8:30 am Chairs Opening Remarks

8:40 am Industry Panel to Address the Most Critical Questions for this Nascent & Emerging Therapeutic

Synopsis

• What is the best neoantigen prediction platform?
• What is the most robust approach to identifying the “right” neoantigen?
• How to best deliver a neoantigen therapy, vaccine or cell therapy?
• What type of vaccine, cell based, virus, RNA, DNA, peptide?
• Which approach is safer and more efficacious?
• Should the focus be on shared neoantigens or private neoantigens?

9:20 am Personalised Neoantigen Immunotherapy – Demonstrating Priming of CD8+ T cell Responses is a Key Step

Synopsis

• Typical solid tumor epithelial cells display class I HLA-presented neoantigens (not class II)
• Consistent priming of strong CD8+ T cell responses to these neoantigens is likely key to effective immunotherapy
• We will show phase one clinical and immunogenicity data from a heterologous prime-boost neoantigen immunotherapy program in
solid tumor patients

9:50 am An Integrated Machine-Learning Approach to Improve the Prediction of Clinically Relevant Neoantigens

Synopsis

• Outline a high-performing machine learning approach, trained on mass spectrometry data, that predicts naturally processed and
presented antigens
• Demonstrate how the predictor is integrated with several immune parameters, such HLA binding, in a deep learning layer to predict
bona fide neoantigens
• Illustrate it’s application to significantly improve the identification of neoantigen targets for personalised cancer immunotherapy

10:20 am Speed Networking

Synopsis

This session is the ideal opportunity to get face-to-face time with many of the brightest minds working to advance cell therapies. Benchmark against the industry leaders and establish meaningful business relationships to pursue for the rest of the conference and beyond

10:20 am Morning Refreshments

PREDICTION & IDENTIFICATION

11.20 Using AI To Accelerate Immunotherapy In Cancer
• Leveraging AI driven prediction tools and identifying challenges in using AI
technology to drive neoantigen clinical therapy
• Uncovering the right data with the right quantity and the right quality to train
your algorithms to predict immunogenicity
• Pushing for fairness in technology comparisons: Assessing advantages and
disadvantages across current methodologies for assay technologies
• Harnessing PIONEER in developing neoantigen based therapeutics

Jens Kringelum, Director, Genomic Immuno-Oncology, Evaxion Biotech

 

11.50 Driving Antigen Discovery in Cervical Cancer
• Evaluating how personalised genome or transcriptome sequencing information is essential for neoantigen discovery using LC-MS technology
• RNAseq allows variant mapping and identification of cryptic antigens
• Pathogen-driven tumours harbour a range of so far unknown targetable, tumourspecific neoantigen

Nicola Ternette, Head of Immunopeptidomics, University of Oxford

 

12.20 Discovery of Immunogenic Dark Antigens™ as Targets for Cancer Immunotherapy
• Ervaxx is pioneering the use of Dark Antigens™ to deliver targeted off-the-shelf cancer immunotherapeutics
• Dark Antigens™ derive from vast untapped expanses of genetic ‘dark matter’ beyond the normal coding regions of the genome and are selectively activated in cancer
• We have developed a platform combining transcriptomics and mass
spectrometry-based immunopeptidomics to identify Dark AntigensTM that are uniquely presented on the surface of cancer cells

Ray Jupp, CSO, Ervaxx LTD

 

 

CLINICAL TRANSLATION & MANUFACTURING

12:50 pm Networking Lunch

PREDICTION & IDENTIFICATION

13.50 Modelling Neoantigen Prediction
• Advancing neoantigen prediction
• Analysing key features
• Improving neoantigen prediction modelling

Maren Lang, Head of Bioinformatics Research & Development, BioNTech SE

 

14.20 Disease-specific Immunopeptides for Generation of Soluble TCR Therapies

Patient tumor and healthy tissues were enriched for MHC class I peptides and identified by mass spectrometry
• Population prevalence was used to prioritize tumor-selective immunopeptides as candidate targets
• Soluble TCRs can be generated against disease-specific immunopeptides for development of targeted therapeutic strategies

Melanie Patterson, Principal Research Scientist II, Abbvie

 

14.50 Prediction and Identification of both HLA Class I & Class II Neo-epitopes

• Quality of T cells are more important than quantity of T cells in the TME
• Tumor mutation burdens do not correlate with PDAC survival, but neoepitope scores, particularly a score system weighing overlapped HLA Class I and Class II epitopes, may correlate better with PDAC survival
• Both HLA Class I and Class II epitope peptides were eluted from PDAC tumor tissue by using anti-pan HLA Class I antibody and anti-pan HLA Class II antibody, respectively, followed by mass spectrometry analysis. Approximately 20% eluted HLA Class I epitopes and Class II epitopes are overlapped
• We propose a strategy of developing neoepitope based vaccine or T cell therapy by selecting HLA Class I and HLA Class II overlapped epitopes.

Lei Zheng, Associate Professor, John Hopkins School of Medicine

 

15.20 Thousands of Novel Unannotated Proteins Expand the MHC I Immunopeptidome in Cancer

• Thousands of novel unannotated ORFs (nuORFs) revealed as translated by ribosome profiling across a range of cancer types

• Mass spectrometry analysis of the MHC I immunopeptidome identifies over 6,000 peptides from nuORFs

• Many nuORFs are translated in cancer-specific manner and can be sources of neoantigens

• NuORFs harbor cancer-specific somatic variants and can contribute to the neoantigen repertoire

Tamara Ouspenskaia, Postdoctoral Fellow, Broad Institute

CLINICAL TRANSLATION & MANUFACTURING

3:50 pm Afternoon Refreshments & Poster Session

4:30 pm ImmunoID NeXT: A Comprehensive Platform for Improving Neoantigen Prediction, Tumour Escape Mechanism Reporting, TME Assessment, & Tumor Heterogeneity Profiling for Immuno-Oncology

  • Sean Boyle Senior Director, Bioinformatics Applications, Personalis

Synopsis

• Improving neoantigen presentation prediction
• Applying immunopeptidomics and machine learning to more accurately predict neoantigens
• Detecting tumour escape mechanisms
• HLA LOH, antigen presentation machinery, and immunogenomics
• Profiling tumour heterogeneity
• Utilizing cell free DNA in order to more comprehensively identify neoantigens and tumor escape mechanisms

5:00 pm Trailblazing the Development of Off-the Shelf Anti-Cancer Vaccines

Synopsis

• Frameshift neoantigens as targets of cancer immunotherapy
• Shared neopeptides emerging from frameshifts
• Frame neopeptide cocktails as off-the-shelf cancer vaccines

5:30 pm A Personal Antigen Selection Calculator (PASCal) for the Design of Off-The-Shelf, Shared NeoantigenBasedPersonal Vaccines

6:00 pm Mastermind Session: Broadening Neoantigen Therapeutic Horizons: Should we Target Unique, Patient-Specific Tumour Mutations or Shared Neoantigens?

Synopsis

• Defining the pros and cons of each therapeutic approach
• Why shared neoantigens are more exciting from a strategy standpoint
• Personlised vaccines targeting neoantigens are designed to prime and amplify neoantigen-specific T cell populations invivo to
augment adoptive antitumor immunity among individuals
• Recognising that off-the-shelf vaccines are more practical then personal vaccines and the potential commercial benefit
• Considerations for time wasted in designing and producing, manufacturing the personlised vaccines
• Realising the economic reality of developing personalised neoantigen based therapies

6:30 pm Chair’s Closing Remarks

6:40 pm End of Day 1 of Neoantigen Summit Europe