Integrated genetic screening reveals FEN1 as a driver of stemness and temozolomide resistance in glioblastoma
Bibliographic record
Abstract
Abstract Glioblastoma (GBM) is a lethal brain tumor with limited response to standard of care chemoradiotherapy. In this study, we conducted genome-wide CRISPR knockout screening in patient-derived glioblastoma stem cells (GSCs) to identify genetic dependencies of cell survival and therapy resistance. Our screening identified flap endonuclease 1 (FEN1) as a key driver of GSC survival, with enhanced dependency under temozolomide (TMZ) treatment. Genetic perturbation of FEN1 reduced GSC self-renewal and proliferation in vitro, and prolonged survival in a patient-derived xenograft model of GBM. FEN1 inhibition (FEN1i) preferentially affected highly aggressive or recurrent GBM models compared with less aggressive GBMs and healthy neural stem cells. Moreover, FEN1 inhibition synergized with TMZ only in these aggressive FEN1i-sensitive GSCs, providing cancer-selective killing and TMZ sensitization in the most untreatable of GBMs. Mechanistically, FEN1i-sensitive GSCs exhibited greater proliferation and sphere formation, while stalling their proliferation conferred resistance to FEN1 inhibition. Single-cell transcriptomics further linked FEN1 expression to stemness and the DNA damage response, elucidating broader determinants of FEN1 dependency. These findings establish FEN1 as a promising therapeutic target in GBM, offering a strategy for both selective targeting and enhancement of TMZ efficacy in aggressive cancers. Statement of Significance This study identifies FEN1 as a key vulnerability of glioblastoma stem cells, revealing its role in therapy resistance and stemness, and proposes FEN1 inhibition as a strategy to enhance temozolomide efficacy.
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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) | 0.001 | 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".