Abstract B040: Uncovering senescence-driven pathways in diffuse midline glioma progression and treatment resistance
Bibliographic record
Abstract
Abstract Diffuse midline gliomas (DMGs) are universally fatal pediatric brain tumors with a median survival of just 11 months. Despite radiation therapy offering temporary benefit, there are no effective long-term treatments, and targeted therapies have shown minimal impact. The infiltrative nature of DMGs, combined with their critical brainstem location, underscores the urgent need to identify novel mechanisms driving tumor progression and resistance. Cellular senescence is a state of permanent cell-cycle arrest induced by stress and has traditionally been viewed as tumor-suppressive. However, recent evidence in adult cancers implicates senescent cells as active contributors to tumor growth, treatment resistance, and relapse through the secretion of pro-tumorigenic factors known as the senescence-associated secretory phenotype (SASP). While senescence has emerged as a hallmark of cancer in adults, its role in pediatric tumors such as DMGs remains largely unexplored. Using single-cell RNA sequencing of treatment-naïve DMG specimens, we identified rare populations of senescent glioma cells and microglia that would be undetectable using conventional approaches. Computational analyses revealed predicted paracrine interactions between senescent and non-senescent cells including pathways previously linked to tumor invasiveness and resistance. This study aims to define how senescent cells and their secretory programs shape the tumor microenvironment, promote DMG progression, and contribute to therapy resistance. By elucidating these mechanisms, we hope to uncover new therapeutic vulnerabilities in DMGs and expand our broader understanding of senescence as a driver of malignancy in pediatric cancers. Citation Format: Timothy H. Chang, Alexander Nassar, Ria Kedia, Shriya M. Rangaswamy, Claudia Kleinman, Pratiti Bandopadhayay. Uncovering senescence-driven pathways in diffuse midline glioma progression and treatment resistance [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Discovery and Innovation in Pediatric Cancer— From Biology to Breakthrough Therapies; 2025 Sep 25-28; Boston, MA. Philadelphia (PA): AACR; Cancer Res 2025;85(18_Suppl_2):Abstract nr B040.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".