Abstract A037 Changes in T-cell repertoire during high-risk neuroblastoma therapy: A report from the Children’s Oncology Group
Bibliographic record
Abstract
Abstract Introduction: High-risk neuroblastoma (HRNB) carries a poor prognosis, with a 5-year survival rate of ∼50% despite intensive treatment. The standard of care (SOC) for HRNB consists of induction chemotherapy, multimodal consolidation therapy, and post-consolidation immunotherapy. While previous research has linked infiltrating T-cells to better prognosis in some pediatric cancers, the importance of the peripheral T-cell repertoire and its T-cell receptors (TCRs) as immune biomarkers for HRNB remains unexplored. Here, we performed a preliminary immunogenomic analysis of a subset of blood samples from the Children’s Oncology Group phase 3 clinical trial NCT03126916, designed to evaluate the effects of adding targeted therapies (including 131I-MIBG) to the SOC on survival and response. Methods: To infer the nucleosome positioning of immune-related sites from coverage information, we generated blood cell-free whole genome sequencing (cfWGS) data for an initial group of 33 patients (N=35 samples). To track the T-cell repertoire, we sequenced TCRs for 12 of the 33 patients from cell-free DNA (cfDNA) (N=14 samples) and peripheral blood mononuclear cells (PBMCs) (N=26 samples) across diagnosis, mid-induction, and end of post-consolidation. Four patients have samples from all timepoints. We computed peripheral TCR diversity and measured TCR specificity using GLIPHII (Grouping Lymphocyte Interactions by Paratope Hotspots II), against a database of 3681 TCR sequences with empirically defined specificities. We included data from 99 children with non-HRNB cancers and 18 healthy adults as controls. Results: Our diversity analysis found a 4-fold decrease in PBMC TCR diversity from diagnosis (Shannon diversity, mean 619.81) to mid-induction (mean 154.55) and a subsequent increase from mid-induction to post-therapy (mean 557.94; Satterthwaite's mixed-effects model, p=0.003). Our specificity analysis detected 112 TCRs in PBMCs and cfDNA that overlap sequences found in ≥3 relapsed pediatric controls with non-HRNB cancers (per patient mean 3.1; range 0-14). This highlights the possibility to identify relapse-associated antigens for detection of early relapse. Our nucleosome positioning analysis indicated that lymphoid- and myeloid-related genes exhibit reduced accessibility in HRNB at diagnosis compared to healthy adult controls (Welch's t-test, central coverage: p<0.0001). By contrast, MYCN shows higher accessibility, consistent with high expression levels of this oncogene (p<0.0001). The differential accessibility at immune sites compared to oncogenic sites suggests potential mechanisms for immune response alterations. Conclusion: Our initial analysis revealed a mid-chemotherapy decrease in TCR diversity in PBMCs, followed by a partial post-therapy recovery. This shift in diversity was accompanied by the presence of relapse-linked TCRs in some patients but not others, highlighting their potential as emergent immune biomarkers. Additional samples are being analyzed for association with outcomes with standard of care with and without 131I-MIBG. Citation Format: Yiyue Jiang, Arash Nabbi, Arnavaz Danesh, Stephanie Pedersen, Jenna Eagles, Arlene Naranjo, Kai Tan, Natalie Collins, Steven G. DuBois, Rochelle Bagatell, Brian D. Crompton, Trevor J. Pugh. Changes in T-cell repertoire during high-risk neuroblastoma therapy: A report from the Children’s Oncology Group [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Advances in Pediatric Cancer Research; 2024 Sep 5-8; Toronto, Ontario, Canada. Philadelphia (PA): AACR; Cancer Res 2024;84(17 Suppl):Abstract nr A037.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 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".