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Do serum biomarkers really measure breast cancer?

Jesneck JL, Mukherjee S, Yurkovetsky Z, Clyde M, Marks JR, Lokshin AE, Lo JY - BMC Cancer (2009)

Bottom Line: Because of significant noise in the data set, we applied iterated Bayesian model averaging to account for model selection uncertainty and to improve generalization performance.Overall, the selected serum proteins showed moderate ability for detecting lesions.However, they are probably more indicative of secondary effects such as inflammation rather than specific for malignancy.

View Article: PubMed Central - HTML - PubMed

Affiliation: Duke Advanced Imaging Labs, Department of Radiology, Duke University Medical Center, Durham, North Carolina 27710, USA. JonathanL_Jesneck@dfci.harvard.edu

ABSTRACT

Background: Because screening mammography for breast cancer is less effective for premenopausal women, we investigated the feasibility of a diagnostic blood test using serum proteins.

Methods: This study used a set of 98 serum proteins and chose diagnostically relevant subsets via various feature-selection techniques. Because of significant noise in the data set, we applied iterated Bayesian model averaging to account for model selection uncertainty and to improve generalization performance. We assessed generalization performance using leave-one-out cross-validation (LOOCV) and receiver operating characteristic (ROC) curve analysis.

Results: The classifiers were able to distinguish normal tissue from breast cancer with a classification performance of AUC = 0.82 +/- 0.04 with the proteins MIF, MMP-9, and MPO. The classifiers distinguished normal tissue from benign lesions similarly at AUC = 0.80 +/- 0.05. However, the serum proteins of benign and malignant lesions were indistinguishable (AUC = 0.55 +/- 0.06). The classification tasks of normal vs. cancer and normal vs. benign selected the same top feature: MIF, which suggests that the biomarkers indicated inflammatory response rather than cancer.

Conclusion: Overall, the selected serum proteins showed moderate ability for detecting lesions. However, they are probably more indicative of secondary effects such as inflammation rather than specific for malignancy.

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Related in: MedlinePlus

Posterior distributions of the model coefficients for the proteins. The distributions are mixtures of a point mass at zero and a normal distribution. The height of the solid line at zero represents the posterior probability that the coefficient is zero. The nonzero part of the distribution is scaled so that the maximum height is equal to the probability that the coefficient is nonzero.
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Figure 4: Posterior distributions of the model coefficients for the proteins. The distributions are mixtures of a point mass at zero and a normal distribution. The height of the solid line at zero represents the posterior probability that the coefficient is zero. The nonzero part of the distribution is scaled so that the maximum height is equal to the probability that the coefficient is nonzero.

Mentions: Complementary to the models' matrix plots for feature strength are the coefficients' marginal posterior probability distribution functions (PDFs), which the BMA technique calculates by including information from all considered models. Figure 4 shows the marginal posterior PDFs for the top coefficients for the BMA models. The coefficients' distributions are mixture models of a normal distribution and a point mass at zero. This point mass is much larger for the benign vs. cancer models than for normal vs. cancer and normal vs. benign models. The higher weight at zero indicates that the proteins are less suitable for distinguishing benign from malignant lesions than they are for distinguishing lesions from normal tissue.


Do serum biomarkers really measure breast cancer?

Jesneck JL, Mukherjee S, Yurkovetsky Z, Clyde M, Marks JR, Lokshin AE, Lo JY - BMC Cancer (2009)

Posterior distributions of the model coefficients for the proteins. The distributions are mixtures of a point mass at zero and a normal distribution. The height of the solid line at zero represents the posterior probability that the coefficient is zero. The nonzero part of the distribution is scaled so that the maximum height is equal to the probability that the coefficient is nonzero.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC2696469&req=5

Figure 4: Posterior distributions of the model coefficients for the proteins. The distributions are mixtures of a point mass at zero and a normal distribution. The height of the solid line at zero represents the posterior probability that the coefficient is zero. The nonzero part of the distribution is scaled so that the maximum height is equal to the probability that the coefficient is nonzero.
Mentions: Complementary to the models' matrix plots for feature strength are the coefficients' marginal posterior probability distribution functions (PDFs), which the BMA technique calculates by including information from all considered models. Figure 4 shows the marginal posterior PDFs for the top coefficients for the BMA models. The coefficients' distributions are mixture models of a normal distribution and a point mass at zero. This point mass is much larger for the benign vs. cancer models than for normal vs. cancer and normal vs. benign models. The higher weight at zero indicates that the proteins are less suitable for distinguishing benign from malignant lesions than they are for distinguishing lesions from normal tissue.

Bottom Line: Because of significant noise in the data set, we applied iterated Bayesian model averaging to account for model selection uncertainty and to improve generalization performance.Overall, the selected serum proteins showed moderate ability for detecting lesions.However, they are probably more indicative of secondary effects such as inflammation rather than specific for malignancy.

View Article: PubMed Central - HTML - PubMed

Affiliation: Duke Advanced Imaging Labs, Department of Radiology, Duke University Medical Center, Durham, North Carolina 27710, USA. JonathanL_Jesneck@dfci.harvard.edu

ABSTRACT

Background: Because screening mammography for breast cancer is less effective for premenopausal women, we investigated the feasibility of a diagnostic blood test using serum proteins.

Methods: This study used a set of 98 serum proteins and chose diagnostically relevant subsets via various feature-selection techniques. Because of significant noise in the data set, we applied iterated Bayesian model averaging to account for model selection uncertainty and to improve generalization performance. We assessed generalization performance using leave-one-out cross-validation (LOOCV) and receiver operating characteristic (ROC) curve analysis.

Results: The classifiers were able to distinguish normal tissue from breast cancer with a classification performance of AUC = 0.82 +/- 0.04 with the proteins MIF, MMP-9, and MPO. The classifiers distinguished normal tissue from benign lesions similarly at AUC = 0.80 +/- 0.05. However, the serum proteins of benign and malignant lesions were indistinguishable (AUC = 0.55 +/- 0.06). The classification tasks of normal vs. cancer and normal vs. benign selected the same top feature: MIF, which suggests that the biomarkers indicated inflammatory response rather than cancer.

Conclusion: Overall, the selected serum proteins showed moderate ability for detecting lesions. However, they are probably more indicative of secondary effects such as inflammation rather than specific for malignancy.

Show MeSH
Related in: MedlinePlus