Microvesicle Proteomic Profiling of Uterine Liquid Biopsy for Ovarian Cancer Early Detection
| Autor: | David Stockheim, Eitan Friedman, Ariella Jakobson-Setton, Tamar Perri, Oranit Zadok, Jacob Korach, Nissim Arbib, Sarit Aviel-Ronen, Shunit Armon, Keren Bahar-Shany, Tamar Geiger, Keren Levanon, Limor Helpman, Yfat Kadan, Mario E. Beiner, Michal Harel, Hadar Brand, Ram Eitan, Anna Blecher, Georgina D. Barnabas, Stav Sapoznik, Guy Katz, Omer Weitzner, Benny Brandt |
|---|---|
| Rok vydání: | 2019 |
| Předmět: |
Proteomics
Oncology medicine.medical_specialty Early cancer education Biochemistry Analytical Chemistry 03 medical and health sciences Cell-Derived Microparticles Internal medicine medicine Humans Ovarian cancer early detection Liquid biopsy Molecular Biology Early Detection of Cancer 030304 developmental biology Ovarian Neoplasms 0303 health sciences Proteomic Profile Proteomic Profiling business.industry Research Microvesicle Uterus 030302 biochemistry & molecular biology Liquid Biopsy Reproducibility of Results medicine.disease Neoplasm Proteins Gene Expression Regulation Neoplastic Female Neoplasm Grading Ovarian cancer business |
| Zdroj: | Mol Cell Proteomics |
| ISSN: | 1535-9476 |
| Popis: | High-grade ovarian cancer (HGOC) is the leading cause of mortality from gynecological malignancies, because of diagnosis at a metastatic stage. Current screening options fail to improve mortality because of the absence of early-stage-specific biomarkers. We postulated that a liquid biopsy, such as utero-tubal lavage (UtL), may identify localized lesions better than systemic approaches of serum/plasma analysis. Further, while mutation-based assays are challenged by the rarity of tumor DNA within nonmutated DNA, analyzing the proteomic profile, is expected to enable earlier detection, as it reveals perturbations in both the tumor as well as in its microenvironment. To attain deep proteomic coverage and overcome the high dynamic range of this body fluid, we applied our method for microvesicle proteomics to the UtL samples. Liquid biopsies from HGOC patients (n = 49) and controls (n = 127) were divided into a discovery and validation sets. Data-dependent analysis of the samples on the Q-Exactive mass spectrometer provided depth of 8578 UtL proteins in total, and on average ∼3000 proteins per sample. We used support vector machine algorithms for sample classification, and crossed three feature-selection algorithms, to construct and validate a 9-protein classifier with 70% sensitivity and 76.2% specificity. The signature correctly identified all Stage I lesions. These results demonstrate the potential power of microvesicle-based proteomic biomarkers for early cancer diagnosis. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|