Image_2_Whole-genome/exome analysis of circulating tumor DNA and comparison to tumor genomics from patients with heavily pre-treated ovarian cancer: subset analysis of the PERMED-01 trial.tif

Introduction

The poor prognosis of ovarian carcinoma (OvC) is due to the advanced stage at diagnosis, a high risk of relapse after first-line therapies, and the lack of efficient treatments in the recurrence setting. Circulating tumor DNA (ctDNA) analysis is a promising tool to assess treatment-resistant OvC and may avoid iterative tissue biopsies. We aimed to evaluate the genomic profile of recurrent heavily pre-treated OvC.

Methods

We performed tumor panel-based sequencing as well as low-coverage whole-genome sequencing (LC-WGS) of tumor and plasma collected in patients with ovarian cancer included in the PERMED-01 trial. Whole-exome sequencing (WES) data of plasma samples were also analyzed and compared to mutation and copy number alteration (CNA) tumor profiles. The prognostic value [progression-free survival (PFS)] of these alterations was assessed in an exploratory analysis.

Results

Tumor and plasma genomic analyses were done for 24 patients with heavily pretreated OvC [67% high-grade serous carcinoma (HGSC)]. Tumor mutation burden was low (median 2.04 mutations/Mb) and the most frequent mutated gene was TP53 (94% of HGSC). Tumor CNAs were frequent with a median of 50% of genome altered fraction. Plasma LC-WGS and WES detected ctDNA in 21/24 cases (88%) with a median tumor fraction of 12.7%. We observed a low correlation between plasma and tumor CNA profiles. However, this correlation was significant in cases with the highest circulating tumor fraction. Plasma genome altered fraction and plasma mutation burden (p = 0.011 and p = 0.041, respectively, log-rank tests) were associated with PFS.

Conclusions

Combination of LC-WGS and WES can detect ctDNA in most pre-treated OvCs. Some ctDNA characteristics, such as genome altered fraction and plasma mutation burden, showed prognostic value. ctDNA assessment with LC-WGS may be a promising and non-expansive tool to evaluate disease evolution in this disease with high genomic instability.

Clinical Trial Registration

https://clinicaltrials.gov/ct2/show/NCT02342158, identifier NCT02342158.