Microvesicle Proteomic Profiling of Uterine Liquid Biopsy for Ovarian Cancer Early Detection

High-grade ovarian cancer accounts for higher mortality rates because of ineffective biomarkers for early diagnosis. Deep proteome profiling of the microvesicles from a total of 187 liquid biopsies of Utero-tubal Lavage, combined with support vector machine algorithms, extracted a 9-protein classifier with high accuracy. The signature predicted all the early stage lesions, and outperformed the known markers CA125 and HE4 with 70‥ sensitivity and 76.2‥ specificity. Our study reveals UtL-microvesicle proteomics as the potential biomarker source for early diagnosis of HGOC. [Display omitted] Highlights •Microvesicle proteomics of 187 utero-tubal lavage samples for early diagnosis of HGOC.•Machine learning-based classification of a 9-protein signature with high predictive power.•Signature has 70‥ sensitivity and 76.2‥ specificity, predicting stage I lesions. 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.