Resumen de Protein signatures in uterine aspirates to improve diagnosis of endometrial cancer: the CEMARK project /
About 30% of endometrial cancer (EC) patients are still diagnosed at an advanced stage of the disease associated with a drastic decrease in the 5-year survival rate. Diagnosis is achieved by the histopathological examination of an endometrial biopsy, which is preferably obtained by a minimally invasive aspiration from the uterine cavity (i.e., uterine aspirate). Unfortunately, the limited number of cells available for examination in these biopsies is associated with two important drawbacks: i) around 22% of undiagnosed patients due to histologically inadequate specimens; and ii) up to 50% of incorrectly assigned EC histotype and grade. The main goal of this thesis is the identification of specific and sensitive proteomic signatures able to: i) accurately discriminate between EC cases and non-EC women with suspicion of EC in uterine aspirate samples, and thus to reduce the number of more invasive sampling methods; and ii) provide clinicians more accurate preoperative information regarding EC histological type and tumor grade that could improve the risk stratification of the patients. In Chapter 1 an extensive literature review was performed, and a list of 506 proteins associated with EC was generated. The analysis of the main characteristics of the studies included in this literature review allowed us to outline approaches not yet exploited that might accelerate the identification of clinically useful proteins for EC diagnosis. These approaches were applied in the workflow described in the next chapters.
In Chapter 2 we demonstrated that those proteins previously associated with EC, mainly studied by immunohistochemistry at the tissue level, can be measured in uterine aspirate samples by highly multiplexing targeted proteomics. Moreover, we selected the fluid fraction of these uterine aspirates as the most appropriate fraction for biomarker identification.
In Chapter 3 we described a stepwise workflow to reduce the initial list of 506 candidate biomarkers generated in Chapter 1 down to 52 proteins that were analyzed in the fluid fraction of uterine aspirates from 20 EC patients and 18 non-EC controls by LC-PRM. The differential expression of 26 proteins was observed, and among them ten biomarkers showed a high sensitivity and specificity (AUC > 0.9). We demonstrated the importance of targeted mass spectrometry-based approaches in biomarker verification studies to prioritize the most promising biomarkers to enter a validation study, and highlighted the benefits of LC-PRM acquisition performed on high-resolution accurate mass spectrometers for this purpose.
The same 52 proteins were then validated in the uterine aspirates from 116 patients by LC-PRM, as described in Chapter 4. The levels of 28 proteins were significantly higher in EC patients (n=69) compared to controls (n=47). The combination of MMP9 and KPYM exhibited 94% sensitivity and 87% specificity for detecting EC cases. This panel perfectly complemented the standard histopathological diagnosis, achieving 100% of correct diagnosis in this dataset. Nine proteins were significantly increased in endometrioid EC (n=49) compared to less common but more aggressive serous EC (n=20). The combination of CTNB1, XPO2 and CAPG achieved 95% sensitivity and 96% specificity for the discrimination of these subtypes.
In order to facilitate the implementation of these biomarker signatures in the clinical practice, the translation of the LC-PRM results to commercially available ELISA kits was then evaluated. Moreover, simplification of the analytical assay and the sample preparation was assessed in Chapter 5.
The two uterine aspirate-based proteomic signatures developed in this thesis are expected to improve EC diagnosis, precluding the use of subsequent invasive sampling methods; and to assist in the prediction of the optimal surgical treatment. In the long term, the implementation of these signatures is expected to improve the management of EC patients and substantially reduce healthcare costs.
