Abstract 103: 64-Cu-NOTA-panitumumab F(ab')2 fragments for PET/CT imaging of pancreatic cancer
Bibliographic record
Abstract
Abstract Background: Pancreatic cancer is a leading cause of cancer related death. Radiolabeled panitumumab (Pmab) has potential as an imaging agent for pancreatic cancer because it targets over-expression of human epidermal growth factor receptor (EGFR) commonly found in the disease. Our aim was to develop a Pmab-based radioimmunoconjugate (RIC) for use for PET imaging of pancreatic cancer. Methods: Pmab Fab fragments (PmabFab), F(ab')2 fragments (PmabF(ab')2), intact IgG (PmabIgG) and irrelevant anti-CD20 rituximab F(ab')2 (RmabF(ab')2) were derivatized with NOTA then 64Cu-labeled to ≥95 % radiochemical purity. RIC immunoreactivity was assessed by binding assays with MDA-MB-468 breast cancer cells (1 × 10E6 EGFR/cell), PANC-1 pancreatic cancer cells (4 × 10E5 EGFR/cell), and a primary pancreatic tumour cell line, OCIP-23 (4 × 10E5 EGFR/cell). RICs (4 MBq, 10 µg) were injected i.v. (tail vein) into non-tumour bearing Balb/c mice and biodistribution was assessed at 18 h post injection (p.i). Tumours were established in NOD-Scid or CD1 nude mice by s.c. inoculation with 1 × 10E7 PANC-1 cells or 1 × 10E7 MDA-MB-468 cells. RICs were injected i.v. (tail vein) into tumour-bearing mice (30 MBq, 10 µg) then PET/CT imaging and biodistribution studies were performed at 24 and 48 h p.i. Results: MDA-MB-468 cell binding assays showed 89.0 ± 0.7 % of applied 50 nM 64Cu-PmabFab and 88.8 ± 4.5 % of 64Cu-PmabF(ab')2 was displaced by 100-fold molar excess PmabIgG, demonstrating specificity for EGFR. Assessment of 64Cu-PmabF(ab')2 binding to pancreatic cell lines showed that 90.1 ± 2.0 % in PANC-1 cells and 80.2 ± 1.0 % in OCIP-23 cells of applied RIC was displaced by PmabIgG. In Balb/c mice, kidney uptake of 64Cu-PmabFab was significantly higher at 90.8 ± 9.0 % ID/g than that of 64Cu-PmabF(ab')2 and PmabIgG with 6.9 ± 0.7 % ID/g and 11.2 ± 1.0 % ID/g, respectively; (p≤0.05). Liver uptake of 64Cu-PmabF(ab')2 was lower at 5.4 ± 0.6 % ID/g than 64Cu-PmabIgG at 8.7 ± 0.9 % ID/g (p≤0.05). 64Cu-PmabF(ab')2 was selected for PET/CT imaging and biodistribution studies. The tumour:blood ratio in MDA-MB-468 xenografted mice was 1:1 at 24 h, but increased at 48 h to 5:1, with tumour uptake of 8.0 ± 0.8 % ID/g. 64Cu-RmabF(ab')2 tumour uptake in MDA-MB-468 xenografted mice at 48 h was 0.5 ± 0.1 % ID/g, significantly lower than that of 64Cu-PmabF(ab')2 (p≤0.005). The tumour:blood ratio in PANC-1 xenografted mice was 1:1 at 24 h, and improved to 2.4:1 at 48 h, with 6.2 ± 1.1 % ID/g tumour uptake. MDA-MB-468 and PANC-1 tumours were imaged by PET/CT. Studies with primary pancreatic tumour OCIP-23 xenografts are in progress. Conclusions: F(ab')2 is the optimal form of Pmab for PET/CT imaging due to lower kidney uptake than Fab fragments, and lower liver uptake than intact IgG. EGFR-positive PANC-1 and MDA-MB-468 xenografts were well visualized with 64Cu-PmabF(ab')2 at 48 h p.i. Supported by a grant from the Canadian Cancer Society Research Institute with funds from the Canadian Cancer Society. Citation Format: Amanda J. Boyle, David W. Hedley, Sachdev S. Sidhu, Mitchell A. Winnik, Raymond M. Reilly. 64-Cu-NOTA-panitumumab F(ab')2 fragments for PET/CT imaging of pancreatic cancer. [abstract]. In: Proceedings of the 105th Annual Meeting of the American Association for Cancer Research; 2014 Apr 5-9; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2014;74(19 Suppl):Abstract nr 103. doi:10.1158/1538-7445.AM2014-103
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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 | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".