Abstract
Abstract Background Survival in high grade serous ovarian cancer (HGSOC) is impacted by the emergence of resistance to platinum chemotherapy. The traditional ethos of killing the greatest number of cancer cells with the maximum tolerated dose of chemotherapy may be flawed in evolutionary terms, facilitating the growth of resistant subclones and accelerating decreased treatment efficacy. Adaptive therapy is a new model of cancer treatment that exploits the competitive interactions between drug-sensitive and resistant subclones. The aim is to maintain a stable tumour burden by keeping a sufficient population of sensitive cells which suppress the proliferation of the ‘less fit’ resistant cells. A challenge in bringing adaptive therapy to the clinical setting is quantifying the relative proportions of sensitive and resistant disease to guide the personalised scheduling of treatment. In HGSOC, chemotherapy resistance is not associated with any common, measurable point mutations, but is correlated with a higher burden of copy number aberrations. We have utilised this to develop a method of quantifying the relative proportion of chemotherapy resistant disease. Methods An in silico dilution series was created using known copy number profiles of paired sensitive and resistant HGSOC OVCAR4 cells. Low pass whole genome sequencing (1×) was performed on cell mixtures of known ratios and bioinformatician-blinded ratios, combined with varied amounts of ‘normal’ DNA extracted from donor leucocytes. Results Our proof of principle experiment verified the method of quantifying copy number aberrations as a biomarker of the resistant population. The technique is now being applied to circulating tumour DNA isolated from serial plasma samples collected from 10 HGSOC patients, using the copy number profile from a diagnostic biopsy as a baseline for sensitive disease. Conclusions We have developed a method to monitor the relative drug-sensitive/resistant composition of tumours that can be applied to liquid biopsies. This will enable real-time serial tracking of tumour composition allowing for tumour heterogeneity, and guide personalised treatment scheduling in adaptive therapy. Legal entity responsible for the study The authors. Funding Cancer Research UK. Disclosure All authors have declared no conflicts of interest.
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