"It is an exciting time for cancer research. We have unprecedented insights into the genetic changes that underpin cancer and an increasing suite of partially effective clinical tools, including immunotherapies. However, we are lacking knowledge in some key areas. The interactions between cancer cells, the normal cells in the body, and even the microbiome (the bacteria that live within us) are key to many emerging therapies; yet they remain poorly understood.
"Personally, I am really excited about applying sophisticated imaging technologies to unpick these interactions and using the ever-increasing power of visual recognition technologies and machine learning to make new breakthroughs. I hope to learn how these interactions change over time and then be able to predict a cancer's 'next move'. The ultimate goal is to then to exploit this knowledge to transform our partially effective tools into transformative advances for cancer patients."
"Over the past five years, arguably the most profound improvement in cancer treatment has been the successful application of immunotherapy. There has been unparalleled success in treating advanced, hitherto terminal cancers. Interestingly, the clinical application of cancer immunology has raced ahead of the basic research programmes that can provide a much fuller understanding of how cells and molecules of the immune system sense and respond to the different stages of cancer initiation and progression, and how those responses vary in different tissues.
"In 2018, researchers will redress this balance. One way that this will be achieved is through increasing integration of the study of cancer immunology with that of other clinical areas, such as rheumatoid arthritis, wound healing, or Type I diabetes, where there are also major infiltrations of active immune cells into the tissues. In sum, 2018 may see the start of a very productive, 'second wave' of cancer immunology, where a deeper understanding will give us far greater refinement in how we harness the immune system to diagnose and treat a much broader range of patients."
"Tumour heterogeneity (diversity within the collection of cells that constitute a tumour) is currently recognised as one of main challenges in targeting cancers efficiently. This includes cellular, genetic, epigenetic and metabolic heterogeneity. I am extremely excited about the state-of-the-art technologies that will allow us to characterise individual tumour cells and understand their interactions, requirements and specific vulnerabilities within intact tumour environments. These platforms include imaging mass spectrometry, mass cytometry and in situ single-cell transcriptomics."
"It is exciting to see that the global cancer survival rate continues to improve with recent advances in cancer research. There are two key factors to improving the survival rate of cancer patients: early diagnosis and finding the right treatment for the right patients. Growing evidence suggests that targeted therapy is an improvement compared to conventional therapies, such as chemotherapy or radiotherapy. Targeted therapy relies on a thorough understanding of the underlying mechanisms of different cancers, which then allows us to target selected cancer types on the basis of specific genetic events.
"One exciting development in recent years has been the 3D organoid culture system. Organoids are essentially stem cell cultures of a tissue or organ that have the potential to expand and mature into different cell types. Researchers can now culture organoids derived from human tumour tissues, which can be used for disease modelling and drug screening. This promising technique opens up exciting avenues for therapeutic development, bringing us a step closer to personalised cancer therapy. Further development of tumour organoid biobanking at a high-throughput scale will likely be one of the emerging trends in cancer research."
"As a computational biologist, I am very excited about the possibility of applying artificial intelligence in order to analyse cancer data. In recent years, the scientific community has spent considerable energy accruing large molecular and clinical datasets on a vast range of cancer types. For the first time we are now in the position to interrogate these high-quality datasets with innovative and creative analytical tools. For example, my group has started applying machine learning to discover the alterations occurring in the genomes of cancer cells which drive the progression of the disease. I am excited about the potential of these analytical techniques to contribute to a better understanding of cancer biology and how the disease can be treated."
"Although it is increasingly appreciated that the specific way cancer cells utilise nutrients (cancer metabolism) is critical for their growth, the rational design of therapeutic strategies that target tumour metabolism remains a great challenge. This is, in part, due to the heterogeneity (diversity) and dynamic nature of metabolic processes within a tumour. I am very excited about using new technological developments in advanced microscopy, in combination with smart biosensors, to visualise metabolism at the single-cell level, and using magnetic resonance spectroscopy to study tumour metabolism in vivo. Such technologies will offer new insights into the heterogeneity and dynamics of essential tumour functions, the understanding of which I expect to be essential for choosing how and when to interfere with cancer metabolism in improved therapies."
