NANETS2023 Trials In Progress (12 abstracts)
Genetic profiling in the randomized controlled phase 3 COMPOSE trial of 177Lu-edotreotide for well-differentiated aggressive grade 2/3 gastroenteropancreatic neuroendocrine tumors
Thorvardur R. Halfdanarson 1 , Jaume Capdevila 2 , Daniel M. Halperin 3 , Ken Herrmann 4 , Grace Kong 5 , Josh Mailman 6 , Diane Reidy-Lagunes 7 , Raj Srirajaskanthan 8 , Cristina Sierras 9 & Amanda Rotger 9
1Mayo Clinic, Rochester, MN; 2Vall d´Hebron University Hospital, Vall d´Hebron Institute of Oncology, Barcelona, Spain; 3MD Anderson Cancer Center, Houston, TX; 4University Hospital Essen, Essen, Germany; 5Peter MacCallum Cancer Centre, Melbourne, Victoria, Australia; 6NorCal CarciNET Community, Ripon, CA; 7Memorial Sloan Kettering Cancer Center, New York, NY; 8Kings College Hospital, London, UK; 9ITM Oncologics GmbH, Garching/Munich, Germany
Background: Targeted radionuclide therapies (TRTs) have changed the treatment paradigm of neuroendocrine tumors (NET) and are expected to be widely available for patients with various gastroenteropancreatic (GEP)-NET phenotypes. However, while they have great potential, TRT-based therapeutic strategies for high-grade GEP-NET demonstrate variable outcomes, and there is a current lack of tools to identify patients who are likely to respond to TRT. To address this need, the randomized, controlled, open-label, phase 3 COMPOSE trial of 177Lu-edotreotide TRT vs best standard of care (CAPTEM, FOLFOX, or everolimus; chosen by the investigator) in patients with well-differentiated, aggressive, G2/G3 (Ki-67 index 15−55%), somatostatin receptor-positive GEP-NET will include a genetic profiling analysis. By applying an integrative, systemic multiomics approach, we aim to identify predictive and prognostic genetic markers to improve understanding of tumor progression and TRT responses and to guide individualized treatment of NET.
Methods: Participation in the genetic profiling analysis will be optional for COMPOSE trial participants and will not affect disease management or trial procedures. The genetic signatures of GEP-NET at the time of diagnosis will be analyzed by whole exome sequencing to understand predisposition and mutational drivers of oncogenesis. In addition, tumor biopsies and blood samples (the latter collected before treatment, during treatment, and at disease progression) will be analyzed to identify relevant genomic and gene expression signatures, with a focus on suppressive/activating genetic traits.
Results: We plan to create a bioinformatic pipeline for predicting TRT efficacy and disease progression. The pipeline will integrate genomic and gene expression signatures with structural and functional imaging data, histopathology, and patients’ clinical characteristics.
Conclusion: Data from this study are expected to contribute to individualized management of GEP-NET by making it easier to predict which treatment(s) individual patients are likely to respond to.
Abstract ID 23655
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Endocrine Abstracts
ISSN 1470-3947 (print) | ISSN 1479-6848 (online)
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