Abstract PR02: Tumor sequestered, neoantigen specific T cells are dynamically increased in circulation following neoadjuvant immunotherapy
Bibliographic record
Abstract
Abstract Improved recurrence free survival is observed following neoadjuvant immune checkpoint blockade (ICB), but the mechanisms underlying this clinical benefit are incompletely understood. To gain insights into changes in the tumor microenvironment (TME) that occur following ICB, we performed paired single-cell transcriptomic and T cell receptor (TCR) RNA sequencing on sorted tumor infiltrating T-lymphocytes before and after neoadjuvant treatment of patients with newly diagnosed oral carcinoma with bintrafusp alfa, a bifunctional PD-L1 mAb and TGF-b neutralizing construct. Analysis of pre- and post-treatment TIL with identical TCRs allowed characterization of changes within individual clonotypes after treatment. The addition of deep TCRb complimentary determining region 3 (CDR3) sequencing of peripheral T cells allowed assessment of dynamic changes in individual T cell clonotypes between the tumor and peripheral blood. Within the TME, multiple clearly defined clusters of CD8+ and CD4+ T cells with distinct transcriptional profiles were observed, including T cells expressing activation, exhaustion, and tissue-resident markers (exhausted) as well as T cells expressing progenitor and memory markers (memory). Multiple TCRs from clonotypes observed to be present in the exhausted and memory compartments were cloned for study of their antigen specificity. Putative neoepitopes were synthesized following in silico prediction of putative mutation derived neoantigens. Several TCRs from the exhausted compartment were specific for mutation-derived neoantigens. Conversely, TCRs from the memory compartment were specific for common viral antigens. With treatment, exhausted tumor-specific T cells transitioned between different exhausted clusters and some transitioned into a proliferating cluster and expanded in number. Expression of genes associated with TCR signaling and glycolysis increased in exhausted clonotypes that expanded with treatment. Expression of GLS, a key gene associated with glutaminolysis, associated with clonotypes that did not expand with treatment. Study of the peripheral blood revealed that CDR3 sequences matching exhausted, tumor specific TCRs from the TME were not detected or detected at very low frequencies prior to treatment. However, detected of CDR3 sequences matching exhausted, tumor specific TCRs from the TME significantly increased after treatment. CDR3 sequences matching memory, viral specific TCRs from the TME were detected at high levels in the peripheral blood prior to treatment, did not significantly change with treatment. Thus, neoadjuvant immunotherapy appears to expand exhausted, tumor specific T cells in the TME and results in increased frequency of these T cells in circulation. These data imply that tumor specific T cells may be largely sequestered into tissues, such as the tumor and possibly draining lymph nodes, in patients with newly diagnosed oral carcinoma and that surgical removal of disease without the use of neoadjuvant immunotherapy may remove a significant proportion of the patient’s anti-tumor T cell immunity. Citation Format: Cem Sievers, Marco Craveiro, Jason Redman, Clint T. Allen. Tumor sequestered, neoantigen specific T cells are dynamically increased in circulation following neoadjuvant immunotherapy [abstract]. In: Proceedings of the AACR-AHNS Head and Neck Cancer Conference: Innovating through Basic, Clinical, and Translational Research; 2023 Jul 7-8; Montreal, QC, Canada. Philadelphia (PA): AACR; Clin Cancer Res 2023;29(18_Suppl):Abstract nr PR02.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| 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) | 0.002 | 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 source (direct Gemma or distilled Codex), 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".