Abstract 60: Functional mapping reveals widespread remodelling and unrecognized pathway dependencies in recurrent glioblastoma
Bibliographic record
Abstract
Abstract Glioblastoma is a highly fatal brain cancer. The underlying functional drivers of treatment resistance and disease recurrence are unclear. By applying a genome-wide CRISPR-Cas9 library to patient-derived glioblastoma stem cell models, we systematically map genetic dependencies in patient-matched pre-treatment primary and post-treatment recurrent tumor cells. These insights reveal a large-scale remodelling of genetic dependency profiles at disease recurrence, arming recurrent tumor cells with newly-acquired genetic drivers and further loss of tumor suppressors. These analyses support parallel tumor-intrinsic mechanisms of treatment resistance which rely on acquisition of immunosuppressive capacity, including a defective mismatch repair pathway, ablation of PTEN activity, and a novel combination of de novo mutations in SWI/SNF components. We map a multilayered genetic and functional response to drive tumor recurrence, identifying protein tyrosine phosphatase 4A2 (PTP4A2) as a novel driver of self-renewal, proliferation and tumorigenicity at glioblastoma recurrence. Mechanistically, genetic perturbation and a small molecule inhibitor of PTP4A2 results in greater survival and reduced tumor growth in patient-derived models of recurrent glioblastoma. Citation Format: Chirayu R. Chokshi, Kevin Brown, Chitra Venugopal, Jason Moffat, Sheila K. Singh. Functional mapping reveals widespread remodelling and unrecognized pathway dependencies in recurrent glioblastoma [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 60.
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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 | 0.001 | 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) | 0.003 | 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".