Early clonal expansion of tumor-infiltrating lymphocytes predicts response to immune checkpoint therapy
Bibliographic record
Abstract
Abstract Immune checkpoint therapy (ICT) causes durable tumor responses in a subgroup of patients. Profiling T cell receptor beta (TCRβ) repertoire structure in ICT responders and non-responders provides mechanistic insight into what constitutes an effective anti-tumor response, and could result in the development of predictive biomarkers of response to identify and stratify patients for ICT. To examine how the TCRβ repertoire dynamics contribute to ICT response, we utilized an established murine model that excludes variation in host genetics, environmental factors and tumor mutation burden, limiting variation between animals to naturally diverse TCRβ repertoires. Oligoclonal expansion of TCRβ clonotypes that corresponded with a low TCRβ diversity was observed in responding tumors prior to ICT. We modeled TCRβ cluster dynamics during ICT and found that select clonotypes expanded slower in responders compared to non- responders. Clonally expanded CD8+ tumor infiltrating T cells in non-responders exhibited a T cell exhaustion phenotype. We conclude that an early burst of clonal expansion followed by a contraction during ICT is associated with response.
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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".