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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

Models selected by BMA of linear models. Features are plotted in decreasing posterior probability of being nonzero. Models are ordered by selection frequency, with the best, most frequently selected models on the left and the weakest, rarest chosen on the right. Coefficients with positive values are shown in red and negative values in blue. Strong, frequently selected features appear as solid horizontal stripes. A beige value indicates that the protein was not selected in a particular model.
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Figure 3: Models selected by BMA of linear models. Features are plotted in decreasing posterior probability of being nonzero. Models are ordered by selection frequency, with the best, most frequently selected models on the left and the weakest, rarest chosen on the right. Coefficients with positive values are shown in red and negative values in blue. Strong, frequently selected features appear as solid horizontal stripes. A beige value indicates that the protein was not selected in a particular model.

Mentions: The iterated BMA algorithm chose the best-fitting probit models. The chosen models and their proteins are shown in Figure 3. The best proteins for each classification task are listed in Table 4. The top protein for both normal vs. cancer and normal vs. benign was macrophage migration inhibitory factor (MIF), a known inflammatory agent [64-66]. Other selected proteins also play roles in inflammation and immune response, such as MMP-9 [67,68], MPO [69], sVCAM-1 [70], ACTH [71], MICA [72], IL-5 [73], IL-12 p40 [74-76], MCP-1 [77], and IFNa [78-80]. For benign vs. cancer, the top protein was CA-125, which is used as a biomarker for ovarian cancer [12,81-83]. However, the greater presence of CA-125 in cancer tissue was still too subtle to allow the classifiers to achieve good classification performance.


Do serum biomarkers really measure breast cancer?

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

Models selected by BMA of linear models. Features are plotted in decreasing posterior probability of being nonzero. Models are ordered by selection frequency, with the best, most frequently selected models on the left and the weakest, rarest chosen on the right. Coefficients with positive values are shown in red and negative values in blue. Strong, frequently selected features appear as solid horizontal stripes. A beige value indicates that the protein was not selected in a particular model.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

Figure 3: Models selected by BMA of linear models. Features are plotted in decreasing posterior probability of being nonzero. Models are ordered by selection frequency, with the best, most frequently selected models on the left and the weakest, rarest chosen on the right. Coefficients with positive values are shown in red and negative values in blue. Strong, frequently selected features appear as solid horizontal stripes. A beige value indicates that the protein was not selected in a particular model.
Mentions: The iterated BMA algorithm chose the best-fitting probit models. The chosen models and their proteins are shown in Figure 3. The best proteins for each classification task are listed in Table 4. The top protein for both normal vs. cancer and normal vs. benign was macrophage migration inhibitory factor (MIF), a known inflammatory agent [64-66]. Other selected proteins also play roles in inflammation and immune response, such as MMP-9 [67,68], MPO [69], sVCAM-1 [70], ACTH [71], MICA [72], IL-5 [73], IL-12 p40 [74-76], MCP-1 [77], and IFNa [78-80]. For benign vs. cancer, the top protein was CA-125, which is used as a biomarker for ovarian cancer [12,81-83]. However, the greater presence of CA-125 in cancer tissue was still too subtle to allow the classifiers to achieve good classification performance.

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