PRRT genomic signature in blood for prediction of 177Lu-octreotate efficacy

  • 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
Original Article



Peptide receptor radionuclide therapy (PRRT) utilizes somatostatin receptor (SSR) overexpression on neuroendocrine tumors (NET) to deliver targeted radiotherapy. Intensity of uptake at imaging is considered related to efficacy but has low sensitivity. A pretreatment strategy to determine individual PRRT response remains a key unmet need. NET transcript expression in blood integrated with tumor grade provides a PRRT predictive quotient (PPQ) which stratifies PRRT “responders” from “non-responders”. This study clinically validates the utility of the PPQ in NETs.


The development and validation of the PPQ was undertaken in three independent 177Lu-PRRT treated cohorts. Specificity was tested in two separate somatostatin analog-treated cohorts. Prognostic value of the marker was defined in a cohort of untreated patients. The developmental cohort included lung and gastroenteropancreatic [GEP] NETs (n = 72) from IRST Meldola, Italy. The majority were GEP (71%) and low grade (86% G1-G2). Prospective validation cohorts were from Zentralklinik Bad Berka, Germany (n = 44), and Erasmus Medical Center, Rotterdam, Netherlands (n = 42). Each cohort included predominantly well differentiated, low grade (86–95%) lung and GEP-NETs. The non-PRRT comparator cohorts included SSA cohort I, n = 28 (100% low grade, 100% GEP-NET); SSA cohort II, n = 51 (98% low grade; 76% GEP-NET); and an untreated cohort, n = 44 (64% low grade; 91% GEP-NET). Baseline evaluations included clinical information (disease status, grade, SSR) and biomarker (CgA). NET blood gene transcripts (n = 8: growth factor signaling and metabolism) were measured pre-therapy and integrated with tumor Ki67 using a logistic regression model. This provided a binary output: “predicted responder” (PPQ+); “predicted non-responder” (PPQ-). Treatment response was evaluated using RECIST criteria [Responder (stable, partial and complete response) vs Non-Responder)]. Sample measurement and analyses were blinded to study outcome. Statistical evaluation included Kaplan-Meier survival and standard test evaluation analyses.


In the developmental cohort, 56% responded to PRRT. The PPQ predicted 100% of responders and 84% of non-responders (accuracy: 93%). In the two validation cohorts (response: 64–79%), the PPQ was 95% accurate (Bad Berka: PPQ + =97%, PPQ- = 93%; Rotterdam: PPQ + =94%, PPQ- = 100%). Overall, the median PFS was not reached in PPQ+ vs PPQ- (10–14 months; HR: 18–77, p < 0.0001). In the comparator cohorts, the predictor (PPQ) was 47–50% accurate for SSA-treatment and 50% as a prognostic. No differences in PFS were respectively noted (PPQ+: 10–12 months vs. PPQ-: 9–15 months).


The PPQ derived from circulating NET specific genes and tumor grade prior to the initiation of therapy is a highly specific predictor of the efficacy of PRRT with an accuracy of 95%.


Biomarker Carcinoid Liquid biopsy Neuroendocrine Prediction PRRT 



Work was performed with an unrestricted grant from the LuGenIum consortium of independent research.

Compliance with ethical standards

Ethical approval

All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards.

Informed consent

Informed consent was obtained from all participants in the study.

Conflict of interest

LB and GP have received consultancy fees from Ipsen and Advanced Accelerator Applications (AAA). EPK and DJK received support from AAA outside the submitted work. MK is employed by Wren Laboratories that undertook the molecular testing. IMM is a consultant for Wren Laboratories. All others have nothing to disclose.

Supplementary material

259_2018_3967_MOESM1_ESM.docx (367 kb)
ESM 1 (DOCX 367 kb)


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Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  • Lisa Bodei
    • 1
    • 2
  • Mark S. Kidd
    • 3
  • Aviral Singh
    • 4
  • Wouter A. van der Zwan
    • 5
  • Stefano Severi
    • 6
  • Ignat A. Drozdov
    • 3
  • Jaroslaw Cwikla
    • 7
  • Richard P. Baum
    • 2
    • 4
  • Dik J. Kwekkeboom
    • 2
    • 5
  • Giovanni Paganelli
    • 6
  • Eric P. Krenning
    • 2
    • 8
  • Irvin M. Modlin
    • 2
    • 9
  1. 1.Molecular Imaging and Therapy Service, Department of RadiologyMemorial Sloan Kettering Cancer CenterNew YorkUSA
  2. 2.LuGenIum Consortium, Milan, Rotterdam, London, Bad BerkaLondonUK
  3. 3.Wren LaboratoriesBranfordUSA
  4. 4.Theranostics Center for Molecular Radiotherapy and ImagingZentralklinik Bad BerkaBad BerkaGermany
  5. 5.Department of Radiology and Nuclear MedicineErasmus University Medical CenterRotterdamThe Netherlands
  6. 6.Nuclear Medicine and Radiometabolic UnitsInstituto Scientifico Romagnolo per lo Studio e la Cura dei Tumori (IRST) IRCCSMeldolaItaly
  7. 7.University of Warmia and MazuryOlsztynPoland
  8. 8.Cyclotron Rotterdam BVErasmus University Medical CenterRotterdamThe Netherlands
  9. 9.Yale School of MedicineNew HavenUSA

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