Defining HLA-II Ligand Processing and Binding Rules with Mass Spectrometry Enhances Cancer Epitope Prediction

Abelin, Jennifer G.; Harjanto, Dewi; Malloy, Matthew; Suri, Prerna; Colson, Tyler; Goulding, Scott P.; Creech, Amanda L.; Serrano, Lia R.; Nasir, Gibran; Nasrullah, Yusuf; McGann, Christopher D.; Velez, Diana; Ting, Ying S.; Poran, Asaf; Rothenberg, Daniel; Chhangawala, Sagar; Rubinsteyn, Alex; Hammerbacher, Jeff; Gaynor, Richard B.; Fritsch, Edward F.; Greshock, Joel; Oslund, Rob C.; Barthelme, Dominik; Addona, Terri A.; Arieta, Christina M.; Rooney, Michael S.

doi:10.1016/j.immuni.2019.08.012

Cited by 196 publications

(239 citation statements)

References 100 publications

(111 reference statements)

Supporting

Mentioning

218

Contrasting

Order By: Relevance

“…Importantly, KPC tumors not expressing MHC‐II NeoAg continued to grow indicating that CD4 + T cell effectors must recognize antigen locally to mediate their antitumor effects. Furthermore, another recent study has demonstrated that across a range of solid tumor types MHC‐II expression is rare among tumor cells, which implies that the local effects of CD4 + T cells previously mentioned are likely dependent on antigen recognition in the context of APCs and stromal cells 82 . Overall, these and other effector roles of CD4 + T cells require further exploration.…”

Section: Help In Therapeutic Contextmentioning

confidence: 98%

“…It has been demonstrated that the inclusion of "helper" epitopes in therapeu- Furthermore, another recent study has demonstrated that across a range of solid tumor types MHC-II expression is rare among tumor cells, which implies that the local effects of CD4 + T cells previously mentioned are likely dependent on antigen recognition in the context of APCs and stromal cells. 82 Overall, these and other effector roles of CD4 + T cells require further exploration. If the dominant role of CD4 + T cells in the antitumor immune response is the provision of help, NeoAg vaccines can simply include promiscuous helper epitopes such as the pan-DR binding epitope.…”

Section: Cancer Vaccinesmentioning

confidence: 99%

See 1 more Smart Citation

Harnessing neoantigen specific CD4 T cells for cancer immunotherapy

Brightman

Naradikian

Miller

et al. 2020

Journal of Leukocyte Biology

View full text Add to dashboard Cite

The goal of precision immunotherapy is to direct a patient's T cell response against the immunogenic mutations expressed on their tumors. Most immunotherapy approaches to‐date have focused on MHC class I‐restricted peptide epitopes by which cytotoxic CD8+ T lymphocytes (CTL) can directly recognize tumor cells. This strategy largely overlooks the critical role of MHC class II‐restricted CD4+ T cells as both positive regulators of CTL and other effector cell types, and as direct effectors of antitumor immunity. In this review, we will discuss the role of neoantigen specific CD4+ T cells in cancer immunotherapy and how existing treatment modalities may be leveraged to engage this important T cell subset.

show abstract

Section: Help In Therapeutic Contextmentioning

confidence: 98%

Section: Cancer Vaccinesmentioning

confidence: 99%

Harnessing neoantigen specific CD4 T cells for cancer immunotherapy

Brightman

Naradikian

Miller

et al. 2020

Journal of Leukocyte Biology

View full text Add to dashboard Cite

show abstract

“…The reasons for this are many and include the complicating factor that both the alpha and beta chains of HLA-DQ and HLA-DP are polymorphic making it non-trivial to assess which of the four possible combinations of alpha and two beta chains are presented as HLA molecules in a given cell. However, careful selection homozygous cell lines or cell lines with only one expressed alpha chain could help resolve this 37 , and in this context EL data covering DQ and DP can without any further complication be integrated in NNAlign_MA modelling framework.…”

Section: Discussionmentioning

confidence: 99%

“…Properties other than antigen processing and HLA binding, such as protein expression, and differential access to the MHC-II presentation pathway, contribute to the likelihood of antigen presentation. In a recent paper Abelin et al 37 have proposed a modelling framework integrating a panel of such properties, suggesting large improvement for prediction of HLA ligandomes. Further work remains to be done to validate the generality of these findings and their potential impact for general rational epitope discovery.…”

Section: Discussionmentioning

confidence: 99%

Improved prediction of MHC II antigen presentation through integration and motif deconvolution of mass spectrometry MHC eluted ligand data

Reynisson

Barra

Kaabinejadian³

et al. 2019

Preprint

View full text Add to dashboard Cite

AbstractMajor Histocompatibility Complex II (MHC II) molecules play a vital role in the onset and control of cellular immunity. In a highly selective process, MHC II presents peptides derived from exogenous antigens on the surface of antigen-presenting cells for T cell scrutiny. Understanding the rules defining this presentation holds critical insights into the regulation and potential manipulation of the cellular immune system. Here, we apply the NNAlign_MA machine learning framework to analyse and integrate large-scale eluted MHC II ligand mass spectrometry (MS) data sets to advance prediction of CD4+ epitopes. NNAlign_MA allows integration of mixed data types, handling ligands with multiple potential allele annotations, encoding of ligand context, leveraging information between data sets, and has pan-specific power allowing accurate predictions outside the set of molecules included in the training data. Applying this framework, we identified accurate binding motifs of more than 50 MHC class II molecules described by MS data, particularly expanding coverage for DP and DQ beyond that obtained using current MS motif deconvolution techniques. Further, in large-scale benchmarking, the final model termed NetMHCIIpan-4.0, demonstrated improved performance beyond current state-of-the-art predictors for ligand and CD4+ T cell epitope prediction. These results suggest NNAlign_MA and NetMHCIIpan-4.0 are powerful tools for analysis of immunopeptidome MS data, prediction of T cell epitopes and development of personalized immunotherapies.

show abstract

“…Two of the studies included experiments that used cell lines engineered to express a single MHC I allele. We refer to these as the MONOALLELIC samples, which comprise 72 samples from the recent publication by Sarkizova et al 10 and 8 samples from Abelin et al 11 . We refer to the other samples, in which exact MHC I restrictions were not experimentally determined, as the MULTIALLELIC samples.…”

Section: Ms Benchmark Construction and Approachmentioning

confidence: 99%

A model of antigen processing improves prediction of MHC I-presented peptides

O’Donnell

Rubinsteyn

Laserson

2020

Preprint

Self Cite

View full text Add to dashboard Cite

Large surveys of peptides naturally presented on major histocompatibility class I (MHC I) proteins have enabled improved MHC I ligand prediction by dramatically expanding the available data for many MHC I alleles. However, it is unclear to what extent antigen processing signals can also be learned from these datasets. Here, we developed a predictor of antigen processing by training neural networks to discriminate mass spec-identified MHC I ligands from unobserved peptides, where both classes of peptides are predicted to be strong MHC I binders. The resulting predictor shows qualitative consistency with established preferences for the transporter associated with antigen processing, proteasomal cleavage, and endoplasmic reticulum aminopeptidases. When we combined the antigen processing predictor with a novel pan-allele MHC I binding predictor in a logistic regression model, the combination model significantly outperformed the two components alone as well as the NetMHCpan 4.0 and MixMHCpred 2.0.2 tools at predicting mass spec-identified MHC I ligands. Our predictors are implemented in the open source MHCflurry package, version 1.6.0

show abstract

Defining HLA-II Ligand Processing and Binding Rules with Mass Spectrometry Enhances Cancer Epitope Prediction

Cited by 196 publications

References 100 publications

Harnessing neoantigen specific CD4 T cells for cancer immunotherapy

Harnessing neoantigen specific CD4 T cells for cancer immunotherapy

Improved prediction of MHC II antigen presentation through integration and motif deconvolution of mass spectrometry MHC eluted ligand data

A model of antigen processing improves prediction of MHC I-presented peptides

Contact Info

Product

Resources

About