Back

DCP, First Generation PET Radiotracer for Diagnosis of Radiation Resistant Head and Neck Cancer

Chen, X.; Kumar Solingapuram Sai, K.; Li, Z.; Zhu, C.; Shukla, K.; Forshaw, T. E.; Wu, H.; Vance, S.; Madonna, M.; Dewhirst, M. W.; Tsang, A. W.; Poole, L. B.; Ramanujam, N.; King, S. B.; Furdui, C. M.

2020-04-30 cancer biology
10.1101/2020.04.28.063537 bioRxiv
Show abstract

Redox metabolism plays essential functions in the pathology of cancer. As tumor redox profiles uniquely reflect cancer stage and in select cases, therapeutic sensitivity, the capability to image redox molecular features is essential to improve diagnosis, treatment, and overall quality-of-life (QOL) of cancer patients. While a number of radiotracers for imaging redox metabolism have been developed, there are no reports of radiotracers for in vivo imaging of protein oxidation. Here we take the first step towards this goal and describe the synthesis and kinetic properties of a new positron emission tomography (PET) [18F]DCP radiotracer for in vivo imaging of protein sulfenylation. Time course biodistribution and PET/CT studies using xenograft animal models of Head and Neck Squamous Cell Cancer (HNSCC) demonstrate feasibility of diagnosing radiation resistant tumors, which display lower [18F]DCP signal. These findings are consistent with our previous reports of decreased protein sulfenylation in clinical specimens of radiation resistant HNSCC. We anticipate further development and implementation of this concept in clinical practice to improve the diagnosis of patients with radiation resistant tumors and the accuracy of prognosis for patients undergoing radiation treatment. Single Sentence SummaryThe study introduces a new PET radiotracer for profiling tumor protein oxidation as a prognostic indicator of resistance to radiation therapy.

Matching journals

The top 7 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.