Oops, you're using an old version of your browser so some of the features on this page may not be displaying properly.

MINIMAL Requirements: Google Chrome 24+Mozilla Firefox 20+Internet Explorer 11Opera 15–18Apple Safari 7SeaMonkey 2.15-2.23

Poster display session: Biomarkers, Gynaecological cancers, Haematological malignancies, Immunotherapy of cancer, New diagnostic tools, NSCLC - early stage, locally advanced & metastatic, SCLC, Thoracic malignancies, Translational research

2166 - Clear Cell Ovarian Cancer (CCOC): Predicting Risk of Relapse (ROR)

Date

20 Oct 2018

Session

Poster display session: Biomarkers, Gynaecological cancers, Haematological malignancies, Immunotherapy of cancer, New diagnostic tools, NSCLC - early stage, locally advanced & metastatic, SCLC, Thoracic malignancies, Translational research

Topics

Tumour Site

Ovarian Cancer

Presenters

Michael-John Devlin

Citation

Annals of Oncology (2018) 29 (suppl_8): viii332-viii358. 10.1093/annonc/mdy285

Authors

M. Devlin1, J.A. Ledermann2, M. Lockley1, N. Wilkinson3, J. McDermott4, R. Kristeleit5, R. Miller1

Author affiliations

  • 1 Medical Oncology, University College London Hospital UCLH NHS Foundation Trust, NW1 2PG - London/GB
  • 2 Cancer Trials Centre, University College London Cancer Institute, WC1E6BT - London/GB
  • 3 Pathology, University College London Hospital UCLH NHS Foundation Trust, NW1 2PG - London/GB
  • 4 Academic Pathology, University College London Hospital UCLH NHS Foundation Trust, NW1 2PG - London/GB
  • 5 Academic Department Of Oncology, UCL Cancer Institute/Paul O'Gorman Building, WC1E 6DD - London/GB

Resources

Login to access the resources on OncologyPRO.

If you do not have an ESMO account, please create one for free.

Abstract 2166

Background

Patients (pts) with advanced CCOC have a significantly poorer prognosis than other Epithelial ovarian cancer (EOC) subtypes. Being able to predict which pts are more likely to relapse could assist with treatment and monitoring decisions. The system inflammatory score (SIS) aims to predict postsurgical prognosis for CCOC by stratifying pts into 3 groups (gps). The Risk of OVarian cAncer Relpase (ROVAR) score aims to predict ROR for EOC following first line treatment and stratifies pts into low/intermediate (int)/high gps. We attempted to validate both scores in a non-trial population.

Methods

We reviewed the medical records for pts with CCOC treated at two UK gynaecological cancer centres between 2002 and 2017. Data comprising pt and tumor characteristics, treatment and outcome. Analysis was performed using Mantel Cox and Fisher Exact Tests.

Results

119 pts; stage I (65), II (19), III (22), IV (10) and unknown (3). ROVAR was calculated for 90 (75%) pts; 24 (20%) had incomplete data, 6 (5%) excluded for other. Pts classified into low (20%), int (44%) and high (36%) gps. ROR for low or int gps vs high p = 0.0001; ROR for low vs int gps p = 1. Compared to low/int, pts in high-risk gp were younger 53.87yrs (34-72) vs 57.81yrs (35-74), had smaller tumours 106mm (45-240) vs 136mm (50-230), with increase in both hypercalcaemia (21% vs 5%; p = 0.306) and thromboembolic events (37.5% vs 10%; p = 0.0047). SIS was calculated for 67 pts (56%); 39 (33%) had insufficient data, 13 (11%) excluded for other. Pts classified into gp 0 (34.3%), 1 (37.3%) and 2 (28.3%) with no statistical difference in PFS (p = 0.9118) or OS (p = 0.849) between gps.

Conclusions

ROVAR significantly predicts ROR in CCOC in pts with high vs low/int risk disease. Our data suggests that the features that promote treatment-resistance are linked to paraneoplastic phenomenon and emerge early in tumour development, resulting in diagnosis at a smaller size in younger women. Another possibility is that these are two pathologically similar, but ultimately distinct, disease entities from the outset. Patients with high risk disease may benefit from more intensive follow-up and, given the chemo-resistant phenotype of the disease, early enrolment in clinical trials.

Clinical trial identification

Legal entity responsible for the study

Michael-John Devlin.

Funding

Has not received any funding.

Editorial Acknowledgement

Disclosure

All authors have declared no conflicts of interest.

This site uses cookies. Some of these cookies are essential, while others help us improve your experience by providing insights into how the site is being used.

For more detailed information on the cookies we use, please check our Privacy Policy.

Customise settings
  • Necessary cookies enable core functionality. The website cannot function properly without these cookies, and you can only disable them by changing your browser preferences.