Neopepsee: accurate genome-level prediction of neoantigens by harnessing sequence and amino acid immunogenicity information

Kim, S.; Kim, H.S.; Kim, E.; Lee, Min Goo; Shin, Eui‐Cheol; Paik, Soonmyung

doi:10.1093/annonc/mdy022

Cited by 131 publications

(123 citation statements)

References 23 publications

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“…17 Neoantigen prediction can be exploited to identify responders to immune checkpoint inhibitors. 18,19 The purposes of this study were to characterize driver genes, mutational signatures and prognosticators in melanoma patients who were treated by ICB. [5][6][7][8][9][10] Findings emerged from this study may be useful for guiding immunotherapy treatment for melanoma patients.…”

Section: Introductionmentioning

confidence: 99%

The new identified biomarkers determine sensitivity to immune check-point blockade therapies in melanoma

et al. 2019

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Immune checkpoint blockade (ICB) therapy has achieved remarkable clinical benefit in melanoma. However, our understanding of biomarkers that predict response to ICB remained obscure. Here we systematically analyzed the association between somatic mutations profile and clinicopathologic information from 336 melanoma patients treated by ICB (CTLA-4/PD-1). We identified eight new significantly mutated genes including COL5A1, SEMA3E, COL28A1, DGKG, RAPGEF5, GLDN, NCF2 and RCAN2. A mutational signature featured by enrichment of T > C mutations was identified to be associated with immune resistance (logistic regression model, OR, 2.59 [95%CI, 1.07 to 7.00], P = .043). High neoantigen quality was associated with prolonged immunotherapy survival (log-rank test, P = .009). This association remained significant after controlling for age, gender, stage and hypermutation (Cox proportional hazards model, HR, 0.56 [95%CI, 0.38 to 0.82], P = .003). Our findings shed new insights on biomarkers that are useful to predict melanoma patients who may benefit from ICB treatment; however, these biomarkers need to be validated in future studies. ARTICLE HISTORY

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Section: Introductionmentioning

confidence: 99%

The new identified biomarkers determine sensitivity to immune check-point blockade therapies in melanoma

et al. 2019

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“…Despite the increased interest in neoantigen-based therapeutic tumor vaccine therapy, prediction algorithms capable of directly predicting for neoantigen immunogenicity are lacking (50), and no neoantigen immunogenicity predictor trained specifically on tumor antigen data exists. Current neoantigen immunogenicity predictors are instead trained on databases containing immunogenicity scores from all potential MHC binding epitopes, of which the biology may not closely match that of mutation-derived tumor antigens.…”

Section: Discussionmentioning

confidence: 99%

Machine-Learning Prediction of Tumor Antigen Immunogenicity in the Selection of Therapeutic Epitopes

Smith

Chai

Washington

et al. 2019

Cancer Immunology Research

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Current tumor neoantigen calling algorithms primarily rely on epitope/major histocompatibility complex (MHC) binding affinity predictions to rank and select for potential epitope targets. These algorithms do not predict for epitope immunogenicity using approaches modeled from tumorspecific antigen data. Here, we describe peptide-intrinsic biochemical features associated with neoantigen and minor histocompatibility mismatch antigen immunogenicity and present a gradient boosting algorithm for predicting tumor antigen immunogenicity. This algorithm was validated in two murine tumor models and demonstrated the capacity to select for therapeutically active antigens. Immune correlates of neoantigen immunogenicity were studied in a pan-cancer data set from The Cancer Genome Atlas and demonstrated an association between expression of immunogenic neoantigens and immunity in colon and lung adenocarcinomas. Lastly, we present evidence for expression of an out-of-frame neoantigen that was capable of driving antitumor cytotoxic T-cell responses. With the growing clinical importance of tumor vaccine therapies, our approach may allow for better selection of therapeutically relevant tumor-specific antigens, including nonclassic out-of-frame antigens capable of driving antitumor immunity.

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“…However, peptides with a predicted high MHC I binding affinity are not necessarily immunogenic. In neoepitope prediction strategies, attempts such as the integration of information concerning the hydrophobicity of the TCR contact region [149,164], amino acid characteristics [140] or binding differences between wild-type and mutant epitopes [149] yield at increasing the probability to identify clinically relevant neoepitopes [149]. Calis et al reported two common properties of neopeptide-MHC combinations, which cause differences in T cell recognition: (1) the composition of amino acids in the position 4-6 of the presented peptide as well as (2) the size and absence/presence of aromatic side chains [140].…”

Section: Alterations In the Antigen Presenting Pathwaymentioning

confidence: 99%

Combination Strategies for Immune-Checkpoint Blockade and Response Prediction by Artificial Intelligence

Huemer

Leisch

Geisberger³

et al. 2020

IJMS

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The therapeutic concept of unleashing a pre-existing immune response against the tumor by the application of immune-checkpoint inhibitors (ICI) has resulted in long-term survival in advanced cancer patient subgroups. However, the majority of patients do not benefit from single-agent ICI and therefore new combination strategies are eagerly necessitated. In addition to conventional chemotherapy, kinase inhibitors as well as tumor-specific vaccinations are extensively investigated in combination with ICI to augment therapy responses. An unprecedented clinical outcome with chimeric antigen receptor (CAR-)T cell therapy has led to the approval for relapsed/refractory diffuse large B cell lymphoma and B cell acute lymphoblastic leukemia whereas response rates in solid tumors are unsatisfactory. Immune-checkpoints negatively impact CAR-T cell therapy in hematologic and solid malignancies and as a consequence provide a therapeutic target to overcome resistance. Established biomarkers such as programmed death ligand 1 (PD-L1) and tumor mutational burden (TMB) help to select patients who will benefit most from ICI, however, biomarker negativity does not exclude responses. Investigating alterations in the antigen presenting pathway as well as radiomics have the potential to determine tumor immunogenicity and response to ICI. Within this review we summarize the literature about specific combination partners for ICI and the applicability of artificial intelligence to predict ICI therapy responses.

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Neopepsee: accurate genome-level prediction of neoantigens by harnessing sequence and amino acid immunogenicity information

Cited by 131 publications

References 23 publications

The new identified biomarkers determine sensitivity to immune check-point blockade therapies in melanoma

The new identified biomarkers determine sensitivity to immune check-point blockade therapies in melanoma

Machine-Learning Prediction of Tumor Antigen Immunogenicity in the Selection of Therapeutic Epitopes

Combination Strategies for Immune-Checkpoint Blockade and Response Prediction by Artificial Intelligence

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