The dog prostate cancer (DPC-1) model: a reliable tool for molecular imaging of prostate tumors and metastases

Background: Clinical applicability of newly discovered reagents for molecular imaging is hampered by the lack of translational models. As the dog prostate cancer (DPC-1) model recapitulates in dogs the natural history of prostate cancer in man, we tested the feasibility of single-photon emission computed tomography (SPECT)/CT imaging in this model using an anti-prostate-specific membrane antigen (PSMA)/17G1 antibody as the radiotracer.

Methods: Immunoblots and immunohistochemistry (IHC) with 17G1 were performed on canine and human prostate cancer cell lines and tissues. Five dogs with DPC-1 tumors were enrolled for pelvic and, in some instances, thoracic SPECT/CT procedures, also repeated over time. Controls included (111)indium (In)-17G1 prior to DPC-1 implantation and (111)In-immunoglobulins (IgGs) prior to imaging with (111)In-17G1 in dogs bearing prostatic DPC-1 tumors.

Results: 17G1 cross-reactivity with canine PSMA (and J591) was confirmed by protein analyses on DPC-1, LNCaP, and PC-3 cell lines and IHC of dog vs. human prostate tissue sections. 17G1 stained luminal cells and DPC-1 cancer cells in dog prostates similarly to human luminal and cancer cells of patients and LNCaP xenografts. SPECT/CT imaging revealed low uptake (≤2.1) of both (111)In-17G1 in normal dog prostates and (111)In-IgGs in growing DPC-1 prostate tumors comparatively to (111)In-17G1 uptake of 3.6 increasing up to 6.5 values in prostate with DPC-1 lesions. Images showed a diffused pattern and, occasionally, a peripheral doughnut-shape-like pattern. Numerous sacro-iliac lymph nodes and lung lesions were detected with contrast ratios of 5.2 and 3.0, respectively. The highest values were observed in pelvic bones (11 and 19) of two dogs, next confirmed as PSMA-positive metastases.

Conclusions: This proof-of-concept PSMA-based SPECT/CT molecular imaging detecting primary prostate tumors and metastases in canines with high cancer burden speaks in favor of this large model's utility to facilitate technology transfer to the clinic and accelerate applications of new tools and modalities for tumor staging in patients.