Abstract B079: The efficacy of CD133 BiTEs and CAR-T cells in preclinical model of recurrent glioblastoma
Bibliographic record
Abstract
Abstract Glioblastoma (GBM) is a uniformly fatal primary brain tumor, characterized by a diverse cellular phenotype and genetic heterogeneity. Despite the use of aggressive cellular multi-modal treatment including surgical resection, radiotherapy and chemotherapy, the outcome of patients with GBM has failed to improve significantly. Numerous studies have implicated CD133+ brain tumor initiating cells (BTICs) as drivers of chemo- and radio-resistance in GBM. We have recently demonstrated that a CD133-driven gene signature is predictive of poor overall survival and targeting CD133+ treatment-refractory cells may be an effective strategy to block GBM recurrence. Chimeric antigen receptors (CARs) and bispecific T-Cell engaging antibodies (BiTEs) present promising immunotherapeutic approaches that have not yet been validated for recurrent GBM. Using CellectSeq, a novel methodology that combines use of phage-displayed synthetic antibody libraries and DNA sequencing, we developed the CD133-specific monoclonal antibody ‘RW03’. We constructed CD133-specific BiTEs or RW03xCD3 that consist of two arms; one arm recognizes the tumor antigen (CD133) while the second is specific to CD3 antigen. The BiTEs were constructed in four different conformations and dual binding specificity was confirmed using flow cytometry. Using CD133high and CD133low primary GBM lines, we validated the binding of BiTEs to CD133+ cells. Further analysis showed binding of BiTEs to human T cells known to express CD3 within a population of healthy donor peripheral blood mononuclear cells. We observed BiTEs redirecting T cells to kill GBMs, with greater efficiency observed in CD133high GBMs, validating BiTE target specificity. Incubating T-cells with BiTEs and the CD133high GBMs resulted in increased expression of T cell activation markers. In parallel, we derived the single chain variable fragment (scFv) from previously generated RW03 and generated a 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. CD133-specific CAR-T cells were cytotoxic to CD133+ GBMs. Co-culturing CD133 CAR-T cells with GBMs triggered T cell activation and proliferation. Treatment of GBM tumor-bearing mice with CD133-specific CAR-T cells yielded extended survival in mice and significant reductions in brain tumor burden. Furthermore, we uniquely adapted the existing chemoradiotherapy protocol for GBM patients for treatment of immunocompromised mice engrafted with human GBMs. Within this model, we have initiated treatment of recurrent GBM directed against CD133+ BTICs, to allow for a direct prospective comparison of toxicity and efficacy of BiTEs and CAR T cell strategies. Citation Format: Parvez Vora, Chirayu Chokshi, Maleeha Qazi, Chitra Venugopal, Sujeivan Mahendram, Mohini Singh, Jarrett Adams, David Bakhshinyan, Max London, Minomi Subapanditha, Nicole McFarlane, James Pan, Jonathan Bramson, Jason Moffat, Sachdev Sidhu, Sheila Singh. The efficacy of CD133 BiTEs and CAR-T cells in preclinical model of recurrent glioblastoma [abstract]. In: Proceedings of the Second CRI-CIMT-EATI-AACR International Cancer Immunotherapy Conference: Translating Science into Survival; 2016 Sept 25-28; New York, NY. Philadelphia (PA): AACR; Cancer Immunol Res 2016;4(11 Suppl):Abstract nr B079.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".