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Mathematical model identifies blood biomarker-based early cancer detection strategies and limitations.

ABSTRACT: Most clinical blood biomarkers lack the necessary sensitivity and specificity to reliably detect cancer at an early stage, when it is best treatable. It is not yet clear how early a clinical blood assay can be used to detect cancer or how biomarker-based strategies can be improved to enable earlier detection of smaller tumors. To address these issues, we developed a mathematical model describing dynamic plasma biomarker kinetics in relation to the growth of a tumor, beginning with a single cancer cell. To exemplify a realistic scenario in which biomarker is shed by both cancerous and noncancerous cells, we primed the model on ovarian tumor growth and CA125 shedding data, for which tumor growth parameters and shedding rates are readily available in published literature. We found that a tumor could grow unnoticed for more than 10.1 years and reach a volume of about ?/6(25.36 mm)(3), corresponding to a spherical diameter of about 25.36 mm, before becoming detectable by current clinical blood assays. Model parameters were perturbed over log orders of magnitude to quantify ideal shedding rates and identify other blood-based strategies required for early submillimeter tumor detectability. The detection times we estimated are consistent with recently published tumor progression time lines based on clinical genomic sequencing data for several cancers. Here, we rigorously showed that shedding rates of current clinical blood biomarkers are likely 10(4)-fold too low to enable detection of a developing tumor within the first decade of tumor growth. The model presented here can be extended to virtually any solid cancer and associated biomarkers.

SUBMITTER: Hori SS

PROVIDER: S-EPMC3423335 | biostudies-literature | 2011 Nov

REPOSITORIES: biostudies-literature

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Mathematical model identifies blood biomarker-based early cancer detection strategies and limitations.

Hori Sharon S SS Gambhir Sanjiv S SS

Science translational medicine 20111101 109

Most clinical blood biomarkers lack the necessary sensitivity and specificity to reliably detect cancer at an early stage, when it is best treatable. It is not yet clear how early a clinical blood assay can be used to detect cancer or how biomarker-based strategies can be improved to enable earlier detection of smaller tumors. To address these issues, we developed a mathematical model describing dynamic plasma biomarker kinetics in relation to the growth of a tumor, beginning with a single cance ...[more]

PMID: 22089452

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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.

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Mathematical model identifies blood biomarker-based early cancer detection strategies and limitations.

Publications

Mathematical model identifies blood biomarker-based early cancer detection strategies and limitations.

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