Abstract A017: The FES tyrosine kinase as an emerging target for cancer immunotherapy
Bibliographic record
Abstract
Abstract Immunotherapies are a promising emerging pillar of cancer treatment, but they still face many barriers due to the immunosuppressive nature of cancer. Cancer immunotherapy relies on the interplay between innate and adaptive immune responses. One way of stimulating such responses, known as immunogenic cell death (ICD), involves the release of tumour-associated antigens and damage associated molecular patterns (DAMPs). These DAMPs function to recruit and activate innate immune cells, including antigen-presenting cells (APCs), through engagement of pattern recognition receptors (PRRs), subsequently leading to production of the pro-inflammatory Signal 3 cytokines required for activation of adaptive immune cells (e.g., cytotoxic T lymphocytes [CTLs] and natural killer [NK] cells). The tyrosine kinase Fes suppresses innate immune responses in APCs by inhibiting components of the PRR signaling cascade. In non-cancer contexts, the negative regulation of APCs by Fes may guard against consequences of overactive innate immunity, including endotoxic shock or autoimmune disease. However, this same inhibitory effect on APC function may also serve as a checkpoint to successful anti-cancer immunotherapy, by obstructing efficient priming of cancer specific CTLs by APCs. Therefore, by inhibiting Fes, we hypothesize there will be greater Signal 3 cytokine production, resulting in greater CTL activation, and therefore improved tumor control. Using bone marrow derived APCs, including macrophages (BMDMs) and dendritic cells (BMDCs), from wildtype (WT) or Fes knockout (fes-/-) mice, we have shown through both Western blotting and flow cytometry analysis, that PRR signal transduction cascades are suppressed by Fes and increase levels of Signal 3 cytokines produced by fes-/- APCs. This includes higher levels of cell associated IL-12 in fes-/- APCs. Using syngeneic orthotopic mouse engraftment models of triple negative breast cancer (EO771) and melanoma (B16-F10) we showed that treatment with doxorubicin (which induces ICD) or anti-PD-1 (immune checkpoint inhibitor) plus doxorubicin controls tumor growth and prolongs survival to a greater extent in fes-/- mice. Immunophenotyping of tumors and spleens from these mice showed higher levels of activated CTLs and skewing of macrophages to a M1 state in fes-/- mice. SIINFEKL peptide loaded-BMDM/BMDCs from fes-/- mice were also more effective at priming CTLs from OT-1 mice (which express a T cell receptor that recognizes the SIINFEKL peptide) in antigen cross-presentation co-culture assays. These results implicate Fes as a potential novel immune checkpoint whose inhibition may enhance anti-cancer immunotherapy by suppressing its role in dampening inflammatory Signal 3 cytokine production by APCs. I will present recent data exploring the role of Fes in regulating the expression and trafficking of IL-12 in APCs to better understand the molecular basis of improved CTL activation by fes-/- APCs. Citation Format: Julian Simonetti, Brian J. Laight, Natasha Dmytryk, Danielle Harper, Yan Gao, Changnian Shi, Madhuri Koti, Sameh Basta, Peter A. Greer. The FES tyrosine kinase as an emerging target for cancer immunotherapy [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Optimizing Therapeutic Efficacy and Tolerability through Cancer Chemistry; 2024 Dec 9-11; Toronto, Ontario, Canada. Philadelphia (PA): AACR; Mol Cancer Ther 2024;23(12_Suppl):Abstract nr A017.
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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 | 0.000 | 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".