ImmunoPET of Ovarian and Pancreatic Cancer with AR9.6, a Novel MUC16-Targeted Therapeutic Antibody
Bibliographic record
Abstract
PURPOSE: Advances in our understanding of the contribution of aberrant glycosylation to the pro-oncogenic signaling and metastasis of tumor cells have reinvigorated the development of mucin-targeted therapies. Here, we validate the tumor-targeting ability of a novel monoclonal antibody (mAb), AR9.6, that binds MUC16 and abrogates downstream oncogenic signaling to confer a therapeutic response. EXPERIMENTAL DESIGN: The in vitro and ex vivo validation of the binding of AR9.6 to MUC16 was achieved via flow cytometry, radioligand binding assay (RBA), and immunohistochemistry (IHC). The in vivo MUC16 targeting of AR9.6 was validated by creating a 89Zr-labeled radioimmunoconjugate of the mAb and utilizing immunoPET and ex vivo biodistribution studies in xenograft models of human ovarian and pancreatic cancer. RESULTS: Flow cytometry, RBA, and IHC revealed that AR9.6 binds to ovarian and pancreatic cancer cells in an MUC16-dependent manner. The in vivo radiopharmacologic profile of 89Zr-labeled AR9.6 in mice bearing ovarian and pancreatic cancer xenografts confirmed the MUC16-dependent tumor targeting by the radioimmunoconjugate. Radioactivity uptake was also observed in the distant lymph nodes (LNs) of mice bearing xenografts with high levels of MUC16 expression (i.e., OVCAR3 and Capan-2). IHC analyses of these PET-positive LNs highlighted the presence of shed antigen as well as necrotic, phagocytized, and actively infiltrating neoplastic cells. The humanization of AR9.6 did not compromise its ability to target MUC16-expressing tumors. CONCLUSIONS: The unique therapeutic mechanism of AR9.6 combined with its excellent in vivo tumor targeting makes it a highly promising theranostic agent. huAR9.6 is poised for clinical translation to impact the management of metastatic ovarian and pancreatic cancers.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".