Neoantigen Peptide Synthesis Service

High-throughput Identification of Intracellular Targets for Solid Tumors

In this webinar, Dr. Marvin Gee, Co-Founder and Head of Target Discovery at 3T Biosciences, discusses 3T's platform for tumor target discovery, processes and current progress. 3T focuses on identifying novel targets for the treatment of solid tumors, particularly targets recognized by T cell receptors (TCRs).

T cells are an integral part of anti-tumor immunity and are able to recognize tumor antigens presented by immune cells, such as human leukocyte antigens (HLA). Once a priming event occurs in the lymph nodes, activated T cells travel to the site of the tumor and target cancer cells for elimination.

3T's Strategy for TCR and Antigen Identification

The use of checkpoint inhibitors in cancer therapy has shown to be an effective strategy in the treatment of melanoma, renal cancer, and head and neck cancers, among others. This outcome is partly attributed to the greater T cell anti-tumor activity enabled by checkpoint inhibitor treatment. Therefore, by focusing on patients responsive to checkpoint inhibitor therapy, the 3T platform aims at identifying the combination of TCRs and antigens leading to tumor elimination. 3T aims to leverage this knowledge for the development of strategies to target tumors in a broad range of patients through T cell therapy.

What Antigen Types are Recognized by T cells in Tumors

Several types of antigens may be specifically found in association with tumor tissue including viral antigens.

With the goal of identifying a broad range of novel antigens, which may be used in T cell therapies for solid tumors, 3T has developed the T cell Receptor Antigen Cross-reactivity Engine or 3T-TRACE platform. The 3T platform allows the unbiased identification of tumor specific T cell responses in patients responsive to checkpoint inhibitor therapy. The 3T-TRACE platform for TCR target discovery is enabled by therapeutic and technological advances including:

• Effective T cell anti-tumor responses- enabled by checkpoint therapies
• Single cell sequencing technology- for identification of TCRs in tumor infiltrating and peripheral T cells
• High diversity and high-throughput yeast display library (peptide-HLA)- to display billions of targets
• Machine learning approaches- for the identification of specific interactions and prediction of TCR’s  off-target activities

By focusing on patients with productive immune responses, the 3T-TRACE platform is able to identify the most active TCRs and most prevalent and immunogenic targets.

3T-TRACE Platform Workflow

The 3T-TRACE workflow starts with tumor samples from patients, which are processed and sequenced to profile T cell immune responses. Next, TCRs of interest are identified and screened in the 3T-TRACE platform for target discovery. Machine learning algorithms aid in the identification of synthetic peptide candidates for interaction with specific TCRs, therefore predicting specificities as well as potential off-target effects. Lastly, candidates are validated through antigen processing, T cell activation, and tumor killing.

The 3T-TRACE platform may also be leveraged for the development of peptide vaccines and to monitor other protein biologics, such as TCR-mimics. Additionally, there is ample opportunity for the application of this unbiased platform to the identification of peptide targets involved in other T cell related disease states including autoimmunity, allergy, and viral infections.

3T-TRACE Platform Target Identification

In order to identify the most immunogenic and prevalent targets in solid tumors, which could aid in developing therapies benefiting a broad population of patients, several properties are evaluated

• TCRs shared across patients
• TCRs enriched in check point inhibitor responding patients
• T cells clonally enriched in the tumor microenvironment
• Markers for activated T cells

By using this platform, scientists at 3T have been able to identify tumor targets which are more widely expressed than other leader targets in the field (e.g., NY-ESO-1 and MAGE-A4).