After previously reporting effective results from 177LU-PSMA-617 treatment in patients with metastatic castration-resistant prostate cancer (mCRPC), Jones T. Nauseef, MD, PhD, sought to refine pre- and post-treatment assessment of prostate-specific membrane antigen (PSMA)-expression in tumor and normal organs “may allow for better patient selection and prediction of toxicities.” Their report, presented at the 2022 American Society of Clinical Oncology (ASCO) Genitourinary Cancers Symposium, associated pre- and post-treatment PSMA expression with prostate-specific antigen (PSA) response and overall survival (OS).
Furthermore, Dr. Nauseef and colleagues observed “associations between patient experience (AEs) and PSMA expression in non-tumor tissues,” supporting the potential of PSMA-expression analysis to “anticipate toxicity and predict treatment response.”
The study administered 177Lu-PSMA-617 to a total of 50 patients, and artificial intelligence (AI) was used to quantify pre- and post-treatment PSMA signal intensity. The investigators calculated scoring cutoffs with a test/re-test subset of patients within 24 hours without intervening therapy. Cox models were used to evaluate associations with survival and Wilcoxon tests were used to assess associations with adverse events (AEs) and PSA response.
According to the authors’ report, 13 of the 50 participants were selected for “AI-based quantification and associated survival analyses.” Among the 13 patients, 10 (77%) experienced any PSA decline, with eight achieving PSA50 (62%) and three achieving PSA90 (23%). The median OS was 17.0 months. Univariate analysis demonstrated that pretreatment mean standardized uptake values (SUV) was associated with improved PFS (hazard ratio [HR = 0.66; 95% confidence interval [CI], 0.49–0.90; p = 0.009), and improved OS (HR = 0.81; 95% CI, 0.65–1.00; p = 0.048). Dr. Nauseef also reported that “subjects with xerostomia had higher salivary gland SUVmax (pretreatment and change in after treatment),” and that “those with pain flare had lower pretreatment SUV scores (Mean, Max, Total) in unaffected portions of bony skeleton.”
Overall, Dr. Nauseef and colleagues felt that their AI-based analysis of pre- and post-treatment PSMA expression on 68Ga-PSMA11-PETs had the potential to improve “patient selection and prediction of toxicities.” They closed their report with the suggestion that “expansion of this algorithm to a larger patient cohort may improve our ability to anticipate toxicity by body-wide PSMA detection and predict treatment response.”
