Molecular profiling of neuroendocrine tumours to predict response and toxicity to peptide receptor radionuclide therapy
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Published on: April 17, 2019
Lisa Bodei1, Heiko Schöder1, Richard P Baum2
1Molecular Imaging and Therapy Service, Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY, USA.
Abstract:
Peptide receptor radionuclide therapy (PRRT) is a type of radiotherapy that targets peptide receptors and is typically used for neuroendocrine tumours (NETs). Some of the key challenges in its use are the prediction of efficacy and toxicity, patient selection, and response optimisation. In this Review, we assess current knowledge on the molecular profile of NETs and the strategies and tools used to predict, monitor, and assess the toxicity of PRRT. The few mutations in tumour genes that can be evaluated (eg, ATM and DAXX) are limited to pancreatic NETs and are most likely not informative. Assays that are transcriptomic or based on genes are effective in the prediction of radiotherapy response in other cancers. A blood-based assay for eight genes (the PRRT prediction quotient [PPQ]) has an overall accuracy of 95% for predicting responses to PRRT in NETs. No molecular markers exist that can predict the toxicity of PRRT. Candidate molecular targets include seven single nucleotide polymorphisms (SNPs) that are susceptible to radiation. Transcriptomic evaluations of blood and a combination of gene expression and specific SNPs, assessed by machine learning with algorithms that are tumour-specific, might yield molecular tools to enhance the efficacy and safety of PRRT.
Insights
A novel blood test accurately predicts peptide receptor radionuclide therapy (PRRT) efficacy in neuroendocrine tumors (NETs). Further research aims to develop molecular tools for predicting and optimizing PRRT toxicity and response.
Area of Science:
- Oncology
- Radiotherapy
- Molecular Diagnostics
Background:
- Peptide receptor radionuclide therapy (PRRT) is crucial for neuroendocrine tumors (NETs).
- Predicting PRRT efficacy, toxicity, and patient selection remain significant challenges.
- Current molecular markers for NETs are limited, especially for predicting treatment outcomes.
Purpose of the Study:
- To review current knowledge on NET molecular profiles.
- To assess strategies and tools for predicting, monitoring, and managing PRRT toxicity.
- To explore potential molecular markers for enhancing PRRT efficacy and safety.
Main Methods:
- Analysis of existing literature on NET molecular profiles and PRRT.
- Evaluation of transcriptomic and gene-based assays for radiotherapy response prediction.
- Assessment of a specific blood-based assay (PRRT prediction quotient [PPQ]) for response prediction.
- Identification of candidate molecular targets for toxicity prediction, including SNPs.
Main Results:
- Limited utility of tumor gene mutations (e.g., ATM, DAXX) for PRRT prediction in NETs.
- A blood-based assay (PPQ) for eight genes shows 95% accuracy in predicting PRRT response in NETs.
- Currently, no molecular markers exist to predict PRRT-related toxicity.
Conclusions:
- The PPQ blood test offers a promising tool for predicting PRRT efficacy in NETs.
- Further development of molecular tools, including transcriptomics and SNP analysis with machine learning, is needed to improve PRRT safety and efficacy.
- Future strategies may involve combining gene expression data with specific SNPs for personalized PRRT management.
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