GENETIC SCREENING REVEALS DEPENDENCY ON FEN1 TO RESIST TEMOZOLOMIDE IN TREATMENT-RESISTANT GLIOBLASTOMA STEM CELLS
Bibliographic record
Abstract
Abstract BTFC travel award recipient Glioblastoma (GBM) is a lethal brain tumor. Despite intensive standard of care (SoC) consisting of surgical resection followed by radiation therapy (RT) and chemotherapy with temozolomide (TMZ), tumor relapse is inevitable with a median overall survival of 14.6 months. Therapeutic failure is attributable to the presence of GBM stem-like cells (GSCs), which are highly resistant to genotoxic therapies, self-renew and differentiate to perpetuate intra-tumoral heterogeneity, and seed tumor recurrence. However, no clinical advances have been made in overcoming resistance to therapy in GSCs, and the mechanisms underlying this resistance remain largely unknown. With the advent of CRISPR-Cas9 technology, functional genetic screens have been used to identify genes that regulate survival and sensitivity to TMZ in GSCs. However, they have not been applied to SoC and a comprehensive set of modulators governing resistance to combination treatment has yet to be determined. Here, we perform genome-wide CRISPR-Cas9 screening in patient-derived treatment-sensitive and treatment-resistant GSC models under normal growth conditions or while exposed to in vitro chemoradiotherapy. By integrating genetic dependencies and treatment-based conditional genetic interactions modulating sensitivity to SoC, we identify flap endonuclease 1 (FEN1) as a driver of GSC survival and therapy resistance in treatment-resistant GBM. Functionally, FEN1 inhibition demonstrates specific killing and synergy with TMZ in treatment-resistant GSCs while sparing healthy neural stem cells (NSCs) and treatment-sensitive GSCs. At the molecular level, FEN1 inhibition induces DNA damage and repair specifically in treatment-resistant GBM, posing FEN1 as an attractive therapeutic target in this population of GBM patients which SoC currently fails.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.000 | 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 teacher head, 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".