ctDNA Sampling May Help Tailor Treatment for Difficult-to-Treat Ovarian Cancer, Study Suggests

ctDNA Sampling May Help Tailor Treatment for Difficult-to-Treat Ovarian Cancer, Study Suggests

An analysis of circulating tumor DNA, or ctDNA, can help guide treatment decisions for patients with difficult-to-treat high-grade serous ovarian cancer, a study shows.

The study, “Prospective Longitudinal ctDNA Workflow Reveals Clinically Actionable Alterations in Ovarian Cancer,” was published in journal Precision Oncology.

Cancers generally develop due to accumulation of genetic mutations in a specific cell. As cancer grows and spreads, tumor cells leak some of their DNA molecules into the bloodstream, so sampling these circulating tumor DNA may help provide a snapshot of the types of genetic mutations occur in cancer.

Additionally, since the effectiveness of some chemotherapy regimens is dependent on the mutational status of the tumor, researchers have suggested that ctDNA analysis may provide useful information for personalized guided treatment.

ctDNA sampling is an attractive assessment technique because it is minimally invasive, requiring only a common blood sample. Unfortunately, the clinical value of ctDNA remains controversial, particularly due to lack of standardized and often poorly described analysis approaches.

To overcome this challenge, Finnish researchers implemented a clinical ctDNA workflow that can help detect clinically relevant mutations in more than 500 cancer-related genes. This workflow results from the combination of ctDNA analysis-tailored bioinformatics pipelines and a Translational Oncology Knowledgebase.

Researchers applied their new workflow to a prospective cohort consisting of 78 ctDNA samples that were collected before, during, and after treatment from 12 patients who had high-grade serous ovarian cancer. They also analyzed the genetic profile of primary tumor tissue samples collected from these patients.

High-grade serous ovarian cancer is the most common and aggressive form of epithelial ovarian cancer with a five-year survival rate of 43%.

Results indicated there was a good correlation between the presence of genetic mutations and copy number alterations in ctDNA and primary tumor samples. Using ctDNA analysis, the team was able to identify mutations in seven patients (58%) that could be treated using clinically available therapies.

Next, the team used this analysis to change the treatment strategy in one patient who had severe ovarian cancer that was not responding to chemotherapy.

The ctDNA analysis revealed that the tumor was carrying a genetic variant known as an ERBB2 amplification, which is, in fact, a predictive biomarker for targeted treatment with Genentech‘s Herceptin (trastuzumab). Based on this finding, the patient started treatment with Herceptin combined with carboplatin and paclitaxel.

With only three treatment cycles, it was possible to detect a reduction in levels of CA-125 (a well-known cancer biomarker) from 840 IU/mL to 19 IU/mL. Further evaluation by computed tomography (CT) scans showed that the patient also had significant tumor shrinkage, which confirmed that, at this point, it had already achieved a partial response to the tailored treatment.

“Early detection of poor-responding patients allows the design of alternative treatment strategies in [high-grade serous ovarian cancer],” the researchers said.

Another benefit associated with the use of ctDNA analysis is that it can help clinicians to manage how a patient is responding to therapy over time. Physicians can sample ctDNA during the course of treatment to monitor changes in the levels of cancer-associated genes.

“ctDNA sampling and sequencing with a large panel offers several benefits to physicians,” they wrote. “Response to therapy can be inferred using two or three consecutive ctDNA samples during therapy.”

The team believes these findings provide “a proof of concept for using ctDNA to guide clinical decisions” for the treatment of cancer.