Abstract PR009: The functional genomic landscape of recurrent glioblastoma
Bibliographic record
Abstract
Abstract Glioblastoma (GBM) is a highly fatal brain cancer where mortality is predominantly caused by disease recurrence and lack of second-line therapies. Here, utilizing genome-wide CRISPR-Cas9 screening, we uncover treatment-based conditional genetic interactions modulating sensitivity to combined radiation therapy (RT) and temozolomide (TMZ), the standard of care treatment administered to over 70% of glioblastoma patients. To investigate therapy-induced changes in the functional landscape of glioblastoma stem cells (GSCs) without confounding patient-to-patient differences, we systematically mapped genetic dependencies in patient-matched pre-treatment primary and post-treatment recurrent GSCs. This comparison highlights a remodelling of genes and pathways regulating tumor cell survival at disease relapse, arming recurrent GSCs with newly acquired genetic drivers and further loss of tumor suppressors. Among these genes, we identify PTP4A2, encoding protein tyrosine phosphatase 4A2, as a major regulator of stemness and tumorigenicity in recurrent GSCs. Genetic perturbation or pharmacological inhibition of PTP4A2 influences the phosphorylation status and expression of interacting proteins belonging to axonal guidance pathways, as phosphoproteomic studies showed specific enrichment in axon guidance regulator Roundabout Guidance Receptor 1 (ROBO1) in recurrent GSCs. We therefore designed and developed efficacious Camelid biotinylated single-domain antibody (bio-sdAb) and tetrameric complexes targeting human ROBO1 (MKRo-20). Subsequently, we orthotopically engrafted recurrent GSCs into immunocompromised mice and administered local treatments of tetrameric MKRo-20. All mice treated with tetrameric MKRo-20 were rendered tumor free, suggesting that modulation of ROBO1 with bio-sdAbs presents a new immunotherapeutic strategy to target recurrent glioblastoma. Our work reveals a context-specific dependence on axonal guidance through a PTP4A2-ROBO axis in recurrent glioblastoma, highlighting new avenues of therapeutic intervention for second-line therapy in GBM. This abstract is also being presented as PO025. Citation Format: Chirayu Chokshi, David Tieu, Kevin Brown, Chitra Venugopal, Lina Liu, Laura Kuhlman, Katherine Chan, Amy Tong, Neil Savage, Dillon McKenna, Nikoo Aghaei, Minomi Subapanditha, Joseph M Salamoun, Martin Rossotti, Peter Wipf, Elizabeth Sharlow, John S. Lazo, Thomas Kislinger, Kevin Henry, Yu Lu, Jason Moffat, Sheila K. Singh. The functional genomic landscape of recurrent glioblastoma [abstract]. In: Abstracts: AACR Virtual Special Conference: Tumor Immunology and Immunotherapy; 2020 Oct 19-20. Philadelphia (PA): AACR; Cancer Immunol Res 2021;9(2 Suppl):Abstract nr PR009.
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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 | 0.001 | 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 | 0.001 |
| 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 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".