Abstract 568: Decoding shared antigenic epitopes and their cognate TCR genes in melanoma TILs using a library of paired human cell-based pHLA multimers and artificial APCs
Bibliographic record
Abstract
HLA-restricted T cell responses toward immunogenic peptides, whether mutated or non-mutated, can induce antitumor responses in patients with advanced cancer. The fact that potential non-mutated antigens are greater in number than mutated antigens by multiple orders of magnitude and the high polymorphism of HLA genes may have hampered comprehensive analyses of the specificity of antitumor T cell responses toward non-mutated antigens than mutated antigens. Unlike shared antigens, the vast majority of neoantigens are not shared and are unique to each patient. The elucidation of T cell epitopes derived from shared antigens may facilitate the robust development of an efficacious and safe adoptive T cell therapy that is readily available to a larger cohort of cancer patients. It is well established that melanoma tumor-infiltrating T lymphocytes (TILs) contain antitumor T cells that are specific for both non-mutated and mutated antigens. The adoptive transfer of TILs can induce sustained clinical responses in some patients with advanced melanoma. However, a precise and extensive understanding of the shared antigen targets of TILs has been lacking. In this study, TILs were isolated from 8 metastatic melanoma patients, polyclonally expanded in vitro, and their shared antigen specificities for all 45 (25 different) class I alleles were examined. The combination of structure-based analysis using human cell-based peptide/HLA (pHLA) multimers and functional analysis using artificial antigen-presenting cells (APCs) were used to determine antigen-specific T cell responses. We were able to determine the specificity of 12.2 ± 7.2% (mean ± SD, max 25.8%, min 4.6%) of CD8+ T cells in an expanded TIL culture toward 3.0 ± 1.8 (mean ± SD, max 6, min 1) previously known or novel peptides derived from shared antigens. Furthermore, we isolated a number of cognate TCR genes with potent tumor reactivity from the CD8+ T cells. The strategy employed in this study using a library of paired human cell-based pHLA multimers and artificial APCs has enabled us to decipher the antigen specificity of tumor-specific T cells for any given HLA class I allele, regardless of allele frequency and for an infinite number of peptides. It has also allowed us to build a large database of class I-restricted peptides and cognate tumor-reactive TCR genes at an unprecedented scale. This database will be a valuable tool in defining the immune response at the level of each individual cancer patient with precision as well as the identification and validation of biomarkers to aid in patient-based selection of a cancer immunotherapy regimen. Furthermore, this database will help the robust development of novel cancer vaccines and TCR gene therapies for patients with a low mutation burden.Citation Format: Kenji Murata, Kayoko Saso, Linh T. Nguyen, Douglas Millar, Marcus O. Butler, Pamela S. Ohashi, Naoto Hirano. Decoding shared antigenic epitopes and their cognate TCR genes in melanoma TILs using a library of paired human cell-based pHLA multimers and artificial APCs [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 568.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".