Abstract 2300: Human CD133-specific chimeric antigen receptor (CAR) modified T cells target patient-derived glioblastoma brain tumors
Bibliographic record
Abstract
Abstract Glioblastoma (GBM), an aggressive primary brain tumor in adults, is feared for its near uniformly fatal prognosis and is characterized by a diverse cellular phenotype and genetic heterogeneity. Despite the use of aggressive multi-modal treatment including surgical resection, radiotherapy and chemotherapy, the outcome of patients with GBM has failed to improve significantly. We developed patient-derived brain tumor initiating cell (BTIC) early passage lines that describe the extent of intertumoral heterogeneity, presenting a powerful preclinical model of GBM. Numerous studies have implicated CD133+ BTICs as drivers of chemo- and radio-resistance in GBM. CD133 expression correlates with disease progression, recurrence, and poor overall survival of GBM patients. Here, we describe the preclinical evaluation of chimeric antigen receptor (CAR) T-cell strategy that specifically targets CD133+ GBM cells. From the previously generated CD133-specific humanized monoclonal antibody, we derived the single chain variable fragment (scFv) and cloned it into an antigen-specific second-generation CAR. Anti-CD133 scFv with a myc tag was cloned in frame with a human CD8 leader sequence, CD8a transmembrane domain, CD28, and hCD3ζ signaling tail in the lentiviral construct pCCL-ΔNGFR vector in two different orientations: Light chain-linker-Heavy chain (CD133 CAR-LH) and Heavy chain-linker-Light chain (CD133 CAR-HL). Following lentiviral preparation, the T cells isolated from PBMCs were transduced with CD133 CAR-LH and CD133 CAR-VH constructs. After successful T cell engineering, the expression of ΔNGFR and myc tag was analyzed using flow cytometry to confirm the efficiency of transduction and surface expression of anti-CD133 respectively. While expression of ΔNGFR was observed in all CAR T cells (including controls), we found expression of the myc tag in both variations of CARs, CD133 CAR-HL and CD133 CAR-LH. Furthermore, we used Presto Blue-based killing assays to test the ability of CD133 CARs to selectively bind and kill CD133+ GBM BTICs. Our data shows that CD133-specific CAR T cells not only recognized, but killed the CD133+ GBM cells selectively, validating this adoptive T-cell therapeutic strategy. CAR-expressing T cells were activated in presence of CD133high GBM cells showed increase surface expression of activation markers CD69 and CD25. Both, CD4+ and CD8+ CD133-specific CAR-T cells showed upregulation in surface expression levels of activation markers. This rigorously obtained data offers compelling evidence that CAR-T induced cytotoxicity against treatment-resistant and evasive CD133+ GBM BTICs could provide a very potent, specific and novel therapeutic strategy for GBM patients. Citation Format: Parvez Vora, Chitra Venugopal, Sujeivan Mahendram, Chirayu Chokshi, Maleeha Qazi, Minomi Subapanditha, Mohini Singh, David Bakhshinyan, Ksenia Bezverbnaya, Jarrett Adams, Nicole McFarlane, Sachdev Sidhu, Jason Moffat, Jonathan Bramson, Sheila Singh. Human CD133-specific chimeric antigen receptor (CAR) modified T cells target patient-derived glioblastoma brain tumors. [abstract]. In: Proceedings of the 107th Annual Meeting of the American Association for Cancer Research; 2016 Apr 16-20; New Orleans, LA. Philadelphia (PA): AACR; Cancer Res 2016;76(14 Suppl):Abstract nr 2300.
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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".