
==== Front
bioRxiv
BIORXIV
bioRxiv
2692-8205
Cold Spring Harbor Laboratory

10.1101/2024.08.21.609031
preprint
1
Article
Tracking clonal evolution of drug resistance in ovarian cancer patients by exploiting structural variants in cfDNA
Williams Marc J.
Vázquez-García Ignacio
Tam Grittney
Wu Michelle
Varice Nancy
Havasov Eliyahu
Shi Hongyu
Satas Gryte
Lees Hannah J.
Lee Jake June-Koo
Myers Matthew A.
Zatzman Matthew
Rusk Nicole
Ali Emily
Shah Ronak H
Berger Michael F.
Mohibullah Neeman
Lakhman Yulia
Chi Dennis S.
Abu-Rustum Nadeem R.
Aghajanian Carol
McPherson Andrew
Zamarin Dmitriy
Loomis Brian
Weigelt Britta
Friedman Claire F.
Shah Sohrab P.
23 8 2024
2024.08.21.609031https://creativecommons.org/licenses/by-nc-nd/4.0/ This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which allows reusers to copy and distribute the material in any medium or format in unadapted form only, for noncommercial purposes only, and only so long as attribution is given to the creator.
http://biorxiv.org/lookup/doi/10.1101/2024.08.21.609031
nihpp-2024.08.21.609031.pdf
ABSTRACT

Drug resistance is the major cause of therapeutic failure in high-grade serous ovarian cancer (HGSOC). Yet, the mechanisms by which tumors evolve to drug resistant states remains largely unknown. To address this, we aimed to exploit clone-specific genomic structural variations by combining scaled single-cell whole genome sequencing with longitudinally collected cell-free DNA (cfDNA), enabling clonal tracking before, during and after treatment. We developed a cfDNA hybrid capture, deep sequencing approach based on leveraging clone-specific structural variants as endogenous barcodes, with orders of magnitude lower error rates than single nucleotide variants in ctDNA (circulating tumor DNA) detection, demonstrated on 19 patients at baseline. We then applied this to monitor and model clonal evolution over several years in ten HGSOC patients treated with systemic therapy from diagnosis through recurrence. We found drug resistance to be polyclonal in most cases, but frequently dominated by a single high-fitness and expanding clone, reducing clonal diversity in the relapsed disease state in most patients. Drug-resistant clones frequently displayed notable genomic features, including high-level amplifications of oncogenes such as CCNE1 , RAB25 , NOTCH3 , and ERBB2 . Using a population genetics Wright-Fisher model, we found evolutionary trajectories of these features were consistent with drug-induced positive selection. In select cases, these alterations impacted selection of secondary lines of therapy with positive patient outcomes. For cases with matched single-cell RNA sequencing data, pre-existing and genomically encoded phenotypic states such as upregulation of EMT and VEGF were linked to drug resistance. Together, our findings indicate that drug resistant states in HGSOC pre-exist at diagnosis and lead to dramatic clonal expansions that alter clonal composition at the time of relapse. We suggest that combining tumor single cell sequencing with cfDNA enables clonal tracking in patients and harbors potential for evolution-informed adaptive treatment decisions.
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