miRquad: first-in-class dPCR multiplex TaqMan™ Advanced clinical research assay for microRNA detection in head and neck cancer
Year: 2025
Type: article
Abstract: BACKGROUND: Cancer resistance is one of the major challenges in oncology, often resulting in disease relapse and poor patient outcomes. Within the RNA family, microRNAs (miRNAs) regulate core biological processes and have been recognized also as critical contributors of tumor resistance and therapy failure. Being pivotal, they are increasingly exploited as biomarkers in various settings. Although in silico analyses facilitate miRNAs identification, PCR-based approaches remain essential to validate their expression. Currently, a plethora of well-established, single-target methods exist but multiplex detection from the same input have been only rarely explored. METHODS: We present miRquad, the first-in-class digital PCR (dPCR) TaqMan™ multiplex clinical research assay for miRNA detection in head and neck (HNC) cancers. Based on a patented prognostic signature including miR-21-5p, miR-96-5p, miR-21-3p and miR-429, the assay would enable simultaneous miRNA analysis via qPCR and dPCR on multiple clinically relevant sample types. RESULTS: We designed and optimized miRquad using both synthetic controls and retrospective patient-derived tissues, sera and saliva. A multicentre ring study was conducted to evaluate assay reliability across different platforms, demonstrating strong correlation with commercial singleplexes, broad applicability, reduced turnaround time (TAT) and cost-effectiveness. Finally, we provide evidence for its potential clinical application to predict disease outcome in HNC, testing miRquad on tumoral and peritumoral tissues, sera and saliva samples collected throughout patient follow up. CONCLUSIONS: The assay overcomes common challenges associated with multiple miRNAs detection, particularly in liquid biopsy samples (e.g., multiple pipetting issues, increased consumption of sample for multiple assessment, extended TAT for complete profiling) and provides robust and accurate detection, demonstrating potential for real-time patient monitoring and prognostication in HNC.
Authors: Matteo Allegretti, David J. Joun, Giulia Urbani, Valentina De Pascale, Federica Ganci, Raul Pellini, Giada Anna Beltramini, Stefano Ferrero, Stefano Fiori, Tania Moccia, Chiara Ciardiello, Elena Di Gennaro, Alfredo Budillon, Luca Sigalotti, Roberta Maestro, Mario Urtis, Eloisa Arbustini, Simona De Summa, Amalia Azzariti, Stella Gagliardi, Antonio Pisani, Gennaro Ciliberto, Paola Cornelia Maria Muti, Junko F. Stevens, Giovanni Blandino
Institutions: Instituto Nazionale Tumori Regina Elena, Thermo Fisher Scientific (United States), Fondazione IRCCS Ca' Granda Ospedale Maggiore Policlinico, University of Milan, Istituto Nazionale Tumori IRCCS "Fondazione G. Pascale", Centro di Riferimento Oncologico, Policlinico San Matteo Fondazione, Istituto Tumori Bari, University of Pavia
Language: English
Subfield: Cancer Research
Domain: Life Sciences
Open Access status: gold
Estimated APC paid: $4,190
Awards: G1864982059, G6327100204
OpenAlex ID: W4417521769