Glioma Stem Cell–Specific Superenhancer Promotes Polyunsaturated Fatty-Acid Synthesis to Support EGFR Signaling
Bibliographic record
Abstract
Abstract Glioblastoma ranks among the most aggressive and lethal of all human cancers. Functionally defined glioma stem cells (GSC) contribute to this poor prognosis by driving therapeutic resistance and maintaining cellular heterogeneity. To understand the molecular processes essential for GSC maintenance and tumorigenicity, we interrogated the superenhancer landscapes of primary glioblastoma specimens and in vitro GSCs. GSCs epigenetically upregulated ELOVL2, a key polyunsaturated fatty-acid synthesis enzyme. Targeting ELOVL2 inhibited glioblastoma cell growth and tumor initiation. ELOVL2 depletion altered cellular membrane phospholipid composition, disrupted membrane structural properties, and diminished EGFR signaling through control of fatty-acid elongation. In support of the translational potential of these findings, dual targeting of polyunsaturated fatty-acid synthesis and EGFR signaling had a combinatorial cytotoxic effect on GSCs. Significance: Glioblastoma remains a devastating disease despite extensive characterization. We profiled epigenomic landscapes of glioblastoma to pinpoint cell state–specific dependencies and therapeutic vulnerabilities. GSCs utilize polyunsaturated fatty-acid synthesis to support membrane architecture, inhibition of which impairs EGFR signaling and GSC proliferation. Combinatorial targeting of these networks represents a promising therapeutic strategy. See related commentary by Affronti and Wellen, p. 1161. This article is highlighted in the In This Issue feature, p. 1143
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".