STEM-27. LEVERAGING FUNCTIONAL GENETIC DEPENDENCIES IN TREATMENT-REFRACTORY GLIOBLASTOMA
Bibliographic record
Abstract
Abstract As the most common primary brain tumor in adults causing death, Glioblastoma (GBM) remains a therapeutic challenge. Unchanged for almost two decades, standard therapy is ineffective at preventing disease relapse with a median patient survival of < 15 months. Stem cell-like subpopulations of tumor cells, known as brain tumor initiating cells (BTICs), evade standard therapy and lead to relapse. Whereas previous studies largely focus on pre-treatment primary GBM (pGBM), we conducted a panel of genome-wide CRISPR-Cas9 gene knockout screens to determine modulators of treatment resistance and de novo genetic vulnerabilities arising at disease recurrence. Using our in vitro model of conventional therapy, we identified genes modulating sensitivity and resistance to Temozolomide and/or radiation therapy in patient-derived pGBM BTICs. Genes modulating sensitivity belong to Fanconi anaemia nuclear complex, interstrand cross link repair, and regulation of stem cell maintenance and differentiation. Following in vitro validation of gene knockouts conferring treatment sensitization in multiple pGBM BTIC lines, we continued to conduct the first genome-wide CRISPR-Cas9 screens in patient-derived rGBM BTICs. Focusing on genetic vulnerabilities arising de novo at disease relapse, we introduce the context-specific role of protein tyrosine phosphatase 4A2 (PTP4A2) in rGBM. Genetic knockout or small molecule targeting of PTP4A2 leads to a context-specific vulnerability of rGBM self renewal capacity and in vivo tumorigenecity. To continue our analysis of treatment-refractory GBM and overcome intertumoral heterogeneity, we conducted genome-wide CRISPR-Cas9 gene knockout screens and whole cell proteomics on patient-matched pGBM and rGBM BTICs. With >1000 differentially essential genes, combined functional genetic and proteomic analyses implicates genes involved in mRNA splicing, nucleotide metabolism, and activation of gene expression by sterol regulatory element-binding protein. Together, our functional genetic approach elucidates novel genes regulating treatment resistance and disease recurrence in GBM.
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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 | 0.001 |
| 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".