2. Academic Validation
  3. Defining HLA-II Ligand Processing and Binding Rules with Mass Spectrometry Enhances Cancer Epitope Prediction

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

  • Immunity. 2019 Oct 15;51(4):766-779.e17. doi: 10.1016/j.immuni.2019.08.012.
Jennifer G Abelin 1,  Dewi Harjanto 1,  Matthew Malloy 1,  Prerna Suri 1,  Tyler Colson 1,  Scott P Goulding 1,  Amanda L Creech 1,  Lia R Serrano 1,  Gibran Nasir 1,  Yusuf Nasrullah 1,  Christopher D McGann 1,  Diana Velez 1,  Ying S Ting 1,  Asaf Poran 1,  Daniel A Rothenberg 1,  Sagar Chhangawala 1,  Alex Rubinsteyn 2,  Jeff Hammerbacher 2,  Richard B Gaynor 1,  Edward F Fritsch 1,  Joel Greshock 1,  Rob C Oslund 1,  Dominik Barthelme 1,  Terri A Addona 1,  Christina M Arieta 1,  Michael S Rooney 3
Affiliations

Affiliations

  • 1 Neon Therapeutics, Cambridge, MA 02139, USA.
  • 2 Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA.
  • 3 Neon Therapeutics, Cambridge, MA 02139, USA. Electronic address: mrooney@neontherapeutics.com.
PMID: 31495665 DOI: 10.1016/j.immuni.2019.08.012
Abstract

Increasing evidence indicates CD4+ T cells can recognize cancer-specific antigens and control tumor growth. However, it remains difficult to predict the antigens that will be presented by human leukocyte antigen class II molecules (HLA-II), hindering efforts to optimally target them therapeutically. Obstacles include inaccurate peptide-binding prediction and unsolved complexities of the HLA-II pathway. To address these challenges, we developed an improved technology for discovering HLA-II binding motifs and conducted a comprehensive analysis of tumor ligandomes to learn processing rules relevant in the tumor microenvironment. We profiled >40 HLA-II alleles and showed that binding motifs were highly sensitive to HLA-DM, a peptide-loading chaperone. We also revealed that intratumoral HLA-II presentation was dominated by professional antigen-presenting cells (APCs) rather than Cancer cells. Integrating these observations, we developed algorithms that accurately predicted APC ligandomes, including Peptides from phagocytosed Cancer cells. These tools and biological insights will enable improved HLA-II-directed Cancer therapies.

Keywords

HLA class II; HLA ligandomics; HLA-II; MHC; RNA-Seq; SILAC; antigen; autophagy; cancer; epitope prediction; isotope labeling; machine learning; mass spectrometry; neoantigen; peptide processing; phagocytosis; proteomics.

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