Abstract PO081: Systematic Generation of Allogeneic Immune-targeting Modalities for Glioblastoma
Bibliographic record
Abstract
Abstract Glioblastoma (GBM) is one of the most common brain tumors in adults. Despite a standard-of-care including surgery and chemoradiotherapy, patients only live to a median of 15 months. This may be due to extensive spatiotemporal, intratumoral heterogeneity observed. This is thought to be created by a small population of stem-like cells marked by expression of CD133. CD133 has been shown to correlate with poor patient prognosis, metastases, relapse and worse overall survival in GBM. We thus created an anti-CD133 CAR-T therapy. Our CD133-targeting CAR-T (CART133) has shown great efficacy and in our patient-derived models of GBM. It has also exhibited safety in sparing healthy CD133-expressing cells, particularly, hematopoietic stem and progenitor cells, in our humanized model of hematopoiesis. However, in looking to the clinic, the generation of autologous CAR-Ts may be difficult as it requires sufficient numbers of a patient's own T-cells who may be immunocompromised after first-line therapy. Thus, we propose to generate allogeneic CAR-Ts that target CD133 (AlloCART133) from healthy donor T- cells. AlloCAR-T cells are genetically-edited to abrogate the T-cell receptor (TCR) to avoid graft-versus- host disease, a phenomenon in which immune-mismatch causes donor tissues to attack recipient tissues. These cells can thus be safe for a recipient while also maintaining their anti-tumor activity. AlloCART133 thus presents a clinically-relevant, off-the-shelf therapy for patients with GBM. Citation Format: Sabra K. Salim, Jiarun Wei, Vassil Dimitrov, Katherine Chen, Chitra Venugopal, Parvez Vora, Jason Moffat, Sheila K. Singh. Systematic Generation of Allogeneic Immune-targeting Modalities for 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 PO081.
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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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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 | 0.000 |
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".