High-throughput, sliding-window algorithm for assessing chemical complementarity between immune receptor CDR3 domains and cancer mutant peptides: TRG-PIK3CA interactions and breast cancer

Abstract

Physicochemical assessments of a vast accumulation of adaptive immune receptor (IR) recombinations have led to correlations of those properties with sub-divisions of various diseases. In the cancer setting, such assessments, particularly for the complementarity determining region-3 (CDR3) immune receptor domain, have been used to establish chemical complementarity matches to mutant amino acids (AA). These matches, in some cases, over very large numbers of tumor samples, have correlated with survival and gene expression distinctions. For example, in melanoma, electrostatic charge based, T-cell receptor CDR3-DNAH9 mutant AA complementarity represents better survival over multiple datasets that represent tumor tissue, T-cell receptor CDR3s. In this report, the complementarity approach has been expanded to include a more comprehensive representation of the interaction of T-cell receptor CDR3s and mutant AAs by incorporating the impact of the wild-type AAs surrounding the mutant AA. This "sliding window" approach was benchmarked against two large datasets of empirically determined CDR3-epitope pairs; showed more significant patient subdivisions; revealed a novel, TRG CDR3-mutant PIK3CA linkage in breast cancer; and was particularly suited to use with big data collections using only modest and widely-available processors. Thus, the algorithm should support more rapid and convenient indications (or prescreens) of CDR3-mutant peptide interactions for more focused studies and more efficient development of patient immunology-related prognostic tools and therapies.

Keywords: Breast cancer; Cancer mutant peptides; Chemical complementarity bioinformatics; Gamma-delta T-cells; Immune receptor CDR3s.