Abstract 1763: BiTEs vs CAR-Ts: Preclinical targeting of CD133+ brain tumor initiating cells using immunotherapy-based treatment strategies
Bibliographic record
Abstract
Abstract Glioblastoma (GBM) is a uniformly fatal primary brain tumor, characterized by extensive cellular heterogeneity. 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. Bispecific T-Cell engaging antibodies (BiTEs) and Chimeric antigen receptors (CARs) present promising immunotherapeutic approaches that have not yet been validated for recurrent GBM. Using CellectSeq, a novel methodology that combines the use of phage-displayed synthetic antibody libraries and DNA sequencing, we developed the CD133-specific monoclonal antibody ‘RW03'. We constructed CD133-specific BiTEs that consist of two arms; one recognizes the tumor antigen (CD133) and the second is specific to the CD3 antigen that binds to human GBMs and PBMC-derived T cells, respectively. 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 RW03 we engineered a second-generation CAR T cell. 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, Jarrett Adams, Mohini Singh, Chitra Venugopal, Nazanin Tatari, Chirayu Chokshi, Maleeha Qazi, Sabra Salim, Sujeivan Mahendram, David Bakhshinyan, Max London, Neil Savage, Minomi Subapanditha, Nicole McFarlane, James Pan, Jonathan Bramson, Sachdev Sidhu, Jason Moffat, Sheila Singh. BiTEs vs CAR-Ts: Preclinical targeting of CD133+ brain tumor initiating cells using immunotherapy-based treatment strategies [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2018; 2018 Apr 14-18; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2018;78(13 Suppl):Abstract nr 1763.
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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) | 0.003 | 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".