Abstract 3377: T-cell receptor repertoire profiling using an augmented transcriptome

Abstract

Abstract Immunotherapy is growing as one of the most promising therapeutic approaches in clinical oncology practice. This brings with it an increasing need for comprehensive immuno-genomic profiling of tumors to better understand the interaction with the immune system. This includes profiling of the T and B-cell receptor repertoires (TCR/BCR), which has traditionally not been feasible with an exome/transcriptome platform. To address these challenges, we developed ImmunoID NeXT, an augmented, immuno-oncology optimized exome/transcriptome platform designed to provide comprehensive information regarding the tumor and tumor microenvironment (TME) from limited FFPE tumor biopsies, including the TCR alpha, beta, gamma and delta chains and BCR heavy and light chains. We show how this platform accurately profiles abundant clones, and can be applied to understand the diversity and activity of the adaptive immune system. We characterize the performance of ImmunoID NeXT at profiling TCR beta from RNA. We analyze the reproducibility of clones identified using replicates of PBMC and FFPE samples, and assess the concordance of top clones from a standalone TCR sequencing approaches to ours. Then, we test LOD by diluting well-characterized clonal T-cell line samples into PBMCs. We also analyze patient-derived FFPE tumors to understand the profiles of tumor-infiltrating immune repertoires. First, we compare TCR beta profiling with IHC quantification of CD3+ cells in both tumor and adjacent normal tissues. Finally, to better understand both B and T cell infiltration, we profile the intra-sample heterogeneity of BCR and TCR in a set of tumors. Between replicates of PBMC samples, abundances for shared clones have high concordance (R2>0.98). We observe strong concordance of the abundances for shared clones between adjacent curls of a tumor FFPE sample (R2>0.87), showing that our approach is robust to degraded FFPE samples. Compared to a standalone approach, we identify over 93% of the top 1000 clones, with highly concordant abundances (R2>0.94). Assessing LOD in dilutions of T cell lines into a PBMC sample, we are able to identify clones present at over 0.00032% RNA by mass. In our analysis of T-cell infiltration, we find a significant T-cell population in normal tissues. We also compare TCR read counts detected by ImmunoID NeXT in tumor and normal tissues with IHC results. Finally, we find substantial inter-sample variations in the number of TCR and BCR clones in tumors. ImmunoID NeXT has been designed to enable sensitive detection of abundant TCR and BCR clones in addition to comprehensive biomarkers from exome/transcriptome data. We demonstrate that our platform is reproducible, sensitive, and concordant with the top-abundance clones derived from targeted TCR methods, as well as feasible with FFPE samples. Finally, we highlight how immune repertoire results from ImmunoID NeXT can be used to gain understanding about the immunological composition of the TME. Citation Format: Eric Levy, Pamela Milani, Sean M. Boyle, Shujun Luo, Gabor Bartha, Charles Abbott, Rena McClory, Robin Li, John West, Richard Chen. T-cell receptor repertoire profiling using an augmented transcriptome [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 3377.