Article Text
Abstract
Introduction/Background High-grade serous ovarian cancer (HGSOC) is the most common clinically diagnosed ovarian cancer, often considered a fatal disease. Despite the increased awareness of this tumor, it is generally diagnosed at a late stage (III or IV), implying that cancer cells have already spread outside the primary site. Although current treatments appeared to provide almost complete remission, the recurrence rate is still high.
Methodology Here we present an innovative tissue engineering approach applied to HGSOC by combining decellularized extra cellular matrix (dECM) and patient-derived organoids (PDOs), with the aim of providing a useful three dimensional (3D) model capable of predicting treatment response.
Results We demonstrated that dECM maintains the structural environment of native tumoral tissue as demonstrated by histology, immunohistochemistry, immunofluorescence and second harmonic generation microscopy. Proteomic analysis and Raman spectroscopy performed on the dECM in comparison to the native tumor revealed a dominant set of functionally-related proteins associated with ECM assembly, organization and morphology consistent with preservation of a tissue-specific niche for later PDOs seeding and infiltration. In parallel, we established a protocol for the PDOs’ derivation with an initiation efficiency of the 83.3%. We compared PDOs with native tumor counterpart using diagnostics markers with an index of concordance of 100% for CK7, P16 and SATB2 and 66% for P53mut, PAX8 and WT1. PDOs were also evaluated for expression of Ki67, E-cadherin, F-actin and β-catenin proteins critical for cell proliferation, adhesion and migration. We treated PDO with Paclitaxel and Paclitaxel plus Carboplatin and IC50 concentrations after treatment resulted 37.10µM and 6.8µM, respectively. dECM recellularized by injection with PDOs and treated with the IC50 concentrations obtained in the PDO displayed a reduced sensitivity to standard first-line chemotherapy.
Conclusion The bio-engineered 3D model could be a reliable preclinical patient specific platform to bridge the gap between in vitro and in vivo drug testing assays.
Disclosures Unpublished data