L Bodei1,2, M Kidd3, I M Modlin4,5, S Severi6, I Drozdov7, S Nicolini6, D J Kwekkeboom2,8, E P Krenning2,8, R P Baum2,9, G Paganelli6. 1. Division of Nuclear Medicine, European Institute of Oncology, Milan, Italy. 2. LuGenIum Consortium, Milan, Rotterdam, Bad Berka, London, Italy, Netherlands, Germany, UK. 3. Wren Laboratories, Branford, CT, USA. 4. LuGenIum Consortium, Milan, Rotterdam, Bad Berka, London, Italy, Netherlands, Germany, UK. imodlin@optonline.net. 5. Yale School of Medicine, 310 Cedar St, New Haven, New Haven, 06510, CT, USA. imodlin@optonline.net. 6. Nuclear Medicine and Radiometabolic Units, Istituto Scientifico Romagnolo per lo Studio e la Cura dei Tumori (IRST) IRCCS, Meldola, Italy. 7. Bering Limited, London, UK. 8. Nuclear Medicine Department, Erasmus Medical Center, Rotterdam, The Netherlands. 9. Theranostics Center for Molecular Radiotherapy and Imaging, Zentralklinik Bad Berka, Bad Berka, Germany.
Abstract
BACKGROUND: Peptide receptor radionuclide therapy (PRRT) is an effective method for treating neuroendocrine tumors (NETs). It is limited, however, in the prediction of individual tumor response and the precise and early identification of changes in tumor size. Currently, response prediction is based on somatostatin receptor expression and efficacy by morphological imaging and/or chromogranin A (CgA) measurement. The aim of this study was to assess the accuracy of circulating NET transcripts as a measure of PRRT efficacy, and moreover to identify prognostic gene clusters in pretreatment blood that could be interpolated with relevant clinical features in order to define a biological index for the tumor and a predictive quotient for PRRT efficacy. METHODS: NET patients (n = 54), M: F 37:17, median age 66, bronchial: n = 13, GEP-NET: n = 35, CUP: n = 6 were treated with (177)Lu-based-PRRT (cumulative activity: 6.5-27.8 GBq, median 18.5). At baseline: 47/54 low-grade (G1/G2; bronchial typical/atypical), 31/49 (18)FDG positive and 39/54 progressive. Disease status was assessed by RECIST1.1. Transcripts were measured by real-time quantitative reverse transcription PCR (qRT-PCR) and multianalyte algorithmic analysis (NETest); CgA by enzyme-linked immunosorbent assay (ELISA). Gene cluster (GC) derivations: regulatory network, protein:protein interactome analyses. STATISTICAL ANALYSES: chi-square, non-parametric measurements, multiple regression, receiver operating characteristic and Kaplan-Meier survival. RESULTS: The disease control rate was 72 %. Median PFS was not achieved (follow-up: 1-33 months, median: 16). Only grading was associated with response (p < 0.01). At baseline, 94 % of patients were NETest-positive, while CgA was elevated in 59 %. NETest accurately (89 %, χ(2) = 27.4; p = 1.2 × 10(-7)) correlated with treatment response, while CgA was 24 % accurate. Gene cluster expression (growth-factor signalome and metabolome) had an AUC of 0.74 ± 0.08 (z-statistic = 2.92, p < 0.004) for predicting response (76 % accuracy). Combination with grading reached an AUC: 0.90 ± 0.07, irrespective of tumor origin. Circulating transcripts correlated accurately (94 %) with PRRT responders (SD+PR+CR; 97 %) vs. non-responders (91 %). CONCLUSIONS: Blood NET transcript levels and the predictive quotient (circulating gene clusters+grading) accurately predicted PRRT efficacy. CgA was non-informative.
BACKGROUND: Peptide receptor radionuclide therapy (PRRT) is an effective method for treating neuroendocrine tumors (NETs). It is limited, however, in the prediction of individual tumor response and the precise and early identification of changes in tumor size. Currently, response prediction is based on somatostatin receptor expression and efficacy by morphological imaging and/or chromogranin A (CgA) measurement. The aim of this study was to assess the accuracy of circulating NET transcripts as a measure of PRRT efficacy, and moreover to identify prognostic gene clusters in pretreatment blood that could be interpolated with relevant clinical features in order to define a biological index for the tumor and a predictive quotient for PRRT efficacy. METHODS: NET patients (n = 54), M: F 37:17, median age 66, bronchial: n = 13, GEP-NET: n = 35, CUP: n = 6 were treated with (177)Lu-based-PRRT (cumulative activity: 6.5-27.8 GBq, median 18.5). At baseline: 47/54 low-grade (G1/G2; bronchial typical/atypical), 31/49 (18)FDG positive and 39/54 progressive. Disease status was assessed by RECIST1.1. Transcripts were measured by real-time quantitative reverse transcription PCR (qRT-PCR) and multianalyte algorithmic analysis (NETest); CgA by enzyme-linked immunosorbent assay (ELISA). Gene cluster (GC) derivations: regulatory network, protein:protein interactome analyses. STATISTICAL ANALYSES: chi-square, non-parametric measurements, multiple regression, receiver operating characteristic and Kaplan-Meier survival. RESULTS: The disease control rate was 72 %. Median PFS was not achieved (follow-up: 1-33 months, median: 16). Only grading was associated with response (p < 0.01). At baseline, 94 % of patients were NETest-positive, while CgA was elevated in 59 %. NETest accurately (89 %, χ(2) = 27.4; p = 1.2 × 10(-7)) correlated with treatment response, while CgA was 24 % accurate. Gene cluster expression (growth-factor signalome and metabolome) had an AUC of 0.74 ± 0.08 (z-statistic = 2.92, p < 0.004) for predicting response (76 % accuracy). Combination with grading reached an AUC: 0.90 ± 0.07, irrespective of tumor origin. Circulating transcripts correlated accurately (94 %) with PRRT responders (SD+PR+CR; 97 %) vs. non-responders (91 %). CONCLUSIONS: Blood NET transcript levels and the predictive quotient (circulating gene clusters+grading) accurately predicted PRRT efficacy. CgA was non-informative.
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Authors: Jarosław B Ćwikła; Lisa Bodei; Agnieszka Kolasinska-Ćwikła; Artur Sankowski; Irvin M Modlin; Mark Kidd Journal: J Clin Endocrinol Metab Date: 2015-09-08 Impact factor: 5.958
Authors: Eric Liu; Scott Paulson; Anthony Gulati; Jon Freudman; William Grosh; Sheldon Kafer; Prasanna C Wickremesinghe; Ronald R Salem; Lisa Bodei Journal: Oncologist Date: 2018-08-29
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Authors: Lisa Bodei; Mark S Kidd; Aviral Singh; Wouter A van der Zwan; Stefano Severi; Ignat A Drozdov; Jaroslaw Cwikla; Richard P Baum; Dik J Kwekkeboom; Giovanni Paganelli; Eric P Krenning; Irvin M Modlin Journal: Eur J Nucl Med Mol Imaging Date: 2018-02-26 Impact factor: 9.236