STEM-42. GENOME-WIDE CRISPR SCREENS IN BRAIN TUMOR INITIATING CELLS (BTICS) IDENTIFY POTENT SENSITIZERS OF CONVENTIONAL CHEMORADIOTHERAPY
Bibliographic record
Abstract
Glioblastoma (GBM) is a highly aggressive and most common form of malignant primary brain tumor in adults (WHO grade IV). Despite surgical and adjuvant therapeutic interventions, including chemotherapy with the alkylating agent TMZ and cranial irradiation, GBM relapse is inevitable with a median survival of <15 months. Extensive intratumoral heterogeneity (ITH) in GBM is believed to be the leading cause of therapy resistance and disease relapse, suggesting that therapy acts as a selection pressure or bottleneck for tumor evolution from minority cell populations present at time of initial diagnosis. Recently, the advent of CRISPR-Cas9 technology has led to the development of genome-wide libraries of sgRNAs capable of introducing insertion-deletion (indels) within exons, leading to a frameshift mutation two-thirds of the time. Here, we present the first genome-wide CRISPR-Cas9 knockout screen in patient-derived GBM BTICs aimed to discover synthetic lethal sensitizers of conventional chemoradiotherapy. Briefly, we performed genome-wide CRISPR-Cas9 screens in treatment-naïve GBM BTICs subjected to in vitro chemotherapy with TMZ and irradiation. By comparing sgRNA dynamics at each doubling period, we were able to identify potent sensitizer genes exclusive to combined chemoradiotherapy, and not TMZ or irradiation alone. Candidate sensitizer genes were validated in an arrayed format to evaluate impact on GBM BTIC self renewal, proliferation, and sensitivity to TMZ and radiation. We aim to further validate these sensitizers of conventional chemoradiotherapy by performing a focused CRISPR-Cas9 genetic screen in our patient-derived xenograft model of treatment-refractory GBM. Ultimately, adjuvants targeting sensitizer genes could greatly enhance the impact of conventional chemoradiotherapy in GBM patients, leading to an increase in patient survival.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".