Investigating T Cell Phenotypes Associated with Clinical Response to PD-1 blockade
Bibliographic record
Abstract
Immunotherapies targeting PD-1/PD-L1 have demonstrated remarkable efficacy in the clinic across a wide variety of cancer types. Despite the success of PD-1 blockade, a significant proportion of cancer patients still do not respond to these therapies. Thus, investigating the immune cells responsible for orchestrating anti-tumor responses following pembrolizumab treatment is critical for improving patient response to therapy. In this thesis, I investigated T cell phenotypes within the peripheral blood and the tumor microenvironment of patients treated with PD-1 blockade in the Investigator-initiated Phase II Study of Pembrolizumab Immunological Response Evaluation (INSPIRE) trial. The INSPIRE trial enrolled 106 patients and was divided into five cohorts: squamous cell carcinoma of the head and neck cancer, triple-negative breast cancer, high-grade serous ovarian cancer, metastatic melanoma, and a mixed solid tumors cohort, which included six patients with Merkel cell carcinoma. In the first half of this thesis, I show in a pan-cancer analysis that patients harboring pre-existing CD8 T cells in the tumor that co-expressed PD-1 and the co-stimulatory molecule 4-1BB were more likely to achieve a favorable response to pembrolizumab treatment. In the second half, I demonstrate the involvement of γδ T cells in the response to PD-1 blockade. I observed that a Merkel cell carcinoma patient exhibiting a complete response to pembrolizumab had a 10-fold expansion in the frequency of γδ T cells in their tumor. Further investigation revealed that their γδ T cells had increased expression of inhibitory receptors and there was clonal expansion of a predominant Vγ2Vδ1 T cell clonotype. Upon characterization of the Vγ2Vδ1 TCR, I found that this TCR recognized various cancer cell lines and that reactivity was not dependent on previously known γδ T cell ligands, suggesting the potential novelty of the ligand. Altogether, these results from the INSPIRE clinical trial have implications for the development of strategies aimed at improving PD-1 blockade efficacy.
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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 | 0.001 |
| 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 | 0.001 |
| 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 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".