Blood Test Tracks Treatment Resistance in High-Grade Serous Ovarian Cancer

Existing monitoring methods fail to distinguish treatment-sensitive from treatment-resistant cell populations, making it hard to predict and prevent relapse. Now, a new method can track the evolution of treatment-resistant cells in ovarian cancer using blood tests, preventing HGSOC from recurring.

The new method, called CloneSeq-SV, developed by researchers at Memorial Sloan Kettering Cancer Center (MSK, New York, NY, USA), combines single-cell whole-genome sequencing with targeted sequencing of structural variants to follow tumor evolution. The method uses structural variants — large DNA rearrangements — as molecular “bar codes” to trace which subclones survive therapy by sampling surgical tumor tissue and serial blood draws. By integrating single-cell and cell-free DNA signals, CloneSeq-SV allows researchers to monitor subpopulation dynamics noninvasively over the course of treatment.

In their study, the team of researchers analyzed longitudinal blood and tissue samples from 18 patients with HGSOC and showed that resistant subpopulations were present at diagnosis and expanded as sensitive cells were eliminated by therapy. Resistant clones carried distinctive features such as oncogene amplifications, chromothripsis, and whole-genome doubling, alterations that explain why they survive standard treatments.

These insights, reported in Nature, create new opportunities to design strategies that target vulnerabilities associated with resistant clones and to match patients to therapies that exploit those weaknesses. For example, one patient’s tumor evolved to be dominated by ERBB2 amplification at recurrence and showed an exceptional response to an ERBB2-targeted drug, illustrating how evolutionary tracking can guide effective second-line therapy. The next steps include studying larger cohorts, collecting richer follow-up samples, and testing the method in other genomically unstable cancers.

“Together, these findings provide new opportunities to develop treatment strategies to attack vulnerabilities associated with those features,” said study first author Marc Williams, PhD, a postdoctoral researcher who uses computational techniques to study cancer evolution.

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