24-26 April, 2018
Amsterdam

Download the Event Guide

Day One
Wednesday 25th April, 2018

Day Two
Thursday 26th April, 2018

08.00
Breakfast & Registration

08.30
Chairman’s Opening Remarks

Optimising the Identification and Prediction of Neoantigens

08.45
Overcoming Neoantigen Prediction Challenges in Clinical Application

Synopsis

• Overview of factors for neoantigen prediction (Mutation detection, antigen presentation, antigen recognition)
• Establishing reliable mutation detection
• Strategies for neoantigen selection
• Mitigating the risks in selection processes
• Emerging factors

09.15
Advances in the Field of In-silico Methods for the Identification of Neoepitopes

  • Morten Nielsen Professor, Group leader Immunoinformatics and Machine-Learning, Center for Biological Sequence Analysis, The Technical University of Denmark

Synopsis

• Due to the high selectivity of the MHC molecules, major efforts have been dedicated to characterize their binding specificity and several in-silico methods have been developed to predict this event
• Overview of the recent advances in prediction methods for rational epitope discovery
• Show how the use of MS peptidomics data in can substantially enhance our ability to predict both MHC class I and MHC class II ligands and epitopes, and how the processing signal contained within MHC class II MS peptidome data can be used to improve predictive accuracy
• Demonstrate how peptide similarity to self serves as an important factor for predicting peptide immunogenicity
• Discuss how T cell receptor data can be used to refine our understanding of peptide immunogenicity, and present results suggesting that the cognate target of a T cell can be predicted from the sequence of T cell receptor
• Discussion on limitations of the state-of-the-art tools, and suggest solutions for how to move the field forward dealing with these limitations

09.45
An Integrated Machine-Learning Approach to Improve the Prediction of Clinically Relevant Neoantigens

Synopsis

  • Current neoantigen discovery algorithms are not optimal to predict presentation to the cell surface
  • Here, we outline a high-performing machine learning approach, trained on mass spectrometry data, that predicts naturally processed and presented antigens
  • The predictor is integrated with several immune parameters, such as HLA binding, in a deep learning layer to predict bonafide neoantigens
  • We illustrate its application to significantly improve the identification of neoantigen
    targets for personalized cancer immunotherapy

10.15
Morning Refreshments & Speed Networking

11.15
Immunopeptidomics: Accelerating the Development of Personalised Cancer Immunotherapy – How can we Improve Identification and Prioritisation of Neoantigens with MS Based Immunopeptidomics?

  • Michal Bassani-Sternberg Immunopeptidomics Unit, Center of Experimental Therapeutics, Department of Oncology, UNIL/CHUV

Synopsis

• We have developed a new high-throughput, reproducible and sensitive immunopeptidomics platform that improves recovery and overall sensitivity for direct mass spectrometry based identification of neoantigens
• We have showed that incorporation of deconvoluted immunopeptidomics data in ligand prediction algorithms can improve their performance
• We have reported that by taking advantage of co-occurring HLA-I alleles one can rapidly and accurately identify HLA-I binding motifs and map them to their corresponding alleles without any a priori knowledge of HLA-I binding specificity. This novel and scalable approach uncovers new motifs for several alleles that had no known ligands
• In addition to providing huge training data to improve the HLA binding prediction, immunopeptidomics also captures other aspects of the natural in vivo presentation that significantly improve prediction of clinically relevant neoantigens

11.45
Panel: Determining Best Practices for Neoantigen Prediction

Synopsis

• How can you best predict the clinical relevance on your neoantigens?
• Use of in silico methods to enable the intelligent selection of mutations likely to result in high-affinity epitopes that bind to MHC molecules
• Analysis of immunoinformatics and computational tools for neoantigen prediction
• How can we harness machine learning to improve the efficiency of how neoantigens are validated?

12.45
Lunch & Networking

13.45
Identification and characterization of presented tumour neo-epitopes from metastatic colon cancer

Synopsis

• Primary cancer data
• Metastatic cancer
• Normal tissue expression
• Functional characterisation

14.15
Sensitive Identification and Functional Profiling of Neoepitopespecific T Cells

Synopsis

• The frequency of neoepitope reactive T cells is decreasing during TIL expansion but this can be circumvented in order to enrich TILs in neoepitope-specific T cells
• The neoepitope repertoire is discordant between circulating and tumor-infiltrating lymphocytes
• Neoepitope-specific T cells are functionally heterogeneous
• High-throughput strategies to validate neoepitopes and to isolate neoepitope-specific T cells are needed to bring mutanome-based immunotherapy into the clinic

14.45
Immunogenicity of Neoantigen Cancer Vaccines

  • Hyewon Phee Senior Scientist & Project Manager, Inflammation, Amgen

Synopsis

• Specific content awaiting internal approval

15.15
Afternoon Refreshments & Networking

15.45
Optimising the Neoantigenic Landscape in Low Mutational Burden Tumours for Optimal T Cells Response

  • Sebastiano Battaglia Assistant Professor of Oncology, Centre of Immunotherapy, Roswell Park Cancer Centre

Synopsis

• Assessing the multidimensional challenge of identifying reactive neoantigens
• As sequence analysis tools and prediction algorithms promise optimal neoantigen calls, T cells status and tumour microenvironment can limit the number of truly reactive neoantigens in cancer patients
• Variant identification is a key limiting factor
• Neoantigen identification can be optimised by ranking epitopes based on combined prediction values
• Magnitude of TILs infiltration does not translate into optimal patient’s response
• TCR repertoire dictates response to neoantigen
• Neoantigens from driver mutations could also be exploited for augmented anti-tumor response

Creating the Appropriate Clinical Trials for Neoantigen Based Therapies

16.15
From Sample to Neoantigens for Vaccines: Key Challenges and Solutions

Synopsis

  • cfDNA neoantigens: dealing with tumor heterogeneity
  • Improving neoantigen identification
  • Elaborating TME, immuno-modulators and vaccine response biomarkers
  • Overcoming poor sample quality and quantity for NGS sequencing
  • Overcoming sequencing gaps that can harbor neoantigens
  • Validation and regulatory issues on the way to commercialization

16.45
Chairman’s Closing Remarks

17.00
Close of Day One