High-throughput spheroid-based assay for functional breast cancer precision medicine facilitated by deep learning

Communications Medicine, vol. 6, pp. 94

Abstract

Despite rapid advances in treatment, breast cancer remains the leading cause of cancer mortality in women, with triple negative breast cancers having a particularly poor prognosis. Some tumors have (epi)genetic alterations causing homologous recombination deficiency, providing opportunities for targeted therapeutics including poly (ADP-ribose) polymerase inhibitors. However, the effects of targeted treatments are variable; therefore, functional assays are needed to predict the best personalized treatment options. We developed a high-throughput spheroid-based assay using patient-derived breast cancer xenograft models sensitive and resistant to cisplatin. Methods were developed for automatic spheroid segmentation using deep learning to measure response of spheroids to treatment with cisplatin, olaparib and radiotherapy. We developed a method to distinguish between sensitive and resistant tumors based on predicting the percentage of responding and non-responding spheroids. Here we show that differences in treatment response between cisplatin-sensitive and resistant tumors faithfully correspond with the expected in vivo responses. The assay is able to discriminate between olaparib-sensitive and resistant tumors based on predicting the percentage of responding and non-responding spheroids. We demonstrate that this assay, guided by automatic spheroid segmentation using deep learning, may report on the tumor’s sensitivity to therapies with the potential to be applied to functional precision oncology for breast cancer. Many women die of breast cancer, especially if tumors are more difficult to treat or spread more easily to other organs. Some of these tumors can be successfully treated with drugs because of faults in their DNA repair. To choose the best treatment for the patient, it would be helpful to know which tumors are vulnerable to treatment. We therefore developed a test. We grew small spheres from tumor material (spheroids) and treated them with different drugs used in cancer therapies. To measure the response, we took pictures of the spheroids and we taught a computer program to recognize the spheroids and measure their size. We tested our assay with patient tumors grown on mice (sensitive and resistant to a drug). We demonstrate that this assay may report on the tumor’s sensitivity to therapies and may be used in the future to help choose the best treatment for breast cancer. Haspels et al. developed a high-throughput assay facilitated by automatic spheroid segmentation using deep learning. Measured differences in treatment response between cisplatin-sensitive and resistant tumors faithfully correspond with expected in vivo responses and the assay is able to discriminate between olaparib-sensitive and resistant tumors.

Authors 9

1. Ben J. Haspels

   Erasmus MC · Erasmus MC Cancer Institute

   Affiliation as printed

   Department of Molecular Genetics, Erasmus MC Cancer Institute, Erasmus University Medical Center, Rotterdam, the Netherlands

   Oncode Institute, Erasmus University Medical Center, Rotterdam, the Netherlands

2. Maarten W. Paul Aachen Department of Cell and Chemical Biology

   Leiden University Medical Center · Erasmus MC · Erasmus MC Cancer Institute

   Affiliation as printed

   Department of Cell and Chemical Biology, Leiden University Medical Center, Leiden, the Netherlands

   Department of Molecular Genetics, Erasmus MC Cancer Institute, Erasmus University Medical Center, Rotterdam, the Netherlands

   Oncode Institute, Erasmus University Medical Center, Rotterdam, the Netherlands

3. Jayant Jagessar Tewari

   Erasmus MC · Erasmus MC Cancer Institute

   Affiliation as printed

   Department of Molecular Genetics, Erasmus MC Cancer Institute, Erasmus University Medical Center, Rotterdam, the Netherlands

4. Mieke Bavelaar

   Erasmus MC · Erasmus MC Cancer Institute

   Affiliation as printed

   Department of Molecular Genetics, Erasmus MC Cancer Institute, Erasmus University Medical Center, Rotterdam, the Netherlands

5. Zofia M. Komar

   Erasmus MC · Erasmus MC Cancer Institute

   Affiliation as printed

   Department of Molecular Genetics, Erasmus MC Cancer Institute, Erasmus University Medical Center, Rotterdam, the Netherlands

6. Jos Jonkers

   Erasmus MC · The Netherlands Cancer Institute

   Affiliation as printed

   Division of Molecular Pathology, The Netherlands Cancer Institute, Amsterdam, the Netherlands

   Oncode Institute, Erasmus University Medical Center, Rotterdam, the Netherlands

7. Roland Kanaar

   Erasmus MC · Erasmus MC Cancer Institute

   Affiliation as printed

   Department of Molecular Genetics, Erasmus MC Cancer Institute, Erasmus University Medical Center, Rotterdam, the Netherlands

   Oncode Institute, Erasmus University Medical Center, Rotterdam, the Netherlands

8. Dik C. van Gent

   Erasmus MC · Erasmus MC Cancer Institute

   Affiliation as printed

   Department of Molecular Genetics, Erasmus MC Cancer Institute, Erasmus University Medical Center, Rotterdam, the Netherlands

   Oncode Institute, Erasmus University Medical Center, Rotterdam, the Netherlands

9. Maayke M.P. Kuijten corresponding

   Erasmus MC · Erasmus MC Cancer Institute

   Affiliation as printed

   Department of Molecular Genetics, Erasmus MC Cancer Institute, Erasmus University Medical Center, Rotterdam, the Netherlands. m.kuijten@erasmusmc.nl

   Oncode Institute, Erasmus University Medical Center, Rotterdam, the Netherlands. m.kuijten@erasmusmc.nl