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 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) | 0.001 | 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 | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".