Abstract 4948: PET/CT clinical protocol design for the novel, first in class 68Ga labeled guanylyl cyclase C targeted peptide MLN6907 ([68Ga]MLN6907)
Bibliographic record
Abstract
Abstract Acquisition and interpretation guidelines for clinical PET/CT imaging in oncology have been designed for whole-body 18F-FDG imaging and may not be optimized for assessment of other PET imaging tracers. Here we describe a methodology of PET/CT study design for the novel first in class 68Ga-labeled Guanylyl cyclase C (GCC) targeted peptide, [68Ga]MLN6907, based on a combination of in vitro, ex vivo, and in vivo preclinical imaging studies and model-based estimation of tumor parameters from simulated clinical PET data. GCC, a protein expressed in GI malignancies, is being targeted by the antibody drug conjugate MLN0264. GCC is also expressed on the healthy apical surface of the intestinal epithelium, which should be inaccessible to intravascular treatment. [68Ga]MLN6907 binds GCC with high affinity and is being developed as an imaging biomarker in an effort to help identify patients likely to respond to GCC-targeted therapy. In a series of experiments, the peptide affinity, internalization rate, and clearance were determined in patient-derived CRC xenografts with varied tumor microenvironmental phenotype. In addition to supporting the clinical development of the imaging agent, this data was used in combination with simulated clinical list-mode PET data to evaluate tumor parameter estimability under several clinically viable acquisition and reconstruction conditions. Specifically, liver CRC metastases of varying tumor diameter, antigen density, and vascularity were simulated in combination with PET imaging acquisition duration and reconstruction with and without partial-volume correction. Tumor, liver, and background time-activity curves (TACs) were generated from the reconstructed data and analyzed using a distributed tumor model to estimate the known tumor antigen density and vascularity. Analysis of the simulation studies revealed: 1) Partial volume correction is required for accurate antigen density and vascularity estimation; 2) Parameter estimation was most accurate within a tumor size range of 1-5 cm; 3) Parameter estimation was robust for all tested TAC reconstruction durations (e.g., 2, 3, 5, and 10 min); 4) Parameter estimation was optimal for common clinical acquisition times of 30-90 minutes; 5) Antigen density estimation was less accurate in poorly vascularized tumors. For the translation of a novel clinical biomarker, well controlled preclinical studies are critical; and, in this case, the findings combined with the distributed tumor model simulations directly guided the clinical image protocol. This rational and data-driven approach has the ability to not only improve the estimation of tumor properties in human subjects but also to guide the design of first-in-human oncology clinical imaging protocols with novel biomarkers. Citation Format: Jacob Y. Hesterman, Kelly D. Orcutt, Ozlem Yardibi, Jerome T. Mettetal, Shu-Wen Teng, Donna Cvet, Jack Hoppin, Thea Kalebic, Daniel P. Bradley. PET/CT clinical protocol design for the novel, first in class 68Ga labeled guanylyl cyclase C targeted peptide MLN6907 ([68Ga]MLN6907). [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 4948. doi:10.1158/1538-7445.AM2014-4948
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".