Immunogenomic profiling of circulating T cells in pediatric cancer patients during standard-of-care
Bibliographic record
Abstract
Abstract While pediatric cancer patients receive intensive chemotherapy, its impact on peripheral T cells and subsequently to disease outcomes are not fully characterized. Here, we assessed T-cell dynamics during treatment, identifying associations with outcomes through immune phenotyping and T-cell Receptor (TCR) sequencing in pediatric solid and hematologic malignancies. We show that while levels of immune checkpoint proteins (PD-1, LAG3, and TIM3) at baseline were highest in lymphomas compared to other cancer groups, they increased significantly in response to therapy in all cancers. Levels of Central Memory (CM) T cells increased in leukemias and solid tumors, while naïve T cells and cell-free TCR diversity decreased in lymphomas. By combining immune cell and TCR repertoire features across all timepoints, we proposed the Dynamic Immunogenomic Score (DIS) to measure patient-specific effects of therapy on the peripheral T-cell population. Higher DIS was associated with high-risk cancer types and logistic regression analysis revealed it may predict incidence of relapse in leukemia patients. TCR specificity analysis revealed patient-specific clonal dynamics and differential detection of virally-associated TCRs in cancer patients compared to healthy individuals. Our results highlight the potential of early upfront immunogenomic profiling in identifying high-risk patients that may be predictive in light of emerging cellular immunotherapies.
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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) | 0.001 | 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".