Team:British Columbia/Description
PROJECT
Description
THE INSPIRATION
Did you know tumours re-engineer their habitat and create their own ecological community within the body?
As science advances and unveils more of tumour biology, we have realized that tumours are defined by not only cancer cells that proliferate indefinitely, but also adipocytes, blood vessels, immune cells, and structural components like collagen fibers, which together make up the tumour microenvironment (TME) that uniquely defines each tumour [1].
The TME is a significant contributor to tumour heterogeneity between and within cancer types. TME-driven heterogeneity can have important therapeutic implications, causing standard of care to fail in select patients. Personalized medicine based on TME characteristics has the potential to be a gamechanger in this space.
THE PROBLEM
Immunotherapy functions by activating the body’s immune system to fight the disease, and has become a promising therapy to combat cancer [2, 3]. Although immunotherapy can be effective for some patients, 60 to 80% of patients observe no changes after the treatment [4]. This may be partially due to tumour microenvironment differences, which influence how the immune cells and cancer cells react to immunotherapy [4]. For example, tumours with higher levels of lactate tend to have reduced immune activities as it lowers the pH of the tumour environment and can suppress immune cell functioning [5]. Currently, there is no consensus on a single biomarker that can be used to predict treatment outcome [7, 8]. There have been attempts at making new types of immunotherapy with synthetic biology, by reprogramming non-pathogenic bacteria to trigger or enhance the immune system [6]. When applied to this context, synthetic biology has the potential to produce tumour-specific targeting technologies that can either sense or treat the tumour.
Patients waste valuable time and resources if they undergo immunotherapy but do not respond.
We need better ways to identify immune evasion strategies that are going on in tumours so that we can choose therapies accordingly.
So the question becomes, how can we find predictive biomarkers and use them to guide personalized treatment decisions?
One factor related to immunotherapy efficacy is the immune status of the tumour [1, 7, 8]. Immune ‘hot’ tumours are characterized by the buildup of proinflammatory cytokines and T cell infiltration, which are able to trigger a strong immune response against the tumour and promote better outcomes for therapeutic interventions [1]. In contrast, immune ‘cold’ tumours lack immune activity, and can actively suppress tumour-eliminating immune response.
Currently the only way to distinguish between hot and cold tumours is via a tissue biopsy, where a biopsy needle is inserted into the tumour to extract a small piece of tumour to analyse [1]. While tissue biopsies are pervasively used in the clinic, they have significant limitations. Tissue biopsies are prone to sampling error, expensive, painful to the patient, and dangerous to acquire in some contexts. These concerns are exacerbated when considering that biopsies may need to be taken repeatedly during a patient’s clinical journey since tumours evolve overtime.
OUR SOLUTION
We are excited to present DetecTME, a synthetic biology-based multifunctional platform to profile tumour microenvironments and support decisions in immunotherapy. We aim to focus on the characterization and monitoring of tumour immune states over time. Our plan is to genetically engineer a strain of non-pathogenic Salmonella with innate tumour-targeting abilities to detect immune biomarkers in-vivo and release a reporter that can be detected in urine.
[1] Duan, Q., Zhang, H., Zheng, J., & Zhang, L. (2020). Turning cold into hot: firing up the tumor microenvironment. Trends in Cancer, 6(7), 605-618.
[2] Ribas, A., & Wolchok, J. D. (2018). Cancer immunotherapy using checkpoint blockade. Science, 359(6382), 1350-1355
[3] Emens, L. A., Ascierto, P. A., Darcy, P. K., Demaria, S., Eggermont, A. M., Redmond, W. L., ... & Marincola, F. M. (2017). Cancer immunotherapy: opportunities and challenges in the rapidly evolving clinical landscape. European Journal of Cancer, 81, 116-129
[4] Sharma, P. et al. (2017). Primary, adaptive, and acquired resistance to cancer immunotherapy. Cell, 168(4), 707–723.
[5] Hinshaw, D. C., & Shevde, L. A. (2019). The tumor microenvironment innately modulates cancer progression. Cancer research, 79(18), 4557-4566
[6] Chien, T., Doshi, A., & Danino, T. (2017). Advances in bacterial cancer therapies using synthetic biology. Current opinion in systems biology, 5, 1-8
[7] McKean, W. B., Moser, J. C., Rimm, D., & Hu-Lieskovan, S. (2020). Biomarkers in precision cancer immunotherapy: promise and challenges. American Society of Clinical Oncology Educational Book, 40, e275-e291
[8] Lei, Y., Li, X., Huang, Q., Zheng, X., & Liu, M. (2021). Progress and Challenges of Predictive Biomarkers for Immune Checkpoint Blockade. Frontiers in Oncology, 11, 609