Several factors make ovarian cancer particularly challenging to treat. This is largely because the cancer often spreads at a microscopic level within the abdomen early on, resulting in diagnosis at an advanced stage. Additionally, while initial treatments with surgery, chemotherapy, and maintenance therapies are successful for many people, most advanced stage ovarian cancers eventually come back.
Now a research team at Memorial Sloan Kettering Cancer Center (MSK) is aiming to find new ways to stop the most common and deadly form of ovarian cancer — high-grade serous ovarian cancer — from recurring with the help of a method they developed for tracking the evolution of treatment resistant cells in ovarian cancer using blood tests.
Their findings, which were published October 1 in Nature , may help develop new approaches for identifying — and ultimately targeting — the specific subpopulations of cells that cause recurrence.
"High-grade serous ovarian cancers — or HGSOCs — contain a diverse array of cells, some of which will be sensitive to our best treatments and some of which will be resistant," says study first author Marc Williams, PhD , a postdoctoral researcher who uses computational techniques to study cancer evolution. "Existing methods to monitor these cancers, however, don't distinguish between one population and the other. So, we decided to build one that could."
Tracking Changes in Ovarian Cancer Over Time Using CloneSeq-SV
To look at the differences between cells within an ovarian cancer, and how their composition changes over time, the research team developed an approach they call CloneSeq-SV. The method combines single-cell whole genome sequencing and targeted sequencing of structural variants, which are large scale alterations or rearrangements in DNA.
This allowed the scientists to monitor the cancer's evolution using initial samples taken during surgery followed by an ongoing series of blood tests — and to track which tumor subpopulations survive treatment and which die off.
"Essentially, we were able to use these structural variants as molecular 'bar codes,' which allowed us to track tumor cells' subpopulations in the bloodstream," says senior study author Sohrab Shah, PhD , who heads MSK's Computational Oncology Service and serves as Director of the Halvorsen Center for Computational Oncology.
The study analyzed blood samples taken from 18 patients with HGSOC from the time of diagnosis through the recurrence of their cancer.
"Using this new method, we could see that the cells that are resistant were present at the time of diagnosis, and that they were able to multiply as cells that were more sensitive to treatment died off," Dr. Shah adds.
The study would not have been possible without extensive collaboration and teamwork between computational researchers and the surgeons, pathologists, and oncologists at MSK who specialize in gynecologic cancers, led by Chief of Gynecologic Medical Oncology Carol Aghajanian, MD , Chief of Gynecology Surgery Nadeem Abu-Rustum, MD , Director of Gynecologic Pathology Lora Ellenson, MD , and Director of the Gynecology Disease Management Team Research Laboratory Britta Weigelt, PhD .
How CloneSeq-SV Could Help Future Patients
How could this new method potentially help future patients?
CloneSeq-SV allowed researchers to identify a clear pattern: The subpopulations of cancer cells that rise to prominence during recurrence have distinctive features, including amplifications of certain cancer-driving genes (oncogenes), chromothripsis (where the chromosome shatters into many pieces and is stitched back together haphazardly), and whole genome doubling .
"Together, these findings provide new opportunities to develop treatment strategies to attack vulnerabilities associated with those features," Dr. Williams says.
He points to an example from the study: One patient had an exceptional response to trastuzumab deruxtecan (a drug that targets the oncogene ERBB2) and remained disease free years later. The analysis revealed why that was the case — at the outset, the tumor contained a mix of cells with and without an amplification of ERBB2, but then first line treatments eliminated the population of cells without the amplification, ultimately resulting in a cancer with many extra copies of ERBB2 during recurrence.
"And because we have a drug that specifically targets ERBB2, this evolutionary shift left the entire tumor sensitive to a new, targeted treatment," Dr Shah says.
Next Steps for the Research
Next steps for the research include studying more patients to identify more patterns that could guide treatment strategies, as well as capturing a richer picture of tumor cell diversity by collecting more tumor samples during follow-up procedures.
Moreover, the same approach might be applied to other types of cancer that are also characterized by high levels of variability (genomic instability), the researchers note.
Key Takeaways
- Researchers at MSK have developed a new computational method — CloneSeq-SV — for analyzing the evolution of treatment-resistant cancer cells in high-grade serous ovarian cancer (HGSOC).
- CloneSeq-SV may lead to new approaches for identifying and targeting the specific subpopulations of cells that cause HGSOC recurrences.
- This method may also be useful in other types of cancer with high genomic instability.
Dr. Shah holds the Nicholls-Biondi Chair in Computational Oncology.
Additional Authors, Funding, and Disclosures
Ignacio Vázquez-García, Grittney Tam, Michelle Wu, Nancy Varice, Eliyahu Havasov, Hongyu Shi, Duaa H. Al-Rawi, Gryte Satas, Hannah J. Lees, Jake June-Koo Lee, Matthew A. Myers, Matthew Zatzman, Nicole Rusk, Emily Ali, Ronak H Shah, Michael F. Berger, Neeman Mohibullah, Yulia Lakhman, Dennis S. Chi, Andrew McPherson, Dmitriy Zamarin, Brian Loomis, and Claire F. Friedman.
The research was funded in part by the Halvorsen Center for Computational Oncology and Cycle for Survival supporting MSK. Additional funding for the work came from Break Through Cancer; the Ovarian Cancer Research Alliance (648007, 650687); the National Institutes of Health (CA281928-01, CA264028, P30-CA008748, CA269382); the National Cancer Institute (K99CA256508, P50-CA247749-01); the Department of Defense (W81XWH-20-1-0565), the Seidenberg Family Foundation; the Cancer Research U.K. Cancer Grand Challenges Program (C42358/A27460); the Marie-Josée and Henry R. Kravis Center for Molecular Oncology; and the Breast Cancer Research Foundation.
The work used the resources of the High-Performance Computing Group , the Molecular Cytology Core Facility , and the Integrated Genomics Operation at MSK.
Dr. Shah reports research funding from AstraZeneca and Bristol Myers Squibb, outside the scope of this work. Several of the other authors also disclosed competing interests; please refer to the journal article for the full list.
Read the study: " Tracking clonal evolution during treatment in ovarian cancer using cell-free DNA ," Nature. DOI: 10.1038/s41586-025-09580-0
