  Cancer is a complex disease characterized by uncontrolled cell growth and
proliferation. T cell receptors (TCRs) are essential proteins for the adaptive
immune system, and their specific recognition of antigens plays a crucial role
in the immune response against diseases, including cancer. The diversity and
specificity of TCRs make them ideal for targeting cancer cells, and recent
advancements in sequencing technologies have enabled the comprehensive
profiling of TCR repertoires. This has led to the discovery of TCRs with potent
anti-cancer activity and the development of TCR-based immunotherapies. In this
study, we investigate the use of sparse coding for the multi-class
classification of TCR protein sequences with cancer categories as target
labels. Sparse coding is a popular technique in machine learning that enables
the representation of data with a set of informative features and can capture
complex relationships between amino acids and identify subtle patterns in the
sequence that might be missed by low-dimensional methods. We first compute the
k-mers from the TCR sequences and then apply sparse coding to capture the
essential features of the data. To improve the predictive performance of the
final embeddings, we integrate domain knowledge regarding different types of
cancer properties. We then train different machine learning (linear and
non-linear) classifiers on the embeddings of TCR sequences for the purpose of
supervised analysis. Our proposed embedding method on a benchmark dataset of
TCR sequences significantly outperforms the baselines in terms of predictive
performance, achieving an accuracy of 99.8\%. Our study highlights the
potential of sparse coding for the analysis of TCR protein sequences in cancer
research and other related fields.
