A large-scale retrospective study in metastatic breast cancer patients using circulating tumor DNA and machine learning to predict treatment outcome and progression-free survival

Beddowes, E. J.; Ortega-Duran, M.; Karapanagiotis, S.; Martin, A.; Gao, M.; Masina, R.; Woitek, R.; Tanner, J.; Tippin, F.; Kane, J.; Lay, J.; Brouwer, A.; Sammut, S.-J.; Chin, S.-F.; Gale, D.; Tsui, D.; Dawson, S. J.; Rosenfeld, N.; Callari, M.; Rueda, O. M.; Caldas, C.

doi:10.1101/2023.03.03.530936

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bioRxiv · 10.1101/2023.03.03.530936

A large-scale retrospective study in metastatic breast cancer patients using circulating tumor DNA and machine learning to predict treatment outcome and progression-free survival

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Abstract

PurposeMonitoring levels of circulating tumor-derived DNA (ctDNA) represents a non-invasive snapshot of tumor burden and potentially clonal evolution. Here we describe how a novel statistical model that uses serial ctDNA measurements from shallow whole genome sequencing (sWGS) in metastatic breast cancer patients produces a rapid and inexpensive assessment that is predictive of treatment response and progression-free survival. Patients and MethodsA cohort of 188 metastatic breast cancer patients had DNA extracted from serial plasma samples (total 1098, median=4, mean=5.87). Plasma DNA was assessed using sWGS and the tumor fraction in total cell free DNA estimated using ichorCNA. This approach was compared with ctDNA targeted sequencing and serial CA 15-3 measurements. The longitudinal ichorCNA values were used to develop a Bayesian learning model to predict subsequent treatment response. ResultsWe identified a transition point of 7% estimated tumor fraction to stratify patients into different categories of progression risk using ichorCNA estimates and a time-dependent Cox model, validated across different breast cancer subtypes and treatments, outperforming the alternative methods. We then developed a Bayesian learning model to predict subsequent treatment response with a sensitivity of 0.75 and a specificity of 0.66. ConclusionIn patients with metastatic breast cancer, sWGS of ctDNA and ichorCNA provide prognostic and predictive real-time valuable information on treatment response across subtypes and therapies. A prospective large-scale clinical trial to evaluate clinical benefit of early treatment changes based on ctDNA levels is now warranted.

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Beddowes, E. J., Ortega-Duran, M., Karapanagiotis, S., Martin, A., Gao, M., Masina, R., Woitek, R., Tanner, J., Tippin, F., Kane, J., Lay, J., Brouwer, A., Sammut, S.-J., Chin, S.-F., Gale, D., Tsui, D., Dawson, S. J., Rosenfeld, N., Callari, M., Rueda, O. M., Caldas, C.. 2023-03-03. A large-scale retrospective study in metastatic breast cancer patients using circulating tumor DNA and machine learning to predict treatment outcome and progression-free survival. https://doi.org/10.1101/2023.03.03.530936

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