Towards individualisation of treatment in endometrial cancer

Molecular profiling of circulating tumor cells links plasticity to the metastatic process in endometrial cancer

BACKGROUND

About 20% of patients diagnosed with endometrial cancer (EC) are considered high-risk with unfavorable prognosis. In the framework of the European Network for Individualized Treatment in EC (ENITEC), we investigated the presence and phenotypic features of Circulating Tumor Cells (CTC) in high-risk EC patients.

METHODS

CTC isolation was carried out in peripheral blood samples from 34 patients, ranging from Grade 3 Stage IB to Stage IV carcinomas and recurrences, and 27 healthy controls using two methodologies. Samples were subjected to EpCAM-based immunoisolation using the CELLection™ Epithelial Enrich kit (Invitrogen, Dynal) followed by RTqPCR analysis. The phenotypic determinants of endometrial CTC in terms of pathogenesis, hormone receptor pathways, stem cell markers and epithelial to mesenchymal transition (EMT) drivers were asked. Kruskal-Wallis analysis followed by Dunn's post-test was used for comparisons between groups. Statistical significance was set at p < 0.05.

RESULTS

EpCAM-based immunoisolation positively detected CTC in high-risk endometrial cancer patients. CTC characterization indicated a remarkable plasticity phenotype defined by the expression of the EMT markers ETV5, NOTCH1, SNAI1, TGFB1, ZEB1 and ZEB2. In addition, the expression of ALDH and CD44 pointed to an association with stemness, while the expression of CTNNB1, STS, GDF15, RELA, RUNX1, BRAF and PIK3CA suggested potential therapeutic targets. We further recapitulated the EMT phenotype found in endometrial CTC through the up-regulation of ETV5 in an EC cell line, and validated in an animal model of systemic dissemination the propensity of these CTC in the accomplishment of metastasis.

CONCLUSIONS

Our results associate the presence of CTC with high-risk EC. Gene-expression profiling characterized a CTC-plasticity phenotype with stemness and EMT features. We finally recapitulated this CTC-phenotype by over-expressing ETV5 in the EC cell line Hec1A and demonstrated an advantage in the promotion of metastasis in an in vivo mouse model of CTC dissemination and homing.

Fourier-transform infrared spectroscopy coupled with a classification machine for the analysis of blood plasma or serum: a novel diagnostic approach for ovarian cancer

Currently available screening tests do not deliver the required sensitivity and specificity for accurate diagnosis of ovarian or endometrial cancer. Infrared (IR) spectroscopy of blood plasma or serum is a rapid, versatile, and relatively non-invasive approach which could characterize biomolecular alterations due to cancer and has potential to be utilized as a screening or diagnostic tool. In the past, no such approach has been investigated for its applicability in screening and/or diagnosis of gynaecological cancers. We set out to determine whether attenuated total reflection Fourier-transform IR (ATR-FTIR) spectroscopy coupled with a proposed classification machine could be applied to IR spectra obtained from plasma and serum for accurate class prediction (cancer vs. normal). Plasma and serum samples were obtained from ovarian cancer cases (n = 30), endometrial cancer cases (n = 30) and non-cancer controls (n = 30), and subjected to ATR-FTIR spectroscopy. Four derived datasets were processed to estimate the real-world diagnosis of ovarian and endometrial cancer. Classification results for ovarian cancer were remarkable (up to 96.7%), whereas endometrial cancer was classified with a relatively high accuracy (up to 81.7%). The results from different combinations of feature extraction and classification methods, and also classifier ensembles, were compared. No single classification system performed best for all different datasets. This demonstrates the need for a framework that can accommodate a diverse set of analytical methods in order to be adaptable to different datasets. This pilot study suggests that ATR-FTIR spectroscopy of blood is a robust tool for accurate diagnosis, and carries the potential to be utilized as a screening test for ovarian cancer in primary care settings. The proposed classification machine is a powerful tool which could be applied to classify the vibrational spectroscopy data of different biological systems (e.g., tissue, urine, saliva), with their potential application in clinical practice.