The clinical applications of a multigene liquid biopsy (NETest) in neuroendocrine tumors

Abstract

Purpose: There are few effective biomarkers for neuroendocrine tumors. Precision oncology strategies have provided liquid biopsies for real-time and tailored decision-making. This has led to the development of the first neuroendocrine tumor liquid biopsy (the NETest). The NETest represents a transcriptomic signature of neuroendocrine tumor (NETs) that captures tumor biology and disease activity. The data have direct clinical application in terms of identifying residual disease, disease progress and the efficacy of treatment. In this overview we assess the available published information on the metrics and clinical efficacy of the NETest.

Material and methods: Published data on the NETest have been collated and analyzed to understand the clinical application of this multianalyte biomarker in NETs.

Results: NETest assay has been validated as a standardized and reproducible clinical laboratory measurement. It is not affected by demographic characteristics, or acid suppressive medication. Clinical utility of the NETest has been documented in gastroenteropancreatic, bronchopulmonary NETs, in paragangliomas and pheochromocytomas. The test facilitates accurate diagnosis of a NET disease, and real-time monitoring of the disease status (stable/progressive disease). It predicts aggressive tumor behavior, identifies operative tumor resection, and efficacy of the medical treatment (e.g. somatostatin analogues), or peptide receptor radionuclide therapy (PRRT). NETest metrics and clinical applications out-perform standard biomarkers like chromogranin A.

Conclusions: The NETest exhibits clinically competent metrics as an effective biomarker for neuroendocrine tumors. Measurement of NET transcripts in blood is a significant advance in neuroendocrine tumor management and demonstrates that blood provides a viable source to identify and monitor tumor status.

Keywords: Carcinoid; CgA; Liquid biopsy; NETest; Neuroendocrine.

Fig. 1.. Numbers of publications (PubMed) versus internet interest (Google Trending) related to the search term “liquid biopsy”.

The general public became aware of liquid biopsies as early as 2004 and academic publications followed a similar time course. Interest in liquid biopsies has however significantly escalated since 2012, when technologies such as cell capture and sequencing adequately evolved as clinical tools. Adapted from Modlin IM, Kidd M, Malczewska A, Drozdov I, Bodei L, Matar S et al. The NETest: The Clinical Utility of Multigene Blood Analysis in the Diagnosis and Management of Neuroendocrine Tumors. Endocrinology and Metabolism Clinics of North America 2018;47(3):485–504.

Fig. 2.. Computational pipeline utilized to derive a set of marker genes, the “NET Marker Panel” that identifies GEP-NEN/NET disease in the blood.

The steps include: the inference of gene co-expression networks and the derivation of a tissue-level GEP-NEN network (Step 1); the derivation of normal and neoplastic networks from other cancers (Step 2); the mathematical derivation of a GEP-NEN “specific network (subtraction of “normal” and “other cancer” networks (Step 3); mapping of upregulated genes to the GEP-NEN network (Step 4); evaluation and expansion of the NETwork to include blood-derived NET genes (Step 5); inclusion of genes from literature and cancer mutation database curation (Step 6) and testing and derivation of the 51 marker gene set (Step 7). Reprinted from Modlin IM, Kidd M, Malczewska A et al. The NETest: The Clinical Utility of Multigene Blood Analysis in the Diagnosis and Management of Neuroendocrine Tumors. Endocrinology and Metabolism Clinics of North America 2018;47(3):485–504, with permission from Elsevier, and Modlin IM, Drozdov I, Kidd M (2013) The Identification of Gut Neuroendocrine Tumor Disease by Multiple Synchronous Transcript Analysis in Blood. PLoS ONE 8(5): e63364.

Fig. 3.. The multi-step protocol used to provide a multianalyte gene expression assay result for GEP-NETs.

A 2-step protocol (mRNA isolation and cDNA synthesis) is undertaken prior to quantitative PCR gene expression. Normalized 51-marker signature is interrogated using mathematical algorithms to provide a score that is scaled 0–100% (the NETest score). The NETest delineates in a specific patient whether the tumor falls into a category of low (< 40%), moderate (40–79%) and high (≥ 80%) risk for disease activity. HRS, hours; qPCR, quantitative PCR.

Fig. 4.. Clinical utility of a multianalyte assay (NETest) for neuroendocrine tumor diagnosis and management

Diagnosis: The NETest can detect lung, thymic, pancreatic, and gastrointestinal tract NETs as well as paragangliomas and pheochromocytomas (PPGL) with ≥ 90% accuracy. Management: NETest has clinical utility in three areas: 1) Defining the status of the disease – as either stable or progressive. 2) Monitoring therapy or evaluating patients in watch-and-wait programs. 3) Determining the effectiveness of a treatment modality e.g. determining residual disease or disease “recurrence” after surgery or evaluating responses to somatostatin analogues (SSA) or peptide receptor radionuclide therapy (PRRT).

Fig. 5.. Comparison of the sensitivity of the blood biomarkers NETest and chromogranin A.

NETest: Positive = red. Negative = yellow. CgA Positive = green.. Negative = yellow. The NETest is overall positive in 96–98% of all NETs (bronchopulmonary, pancreatic and small bowel). CgA is significantly less accurate in small bowel - 60% positive and only ~25% in BPNETs and PNETs. The low diagnostic sensitivity indicates very limited clinical utility for CgA as a biomarker. BPNET – Bronchopulmonary NET; PNET – Pancreatic NET.

Fig. 6.. Relationship between NETest and progression free survival in a prospective observational Registry Cohort (N = 100).

6A. Watch-and-wait cohort: a low NETest score was associated with mPFS of 12 months. A high score was associated with an mPFS of 3 months (HR 30.4, p < 0.0001). 6B. Treatment cohort: a low score was associated with an mPFS that was not reached at 12-months. A high score was associated with an mPFS of 5 months (HR 60.2, p < 0.0001). HR = hazard ratio. Reprinted from Modlin IM, Kidd M, Malczewska A et al. The NETest: The Clinical Utility of Multigene Blood Analysis in the Diagnosis and Management of Neuroendocrine Tumors. Endocrinology and Metabolism Clinics of North America 2018;47(3):485–504, with permission from Elsevier.

Fig. 7.. Comparative clinical utility for CgA and NETest

Of the one hundred patients enrolled (all of whom had a NETest), fifty-three had both a NETest and CgA. NETest was positive in all 53 samples while CgA was elevated in 13 (25%) and were normal in 40 (75%). High NETest scores were noted in 18 (34%) of the 53 patients. Alterations in clinical management (intervene) were made in 78%. All demonstrated disease stabilization at subsequent follow-up (12 months). Low scores were associated with a management change in 1 patient (4%). This patient, progressed on Affinitor. All other patients (96%) with low scores exhibited disease stabilization. CgA was associated with alterations in clinical management in ~30% of patients, irrespective of whether the CgA level was elevated or not. Disease stabilization ranged from 6 to 62% based on intervention and score. CgA levels therefore are unable to effectively guide disease management. *p < 0.0001 vs. high score. F/ Up = Follow-up; Mo = months; +ve, positive. Reprinted from Modlin IM, Kidd M, Malczewska A et al. The NETest: The Clinical Utility of Multigene Blood Analysis in the Diagnosis and Management of Neuroendocrine Tumors. Endocrinology and Metabolism Clinics of North America 2018;47(3):485–504, with permission from Elsevier.

Fig. 8.. Strategy for utilizing the PRRT Predictive Quotient (PPQ) to predict an individual patient response to PRRT (¹⁷⁷Lu-Octreotate therapy).

The PPQ is derived from algorithmic analysis of growth factor signaling and metabolic pathways. Individuals are stratified into responders (green) and non-responders (red) to PRRT. Responders exhibit intact regulated growth factor signaling pathways and low-level metabolic pathways which indicate the tumor sensitivity/susceptibility to radiation and predict significant tumor DNA damage cell/ apoptosis. The non-responder group are defined by an autonomous growth factor signalome and a high-level metabolome and will not respond to PRRT. The majority (89%) of the predicted non-responders develop disease progression after PRRT. Alternative or combination therapies e.g., chemotherapy, immunotherapy or external beam radiation therapy (EBRT) may improve the likelihood or a response to PRRT.