Abstract B003: Communication is key: Understanding the modulating effect of immune cell crosstalk on T cell therapy function
Bibliographic record
Abstract
Abstract T cell therapies have shown great promise, especially for the treatment of hematological malignancies, but this success has not been translatable for most solid tumors. Therefore, efforts have been put towards improving their efficacy, including optimization of the subset composition of the T cell product and development of strategies to overcome immunosuppression by the tumor microenvironment. Here, we combined advanced immune-organoid co-cultures and live 3D imaging to understand how different T cell subsets and immune cells in the tumor microenvironment can affect T cell therapy efficacy. We found that CD4+ and CD8+ subsets of therapeutic T cells cooperate to boost anti-tumor function, while tumor-associated macrophages (TAMs) have an opposing effect and reduce tumor-targeting by engineered T cells. Through our dynamic imaging-readout (Dekkers, Alieva Nature Biotech. 2022), we observed that a high level of interactions are involved in these modulating events, indicating direct crosstalk between the different cell types. To further investigate the impact of these interactions, we developed a 3D interactome mapping tool which enables us to profile all occurring cell-cell interactions in our 3D imaging datasets and determine their functional outcome. With this tool we identified a specific interaction between CD4+ and CD8+ T cells, where a subset of CD4+ T cells acquired a potent helper function over time by first engaging with tumor organoids and subsequently inducing CD8+ T cell target localization via direct contact. Multi-omics integration with single cell transcriptomic data allowed us to study the underlying molecular pathways involved in this interaction, leading to the identification of a crucial binding partner involved in this cooperative tumor-targeting behavior. Using the same strategy, we are currently investigating how interactions between TAMs and therapeutic T cells modulate their tumor-targeting activity. Together, these data highlight how cellular interactions can play an important role in engineered T cell function and we are currently exploring strategies to target these axes of immune cell communication to maximize therapy efficacy. Citation Format: Amber K.L. Wezenaar, Celina Honhoff, Helena van Eck, Elena Jiménez Curiel, Annelisa M Cornel, Femke C.A. Ringnalda, Tineke Aarts-Riemens, Jürgen Kuball, Zsolt Sebestyen, Hans Clevers, Stefan Nierkens, Johanna F. Dekkers, Ellen J. Wehrens, Maria Alieva, Anne C. Rios. Communication is key: Understanding the modulating effect of immune cell crosstalk on T cell therapy function [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Tumor Immunology and Immunotherapy; 2023 Oct 1-4; Toronto, Ontario, Canada. Philadelphia (PA): AACR; Cancer Immunol Res 2023;11(12 Suppl):Abstract nr B003.
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) | 0.001 | 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 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.055 | 0.013 |
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".