Abstract PR-011: The role of tumor microenvironment in metastasis and relapses of medulloblastoma using immunocompetent mouse models
Bibliographic record
Abstract
Abstract Medulloblastoma (MB) is an embryonal malignant tumor that arises in the cerebellum of infants, children, and adolescents. Despite highly aggressive therapies, including surgery, radiation, and chemotherapy, metastasis remains the primary cause of death in these patients. The tumor microenvironment (TME) has an important role in metastasis and relapse in several cancer types, but it’s role in high-risk MBs (metastatic and relapse) has been understudied. Here, we investigated the process of relapse in an immunocompetent Shh MB mouse model based on somatic mutagenesis induced by Sleeping Beauty (SB)-mediated transposition on a genetically engineered strain carrying a loss-of-function Patched 1 gene (Ptch1) mutation. Lack of one functional copy of the Ptch1 gene predisposes this strain to develop MB. Somatic mutations induced by the transposon increase penetrance of tumor formation in the triple mutant mice carrying the SB transposon system and the Ptch1 mutation. We used this model to study the mechanisms of chemoresistance to cisplatin and cyclophosphamide, that constitute the backbone of the high-dose chemotherapy used to treat patients under the age of 3, who cannot receive craniospinal irradiation. By treating the SB mouse model with surgery followed by high-dose fractionated chemotherapy, we developed a model of MB chemoresistance which is relevant to the infant SHH-MB patient population. While chemotherapy improved mouse survival, only a small number of animals were cured, mirroring the outcome seen in infant patients with Shh MB. Deep sequencing and mapping of transposon-gDNA junctions, along with gene-centric common insertion site analysis (gCIS), revealed that Trp53 transposon insertions are found exclusively at relapse. In the metastatic compartment we observed convergence of gCISes on genes coding for epigenetic regulators, among them a prominent enrichment was found for PRC2 complex genes. RNA-seq analysis on the transcriptome of primary and recurrent tumors indicated that a majority of the differentially expressed genes are associated with inflammatory responses, IL6, and TNF-α/NF-κB signaling. Immunohistochemistry to visualize the expression levels of inflammatory markers: IL-6, CCL2, and F4-80 and spatial targeted multiplexed assays (Geomx) confirmed the increase of immune cells at progression. ChIP-qPCR on cytokine loci revealed binding of PRC2 and deposition of PRC2 mediated repressive histone mark H3K27me3 on the promoter of CCL2 highlighting LOF mutations in PRC2 components as potential epigenetic mechanisms for immune recruitment at progression. Inhibition of immune activation through pharmacological strategies might be a possible therapeutic option for infant SHH-MB. Citation Format: Niusha Khazaei, Ana Castillo Orozco, Wajih Jawhar, Geoffroy Danieau, Kiran Narta, Michael Taylor, Sorana Morrissy, Ana Stuecklin, Livia Garzia. The role of tumor microenvironment in metastasis and relapses of medulloblastoma using immunocompetent mouse models [abstract]. In: Proceedings of the AACR Special Conference on Brain Cancer; 2023 Oct 19-22; Minneapolis, Minnesota. Philadelphia (PA): AACR; Cancer Res 2024;84(5 Suppl_1):Abstract nr PR-011.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".