550 Novel 3D model systems to assess heterogeneity in response to platinum therapy in high grade serous ovarian cancer. (4th December 2020)
- Record Type:
- Journal Article
- Title:
- 550 Novel 3D model systems to assess heterogeneity in response to platinum therapy in high grade serous ovarian cancer. (4th December 2020)
- Main Title:
- 550 Novel 3D model systems to assess heterogeneity in response to platinum therapy in high grade serous ovarian cancer
- Authors:
- Ploski, Jennifer
Nixon, Katherine
Demchenko, Nikita
Cunnea, Paula
Fotopoulou, Christina - Abstract:
- Abstract : Introduction/Background: High-grade serous ovarian cancer (HGSOC) is the most common subtype of ovarian cancer, characterised by vast genomic instability and heterogeneity and acquired resistance to platinum-based chemotherapy. However, matching the most beneficial treatment options to patients is difficult to predict due to different platinum resistance mechanisms and limited effective predictive biomarkers. A study characterising intra-tumoural heterogeneity in HGSOC has identified variations in phenotypic responses to platinum treatment between different metastatic sites. In this study, we aim to develop novel clinically-relevant 3D ex-vivo models of HGSOC to investigate the effect of the local microenvironment on metastatic tumour cells' response to treatment, and potential use as a screening tool to predict drug responses. Methodology: Three different ex-vivo models were developed: organotypic, organoid and tumour slice. For organotypic and organoid models, tumour cells were extracted from metastatic deposits obtained from defined anatomical regions during upfront radical debulking surgery of advanced stage HGSOC patients. Organotypic models were assembled using normal omental stromal cells embedded in Collagen-1 and tumour cells were added. Organoid models were propagated from tumour cells and embedded in basement membrane extract. For slice culture models, tumours were sliced into 350μm sections using a vibratome and cultured on cell culture inserts. AllAbstract : Introduction/Background: High-grade serous ovarian cancer (HGSOC) is the most common subtype of ovarian cancer, characterised by vast genomic instability and heterogeneity and acquired resistance to platinum-based chemotherapy. However, matching the most beneficial treatment options to patients is difficult to predict due to different platinum resistance mechanisms and limited effective predictive biomarkers. A study characterising intra-tumoural heterogeneity in HGSOC has identified variations in phenotypic responses to platinum treatment between different metastatic sites. In this study, we aim to develop novel clinically-relevant 3D ex-vivo models of HGSOC to investigate the effect of the local microenvironment on metastatic tumour cells' response to treatment, and potential use as a screening tool to predict drug responses. Methodology: Three different ex-vivo models were developed: organotypic, organoid and tumour slice. For organotypic and organoid models, tumour cells were extracted from metastatic deposits obtained from defined anatomical regions during upfront radical debulking surgery of advanced stage HGSOC patients. Organotypic models were assembled using normal omental stromal cells embedded in Collagen-1 and tumour cells were added. Organoid models were propagated from tumour cells and embedded in basement membrane extract. For slice culture models, tumours were sliced into 350μm sections using a vibratome and cultured on cell culture inserts. All models were treated with cisplatin and assessed for apoptosis and viability read-outs. Results: Organotypic models showed that tumour cells cultured in 3D showed heterogeneity in response to cisplatin treatment, data showed a trend towards reduced response to treatment within 3D models compared to 2D (n=8). Changes in patterns of response to treatment between samples from 2D to 3D within the same patient was also demonstrated (n=5). Organoid models were successfully propagated from different metastatic sites and maintained long term growth (>15 passages). Histological read-outs for slice culture models demonstrated slices from different metastatic sites maintained viability in culture for up to 5 days. Conclusion: We have established growth, drug treatment conditions and assay read-outs for 3 different ex-vivo models of metastatic HGSOC. We have established that organoid culture must be generated within 24hours of tumour cell extraction. Furthermore, both fresh and viably frozen tumours can be used to generate organotypic and organoid models. The broader implication of establishing clinically-relevant complex tumour models as routine methodologies for screening novel therapeutics and capturing the complex heterogeneity of individual patients, may lead to better development of therapeutic strategies including tumour/microenvironment combination strategies and also better personalisation of therapy for patients with HGSOC. Disclosures: CF: advisory boards and honoraria from Roche, Tesaro, Sequana, Olympus, Astra Zeneca. Other authors have no disclosures to declare. … (more)
- Is Part Of:
- International journal of gynecological cancer. Volume 30(2020)Supplement 4
- Journal:
- International journal of gynecological cancer
- Issue:
- Volume 30(2020)Supplement 4
- Issue Display:
- Volume 30, Issue 4, Part 4 (2020)
- Year:
- 2020
- Volume:
- 30
- Issue:
- 4
- Part:
- 4
- Issue Sort Value:
- 2020-0030-0004-0004
- Page Start:
- A99
- Page End:
- A99
- Publication Date:
- 2020-12-04
- Subjects:
- Generative organs, Female -- Cancer -- Periodicals
616.99465 - Journal URLs:
- http://journals.lww.com/ijgc/pages/default.aspx ↗
http://www3.interscience.wiley.com/journal/118544021/toc ↗
https://ijgc.bmj.com/ ↗
http://journals.lww.com ↗ - DOI:
- 10.1136/ijgc-2020-ESGO.172 ↗
- Languages:
- English
- ISSNs:
- 1048-891X
- Deposit Type:
- Legaldeposit
- View Content:
- Available online (eLD content is only available in our Reading Rooms) ↗
- Physical Locations:
- British Library DSC - 4542.273500
British Library DSC - BLDSS-3PM
British Library HMNTS - ELD Digital store - Ingest File:
- 19711.xml