Title: Using machine learning to predict subclone evolution and response during chemotherapy
Funder: Health and Care Research Wales (Welsh Government)

Abstract: Genomics and artificial intelligence (AI) are two technologies with the potential to transform healthcare. They are driving a move to precision medicine, allowing personalized treatment with a focus on identifying what works for an individual and avoiding what does not. Cancer is a leading cause of death worldwide.. Every year in Wales, around 2,800 people are diagnosed with breast cancer and, in the UK, approximately 12,000 people die from the disease. Chemotherapy is a mainstay of the treatment of metastatic (secondary) cancer. However, not all patients respond and, in those who do, the cancer eventually becomes resistant to treatment. Currently, we are not able to predict who will respond to chemotherapy or how long the response will last. Cancer is a disease of the genome. Tumours consist of distinct populations of cancer cells, subclones, which can be defined by their complement of genetic mutations. The subclone populations and the underlying mutational landscape of a cancer evolve over time. When a treatment such as chemotherapy is given, sensitive subclones may be eliminated but resistant subclones will continue to grow. To capture changes in the genetic heterogeneity of a cancer, it would be desirable to obtain representative samples from the tumour and all metastatic sites at multiple time points. This could be done by the use of cell-free circulating tumour DNA (ctDNA). This consists of DNA fragments released from tumours into the circulation. The ctDNA is extracted from blood samples and sequenced. Our aim is to apply machine learning techniques to an integrated ctDNA and clinical data set to build data-driven models that predict subclone evolution and of response to chemotherapy in metastatic breast cancer. The study analysis population will be 60 patients with metastatic breast cancer, being treated with the chemotherapy drug capecitabine at oncology centers in North, South West, and South East Wales. Longitudinal clinical data will be collected. Samples of ctDNA will be obtained before starting chemotherapy and at progression. Sequencing of ctDNA profiling will be done using the Illumina TruSight ctDNA panel which targets 523 cancer associated genes. Dimensionality reduction and feature selection techniques will be used to reduce the dimension of the data space and identify influential variables that are predictive for subclone evolution and response to chemotherapy. Once the influential variables are identified, we will build parsimonious predictive models with our ctDNA data for subclone evolution. We will integrate ctDNA and clinical data to predict response in terms of progression free survival. Bootstrapping and resampling techniques will be used to improve cross validation schemes with different measurements - positive predictive values (precision), sensitivity, Fß score, balanced accuracy, and Matthews correlation coefficient used to assess model performance. It is likely, in the near future, that all cancer patients in Wales will receive comprehensive genomic profiling of their cancer as part of routine NHS care. By applying artificial intelligence approaches to this large-scale genomic and clinical data, we could further refine our models and deploy them into clinical practice. Our predictive models could improve outcomes, avoid unnecessary toxicities and make effective use of high-cost drugs, leading to better quality and value cancer care within Wales.

Lay abstract: This research aims to apply artificial intelligence (AI) approaches to genomic and clinical data to develop models that predict response to chemotherapy in breast cancer. AI and genomics are two technologies with the potential to transform cancer care. They are driving precision oncology, allowing personalized treatment focusing on identifying what works for an individual and avoiding what does not. Chemotherapy is a mainstay of the treatment of metastatic breast cancer,that is breast cancer that has spread within the body. The response to chemotherapy is very variable. For some people treatment works very well, at least initially. For others, treatment has little or no effect. Being able to predict who will respond would allow treatment to be targeted to those most likely to benefit. Those unlikely to respond could be offered alternative treatments and spared unnecessary toxicities. A complex interaction between drugs, cancer cells and the patient determines the response to chemotherapy. Much of a cancer’s behaviour is driven by mutations in genes controlling cellular functions, such as growth and division. The patterns of mutation can vary from one area of cancer to another and can change over time. As a result, genetically distinct subpopulations of cancer cells (subclones) can arise, which vary in their sensitivity to chemotherapy. Our approach to capture the genetic profiles of subclones is to analyse circulating tumour DNA (ctDNA). This is DNA that has been shed from the cancer into the bloodstream and which can be extracted from a blood sample. Understanding the complex interactions that determine response to chemotherapy requires sophisticated data analysis techniques. Machine learning is a branch of AI that provides the ability to learn patterns from data. In this project, we will apply cutting edge machine learning techniques to an integrated ctDNA and clinical data set. Our models will predict which subclones will become predominant. This would allow treatment to be modified to target these subclones. Our models will also predict how well a patient will respond, allowing therapy to be individualized. Insights from our public partners have guided both the strategic direction of our research plans, and specific issues relating to this project. A public representative is a co-applicant, enabling ongoing public partner interaction. We will disseminate results by submitting papers to peer reviewed journals and presenting at conferences. We will seek to engage with service users, social care planners, practitioners and policy makers. Our study can be seen as an exemplar for integration of clinical and genomics data. It is likely, in the near future, that all cancer patients in Wales will receive comprehensive genomic profiling of their cancer as part of routine NHS care. By applying artificial intelligence approaches to this large-scale genomic and clinical data, we could further refine our models and deploy them into clinical practice. These approaches could extended to other treatments and cancer types. This could improve outcomes, avoid unnecessary toxicities and make effective use of high-cost drugs, leading to better quality and value cancer care within Wales.

Research activity codes: 4.1 DISCOVERY AND PRECLINICAL TESTING OF MARKERS AND TECHNOLOGIES
Health categories: CANCER AND NEOPLASMS