Integrating synthetic biology to pave the way for innovative solutions in cancer treatment, ensuring it's responsible and beneficial for humanity.
Humanity has achieved thousands of setbacks over a few decades. We have eradicated a certain number of infectious diseases once believed to be caused by God. Nowadays, The healthcare system has become so good that life expectancy has increased significantly. However, humanity is now facing a more challenging problem, one which requires us to understand a variety of fields in order to win it. Cancer has become among the top causes of death in the modernized world. Researchers from different parts of the world have tried their best to integrate the knowledge in the field of synthetic biology into a variety of fields with the hope of helping humankind overcome this struggle, just like what we have achieved in the past.
The DC vaccine is one of the attempts of united scientists trying to ease the suffering of cancer patients. The Queen Sirikit Centre for Breast Cancer has a dedicated team working on the vaccines for treating breast cancer patients. The center inspired our team by the fact that an advanced understanding of the matter can lead to a successful cure of the developing tumor and have thankful patient smiles in return. Our team wishes to be part of those smiles and hopes to expand them to a wider range of the population. The team is composed of diverse members, from high school students to medical and engineering undergraduates, whose passions are in a variety of fields. We have brainstormed ideas for integrating the software engineering process and the knowledge in synthetic biology that could aid the DC vaccine process. We proposed our ideas to the advisors from the center and the medical school to have their opinions guide our project further.
Simply put, the work of DC vaccines is done by collecting the patient's blood to check for a specific immunological protein called HLA. There are a variety of HLA protein types that can be defined into different classes. Each individual has a specific set of HLA based on the encoded gene. These specific immunological proteins play an essential role in the human immune system generally by fusing with certain lysed peptides to form MHC complexes and present themselves on the surface of the cells, which will then be recognized and dealt with by our immune system. HLA also helps present the peptides transcribed from the mutated genes so that our (t cell??) dendritic cells (DC cells) can recognize the tumour cells and deal with them.
What will happen if the DC cells fail their job? That’s what happens with the patient with breast cancer; the DC cells in the cancer patient fail to deal with the tumor cells at a certain point (or points) in their normal working process, causing the mutated cells to progress. Researchers came up with the idea of “teaching” those DC cells to proceed to do their job properly by combining DC cells with that specific MHC complex specific to each patient so that DC cells can easily recognize the complexes. The process of the DC vaccine may seem very simple; however, the cost of the DC vaccine is very high, and the time needed for each patient is also long.
Why so? Why does this simple process demand that much time and money? The actual process is much more complicated. Forming an MHC complex is not an easy job given the number of transcribed peptides. Not every peptide is a neoantigen, the mutated peptide that will form a MHC complex. Which peptide is neoantigen? That is the question that leads the researchers into the process of “neoantigen prediction,” in which they have to carefully predict the correct peptide because if the wrong peptide is chosen, it will be a huge waste of money given the cost of the procedure. This process can be done based on the fact that each HLA has its own tendency to form an MHC complex with certain peptide chains that have certain observed patterns. The patterns are recorded and published. They have to review the published data, go through each peptide, and rank its tendency to be neoantigen. This is the reason why it takes a lot of time and cost. Apart from predicting the neoantigen, there are also a lot more predictions after this process, for example, “t cell prediction.”
With the knowledge our team has on the matter, our team sees the potential of adding an algorithm with a database into the processes with the aim of making the vaccine more efficient. The initial phase of our team will, however, focus only on the “neoantigen prediction” phase. We have summoned the HLA database and ran it on our program so that it could predict the neoantigen by just providing the normal and tumor cells' DNAs. We decided that, for the greatest engineering result, we would have a professional software engineer to help guide us.
Our software engineering process consists of data preparation, neoantigen prediction service development, a demo of the service, validation of the software’s performance, and troubleshooting. Our team has encountered many problems with the engineering process, which our team perceives to be a worthy lesson, just like ones from attending university classes. After we sought professional help for the feedback, we found solutions to the problems. We have learned a lot and thought that sharing our problems with our solutions would be a great lesson for other software engineers as well.
The final result of our project may have been different from the point where we could use it with the patient due to>. However, we have downloaded the example file from the Nextneopi website so that we could see how the overall program runs. We are currently working on debunking the software so that it can be used with real patients to help researchers predict the correct neuroantigens. As was our initial goal, we were not just doing the project for the iGems competition. We aimed to create a full system that could help predict and facilitate the entire process. After we finish with the phase of real patient neoantigen prediction with our medical team evaluating the prediction, we will do the same with the prediction of T-cell matching.