples were randomly separated from a dataset of peripheral blood of breast cancer that is downloaded from Gene Express Omnibus. First, we identified the 42 differentially expressed biomarkers between "normal" and "cancer". Then, with the SVM-RFE-CV we extracted 15 biomarkers that yield zero cross validation score. Lastly, we compared the classification and prediction performance of SVM-RFE-CV with that of SVM and SVM Recursive Feature Elimination (SVM-RFE). CONCLUSIONS: We found that 1) the SVM-RFE-CV is suitable for analyzing noisy high-throughput microarray data, 2) it outperforms SVM-RFE in the robustness to noise and in the ability to recover informative features, and 3) it can improve the prediction performance (Area Under Curve) in the testing data set from 0.5826 to 0.7879. Further pathway analysis showed that the biomarkers are associated with Signaling, Hemostasis, Hormones, and Immune System, which are consistent with previous findings. Our prediction model can serve as a general model for biomarker discovery in early detection of other cancers. In the future, Polymerase Chain Reaction (PCR) is planned for validation of the ability of these potential biomarkers for early detection of breast cancer.

Dataset Information

High-sensitivity array analysis of gene expression for the early detection of disseminated breast tumor cells in peripheral blood.

Publications

High-sensitivity array analysis of gene expression for the early detection of disseminated breast tumor cells in peripheral blood.

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