Rapid and Continued T-Cell Differentiation into Long-term Effector and Memory Stem Cells in Vaccinated Melanoma Patients
Bibliographic record
Abstract
Abstract Purpose: Patients with cancer benefit increasingly from T-cell–based therapies, such as adoptive T-cell transfer, checkpoint blockade, or vaccination. We have previously shown that serial vaccinations with Melan-AMART-126-35 peptide, CpG-B, and incomplete Freund adjuvant (IFA) generated robust tumor-specific CD8 T-cell responses in patients with melanoma. Here, we describe the detailed kinetics of early- and long-term establishment of T-cell frequency, differentiation (into memory and effector cells), polyfunctionality, and clonotype repertoire induced by vaccination. Experimental Design: Twenty-nine patients with melanoma were treated with multiple monthly subcutaneous vaccinations consisting of CpG-B, and either the native/EAA (n = 13) or the analogue/ELA (n = 16) Melan-AMART-126-35 peptide emulsified in IFA. Phenotypes and functionality of circulating Melan-A–specific CD8 T cells were assessed directly ex vivo by multiparameter flow cytometry, and TCR clonotypes were determined ex vivo by mRNA transcript analyses of individually sorted cells. Results: Our results highlight the determining impact of the initial vaccine injections on the rapid and strong induction of differentiated effector T cells in both patient cohorts. Moreover, long-term polyfunctional effector T-cell responses were associated with expansion of stem cell–like memory T cells over time along vaccination. Dominant TCR clonotypes emerged early and persisted throughout the entire period of observation. Interestingly, one highly dominant clonotype was found shared between memory and effector subsets. Conclusions: Peptide/CpG-B/IFA vaccination induced powerful long-term T-cell responses with robust effector cells and stem cell-like memory cells. These results support the further development of CpG-B–based cancer vaccines, either alone or as specific component of combination therapies. Clin Cancer Res; 23(13); 3285–96. ©2016 AACR.
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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".