MétaCan
Menu
Back to cohort
Record W4414465438 · doi:10.1158/2326-6074.cimm25-b001

Abstract B001: Leveraging peripheral leukocyte recruitment to improve efficacy and mitigate toxicities following checkpoint blockade

2025· article· en· W4414465438 on OpenAlexaboutno aff
Oluwatoyosi Adewunmi, Arielle G. Dessens, Aloukick Kumar Singh, Ashley Sunderland, Sanjana Ananthula, Rachel Morris, Jason M. Schenkel, Kristen E. Pauken

Bibliographic record

VenueCancer Immunology Research · 2025
Typearticle
Languageen
FieldMedicine
TopicCancer, Stress, Anesthesia, and Immune Response
Canadian institutionsnot available
Fundersnot available
KeywordsBlockadeCytotoxic T cellImmune checkpointT cellCD8Immune systemCancer immunotherapyImmunotherapyEffector

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.291
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.088
GPT teacher head0.409
Teacher spread0.320 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2025
Admission routes1
Has abstractyes

Explore more

Same venueCancer Immunology ResearchSame topicCancer, Stress, Anesthesia, and Immune ResponseFrench-language works237,207