Evaluating the Metabolic and Anti-tumor Properties of CD8+ T Cell Lineages
Bibliographic record
Abstract
Harnessing the immune system to attack cancerous cells is an exciting treatment strategy that has shown great promise clinically. Many immune cell types are involved in mediating tumor rejection with CD8+ T cells playing an important role. Typically effector CD8+ T cells are regarded as being a homogenous population of cytotoxic cells that produce the cytokine interferon (IFN)-γ. However, this outlook does not fully encompass the diversity of the CD8+ T cell population as multiple subsets of CD8+ T cells (Tc subsets) have been identified each demonstrating distinct effector functions. As many of these Tc subsets are not well defined, the first part of this Thesis was aimed at defining and characterizing the different Tc lineages. I identified the polarizing conditions to induce an interleukin (IL)-22 producing CD8+ Tc22 subset, which we found to be dependent on IL-6 and the aryl hydrocarbon receptor transcription factor. Further characterization showed that this subset is highly cytolytic and expresses a distinct cytokine profile and transcriptome relative to other subsets. Moreover, Tc22 cells demonstrate robust anti-tumor properties that were attributed to increased mitochondrial metabolism. Given the potential role of T cell metabolism in mediating the Tc22 anti-tumor responses, the second part of the Thesis was aimed at understanding the metabolic pathways involved in Tc22 polarization. Here, I found that Tc22 were distinct from other Tc subsets in that Tc22s required oxidative phosphorylation for polarization. Moreover, I identified coenzyme a (CoA) as a reagent to induce Tc22 cells in the absence of polarizing cytokines. CoA-treated cells demonstrate robust anti-tumor properties in multiple mouse models. Importantly, I found the CoA precursor pantothenic acid to be a marker of complete response to anti-PD1 therapy in human melanoma patients. In summary, the findings presented in this Thesis identify Tc22 cells as a robust anti-tumor subset with potential therapeutic implications in the context of checkpoint blockade in addition to chimeric antigen receptor (CAR)-T or T cell receptor (TCR) transduction based immunotherapies.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".