Abstract PO-068: Harmony between transdifferentiated neuron and immunosuppressive tumor microenvironment
Bibliographic record
Abstract
Abstract Solid tumors sculpt their microenvironments to maximize their growth and metastatic potential. Previously, we showed that the loss of p53 in head and neck squamous cell carcinoma (HNSCC) cells reprograms tumor-associated neurons (TANs) towards an adrenergic phenotype and promotes tumor progression. The impact of this neuronal reprogramming on the tumor immune microenvironment remains undefined. To understand how neuronal transdifferentiation regulates the antitumor immune response, we tested our hypothesis that shuttling cancer-derived microRNAs to TANs via exosomes induces an immunosuppressive neuronal phenotype in the tumor microenvironment (TME) that undermines the anti-tumor immune response. We first checked the stability of HNSCC-derived exosomes and their cargo (i.e., microRNA [miR]). Then, we assessed the miR transfer to TANs both in vitro and in vivo using HNSCC syngeneic models, and we measured miR enrichment in the trigeminal ganglia neurons after exosome injection. We used RNA sequencing to assess the trigeminal ganglia’s transcriptome and a single-cell multiplex enzyme-linked immunosorbent assay to evaluate the functional status of CD8+ T cells cocultured with cancer cells with or without TANs. Cancer cell–derived exosomes and their cargo were stable for up to 14 days in the TME and were taken up by TANs both in vitro and in vivo. The injection of exosomes into the peripheral nerves led to miR324a and miR21 upregulation in the associated trigeminal ganglionic soma, indicating that cancer-derived exosomes mediated crosstalk between cancer and neuronal cells. The RNA sequencing of human and mouse neuronal cells transfected with miR21 and miR324a revealed many differentially regulated immune pathways, including the neuroinflammation, T-cell receptor signaling, toll-like receptor, macrophage differentiation and growth inhibition, antigen-processing, interleukin (IL)-6, programmed cell death protein 1/programmed death-ligand 1, acute-phase response signaling, IL-8, wound-healing, IL-9, IL-17A, IL-23, IL-2, IL-10, IL-7, IL-4, IL-33, IL-36, CD28, CD40, signal transducer and activator of transcription 3, and nuclear factor kappa B pathways. Furthermore, the single-cell proteomic assay indicated that the CD8+ T cells cultured with neuronal cells were functionally distinct from the activated T cells co-cultured with neuronal and cancer cells. The co-culture with cancer cells and neuronal cells significantly downregulated the effector phenotype markers (e.g., granzyme B, interferon gamma, tumor necrosis factor alpha) in activated T cells, indicating that the interaction of cancer cells with neurons regulates the immunosuppressive TME. Our study showed the ability of cancer-derived exosomes to reprogram neurons and downstream to that to regulate the tumor immune environment in HNSCC. Our data suggest that transdifferentiating of TANs induce immunosuppression by direct inhibitory signal and immune checkpoint expression in the TME of HNSCC. Future studies are needed to investigate TANs as a potential target in immune oncology. Citation Format: Shamima Akhter, Tongxin Xie, Simone Anfossi, Adewale Adebayo, Frederico O. Gleber-Netto, Melanie Winkle, Masayoshi Shimitzu, Weilu Zhao, Megan L. Uhelski, Yan Li, George A. Calin, Moran Amit. Harmony between transdifferentiated neuron and immunosuppressive tumor microenvironment [abstract]. In: Proceedings of the AACR-AHNS Head and Neck Cancer Conference: Innovating through Basic, Clinical, and Translational Research; 2023 Jul 7-8; Montreal, QC, Canada. Philadelphia (PA): AACR; Clin Cancer Res 2023;29(18_Suppl):Abstract nr PO-068.
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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 | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".