Background

Neuroendocrine neoplasms (NENs) are clinically diverse tumors that likely arise from neuroendocrine cells scattered throughout the body. The incidence of NEN has risen significantly globally over the last three decades making NEN a growing healthcare concern.¹

Initial diagnosis of NEN is challenging due to heterogeneous clinical presentation with often vague abdominal symptoms. Further complicating this is the high tumor diversity and subtle histologic differences between pathological types. The factors can lead to a delay in diagnosis and studies have found a median delay of almost 54 months from onset of first symptoms.² This delay results in later stage diagnosis with significantly poorer outcomes of some NENs compared with non-NENs at the same anatomical site.³

Current diagnostic testing for NEN detection is a combination of imaging to identify the tumor and pathologic evaluation of biopsy or resection material. The latter technique combines immunohistochemistry and mitotic count figures to predict primary tumour site and grade but has accuracy limitations. With the above limitations, and the high expense of existing diagnostics, there is an urgent need to identify better biomarkers enable faster, cheaper, and more accurate diagnosis of NENs.

White, B.E. et al. Evaluating cost-effectiveness in the management of neuroendocrine neoplasms. Reviews in Endocrine and Metabolic Disorders. Nov.6, 2020: Online ahead of print.
Basuroy R. et al. Delays and routes to diagnosis of neuroendocrine tumours. BMC Cancer 18: Article number:1122, 2018.
Tracey S.E. et al. Impact of neuroendocrine morphology on cancer outcomes and stage at diagnosis: a UK nationwide cohort study 2013-2015. Br J Cancer. 121(11):966-972, 2019.

Technology Overview

MicroRNAs (miRNAs) are small (19‑24 nucleotide) regulatory molecules that can be used as biomarkers to classify cancer. miRNAs are highly informative tissue markers due to their abundance and stability, as well as their cell‑type and disease‑stage specificity. miRNAs are also known to have predictable interactions with messenger RNAs, meaning that these molecules can also provide mechanistic information regarding cellular processes.

Using barcoded small RNA sequencing and data mining, Queen’s researchers have generated comprehensive microRNA expression profiles for 15 different NEN pathology types. These data have allowed the researchers to create a miRNA‑based hierarchical classifier which is able to first identify whether a tumor is a NEN and subsequently sub‑classify the actual pathological type.

Queen’s researchers have identified miR‑375 as a potential universal marker of neuroendocrine cell differentiation (Figure 1). The current leading NEN biomarkers, chromogranin A (CgA) and synaptophysin, have demonstrated inconsistent results depending on the particular NEN subtype. Chromogranin A has shown specificity ranging from 68‑100% and sensitivity ranging from 42‑93% based on tumor primary site, grade, or disease burden¹. Similarly, synaptophysin, despite being considered to be less specific than CgA, has been found to be expressed in as low as 41‑75% of certain NEN tumor types². As such, there is a clear need for a universal biomarker that can detect NEN tumors with a high degree of sensitivity and specificity.

Figure 1: miR-375 expression in NEN and non-NEN samples

Their feature selection algorithm also identified 17 miRNAs to discriminate 15 NEN pathological types (Figure 2). Queen’s researchers subsequently constructed a multilayer classifier for discriminating NEN pathological types and successfully identified 217 of 221 samples (98%)³.

Figure 2: Multilayer miRNA-based Classifier for Predicting NEN Pathological Types

This miRNA expression profiling has resulted in the identification of universal and classificatory markers to both identify NEN tumors and determine the NEN pathology type. These improvements in NEN classification can help enable more effective and timely clinical treatment while also allowing for more targeted NEN classification in the context of future clinical trials.

Malczewska A. et al. An Assessment of Circulating Chromogranin A as a Biomarker of Bronchopulmonary Neuroendocrine Neoplasia: A Systematic Review and Meta-Analysis. Neuroendocrinology. 110:198–216, 2020.
Rooper, L.M. et al. INSM1 Demonstrates Superior Performance to the Individual and Combined Use of Synaptophysin, Chromogranin and CD56 for Diagnosing Neuroendocrine Tumors of the Thoracic Cavity. Am J Surg Pathol. 41:1561–1569, 2017.
Nanayakkara J. et al. Characterizing and classifying neuroendocrine neoplasms through microRNA sequencing and data mining. NAR Cancer. 2:3, 2020.

Benefits

Candidate universal marker to determine whether a tumor is a NEN
Hierarchical classification can then be used to determine the specific NEN pathological type
Faster and more accurate diagnosis of NENs, enabling more effective clinical treatment

Applications

Potential universal NEN biomarker
Faster and more accurate diagnosis of NENs, including the primary tumor type
Enables more specific patient selection in clinical trials focused on treating particular NEN sub‑types
Companion diagnostic for treatments focused on NEN tumors, especially treatments targeting microRNAs

Opportunity

Seeking exclusive licensee, research collaborator or venture investment.

Novel Diagnostic for Identifying and Classifying Neuroendocrine Neoplasms

Patents

IP Status

Seeking