The work focuses on the prediction process of T-cell peptides called neoantigens to be used in the treatment of cancer patients. The primary process involves obtaining the DNA sequence of T-cells that matches the desired neoantigen characteristics. This neoantigen is then introduced into the body of the cancer patient. Once the neoantigen is present in the patient's body, T-cells will interact with it through their T-cell receptor (TCR) and bind to the HLA (Human Leukocyte Antigen) proteins on the surface of cells. This process activates T-cells to mount an immune response explicitly targeting the neoantigen. This immune response aims to destroy or eliminate cancer cells through the body's immune system.
Developing DC vaccines for cancer treatment involves multiple steps and takes a significant amount of time. It begins with obtaining cells from the patient and comparing them to cancer cells, followed by predicting peptides to be used as neoantigens and incorporating them into the DC vaccine. Currently, in Thailand, DC vaccines have been developed by the Queen Sirikit National Convention Center. However, treating each patient requires a long time, with months spent on each step. It also requires a substantial amount of human resources and financial resources. Therefore, we aim to create an application that streamlines and accelerates these processes, making them more user-friendly and widely accessible by consolidating the entire pipeline for neoantigen prediction into a user-friendly program. With a dedicated and experienced team, we're confident in making this vision a reality. We understand the challenges of DC vaccine development and are driven by our passion for innovation. By creating an application that revolutionizes the field, we hope to bring faster and more efficient cancer treatment to as many people as possible.
Our project is to create an application to best predict the Neoantigen to kill the tumor cell in cancer patients. The genes in tumor cells are not homogeneous as they constantly mutate at different positions at different rates, so we have to personalize the medicine to treat different types of mutations. However, finding a specific protein sequence that can decrease the chance of curing takes a significant amount of time. We are creating an application to help shorten the time to figure out the specific peptide sequence. The application's input is the RNA sequence data, which is used to run prediction. And the output will be the personalized protein sequence. After running the application, we can use the results to activate antigen-specific T cells, which will be created into a vaccine and will kill the cancer cells.
Our project is based on the method of neoantigen prediction, which will require a comparison between the genetics of the patient’s healthy cells and their tumor cells. This process aims to identify the correct specific peptide sequence for future DC cell Pulsing treatment. First, After retrieving our patient's cell samples, we will use the BWA-MEM algorithm to align the DNA sequence and find the misalignment, which indicates tumor-specific mutations. We also used RNA transcribed from our patient's tumor DNA to calculate the gene expression of the cancer cell using the STAR sequence aligner. We can narrow down the sequence and find the expressed tumor-specific mutation with the information above, which will result in tumor-specific peptides. Later, by doing HLA-typing, we will have to determine if the peptide we got is compatible with our HLA. Finally, we will go through the method of DC cell pulsing, which will not be a part of the project.