Peripheral Blood TCR Repertoire Profiling May Facilitate Patient Stratification for Immunotherapy against Melanoma
Bibliographic record
Abstract
Abstract Many metastatic melanoma patients experience durable responses to anti-PD1 and/or anti-CTLA4; however, a significant proportion (over 50%) do not benefit from the therapies. In this study, we sought to assess pretreatment liquid biopsies for biomarkers that may correlate with response to checkpoint blockade. We measured the combinatorial diversity evenness of the T-cell receptor (TCR) repertoire (the DE50, with low values corresponding to more clonality and lack of TCR diversity) in pretreatment peripheral blood mononuclear cells from melanoma patients treated with anti-CTLA4 (n = 42) or anti-PD1 (n = 38) using a multi-N-plex PCR assay on genomic DNA (gDNA). A receiver operating characteristic curve determined the optimal threshold for a dichotomized analysis according to objective responses as defined by RECIST1.1. Correlations between treatment outcome, clinical variables, and DE50 were assessed in multivariate regression models and confirmed with Fisher exact tests. In samples obtained prior to treatment initiation, we showed that low DE50 values were predictive of a longer progression-free survival and good responses to PD-1 blockade, but, on the other hand, predicted a poor response to CTLA4 inhibition. Multivariate logistic regression models identified DE50 as the only independent predictive factor for response to anti-CTLA4 therapy (P = 0.03) and anti-PD1 therapy (P = 0.001). Fisher exact tests confirmed the association of low DE50 with response in the anti-CTLA4 (P = 0.041) and the anti-PD1 cohort (P = 0.0016). Thus, the evaluation of basal TCR repertoire diversity in peripheral blood, using a PCR-based method, could help predict responses to anti-PD1 and anti-CTLA4 therapies.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".