Abstract LB188: Identification of intrinsic molecular vulnerabilities in inherited and treatment-related hypermutant patient-derived glioma cell line models
Bibliographic record
Abstract
Abstract Background: Hypermutant gliomas in children are caused by inherited or therapy related replication repair deficiency (RRD), the latter typically following treatment with the alkylating agent temozolomide (TMZ). Recently, immune checkpoint inhibitors (ICIs) revealed clinical responses and prolonged survival in 30% of pediatric-inherited hypermutant gliomas but none of the treatment-related adult hypermutant gliomas. We hypothesized that high-throughput screening of hypermutant and RRD patient derived glioma cell lines from both glioma types (PDGCL) would reveal specific molecular vulnerabilities immediately translatable as targeted therapies to be used in combination with ICIs. Methods: We screened two pediatric hypermutant and RRD PDGCLs (1806, 1260); one pediatric non-hypermutant non-RRD PDGCL (477); an adult non-hypermutant non-RRD PDGCL (189EW); matched recurrent post-TMZ treatment hypermutant RRD PDGCL (248z, 248xy) from patient 189 against a library of 3336 bioactive compounds, including 1500 FDA approved drugs supplemented with 16 additional compounds (BET, PLK1, EZH2, PARP1 inhibitors) at 100nM.Each plate screened contained negative (0.2% DMSO) and positive controls (50nM bortezomib). Cell viability was calculated at 96hr by ATP measurement. Raw values from negative and positive control wells were aggregated and used to calculate Z’-factors for each assay plate, as a measure of assay performance and data quality, with a Z’-factor >0.5 representing high quality data. Only drugs scoring Normalized Percentage of Inhibition (NPI) >50% and z-score>3 were considered potential hits for follow-up evaluation, focusing on those enriched in the pediatric and adult hypermutant PDGCLs. Results: We identified 45 drugs active in all three of the pediatric PDGCLs and 23 common hits in the adult cell lines. Broad target classes were identified such as microtubule associated inhibitors, heat shock protein inhibitors for PDGCLs and proteasome inhibitors for adult cell lines. Unique targets were also identified. Importantly, the inherited and treatment related mismatch repair deficient glioma cells (MMR1260,248z, 248xy) and the hypermutant pediatric cell line (1806) exhibited significant response to PI3K/mTOR inhibitors. Ultrahypermutant RRD cell line and the treatment-related hypermutant cell lines (1806 248z 248xy ) displayed sensitivity to topoisomerase I inhibitors. Preliminary in vitro data confirm sensitivity to topotecan of hypermutant pediatric and adult cell lines. Conclusion: We have identified both mechanism specific and common intrinsic vulnerabilities in hypermutant PDGCLs that can be tested as adjuvant therapies for ICI. Functional validation of the lead candidates is ongoing and will be reported. Citation Format: Laura Scolaro, Nuno Miguel Nunes, Michelle Kushida, David C. Schultz, Sara Cherry, Kanupriya Whig, Peter Dirks, Uri Tabori, John M. Maris. Identification of intrinsic molecular vulnerabilities in inherited and treatment-related hypermutant patient-derived glioma cell line models [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 LB188.
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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) | 0.003 | 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".