A scoring system for the electrostatic complementarities of T‐cell receptors and cancer‐mutant amino acids: multi‐cancer analyses of associated survival rates

Abstract

Summary The anti‐tumor immune response is considered to be due to the T‐cell receptor (TCR) binding to tumor antigens, which can be either wild‐type, early stem cell proteins, presumably foreign to a developed immune system; or mutant peptides, foreign to the immune system because of a mutant amino acid (aa) or otherwise somatically altered aa sequence. Recently, very large numbers of TCR complementarity‐determining region‐3 (CDR3) aa sequences obtained from tumor specimens have become available. We developed a novel algorithm for assessing the complementarity of tumor mutant peptides and TCR CDR3s, based on the retrieval of TCR CDR3 aa sequences from both tumor specimen and patient blood exomes and by using an automated process of assessing CDR3 and mutant aa electrical charges. Results indicated many instances where high electrostatic complementarity was associated with a higher survival rate. In particular, our approach led to the identification of specific genes contributing significantly to the complementary, TCR CDR3‐mutant aa. These results suggest a novel approach to tumor immunoscoring and may lead to the identification of high‐priority neo‐antigen, peptide vaccines; or to the identification of ex vivo stimulants of tumor‐infiltrating lymphocytes.

Chemical complementarity between immune receptors and cancer mutants, independent of antigen presentation protein binding, is associated with increased survival rates

“…The survival distinction was consistent with the higher level of the expression of proteins related to protein translation in the lower surviving groups, as a higher level of protein translation would be expected in growing and thus more aggressive, more deadly tumors [ 23 , 24 ]. The results of the CS approach applied in this study are consistent with several previous studies representing other cancers [7] , [8] , [9] , [10] , but as discussed below, the results from this study were explored in new ways. However, as all of these CS algorithm studies are correlative studies, underlying mechanisms explaining the basis of the association between TRA CD3-mutant AA complementarity and survival need to be determined.…”

“…Another potential confounding molecular factor has been HLA binding, in that tumor samples where there is a complementary relationship between CDR3s and mutant AAs may simply represent a condition where the patient's HLA type facilitates binding of neoantigens common to the tumor. This issue has not been addressed in any of the past CS-based survival distinction discoveries [7] , [8] , [9] , [10] . In this case, we focused on HLA class II binding, due to the apparent high level of antigen presenting cells in the endometrioid uterine cancer microenvironment.…”

“…The method of complementarity scoring is based on an approach documented in references [7] , [8] , [9] , [10] . Missense mutants for LRP2 and TTN were assigned a value based on change in charge from wild-type to mutant AA sequence.…”

“…In addition, cancer mutant peptides have been shown to bind both the HLA antigen presenting molecules and patient TCRs [ 5 , 6 ]. Recently, substantial correlative data have indicated that the CDR3 is an important part of the TCR for binding to cancer mutant peptides [7] , [8] , [9] , [10] . For example, survival distinctions representing numerous cancers have been observed with higher chemical complementarity between CDR3s and cancer mutant peptides, with greater complementarity associated with greater survival rates [7] , [8] , [9] , [10] .…”

Immune receptor CDR3 chemical features that preserve sequence information are highly efficient in reflecting survival distinctions: A pan‑cancer analysis

“…This approach is based on the simple idea that the CDR3 is the most important region of the IR for antigen recognition (11), and that, particularly in a big data setting, a dominant physicochemical feature could represent a minimum for successful antigen binding and immune mediated reductions in tumor cell numbers (12). From the outset, algorithms were developed to exploit the physicochemical features, and their correlations with data represented by other platforms, such as survival and immune biomarker expression, to identify possible antigen partners (9,(12)(13)(14)(15)(16). For example, single value, physicochemical assessments of tumor resident…”