Evaluating T-cell cross-reactivity between tumors and immune-related adverse events with TCR sequencing: pitfalls in interpretations of functional relevance
Bibliographic record
Abstract
T-cell receptor sequencing (TCRseq) enables tracking of T-cell clonotypes recognizing the same antigen over time and across biological compartments. TCRseq has been used to test if cross-reactive antitumor T cells are responsible for development of immune-related adverse events (irAEs) following immune checkpoint blockade. Prior studies have interpreted T-cell clones shared among the tumor and irAE as evidence supporting this, but interpretations of these findings are challenging, given the constraints of TCRseq. Here we capitalize on a rare opportunity to understand the impact of potential confounders, such as sample size, tissue compartment, and collection batch/timepoint, on the relative proportion of shared T-cell clones between an irAE and tumor specimens. TCRseq was performed on tumor-involved and -uninvolved tissues, including an irAE, that were obtained throughout disease progression and at the time of rapid autopsy from a patient with renal cell carcinoma treated with programmed death-1 (PD-1) blockade. Our analyses show significant effects of these confounders on our ability to understand T-cell receptor overlap, and we present mitigation strategies and study design recommendations to reduce these errors. Implementation of these strategies will enable more rigorous TCRseq-based studies of immune responses in human tissues, particularly as they relate to antitumor T-cell cross-reactivity in irAEs following checkpoint blockade.
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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 | 0.001 | 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 | 0.000 | 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 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".