Abstract 1481: Preclinical validation of a novel CD133/CD3 bispecific T-cell engager (BiTE) antibody to target patient-derived glioblastoma cells
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 a recombinant RW03xCD3 bispecific T-cell engager (BiTE) antibody that redirects human polyclonal T cells to CD133+ GBM cells, inducing very potent anti-tumor response. Using CellectSeq, a novel methodology that combines use of phage-displayed synthetic antibody libraries and high-throughput DNA sequencing technology, 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. In order to test the ability of BiTEs to functionally elicit CD133-specific cytotoxic responses in vitro, we performed Presto blue-based killing assays. We observed CD133-specific BiTEs redirect T cells to kill CD133-expressing GBM cells in a coculture of T cells and GBM cells. The killing was more efficient in CD133high GBMs compared to CD133low GBMs, validating its specificity to target CD133+ BTICs. Incubating T cells with BiTEs and GBMs resulted in increased surface expression of T-cell activation markers CD69 and CD25 in both, CD4+ and CD8+ T cells populations. Treatment with BiTEs yielded extended survival in mice and significant reductions in brain tumor burden. This rigorously obtained data offers compelling evidence that BiTE-mediated 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, Jarrett Adams, James Pan, Chirayu Chokshi, Maleeha Qazi, Minomi Subapanditha, Mohini Singh, David Bakhshinyan, Ksenia Bezverbnaya, Nicole McFarlane, Jonathan Bramson, Sachdev Sidhu, Jason Moffat, Sheila Singh. Preclinical validation of a novel CD133/CD3 bispecific T-cell engager (BiTE) antibody to target patient-derived glioblastoma cells. [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 1481.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".