Abstract
Background: Breast cancer bone metastasis is a complication that significantly compromises patient survival due, in part, to the lack of disease-specific biomarkers that allow early and accurate diagnosis. Methods: Using mass spectrometry protein profiling, plasma samples were screened from three independent breast cancer patient cohorts with and without clinical evidence of bone metastasis. Results: The results identified 13 biomarkers that classified all 110 patients with a sensitivity of 91% and specificity of 93% [receiver operating characteristics area under the curve (AUC = 1.00)]. The most discriminatory protein was subsequently identified as a unique 12-48aa peptide fragment of parathyroid hormonerelated protein (PTHrP). PTHrP(12-48) was significantly increased in plasma of patients with bone metastasis compared with patients without bone metastasis (P < 0.0001). Logistic regression models were used to evaluate the diagnostic potential of PTHrP(12-48) as a single biomarker or in combination with the measurement of the clinical marker N-telopeptide of type I collagen (NTx). The PTHrP(12-48) and NTx logistic regression models were not significantly different and classified the patient groups with high accuracy (AUC = 0.85 and 0.95), respectively. Interestingly, in combination with serum NTx, the plasma concentration of PTHrP(12-48) increased diagnostic specificity and accuracy (AUC = 0.99). Conclusions: These data show that PTHrP(12-48) circulates in plasma of patient with breast cancer and is a novel and predictive biomarker of breast cancer bone metastasis. Importantly, the clinical measurement of PTHrP(12-48) in combination with NTx improves the detection of breast cancer bone metastasis. Impact: In summary, we present the first validated, plasma biomarker signature for diagnosis of breast cancer bone metastasis that may improve the early diagnosis of high-risk individuals. Cancer Epidemiol Biomarkers Prev; 22(5); 972-83.
| Original language | English (US) |
|---|---|
| Pages (from-to) | 972-983 |
| Number of pages | 12 |
| Journal | Cancer Epidemiology Biomarkers and Prevention |
| Volume | 22 |
| Issue number | 5 |
| DOIs | |
| State | Published - May 2013 |
UN SDGs
This output contributes to the following UN Sustainable Development Goals (SDGs)
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SDG 3 Good Health and Well-being
All Science Journal Classification (ASJC) codes
- General Medicine
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