Abstract IA004: Targeting clonal heterogeneity in treatment-refractory Glioblastoma with novel and empiric immunotherapies
Bibliographic record
Abstract
Abstract Despite aggressive multimodal therapy, glioblastoma (GBM) remains the most common malignant primary brain tumor in adults. With the advent of therapies that revitalize the anti-tumor immune response, several immunotherapeutic modalities have been developed for the treatment of GBM. In this talk, we summarize recent clinical and pre-clinical efforts to develop immunotherapeutic and chimeric antigen receptor (CAR) T cell treatment strategies for GBM recurrence, including challenges in the form of fundamental mechanisms of therapy evasion by tumor cells, such as immense intratumoral heterogeneity, suppression of the tumor immune microenvironment, and low mutational burden. Insights from these advances and challenges have shaped our development of a translational research pipeline from initial target discovery, through target validation and exploration of mechanism, to building new biotherapeutics against novel cancer targets, and preclinical testing in our patient-derived animal models of treatment-resistant GBM. We have tracked GBM cell populations that undergo clonal evolution as a result of selective pressures exerted by standard treatments to identify the cellular composition of the recurrence, to allow us to determine the intracellular signaling pathways that drive GBM relapse. Ultimately, we will design rational combinations of therapeutics that can reignite the anti-tumor immune response, effectively and specifically target tumor cells, and reliably decrease tumor burden for GBM patients. Citation Format: Sheila K Singh. Targeting clonal heterogeneity in treatment-refractory Glioblastoma with novel and empiric immunotherapies [abstract]. In: Proceedings of the AACR Special Conference on Brain Cancer; 2023 Oct 19-22; Minneapolis, Minnesota. Philadelphia (PA): AACR; Cancer Res 2024;84(5 Suppl_1):Abstract nr IA004.
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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 | 0.001 | 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".