Evaluation of novel highly specific antibodies to cancer testis antigen Centrin‐1 for radioimmunoimaging and radioimmunotherapy of pancreatic cancer
Bibliographic record
Abstract
Abstract Background Pancreatic ductal adenocarcinoma (PDAC) accounts for >90% of pancreatic malignancies, and has median survival of <6 months. There is an urgent need for diagnostic and therapeutic options for PDAC. Centrin1 (CETN1) is a novel member of Cancer/Testis Antigens, with a 25‐fold increase of CETN1 gene expression in PDX from PDAC patients. The absence of selective anti‐CETN1 antibodies is hampering CETN1 use for diagnosis and therapy. Here we report the generation of highly specific for CETN1 antibodies and their evaluation for radioimmunoimaging and radioimmunotherapy (RIT) of experimental PDAC. Methods The antibodies to CETN1 were generated via mice immunization with immunogenic peptide distinguishing CETN1 from CETN2. Patient tumor microarrays were used to evaluate the binding of the immune serum to PDAC versus normal pancreas. The antibodies were tested for their preferential binding to CETN1 over CETN2 by ELISA. Mice bearing PDAC MiaPaCa2 xenografts were imaged with microSPECT/CT and treated with 213 Bi‐ and 177 Lu‐labeled antibodies to CETN1. Results Immune serum bind to 50% PDAC cases on patient tumor microarrays with no specific binding to normal pancreas. Antibodies demonstrated preferential binding to CETN1 versus CETN2. Antibody 69‐11 localized to PDAC xenografts in mice in vivo and ex vivo. RIT of PDAC xenografts with 213 Bi‐labeled antibodies was effective, safe, and CETN1‐specific. Conclusions The results demonstrate the ability of these novel antibodies to detect CETN1 both in vitro and in vivo; as well, the RIT treatment of experimental PDAC when radiolabeled with 213 Bi is highly efficient and safe. Further evaluation of these novel reagents for diagnosis and treatment of PDAC is warranted.
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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) | 0.000 | 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 | 0.001 |
| 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".