Team:EPFL/Description
What if cancer was only a minor inconvenience, which upon diagnosis could be cured rapidly, using just a vaccine-shot? This could become reality in the near future as more and more people are working on immunotherapy, a therapeutic approach which boosts the immune system’s response against cancerous cells. One promising immunotherapy is cancer vaccine: it works like any vaccine, except that instead of presenting the antigen of an infectious agent, it presents an antigen specific to the patient’s cancer cells, personalized. However, it is not always as simple as it seems. The field is promising, but how can we offer such a treatment, that would work better than any current one, could be rapidly produced, at a low cost and be widely accessible in any medical facility? This is what EPFL iGEM team is working on: CAPOEIRA! Our goal this year is to provide a comprehensive solution to melanoma patients and their caretakers to utilize the full potential of personalized medicine: establishing both fully personalized vaccine and a monitoring system to evaluate the patient’s response to treatment in a fast, reliable, and convenient fashion using novel and innovative approaches in synthetic biology. If we are to produce a vaccine, we need to know what we’re up against! We have to find something that makes cancer cells different from healthy cells in the body so that we can point this difference to the immune system. To do this our team has developed GINGA, inspired the fundamental movement of the capoeira martial art. As in the martial art, Ginga, serves as the starting point for our project CAPOEIRA, and connects all the parts of the project back to the patient, an open source software. It’s straightforward: we take the whole-exome sequence of healthy cells and compare it directly with the exome sequence of the cancer tissue from the same patient. If the software finds a difference, based on the mutated version of the DNA sequence it finds what proteins this mutation codes for (as a ‘signature’ of the cancer). Now that we have a set of these signatures, we can select some of them according to how well they could be presented to the immune system and “trigger” it to fight back. When we find a good signature candidate, we can use it as neo-antigen (antigens encoded by tumour specific mutations) for our vaccine. We now have potential neo-antigen candidates, however there is much more to be done. If we inject this protein as is, it will mostly get “ignored” by the immune system and quickly be digested. We need to bind it to what is called an adjuvant, that will potentially activate the immune system and initiate an immune response that would target the cancer cells. This is where the key components of our project come into action. Encapsulin is a protein that comes from a bacteria called Thermotoga maritima, and it can do pretty cool things. Its main feature is that several (60 to be exact) encapsulin subunits can assemble into an Encapsulin nanocompartment, a sphere shaped, bacterial organelle. Although its main purpose is to transport proteins inside of the organelle, we can use it to attach neoantigens to its surface. Hopefully, our encapsulin nanocompartment can now act as an adjuvant, and lead to the intake of the neoantigen by dendritic cells, which could then start an immune response against the neoantigen we selected. Resulting in the cancer cells being killed by the immune system cells. The fight against cancer does not stop at a vaccine! CAPOEIRA aims to accompany the patient throughout his life post-treatment using an easy-to-use system that requires just a small amount of blood to detect if the cancer will relapse. We have designed a system using Cas12a protein which give us the ability to accurately detect biomarkers in the blood that could help clinicians do amazing things; circulating tumour DNA (ctDNA: fragmented cancer DNA) and microRNA (miRNA: short non-coding RNA). It would monitor the vaccine efficiency so that a change of vaccine treatment could be administered immediately! Our system also enables the prediction of cancer relapse in a lead time compared to current methods, allowing doctors to act faster and patients to stay healthy! Description
