Student Projects

Neoantigen-Associated Mutations inMicrosatellite Unstable Colorectal Cancer

A Data Analysis Project

by Charlotte Boys

Cancer is a group of diseases characterized by several hallmarks including sustained proliferative signaling and the acquired ability to resist cell death mechanisms [1] . The progression from normal cell to malignant tumor is understood as a multi-step process, where a cell must accumulate a set of so-called driver mutations[2] before it grows into a malignant lesion. A further emerging hallmark of cancer is the avoidance of immune destruction [3] . To further our understanding of how to prevent and cure different types of cancer, it is crucial to understand this process of carcinogenesis, as well as the interaction between the tumor and its microenvironment.

The fields of mathematical and computational oncology are well-placed to help develop this understanding, by providing new ways to analyze ever-increasing amounts of genomic and transcriptomic data. Conversely, such data is also used to inform and validate mathematical models of cancer.

This project focused on the visualization and analysis of mutation data from microsatellite unstable (MSI) colorectal cancers. Specifically, we look at mutation and expression data for genes which are associated with the generation of proteins which are novel to the host’s immune system, called neoantigens, as well as the B2M gene, which plays a key role in antigen presentation. In the special case of MSI tumors, these genes are of particular importance for immune surveillance, a mechanism through which the body is able to detect and kill tumor cells. Of particular note is the potential for novel immune therapies which exploit the relationship between tumor-specific neoantigens and immune surveillance, for instance in the form of a vaccine. In the context of Lynch Syndrome, a hereditary disposition to developing MSI colorectal cancer (MSI CRC), such a vaccine would be of particular importance for catching pathways of carcinogenesis which can’t be easily detected through regular colonoscopy.

Of the many neoantigens generated by MSI tumors, only a handful can be chosen as vaccine targets, and the ongoing investigation into the most promising neoantigen candidates for a vaccine[4] forms the wider context of this project. The key idea is that the difference between mutational landscapes heavily influenced by immune surveillance and those less heavily influenced by immune surveillance might in turn illuminate mutations which are associated with the production of more immuno- genic neoantigens. As such, our main aim will be to explore differences in neoantigen-associated mutational patterns between tumors where immune surveillance has been compromised through B2M mutation (B2M-mutated tumors) and tumors where immune surveillance hasn’t been compromised in this particular way (B2M-wildtype tumors). To conduct the data exploration and visualization we make use of ideas from the lecture course Mathematical Structures of Complex Systems including graphs, hypergraphs, the notions of distance that these representations

[1]: Hanahan et al. (2000), ‘The hallmarks of cancer’

[2]: Vogelstein et al. (1993), ‘The multistep nature of cancer’

[3]: Hanahan et al. (2011), ‘Hallmarks of cancer: the next generation’