Girotti, M. R. | bioRxiv Science

bioRxiv2022

MHC-I binding affinity derived metrics fail to predict tumor specific neoantigen immunogenicity

Tumor-specific antigens emerging through somatic genomic rearrangements, known as neoantigens, play a critical role in current anticancer immunotherapy. They may or may not elicit an immune response when presented on the tumor cell surface bound to the MHC-I molecule, whose strength has been assumed as an indicator of immunogenicity. Several in silico peptide-MHC-I binding affinity predictors are used to prioritize putative immunogenic neoantigens to be experimentally and clinically explored either as biomarkers or targets for anticancer vaccines. This claims for a fair evaluation of such predictors, making essential the development of appropriate databases with experimentally validated, immunogenic/non-immunogenic neoantigens. Thus far, such a database is lacking. We herein present ITSNdb, a new and curated immunogenic neoantigen database and use it to benchmark current neoantigen immunogenicity predictors. Benchmark results failed to support the application of the predicted peptide- MHC-I binding affinity or its derived metrics as a tool to estimate neoantigen immunogenicity and the tumor neoantigen burden as an immunotherapy response biomarker. Moreover, binding affinity based immunogenicity definition leads to identifying wild-type peptide counterparts as predictors of immunotherapy response. We demonstrate that MHC-I binding affinity is insufficient to define neoantigen immunogenicity, despite being necessary for neoantigen tumor cell presentation suggesting that a paradigm shift for the emergence of new rules to identify immunogenic neoantigens is required.

bioinformatics↗

bioRxiv2019

MIXTURE: an improved algorithm for immune tumor microenvironment estimation based on gene expression data.

RNA sequencing has proved to be an efficient high-throughput technique to robustly characterize the presence and quantity of RNA in tumor biopsies at a given time. Importantly, it can be used to computationally estimate the composition of the tumor immune infiltrate and to infer the immunological phenotypes of those cells. Given the significant impact of anti-cancer immunotherapies and the role of the associated immune tumor microenvironment (ITME) on its prognosis and therapy response, the estimation of the immune cell-type content in the tumor is crucial for designing effective strategies to understand and treat cancer. Current digital estimation of the ITME cell mixture content can be performed using different analytical tools. However, current methods tend to over-estimate the number of cell-types present in the sample, thus under-estimating true proportions, biasing the results. We developed MIXTURE, a noise-constrained recursive feature selection for support vector regression that overcomes such limitations. MIXTURE deconvolutes cell-type proportions of bulk tumor samples for both RNA microarray or RNA-Seq platforms from a leukocyte validated gene signature. We evaluated MIXTURE over simulated and benchmark data sets. It overcomes competitive methods in terms of accuracy on the true number of present cell-types and proportions estimates with increased robustness to estimation bias. It also shows superior robustness to collinearity problems. Finally, we investigated the human immune microenvironment of breast cancer, head and neck squamous cell carcinoma, and melanoma biopsies before and after anti-PD-1 immunotherapy treatment revealing associations to response to therapy which have not seen by previous methods.

bioinformatics↗