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Background: Adoptive T-cell therapies would benefit from accurate computational prediction of T cell receptor (TCR) specificity to its target antigen. Despite the diversity of complementarity determining region (CDR) among T cells, many sequence features of CDRs are conserved. Conserved CDR sequences allow for shared specificity, that is, recognition of the same antigen, potentially increasing the pool of candidate TCRs available for treating patients. Utilizing sequence similarity, enrichment of V-genes, CDR lengths, and evidence of clonal expansion, several computational algorithms have been developed to predict the specificity of TCRs. Using neoantigen-specific TCRs (neoTCRs) in our phase 1 clinical trial (NCT039703820), we investigated the shared specificity of TCRs in the context of personalized autologous T cell therapy for cancer patients. Methods: Leveraging a high throughput TCR discovery and validation platform, neoantigen-specific T cells were isolated from patient peripheral blood mononuclear cells (PBMCs) and their neoTCR sequences were identified. RNA-seq was performed on tumor biopsies and TCR sequences derived from tumor-infiltrating lymphocytes (TILs) were extracted using MiXCR software. TCR specificity algorithms TCRdist3, GLIPH2, and TCRmatch were then used to identify specificity groups within and between neoTCR sequences derived from patient PBMCs, TCR sequences derived from TILs, and publicly available TCR sequences of known specificities. Results: NeoTCRs identified during our phase 1 clinical trial and their known neoantigens provided an experimentally validated benchmark for testing accuracy of TCR specificity prediction. TCR specificity algorithms accurately clustered neoTCR β chains into groups of shared antigen specificity. TCR chain sequences detected from TILs with high similarity to TCR sequences of neoTCRs from PMBCs were found, suggesting shared antigen specificity between circulating T cells and those found in the tumor. In addition, TCR specificity algorithms identified a public TCR sequence known to recognize an HPV-derived epitope presented by HLA-A*02:01 with high similarity to a TCR sequence identified in a tumor biopsy of an HPV16+ HLA-A*02:01+ patient with head and neck cancer. Conclusion: Neoantigens and their associated neoTCRs identified by our TCR discovery and validation platform can be used as a benchmark for TCR specificity prediction algorithms. TCR specificity algorithms provide insight into TCRs with predicted shared specificity in the blood and within the tumor. Accurate identification of TIL TCRs with shared specificity to neoTCRs could inform on tumor trafficking and aid in therapeutic product selection. Similarly, accurate specificity matching of TIL TCRs to public TCRs with known targets could aid in identification of efficacious targets within the tumor. Citation Format: Tyler Borrman, Eric Stawiski, Zheng Pan, Chad Smith, Susan Foy, Stefanie J. Mandl. TCR specificity prediction of circulating T cells and TILs in personalized adoptive neoTCR T cell therapy [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 3125.

Background: NeoTCR-P1 is a personalized autologous T cell therapy for treatment of patients with solid tumors. Neoantigen-specific T cell receptors (neoTCRs) were isolated from the patients’ circulating CD8 T cells followed by non-viral precision genome engineering into autologous apheresis-derived T cells for infusion back into the patient. We present comprehensive immune monitoring data for 2 patients treated in a first-in-human Phase 1 clinical trial (NCT03970382). Each patient was infused with their own personalized cell therapy product consisting of 3 unique neoTCRs targeting neoantigens identified as truncal in screening biopsies. Methods: We identified infused neoTCR specific T cells in the blood by using patient-specific peptide HLA reagents with single cell RNA seq and in the tumor using RNA scope probes or bulk transcriptome analysis. Results: Patient 0503, a patient with MSS colorectal cancer, was treated with 3 unique neoTCRs (1.3x109 neoTCR+ cells) which targeted 2 distinct neoantigens presented by HLA-B*39:01 or HLA-C*12:02. Analysis of longitudinal biopsies confirmed that targeted mutations were truncal, with both neoantigens present pre- and post-dosing. NeoTCR+ T cells were identified in the tumor by RNA seq and imaging, with neoTCR+ T cells also expressing Granzyme B. Furthermore, single cell RNA seq of neoTCR+ T cells from the pre-infusion product as compared neoTCR T cells isolated from the patients’ blood 2 months post-infusion demonstrated evidence of antigen engagement in 1 out of 3 neoTCR T cell products, with a shift from a predominantly Tcm/Tem-like phenotype pre-infusion to a Teff-like state post-infusion. This neoTCR T cell product also increased slightly in the blood from 1.1 to 3.4% between month 1 and 2, while the other 2 T cell products remained predominantly in the Tcm-like phenotype and remained stable in the blood between month 1 and 2. Patient 0612, a patient with melanoma, was treated with 3 unique neoTCRs (4.0x109 neoTCR+ cells) which targeted 3 distinct neoantigens presented by HLA-B*08:01 or HLA-C*07:02. Longitudinal biopsies support the assessment that 2/3 mutations were truncal and 1 targeted mutation was sub-clonal based on their presence in each biopsy. Retrospective analysis showed HLA loss of heterozygosity at HLA-C07:02 in all 3 biopsies, affecting 2/3 infused TCR products. The remaining TCR showed evidence of a shift from a predominantly Tcm-like phenotype pre-infusion to more differentiated Teff/Tcm-em phenotypes post-infusion compared to the 2 TCRs targeting mutations expressed by the lost HLA allele. Conclusion: These case studies illustrate for the first time tracking of 3 unique personalized T cell products within a single patient. Shifts in the T cell phenotype of the neoTCR T cell products post-infusion for some, but not all, of the products suggest early signs of T cell engagement with the target. Citation Format: Susan Foy, Cliff Wang, Benjamin Yuen, Sara Said, Boi Quach, Jonathan Hoover, Saparya Nayak, Theresa Hunter, Zheng Pan, Chad Smith, Tyler Borrman, Eric Stawiski, Alphonsus H. C. Ng, Yue Lu, James Heath, Stefanie Mandl. Monitoring of neoantigens and adoptively transferred personalized neoTCR T cell populations in the tumor and PBMCs of patients with solid tumors [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 901.

PACT Pharma has developed an ultra-sensitive approach to validate predicted neoantigens and the cognate T cell receptors (neoTCRs) from tumor specific somatic mutations by capturing neoantigen-specific T cells from peripheral blood. This process is used in clinical trials (NCT03970382) of personalized neoTCR-T therapy for persons with solid cancers. Using the state of the art prediction pipeline and screening process, more than one hundred twenty neoantigens were predicted from 5 type of solid cancers that were validated by characterizing cognate T cells and their close to 200 unique TCRs captured from the blood of the same individual. These validated neoepitopes of 8 to 11 amino acids represent broad HLA class I coverage with >30 alleles to date. Our analysis revealed that mutations can occur in all positions within the epitopes. Epitope immunogenicity is potentially affected by different mechanisms including mutation position, agretopicity, as well as HLA interacting positions and/or by interactions between mutated residues and its cognate TCRs. It was observed that these validated neoepitopes comprise broad ranges of predicted HLA binding affinities, stability, and neoantigen expression levels. The analysis presented here offers insights to enable machine learning to advance rules for epitope selection and prioritization that may be important for immunological approaches to address a broad range of diseases, including cancer. Citation Format: Zheng Pan, Olivier Dalmas, Songming Peng, Kyle Jacoby, Barbara Sennino, Yan Ma, Chad Smith, Amin Momin, Allison Xu, Katharine Heeringa, Jonathan Johnston, Duo An, Boi Quach, William Lu, Diana Nguyen, Andrew Conroy, Bhamini Purandare, Eva Huang, Eric Stawiski, Alex Franzusoff, Stefanie Mandl. Tumor neoantigen profiling with validated patient-specific TCR characterization to improve neoepitope prediction [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2021; 2021 Apr 10-15 and May 17-21. Philadelphia (PA): AACR; Cancer Res 2021;81(13_Suppl):Abstract nr 2177.