Association of integrated biomarkers and progression-free survival prediction in patients with gastroenteropancreatic neuroendocrine tumors undergoing [177Lu]Lu-DOTA-TATE therapy.

Integrated biomarkers that predict survival in patients with gastroenteropancreatic neuroendocrine tumors (GEP-NET) receiving peptide receptor radionuclide therapy (PRRT) are still limited. This study aims to identify predictors of progression-free survival (PFS) in patients with GEP-NET undergoing two cycles of PRRT.

Methods: This single-center retrospective study included 178 patients with GEP-NET (G1 and G2) who received at least two consecutive cycles of PRRT with [177Lu]Lu-DOTA-TATE and underwent somatostatin receptor (SSTR)-PET/CT before and after therapy. At baseline, Krenning score (KS) > 2, clinical, pathological and laboratory parameters were collected and correlated to PFS. Survival predictors were analyzed using univariate and multivariate models. For goodness-of-fit analysis, the Akaike information criterion and Harrell concordance index were determined. To determine the impact on the regression model the Wald-Test was performed.

Results: In univariate analysis, KS 3 (vs. KS 4; HR, 2.02; 95% CI, 1.27-3.22; p = 0.012), Ki-67 > 5 % (HR, 2.00; 95% CI, 1.31-3.04; p = 0.008), CgA > 200 ng/mL (HR, 1.77; 95% CI, 1.14-2.76; p = 0.027) and NSE > 35 ng/mL (HR, 2.37; 95% CI, 1.44-3.89; p < 0.008) were significantly associated with shorter PFS, with CgA providing the highest C-index (0.6). In multivariate analysis , KS 3 (vs. KS 4; HR, 1.94; 95% CI, 1.17-3.21; p = 0.01), CgA > 200 ng/mL (HR, 1.76; CI, 1.08-2.87; p = 0.024), NSE > 35 ng/mL (HR, 1.98; 95% CI, 1.17-3.36; p = 0.011), and Ki-67 > 5 % (HR, 1.89; 95% CI, 1.18-3.02; p = 0.008) were significantly associated with reduced PFS. Including KS into multivariate analysis significantly improved the Cox regression model performance, as shown by a reduction in Akaike Information Criterion (592/596) and an increase in concordance index (0.66/0.65). The Wald test for individual variables supported the significance of both Ki-67 (7.1) and KS (6.7) as independent predictors of PFS.

Conclusions: NSE, CgA, KS and Ki-67 emerged as independent predictors of PFS in GEP-NET patients scheduled for two cycles of PRRT, thereby emphasizing the importance of integrated diagnostics including in- and ex-vivo biomarkers to identify high-risk individuals prone to disease progression.