Abstract 2084: Single-cell multimodal glioma analyses reveal epigenetic regulators of cellular plasticity and environmental stress response
Bibliographic record
Abstract
Abstract Glioma intratumoral heterogeneity enables adaptation to challenging microenvironments and contributes to universal therapeutic resistance. Here, we integrated 914 single-cell DNA methylomes, 55,284 single-cell transcriptomes, and bulk multi-omic profiles across 11 adult IDH-mutant or IDH-wild-type gliomas to delineate sources of intratumoral heterogeneity. We found that local DNA methylation instability, or epimutation burden, was elevated in more aggressive tumors, reflected intratumoral variability, linked with transcriptional disruption, and associated with environmental stress response. We show that the activation of cell-state specific transcription factors is impacted by epimutations and that loosened epigenetic control may facilitate cellular plasticity. We validated the impact that the common environmental stressor hypoxia has on the epigenetic stability and shifts in cell states through perturbation-based in vitro experiments coupled with single-cell genomics. Our analyses support that glioma cells under stress hijack epigenetic mechanisms that regulate cell state transitions to overcome challenges posed by nutrient-poor microenvironments and therapeutic insults. We confirmed the link between cellular stress and epigenetic instability by analyzing larger cohorts of bulk longitudinally collected and spatially separated DNA methylation data. Increased DNA methylation instability was associated with accelerated disease progression, and recurrently selected DNA methylation changes were enriched for environmental stress response pathways. Our work provides an integrative framework to better understand glioma evolution and highlights the importance of epigenetic heterogeneity in shaping therapeutic response. Citation Format: Kevin C. Johnson, Kevin J. Anderson, Elise T. Courtois, Amit D. Gujar, Floris P. Barthel, Frederick S. Varn, Diane Luo, Martine Seignon, Eunhee Yi, Hoon Kim, Marcos R. Estecio, Ming Tang, Nicholas E. Navin, Rahul Maurya, Chew Yee Ngan, Niels Verburg, Philip C. De Witt Hamer, Ketan Bulsara, Michael L. Samuels, Sunit Das, Paul Robson, Roel G. Verhaak. Single-cell multimodal glioma analyses reveal epigenetic regulators of cellular plasticity and environmental stress response [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2021; 2021 Apr 10-15 and May 17-21. Philadelphia (PA): AACR; Cancer Res 2021;81(13_Suppl):Abstract nr 2084.
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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 | 0.001 | 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".