Designing Personalised Medicine Using Single-Cell Sequencing Analysis
Project Description: The use of chemotherapy or immunotherapy prior to surgery, known as neoadjuvant therapy (NAT), is a common treatment option in breast cancer. However, not all women will respond to the NAT drugs, and they can have negative side effects. Dr Mun Hui (The Garvan Institute of Medical Research/ University of New South Wales) has developed a new single-cell analysis technique to better understand which women will respond to NAT.
Why this work is needed: Although the risks of NAT prior to surgery for breast cancer are relatively low, some women can develop organ damage or other complications. As such, there is an urgent need to select the right patients for NAT, and to avoid treating those who will not respond.
Expected outcomes: This study will provide insights into how tumours respond to NAT, enabling personalised medicine for women with breast cancer. In addition, the data may identify new therapeutic opportunities to overcome treatment resistance in breast cancer.
The use of neoadjuvant therapy (NAT) before surgery to render large tumours operable in breast cancer is increasingly common. However, only <30% of breast cancers treated with neoadjuvant chemotherapy show complete disappearance of cancer cells following treatment. Even with the addition of immunotherapy, the best response is only 60%. In addition, some women can experience adverse effects such as organ damage.
Dr Hui and her team aim to learn more about the variation in response to NAT, using a novel single cell sequencing analysis. In the study, tissue samples will be collected from women throughout the course of their treatment (at diagnosis, during NAT and at the time of surgery). The behaviour of single cells within the tissue, and how these cells interact with their environment, will be assessed. The method will be able to identify which cells are not responding to treatment, providing insight into how some tumours become resistant to NAT.
The data collected will provide information on single cell behaviour, including gene expression patterns. This will then be used to identify new possible drug treatments, and to enable personalisation of cancer treatment.