Deep Learning the Ecological Niches of Cancer Cells for Combating Treatment Resistance
Tumours consist of not only cancer cells, but also normal cells such as immune cells that can be critical in eliminating cancer cells. These different types of cells co-exist in different parts of the same tumour with profound clinical implications. Just like in ecology where spatial organisation of animals, their predators and habitats is central for understanding the ecosystem and make prediction, It is becoming increasingly evident that we need to use a similar spatial approach to evaluate tumour heterogeneity.
My team at the Institute of Cancer Research develops machine learning and deep learning approaches to identify different types of cells in digital pathological images of tumour sections based on their differences in appearance. Such automated image analysis allows us to map their spatial distribution within the tumour of a patient. The next step is to quantify spatial variability of these cells, usually in the order of millions, using spatial statistics.
Our recent study on breast cancer and lung cancer underscored the importance of examining spatial heterogeneity of the tumour. We studied how immune cells are spatially arranged within the tumours, and detected the so-called immune hotspots, which are tumour regions that contain spatial clustering of immune cells. This uses a spatial statistical method called Getis-Ord Hotspot analysis, which is commonly used for detecting crime hotspots in cities. High amount of immune hotspots, but not the amount of immune cells, correlates with high probability of cancer recurrence. This study provides a new way to predict patient prognosis, and open the door to new therapeutic opportunities using immunotherapy across cancer types.
Yinyin Yuan joined the ICR in 2012 as the leader of the Computational Pathology and Integrative Genomics team. Currently, her team is part of the Centre for Evolution and Cancer and the Division of Molecular Pathology. Yinyin was trained in computer science and bioinformatics. She obtained her academic degrees in computer science during her education at the University of Science and Technology of China (BSc 2003) and University of Warwick (MSc by research 2005, computer vision and steganography; PhD 2009, machine learning and bioinformatics).