Tumor-informed liquid biopsy detection of structural variants in high grade serous ovarian cancer

TL;DR

This study explores using structural variants in liquid biopsies to detect and monitor high grade serous ovarian cancer more effectively.

Contribution

A tumor-informed, ddPCR-based method for detecting structural variants in cfDNA specific to HGSOC is developed and validated.

Findings

Tumor-specific SV breakpoints were successfully designed and validated in multisite biopsies.

Nine out of 15 selected SVs were measurable in liquid biopsies using ddPCR.

The method shows feasibility for monitoring disease burden during treatment.

Abstract

Background: High grade serous ovarian cancer (HGSOC) recurs frequently and commercial tests have emerged for tumor-informed, cell-free DNA (cfDNA)-based detection of minimal residual disease. These tests are based on somatic single nucleotide variants prevalent in many cancers and thus are not well matched to HGSOC, which is dominated by structural genomic rearrangements. The purpose of this study was to evaluate the feasibility of a structural-variant (SV)-informed, cfDNA-based method for detecting clonal and subclonal HGSOC disease burden.

Methods: A method was developed for detecting patient-specific SV breakpoints using digital droplet PCR (ddPCR) with custom tumor-informed primer/probe pairs. Test parameters were first estimated using synthetic cfDNA generated by ultrasonication of genomic DNA from ovarian cancer cell lines. The optimized workflow was implemented in which whole genome sequencing of multisite pre-treatment HGSOC biopsies performed and high confidence SVs were called by multiple published SV callers. Real-time PCR and ddPCR were used for assay development.

Results: Following the optimized workflow, tumor-specific SV breakpoint-spanning primers/probe sets of four HGSOC patients’ multisite biopsies were designed and validated by real-time PCR and ddPCR. Together with four HGSOCs, a total of 29 SVs breakpoints-spanning tumor-informed primers/probe sets were designed and validated in multisite biopsies. 15 validated tumor-specific SVs were selected for quantification in their corresponding liquid biopsies using the validated ddPCR, and 9 had measurements in liquid biopsies.

Conclusions: Our result shows the detection of SVs from pre-treatment cfDNA using tumor-informed breakpoints-spanning ddPCR is feasible and may enable a novel and sensitive method for monitoring on-treatment disease burden.

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