IMMU-03. THERAPEUTIC TARGETING OF TUMORIGENIC EphA2+/EphA3+ BRAIN TUMOR INITIATING CELLS WITH BISPECIFIC ANTIBODY IN HUMAN GLIOBLASTOMA
Bibliographic record
Abstract
Human glioblastoma (hGBM) carries a dismal prognosis and inevitably relapses despite aggressive therapy. Many of the 14 members of the Eph receptor tyrosine kinase family are expressed in hGBM initiating cells (GICs) and constitute potential molecular targets. We hypothesize that multiple members of the EphR family play a critical role in hGBM recurrence. Using a highly specific human EphR antibody panel, we identified differential expression of EphRs in recurrent hGBM (rGBM). We further characterized EphR co-expression along with multiple GIC markers using mass cytometry (CyTOF). Here we show that EphA2 and EphA3 co-expression marks a highly tumorigenic cell population in rGBM that is enriched in GIC marker expression, and exhibits higher in vitro and in vivo self-renewal and proliferation capacity as compared to EphA2+/EphA3-, EphA2-/EphA3+ or EphA2-/EphA3- cells. Knockdown of EphA2 and EphA3 blocks this self-renewal and proliferation capacity, and is marked by increase in the expression of differentiation marker GFAP. Next, we generated and tested a bispecific antibody (BsAb) that co-targets EphA2 and EphA3. In vitro treatment of rGBM with BsAb led to phosphorylation of EphA2 and EphA3, eventually leading to receptor internalization and degradation. The cellular effect of EphA2/A3 blockade was mediated through the down regulation of Akt and MAPK. Intracranial treatment of immune-deficient mice harboring hGBM with BsAb resulted in non-invasive and significantly smaller tumors. Hence, EphA2 and EphA3 co-expression marks an even more potent GIC population in rGBM, and targeting either single EphA2+ or EphA3+ populations alone will allow the remaining GICs to drive tumor recurrence. For the first time, we show that strategic co-targeting of both EphA2 and EphA3 with a BsAb presents a novel and rational therapeutic approach to recurrent GBM, where multiple GIC populations may be driving the intra-tumoral heterogeneity underlying disease progression.
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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) | 0.000 | 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) | 0.001 | 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".