By: Mahzabin Ahmed
In a rapidly evolving world, artificial intelligence (AI) has been at the forefront of several discussions in the scientific community. Recently, cancer vaccines have gained significant attention as a promising avenue for personalized cancer treatments for various forms of cancer- primarily in the earlier stages. With the constant search to adapt new ways in advancing cancer therapies, the integration of artificial intelligence coupled with CRISPR/CAS9 systems offers a unique opportunity to enhance both the specificity and efficiency of cancer vaccines.
The first step in creating these personalized cancer vaccines is having an integrated computational approach. This involves the utilization of multiple data sources, including genomic, transcriptomic, and proteomic data, to identify neoantigens specific to an individual's tumor. AI techniques, such as machine learning algorithms, can analyze large datasets and identify unique features that distinguish cancer cells from healthy cells. In the case of examining neoantigens, these proteins (MHCs) can be marked as “foreign” by modified T cells allowing the immune response to hinder tumor growth. There have been studies as well with Chimeric Antigen Receptors (CARS) which has been shown to be faster in killing cancerous cells than any immune cells thus far.
Integration of AI algorithms with CRISPR/Cas9-based gene editing tools enables the precise manipulation of the tumor genome by overcoming the challenges of non-specific genome targeting and thus increasing the chances of accurate predictions. Integrated models and architectures improve prediction accuracy by combining machine learning models. MARIA, a specific model, is effective in detecting immunogenic epitopes in various cancers and autoimmune diseases. To achieve accurate and comprehensive predictions, it is important to identify and combine machine learning models as integrated algorithms enhance the accuracy and effectiveness of predictions. As is found in science, the more trials and data, the better!
In a study to investigate the application of AI and breast cancer treatments. The TUGROVIS project allowed them to find applications including modeling, tumorigenicity, distinguishing tumor cells, predicting drug synergism, mammography, changes in breast density and even detecting chances of metastasis. To go further in detail, for breast cancer modeling AI algorithms like DeepCode can classify tumor subtypes with high accuracy, monitor molecular events during tumorigenesis, and identify interactions between genes while for tumorigenicity AI enables specific cell and tissue targeting, allowing for investigations into the molecular basis of breast cancer tumorigenesis and the manipulation of tumor genomes using gene-editing technologies.
While the integration of AI and CRISPR/Cas9 in cancer vaccine design shows tremendous potential, it also raises ethical concerns and faces certain challenges. Privacy and security of patient data, potential biases in AI algorithms, and the risk of off-target effects in genome editing are some of the ethical considerations all pose as risks when exploring these innovative applications. Not to mention, there is the factor of high cost and accessibility to implementing AI technology and even CRISPR itself. As AI research is still at the surface, with continued research and collaboration among the scientific community, the integration of AI and CRISPR/Cas9 can help in advancing personalized cancer vaccine development and improving patient outcomes.
Requirements of integrated computational approach for developing personalized cancer vaccines
Current methods of epitope identification for cancer vaccine design
Integration of Artificial Intelligence and CRISPR/Cas9 System for Vaccine Design.