Molecular profiling of neuroendocrine tumours to predict response and toxicity to peptide receptor radionuclide therapy

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.

Figure 1.. Radiation sensitivity and toxicity: pathobiological features, pathways and candidate genes.

Tumor features relevant to radiation sensitivity (pink – top left): include proliferation, hypoxia, “stemness” and mutations particularly in TP53 and the ATM genes. Molecular profiling studies have identified pathways common to radiation responsiveness (green – bottom left). These include DNA damage recognition and repair; senescence induction and apoptosis. Radiation toxicity affects the kidneys, individual blood cell populations e.g., platelets, white cells and the bone marrow (pink – top right). Candidate factors related to toxicity (green – bottom right) include intrinsic sensitivity to radiation which may be related to SNPs or mutations in DNA damage/repair pathways including ATM, NBS1 and LIG4. A variety of genes validated as radiation-sensitive genes in other cancers have been identified as relevant to PRRT lung and gut sensitivity, that as they are. Furthermore, intrinsic radiation toxicity has been found for TNFα and TANC1 genes. None of these candidate genes have been evaluated for PRRT.

Figure 2.. Clinical Factors commonly used to “predict” PRRT response

A panoply of clinical factors has been evaluated as “predictors” of PRRT response. Factors that predict clinical outcomes and survival have been misperceived as predictors of PRRT response. Thus, performance status, primary location, and disease extent are all prognostic and unrelated to the prediction of PRRT response. Similarly, glucose-based metabolism (FDG-positive) and tumor grade are also prognostic features. Circulating monoanalyte biomarkers, if elevated, reflect tumor burden, and are prognostic. Other parameters such as somatostatin receptor imaging intensity or IHC SSTR expression provide evidence of target existence and properly are “inclusion” factors. They cannot predict PRRT response but represent indices of target acquisition likelihood. CgA = chromogranin A; IHC = immunohistochemistry; KPS = Karnofsky performance score; NSE = neuron-specific enolase; ORR = objective response rate; OS = overall survival; PFS = progression free survival; SSTR = somatostatin receptor; IHC= immunohistochemical (Figure adapted from Bodei et al. EJNMMI 2018; 45(7): 1155–69).

Figure 3.. Current and future targets for predicting sensitivity and toxicity.

Sensitivity. PRRT-sensitivity prediction can currently be measured using the blood-based transcriptome assay (PPQ). Different tissue-based multigene radiation-sensitivity assays (10–31 genes) for other cancers have not yet been tested for PRRT. Molecular profiling indicates that somatic mutations of chromatin remodeling genes, DNA damage/repair and apoptosis may be viable targets for evaluation. For lung, this may include TTF1, MEN and ARID genes. In pancreatic NETs a series of mutations in ATM, BRCA2, CHECK2, MUTYH and DAXX might be relevant. Given the absence of mutations in small intestinal NETs, the most likely candidate is chromosome 18q loss. Toxicity: Germline SNPs in a series of genes that are proven radiation sensitivity intrinsic factors require assessment. Fibrosis can be evaluated using a 9, circulating gene-based fibrosis assay. Separately, epigenetic or CTC evaluation may provide alternative molecular strategies for either sensitivity or toxicity prediction.