In-silico discovery of cancer-specific peptide-HLA complexes for targeted therapy

Abstract

BackgroundMajor Histocompatibility Complex (MHC) or Human Leukocyte Antigen (HLA) Class I molecules bind to peptide fragments of proteins degraded inside the cell and display them on the cell surface. We are interested in peptide-HLA complexes involving peptides that are derived from proteins specifically expressed in cancer cells. Such complexes have been shown to provide an effective means of precisely targeting cancer cells by engineered T-cells and antibodies, which would be an improvement over current chemotherapeutic agents that indiscriminately kill proliferating cells. An important concern with the targeting of peptide-HLA complexes is off-target toxicity that could occur due to the presence of complexes similar to the target complex in cells from essential, normal tissues.ResultsWe developed a novel computational strategy for identifying potential peptide-HLA cancer targets and evaluating the likelihood of off-target toxicity associated with these targets. Our strategy combines sequence-based and structure-based approaches in a unique way to predict potential off-targets. The focus of our work is on the complexes involving the most frequent HLA class I allele HLA-A*02:01. Using our strategy, we predicted the off-target toxicity observed in past clinical trials. We employed it to perform a first-ever comprehensive exploration of the human peptidome to identify cancer-specific targets utilizing gene expression data from TCGA (The Cancer Genome Atlas) and GTEx (Gene Tissue Expression), and structural data from PDB (Protein Data Bank). We have thus identified a list of 627 peptide-HLA complexes across various TCGA cancer types.ConclusionPeptide-HLA complexes identified using our novel strategy could enable discovery of cancer-specific targets for engineered T-cells or antibody based therapy with minimal off-target toxicity.Electronic supplementary materialThe online version of this article (doi:10.1186/s12859-016-1150-2) contains supplementary material, which is available to authorized users.

General Prediction of Peptide-MHC Binding Modes Using Incremental Docking: A Proof of Concept

“…However, T-cell cross-reactivity can be triggered even by peptides with no sequence identity and low biochemical similarity 78 , and might be driven by specific structural similarities in hot-spots over the TCR-interacting surface 79 . In this context, structure-based methods for cross-reactivity prediction have been proposed, either clustering pMHCs of interest based on structural similarity 78 , 80 , or integrating structural information and protein expression levels into sequence-based proteomic searches 81 , 82 . This field will spawn significant developments in the coming years, particularly considering the importance of cross-reactivity prediction for T-cell-based immunotherapy 11 .…”

Interpreting T-Cell Cross-reactivity through Structure: Implications for TCR-Based Cancer Immunotherapy

“…Some of these methods involve assessing peptide sequence similarity, while also accounting for protein tissue expression and MHC binding ( 44 , 45 ). Others are based on pMHC structural similarity ( 46 – 48 ) or some combination of previously mentioned features ( 49 , 50 ). Despite the incredible challenge at hand and the current limitations of these computational methods, encouraging results are being reported.…”

Specificity of bispecific T cell receptors and antibodies targeting peptide-HLA

“…For instance, even on individual cell types, data from our in-house mass spectrometry database and published direct evidence demonstrate that the number of unique peptides can be in the range of tens of thousands (39)(40)(41)(42). Considering the full human protein-coding genome, the number of peptides presented has been estimated to be over 11 million (43).…”