Literature DB >> 27622028

Current tools for predicting cancer-specific T cell immunity.

David Gfeller1, Michal Bassani-Sternberg2, Julien Schmidt3, Immanuel F Luescher3.   

Abstract

Tumor exome and RNA sequencing data provide a systematic and unbiased view on cancer-specific expression, over-expression, and mutations of genes, which can be mined for personalized cancer vaccines and other immunotherapies. Of key interest are tumor-specific mutations, because T cells recognizing neoepitopes have the potential to be highly tumoricidal. Here, we review recent developments and technical advances in identifying MHC class I and class II-restricted tumor antigens, especially neoantigen derived MHC ligands, including in silico predictions, immune-peptidome analysis by mass spectrometry, and MHC ligand validation by biochemical methods on T cells.

Entities:  

Keywords:  Cancer; MHC; T cells; exome; mutation; peptide; peptidome; transcriptome

Year:  2016        PMID: 27622028      PMCID: PMC5006903          DOI: 10.1080/2162402X.2016.1177691

Source DB:  PubMed          Journal:  Oncoimmunology        ISSN: 2162-4011            Impact factor:   8.110


  95 in total

1.  Use of selected reaction monitoring mass spectrometry for the detection of specific MHC class I peptide antigens on A3 supertype family members.

Authors:  Kevin T Hogan; Jennifer N Sutton; Kyo U Chu; Jennifer A C Busby; Jeffrey Shabanowitz; Donald F Hunt; Craig L Slingluff
Journal:  Cancer Immunol Immunother       Date:  2004-09-16       Impact factor: 6.968

2.  Generation of peptide-MHC class I complexes through UV-mediated ligand exchange.

Authors:  Boris Rodenko; Mireille Toebes; Sine Reker Hadrup; Wim J E van Esch; Annemieke M Molenaar; Ton N M Schumacher; Huib Ovaa
Journal:  Nat Protoc       Date:  2006       Impact factor: 13.491

Review 3.  Discovering protective CD8 T cell epitopes--no single immunologic property predicts it!

Authors:  Pavlo Gilchuk; Timothy M Hill; John T Wilson; Sebastian Joyce
Journal:  Curr Opin Immunol       Date:  2015-02-06       Impact factor: 7.486

4.  Peptide binding to HLA class I molecules: homogenous, high-throughput screening, and affinity assays.

Authors:  Mikkel Harndahl; Sune Justesen; Kasper Lamberth; Gustav Røder; Morten Nielsen; Søren Buus
Journal:  J Biomol Screen       Date:  2009-02-04

5.  Mutant MHC class II epitopes drive therapeutic immune responses to cancer.

Authors:  Sebastian Kreiter; Mathias Vormehr; Niels van de Roemer; Mustafa Diken; Martin Löwer; Jan Diekmann; Sebastian Boegel; Barbara Schrörs; Fulvia Vascotto; John C Castle; Arbel D Tadmor; Stephen P Schoenberger; Christoph Huber; Özlem Türeci; Ugur Sahin
Journal:  Nature       Date:  2015-04-22       Impact factor: 49.962

6.  Predicting peptides that bind to MHC molecules using supervised learning of hidden Markov models.

Authors:  H Mamitsuka
Journal:  Proteins       Date:  1998-12-01

7.  The human leukocyte antigen-presented ligandome of B lymphocytes.

Authors:  Chopie Hassan; Michel G D Kester; Arnoud H de Ru; Pleun Hombrink; Jan Wouter Drijfhout; Harm Nijveen; Jack A M Leunissen; Mirjam H M Heemskerk; J H Frederik Falkenburg; Peter A van Veelen
Journal:  Mol Cell Proteomics       Date:  2013-03-12       Impact factor: 5.911

8.  Peptide microarray-based identification of Mycobacterium tuberculosis epitope binding to HLA-DRB1*0101, DRB1*1501, and DRB1*0401.

Authors:  Simani Gaseitsiwe; Davide Valentini; Shahnaz Mahdavifar; Marie Reilly; Anneka Ehrnst; Markus Maeurer
Journal:  Clin Vaccine Immunol       Date:  2009-10-28

9.  A microfluidic platform for high-throughput multiplexed protein quantitation.

Authors:  Francesca Volpetti; Jose Garcia-Cordero; Sebastian J Maerkl
Journal:  PLoS One       Date:  2015-02-13       Impact factor: 3.240

10.  NetMHC-3.0: accurate web accessible predictions of human, mouse and monkey MHC class I affinities for peptides of length 8-11.

Authors:  Claus Lundegaard; Kasper Lamberth; Mikkel Harndahl; Søren Buus; Ole Lund; Morten Nielsen
Journal:  Nucleic Acids Res       Date:  2008-05-07       Impact factor: 16.971

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  14 in total

Review 1.  The Flipside of the Power of Engineered T Cells: Observed and Potential Toxicities of Genetically Modified T Cells as Therapy.

Authors:  Felipe Bedoya; Matthew J Frigault; Marcela V Maus
Journal:  Mol Ther       Date:  2017-02-01       Impact factor: 11.454

2.  Modified Vaccinia virus Ankara-based vaccines in the era of personalized immunotherapy of cancer.

Authors:  Kaïdre Bendjama; Eric Quemeneur
Journal:  Hum Vaccin Immunother       Date:  2017-08-28       Impact factor: 3.452

3.  A comprehensive review and performance evaluation of bioinformatics tools for HLA class I peptide-binding prediction.

Authors:  Shutao Mei; Fuyi Li; André Leier; Tatiana T Marquez-Lago; Kailin Giam; Nathan P Croft; Tatsuya Akutsu; A Ian Smith; Jian Li; Jamie Rossjohn; Anthony W Purcell; Jiangning Song
Journal:  Brief Bioinform       Date:  2020-07-15       Impact factor: 11.622

4.  Neoantigen prediction and the need for validation.

Authors:  Antonella Vitiello; Maurizio Zanetti
Journal:  Nat Biotechnol       Date:  2017-09-11       Impact factor: 54.908

Review 5.  Trial watch: Immunogenic cell death induction by anticancer chemotherapeutics.

Authors:  Abhishek D Garg; Sanket More; Nicole Rufo; Odeta Mece; Maria Livia Sassano; Patrizia Agostinis; Laurence Zitvogel; Guido Kroemer; Lorenzo Galluzzi
Journal:  Oncoimmunology       Date:  2017-10-04       Impact factor: 8.110

6.  Proteomics-inspired precision medicine for treating and understanding multiple myeloma.

Authors:  Matthew Ho; Giada Bianchi; Kenneth C Anderson
Journal:  Expert Rev Precis Med Drug Dev       Date:  2020-02-24

7.  High-Throughput Prediction of MHC Class I and II Neoantigens with MHCnuggets.

Authors:  Xiaoshan M Shao; Rohit Bhattacharya; Justin Huang; I K Ashok Sivakumar; Collin Tokheim; Lily Zheng; Dylan Hirsch; Benjamin Kaminow; Ashton Omdahl; Maria Bonsack; Angelika B Riemer; Victor E Velculescu; Valsamo Anagnostou; Kymberleigh A Pagel; Rachel Karchin
Journal:  Cancer Immunol Res       Date:  2019-12-23       Impact factor: 12.020

8.  Deciphering HLA-I motifs across HLA peptidomes improves neo-antigen predictions and identifies allostery regulating HLA specificity.

Authors:  Michal Bassani-Sternberg; Chloé Chong; Philippe Guillaume; Marthe Solleder; HuiSong Pak; Philippe O Gannon; Lana E Kandalaft; George Coukos; David Gfeller
Journal:  PLoS Comput Biol       Date:  2017-08-23       Impact factor: 4.475

Review 9.  Predicting Antigen Presentation-What Could We Learn From a Million Peptides?

Authors:  David Gfeller; Michal Bassani-Sternberg
Journal:  Front Immunol       Date:  2018-07-25       Impact factor: 7.561

10.  Footprints of antigen processing boost MHC class II natural ligand predictions.

Authors:  Carolina Barra; Bruno Alvarez; Sinu Paul; Alessandro Sette; Bjoern Peters; Massimo Andreatta; Søren Buus; Morten Nielsen
Journal:  Genome Med       Date:  2018-11-16       Impact factor: 11.117

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