Identification of a serum-detectable metabolomic fingerprint potentially correlated with the presence of micrometastatic disease in early breast cancer patients at varying risks of disease relapse by traditional prognostic methods
| Autor: | Ivano Bertini, Patrick G. Morris, Leonardo Tenori, Edoardo Saccenti, Patrizia Bernini, A. Di Leo, Stefano Nepi, Claudio Luchinat, Elena Zafarana, Silvia Cappadona, Catherine Oakman, Laura Biganzoli, Monica Fornier, Wederson M. Claudino, A. Battaglia |
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| Jazyk: | angličtina |
| Rok vydání: | 2011 |
| Předmět: |
Oncology
Adult Risk medicine.medical_specialty Pathology Breast Neoplasms Disease 03 medical and health sciences 0302 clinical medicine Breast cancer Metabolomics Internal medicine medicine Biomarkers Tumor Humans Risk factor Early Detection of Cancer 030304 developmental biology Aged Aged 80 and over 0303 health sciences business.industry Micrometastasis Cancer Hematology Middle Aged medicine.disease Prognosis Metastatic breast cancer 3. Good health Neoplasm Micrometastasis 030220 oncology & carcinogenesis Female Breast disease Neoplasm Recurrence Local business |
| Zdroj: | Annals of Oncology; Vol 22 |
| ISSN: | 1569-8041 |
| DOI: | 10.1093/annonc/mdq606 |
| Popis: | Background Prognostic tools in early breast cancer are inadequate. The evolving field of metabolomics may allow more accurate identification of patients with residual micrometastases. Patients and methods Forty-four early breast cancer patients with pre- and postoperative serum samples had metabolomic assessment by nuclear magnetic resonance. Fifty-one metastatic patients served as control. Differential clustering was identified and used to calculate individual early patient ‘metabolomic risk’, calculated as inverse distance of each early patient from the metastatic cluster barycenter. Metabolomic risk was compared with Adjuvantionline 10-year mortality assessment. Results Innate serum metabolomic differences exist between early and metastatic patients. Preoperative patients were identified with 75% sensitivity, 69% specificity and 72% predictive accuracy. Comparison with Adjuvantionline revealed discordance. Of 21 patients assessed as high risk by Adjuvantionline, 10 (48%) and 6 (29%) were at high risk by metabolomics in pre- and postoperative settings, respectively. Of 23 low-risk patients by Adjuvantionline, 11 (48%) preoperative and 20 (87%) postoperative patients were at low risk by metabolomics. Conclusions This study identifies metabolomic discrimination between early and metastatic breast cancer. Micrometastatic disease may account for metabolomic misclassification of some early patients as metastatic. Metabolomics identifies more patients as low relapse risk compared with Adjuvantionline. Further exploration of this metabolomic fingerprint is warranted. |
| Databáze: | OpenAIRE |
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