Targeting a Tumor-Specific Epitope on Podocalyxin Increases Survival in Human Tumor Preclinical Models
Bibliographic record
Abstract
Podocalyxin (Podxl) is a CD34-related cell surface sialomucin that is normally highly expressed by adult vascular endothelia and kidney podocytes where it plays a key role in blocking adhesion. Importantly, it is also frequently upregulated on a wide array of human tumors and its expression often correlates with poor prognosis. We previously showed that, in xenograft studies, Podxl plays a key role in metastatic disease by making tumor initiating cells more mobile and invasive. Recently, we developed a novel antibody, PODO447, which shows exquisite specificity for a tumor-restricted glycoform of Podxl but does not react with Podxl expressed by normal adult tissue. Here we utilized an array of glycosylation defective cell lines to further define the PODO447 reactive epitope and reveal it as an O-linked core 1 glycan presented in the context of the Podxl peptide backbone. Further, we show that when coupled to monomethyl auristatin E (MMAE) toxic payload, PODO447 functions as a highly specific and effective antibody drug conjugate (ADC) in killing ovarian, pancreatic, glioblastoma and leukemia cell lines in vitro. Finally, we demonstrate PODO447-ADCs are highly effective in targeting human pancreatic and ovarian tumors in xenografted NSG and Nude mouse models. These data reveal PODO447-ADCs as exquisitely tumor-specific and highly efficacious immunotherapeutic reagents for the targeting of human tumors. Thus, PODO447 exhibits the appropriate characteristics for further development as a targeted clinical immunotherapy.
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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 | 0.000 | 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".