Abstract 1134: Therapeutic potential of an antibody-drug conjugate directed against a tumor-specific epitope on podocalyxin
Bibliographic record
Abstract
Abstract Podocalyxin (Podxl) is a cell surface sialomucin that is frequently upregulated in tumors with high metastatic potential and its expression is associated with poor outcome in several human cancers. As such, Podxl is emerging as an important prognostic and theragnostic marker. While Podxl is expressed on normal vascular endothelia and kidney podocytes, we sought to produce a novel anti-Podxl antibody that selectively recognizes a tumor-restricted glycoepitope on the extracellular mucin domain of Podxl. The antibody we have produced, PODO447, is specific to the tumor glycoform of Podxl, demonstrated by a lack of binding to normal tissues that are known to express Podxl. In contrast, we show binding of the antibody to tumor cell lines, patient-derived cell lines, and primary tumor tissues. We have previously shown that the majority of tumors in an ovarian carcinoma array (219 cases), including 65% of the high-grade serous histotype, are positive for PODO447. Here, we further show the presence of PODO447 in tumors of the breast, urothelium, pancreas, endometrium, colon, prostate, lung (both small cell and non-small cell), and glioblastoma. To assess the therapeutic potential of our antibody as an antibody drug conjugate (ADC), we coupled PODO447 to the microtubule disruptor monomethyl auristatin E (MMAE) with an enzyme cleavable linker carbamoyl p-aminobenzyl carbamate (PABC), resulting in the ADC PODO447-Vedotin. We demonstrate promising in vitro activity of the ADC to various human tumor cell lines as well as in vivo efficacy to xenografted ovarian and pancreatic tumor lines. Our data reveals PODO447-Vedotin as a tumor-specific and highly efficacious therapeutic agent for the targeting of human tumors and as such, PODO447 exhibits potential for further development as a targeted clinical immunotherapy. Citation Format: Pamela M. Austin Dean, Diana Canals Hernaez, Julyanne Brassard, Michael R. Hughes, Erin M. Bell, Kelly M. McNagny, Calvin D. Roskelley. Therapeutic potential of an antibody-drug conjugate directed against a tumor-specific epitope on podocalyxin [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 1134.
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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) | 0.002 | 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".