Targeting IL-5Rα with antibody-conjugates reveals a strategy for imaging and therapy for invasive bladder cancer
Bibliographic record
Abstract
Despite the high interest and concern due to an increasing incidence and death rate, patients who develop muscle invasive bladder cancer (MIBC) have few options available. However, the past decade has produced many candidate bladder tumor-specific markers but further development of these markers is still needed for creating effective targeted medications to solve this urgent need. Interleukin-5 receptor α-subunit (IL-5Rα) has recently been reported to be involved in MIBC progression. Thus, we aimed to validate IL-5Rα as a target for antibody-conjugates to better manage patients with MIBC. Patients were recruited and their tumors were processed for IL-5Rα immunohistochemical analysis. NOD/SCID mice were also heterotopically implanted with the human MIBC HT-1376 and HT-B9 cell lines and established xenografts immunohistochemically evaluated for IL-5Rα and compared against patient tumors. Using the mAb A14, an antibody-drug conjugate (ADC) and a radiolabeled immunoconjugate (RIC) were developed by conjugating to vinblastine and to the positron emitter copper-64 (64Cu), respectively. As a proof-of-concept for ADC and RIC efficacy, in vitro cytotoxicity and in vivo positron emission tomography (PET) imaging in tumor-bearing mice were performed, respectively. In addition, as rapid internalization and accumulation are important components for effective antibody-conjugates, we evaluated these aspects in response to IL-5 and 64Cu-A14 treatments. Our findings suggest that although IL-5Rα protein expression is preferentially increased in MIBC, it is rapid IL-5Rα-mediated internalization allowing vinblastine-A14 to have cytotoxic activity and 64Cu-A14 to detect MIBC tumors in vivo. This is the first report to elucidate the potential of IL-5Rα as an attractive MIBC target for antibody-conjugate applications.
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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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".