A tumor-restricted glycoepitope of podocalyxin correlates with immune evasion in high-grade serous ovarian carcinoma.
Bibliographic record
Abstract
Abstract High-grade serous ovarian carcinoma (HGSOC) is an aggressive tumor with a 5-year disease-free survival of roughly 15%, partly because it is usually diagnosed at an advanced stage. Podocalyxin (Podxl) is a highly glycosylated sialomucin normally expressed by vascular endothelia and kidney podocytes. Strikingly, Podxl expression is frequently upregulated by a variety of tumors (including HGSOC) and is consistently associated with poor prognosis. We capitalized on the fact that glycosylation pathways are frequently dysregulated in cancer to develop an antibody, PODO447, that recognizes a tumor-restricted glycoform of Podxl not expressed on normal tissue. While the exact epitope remains to be identified, our results suggest that PODO447 binds an epitope comprising a peptide domain of Podxl in combination with the core 1 O-GalNAc glycan (T-antigen). When coupled to a cytotoxin, a PODO447-antibody-drug conjugate (ADC) effectively kills human tumor cells in vitro and in xenografted mice. While the vast majority of ovarian tumors highly express the Podxl core protein, only a subset of these express the PODO447 epitope. Strikingly, tumors that express a high level of PODO447 epitope tend to be those that lack infiltrating CD8+ T cells and CD20+ B cells: a phenotype that has previously been linked to immune evasion and poorest disease-free survival. Furthermore, we find that PODO447 is a more consistent marker of these immunologically “cold” tumors than a number of other markers, including CA125, mesothelin and folate receptor α. These results highlight the PODO447-epitope as a highly selective diagnostic marker of poor outcome tumors and the PODO447-ADC as a novel strategy for therapeutic intervention. This research was supported by the Canadian Institutes of Health Research (Grant Number: PJT-166180), the School of Biomedical Engineering (The University of British Columbia) postdoctoral fellowship and the Michael Smith Foundation for Health Research (MSFHR) research trainee award.
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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".