Abstract 6667: Pan-cancer assessment of tumour and peripheral T-cell receptor repertoire dynamics in patients treated with immune checkpoint inhibitors
Bibliographic record
Abstract
Abstract Introduction: Clinical benefit from Immune Checkpoint Blockade (ICB) is a function of local T-cell specificity for tumor-associated antigens. However, overcoming local T-cell dysfunction necessitates systemic immunity engagement. Therefore, studying the dynamics of both local and peripheral T-cell repertoires in response to ICB is required to identify features of T-cell repertoires associated with pathological response. Methods: We conducted TCRβ-sequencing on tumor-residing T-cells (n=59), Peripheral Blood Mononuclear Cells (PBMCs) (n=306) and cell-free DNA (cfDNA) (n=73) from pre- and multiple on-ICB timepoints collected from patients enrolled in the pan-cancer INvestigator-initiated Phase II Study of Pembrolizumab Immunological Response Evaluation (INSPIRE; NCT02644369) trial. To assess specificity-agnostic shifts in TCR repertoires, we first compared TCR diversity and clonal expansion in longitudinal tumor and PBMC samples. Then, to temporally track the specificity-associated features of local and systemic TCR repertoires, we leveraged a Graph Neural Network (GNN) model that took in unique TCRβ chains as nodes. The connectivity between the nodes was defined by multi-relational edges that represented VJ-gene usage and GLIPHII-identified (Grouping Lymphocyte Interactions by Paratope Hotspots) specificities derived from a compendium of TCR sequences with empirically confirmed specificities. Results: While absolute diversity and clonal expansion values in baseline tumor (n=33) were not associated with response to ICB, changes in these values were informative between pre- and on-ICB tumors. All patients (n=4) with low baseline tumor TCR diversity and lack of clonotypic re-structuring in tumor TCR repertoire on-ICB had either progressive or short-term stable disease. Furthermore, pairwise comparison of pre- and on-ICB tumors for each patient (n=17) revealed that all the patients, irrespective of their pathological response, experienced emergence of new TCR clonotypes (i.e., clonal replacement) in response to ICB, suggesting only a minority of these TCRs might consist of tumor-associated clonotypes. Patients with clinical benefit also had higher degree of GLIPHII-identified clustering at baseline tumor, highlighting the role of both specificity-agnostic and specificity-centric TCR analysis in determining the response to ICB. Analysis of TCR sequences in blood plasma found cfDNA contains a small number of TCR sequences (median 32, range 12-89) enriched for TCRs found in matched tumor tissues, suggesting that cfDNA TCR repertoire may provide an indirect measurement of tumor-residing T-cells. Conclusions: TCR diversity and functional clonal annotation are emerging biomarkers of ICB response and cfDNA TCR repertoire can potentially be exploited for clinical diagnostics and monitoring. Citation Format: Shirin Soleimani, Ben X. Wang, Stephanie Pedersen, Jenna Eagles, Jacob Brick, Marcus O. Butler, Scott V. Bratman, Lillian L. Siu, Pamela S. Ohashi, Trevor J. Pugh. Pan-cancer assessment of tumour and peripheral T-cell receptor repertoire dynamics in patients treated with immune checkpoint inhibitors [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 6667.
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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".