Abstract B064: Uncovering the lineage state specific regulation of STING in neuroblastoma
Bibliographic record
Abstract
Abstract Study Aim: This study aims to leverage our investigation of the transcriptional regulation of cGAS-STING signaling to identify the mechanisms that lead to differences in inflammatory signaling and immunogenicity between neuroblastoma lineage states.Neuroblastomas cells can exist in two distinct lineage states - a chemosensitive adrenergic state and a chemoresistant mesenchymal state. We previously found that the mesenchymal state has higher expression of pattern recognition receptors, including STING (stimulator of interferon genes), which transduces signals from the cytosolic DNA sensor cGAS (cyclic GMP-AMP synthase). We hypothesize that identifying modifiers of STING signaling will uncover drivers of the lineage state differences in inflammatory signaling. Methods: We examined cGAS and STING expression at the RNA and protein level in cell lines, patient-derived xenografts, and patient tumors. We also assessed CpG island and histone methylation and how they differed based on lineage state.Through immunoblots and methylation specific PCR we assessed the restoration of cGAS-STING pathway specific signaling through the use of DNMT inhibitors and a DOX inducible system state switch system. We performed a genome wide flow-based CRISPR screen, using an IRF3-responsive GFP reporter, to assess genes involved in STING expression and function. Results: We found that STING is universally expressed in mesenchymal, but not adrenergic cell lines, while cGAS is nearly universally silenced in all neuroblastoma cell lines. Moreover, we found that neuroblastoma cell lines have extensive methylation at the cGAS promoter CpG islands.Using methylation-specific PCR, we showed that the DNMT inhibitor decitabine reduced CpG island methylation and rescued cGAS expression, which restored the immunogenic functionality of the cGAS-STING pathway measured through expression of pIRF3 and pSTAT1.Furthermore, through the use of DOX inducible expression of PRRX1, we showed that STING expression was restored when cells switch from the adrenergic to the mesenchymal state.Finally, we performed a genome wide CRISPR screen using an IRF3 reporter to identify targets responsible for STING expression in the mesenchymal state. This screen identified known components of the cGAS-STING signaling pathway as well as chromatin modifiers and transcription factors not previously associated with cGAS-STING signaling. Conclusions: The cGAS-STING pathway is inactivated in neuroblastoma cell lines due to silencing of cGAS, but can be restored in the mesenchymal state due to its expression of STING.When cGAS functionality is restored, downstream immunogenic signaling returns.We have identified known and novel genes required for cGAS-STING signaling in mesenchymal neuroblastoma cells. Further validation and mechanistic work on these genes is ongoing, including understanding how they alter inflammatory signaling more broadly and how to use this information to improve immunotherapeutic responses. Citation Format: Matthew Shapiro, Pamela Mishra, Alaa Narch, Jessica Dessau, Chi V. Dang, Adam Wolpaw. Uncovering the lineage state specific regulation of STING in neuroblastoma [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Advances in Pediatric Cancer Research; 2024 Sep 5-8; Toronto, Ontario, Canada. Philadelphia (PA): AACR; Cancer Res 2024;84(17 Suppl):Abstract nr B064.
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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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".