Abstract B001: Leveraging peripheral leukocyte recruitment to improve efficacy and mitigate toxicities following checkpoint blockade
Bibliographic record
Abstract
Abstract CD8 T cells are effector cells of the adaptive immune system responsible for mediating protective immune responses in cancer. However, CD8 T cells can also be critical drivers of pathologic responses including autoimmunity. A key feature of these cells is that they must infiltrate tissues to execute their primary function of cell killing. It is well established that naive T cell responses do not originate in these tissues. Rather, prior to antigen-driven activation, naïve T cells are restricted to lymph nodes, spleen, and blood. T cell activation by cognate antigen drives expression of multiple immune checkpoint inhibitors, including programmed cell death protein-1 (PD-1) and/or cytotoxic T lymphocyte associated protein-4 (CTLA-4), as well as migration to nonlymphoid tissues. Work over the last two decades has harnessed T cell effector activities by blocking PD-1 and/or CTLA-4, which has revolutionized cancer care and demonstrated remarkable efficacy in diverse cancer types. However, many patients do not respond to checkpoint blockade therapy, and worse, other patients develop autoimmune-like immune-related adverse events (irAEs), limiting the utility of this approach. To maximize the therapeutic benefit of checkpoint blockade without accelerated irAE development, targeted approaches are needed to increase recruitment of protective CD8 T cells to tumors while preventing pathogenic T cell entry into sensitive tissues. However, an inability to granularly measure recruitment into non-lymphoid tissues has created a significant gap in knowledge in: 1) how checkpoint inhibitors impact T cell entry rates into tissues, 2) whether these entry rates are different for tumors and lesional irAE tissues, 3) where recruited cells localize, and 4) the precise functional contributions of recruited versus resident T cells in driving tumor regression or autoimmunity. To fill this gap in knowledge, we are utilizing an intravenous antibody labeling approach to mark CD8 T cells in the blood prior to entry into tissues, an approach which enables a 72-hour window to track tissue entry. Using this approach in the preclinical setting, we are interrogating how PD-1 inhibitors impact CD8 T cell recruitment to tumors as well as the pancreas in the context of Type 1 Diabetes. In our tumor studies, we have found that PD-1 inhibitors induce an increase in the number of recently recruited CD8 T cells, and that these recruited T cells have a less exhausted phenotype than the T cells that have been in the tumor for longer. In our diabetes studies, we have found that PD-1 inhibitors impact CD8 T cells both in the periphery and the pancreas, and in some mice cause an increase in migration to the pancreas. These studies support a model where peripheral CD8 T cell recruitment can positively contribute to productive anti-tumor immunity but can also exacerbate autoimmunity. Thus, our studies emphasize the doubled-edged nature of T cell recruitment, and our future studies will examine ways to both improve recruitment to tumors and mitigate recruitment to lesional irAE tissues. Citation Format: Oluwatoyosi Adewunmi, Arielle G. Dessens, Aloukick K. Singh, Autumn M. Sunderland, Sanjana Ananthula, Rachel M. Morris, Jason M. Schenkel, Kristen E. Pauken. Leveraging peripheral leukocyte recruitment to improve efficacy and mitigate toxicities following checkpoint blockade [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Mechanisms of Cancer Immunity and Cancer-related Autoimmunity; 2025 Sep 24-27; Montreal, QC, Canada. Philadelphia (PA): AACR; Cancer Immunol Res 2025;13(9 Suppl):Abstract nr B001.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".