A Noninvasive Blood-based Combinatorial Proteomic Biomarker Assay to Detect Breast Cancer in Women Under the Age of 50 Years
| Autor: | David E. Reese, Ana Lourenço, Rebecca Yang, Fred Kass, Carrie Costantini, Quynh Tran, Elias Letsios, Rao Mulpuri, Meredith C. Henderson, Kelly J. Gordon, Nitin Rohatgi, Christa Corn, Kasey Benson, Josie Alpers, Michael Morris, Wendy Smith, Sherri Borman, Michael Silver, Ali Haythem, Shah Biren |
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| Rok vydání: | 2017 |
| Předmět: |
Adult
Proteomics 0301 basic medicine Oncology Cancer Research medicine.medical_specialty Proteome Breast imaging Breast Neoplasms BI-RADS Multimodal Imaging 03 medical and health sciences 0302 clinical medicine Breast cancer Internal medicine Biomarkers Tumor medicine Humans Mammography Prospective Studies Prospective cohort study medicine.diagnostic_test business.industry Carcinoma in situ Middle Aged Prognosis medicine.disease Carcinoma Lobular 030104 developmental biology 030220 oncology & carcinogenesis Cohort Immunology Biomarker (medicine) Female business Carcinoma in Situ Follow-Up Studies |
| Zdroj: | Clinical Breast Cancer. 17:516-525.e6 |
| ISSN: | 1526-8209 |
| Popis: | Background Despite significant advances in breast imaging, the ability to detect breast cancer (BC) remains a challenge. To address the unmet needs of the current BC detection paradigm, 2 prospective clinical trials were conducted to develop a blood-based combinatorial proteomic biomarker assay (Videssa Breast) to accurately detect BC and reduce false positives (FPs) from suspicious imaging findings. Patients and Methods Provista-001 and Provista-002 (cohort one) enrolled Breast Imaging Reporting and Data System 3 or 4 women aged under 50 years. Serum was evaluated for 11 serum protein biomarkers and 33 tumor-associated autoantibodies. Individual biomarker expression, demographics, and clinical characteristics data from Provista-001 were combined to develop a logistic regression model to detect BC. The performance was tested using Provista-002 cohort one (validation set). Results The training model had a sensitivity and specificity of 92.3% and 85.3% (BC prevalence, 7.7%), respectively. In the validation set (BC prevalence, 2.9%), the sensitivity and specificity were 66.7% and 81.5%, respectively. The negative predictive value was high in both sets (99.3% and 98.8%, respectively). Videssa Breast performance in the combined training and validation set was 99.1% negative predictive value, 87.5% sensitivity, 83.8% specificity, and 25.2% positive predictive value (BC prevalence, 5.87%). Overall, imaging resulted in 341 participants receiving follow-up procedures to detect 30 cancers (90.6% FP rate). Videssa Breast would have recommended 111 participants for follow-up, a 67% reduction in FPs ( P Conclusions Videssa Breast can effectively detect BC when used in conjunction with imaging and can substantially reduce unnecessary medical procedures, as well as provide assurance to women that they likely do not have BC. |
| Databáze: | OpenAIRE |
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