Standardized and simplified reporting of next-generation sequencing results in advanced non-small-cell lung cancer: Practical indications from an Italian multidisciplinary group
Umberto Malapelle, Alessandro Delle Donne, Fabio Pagni, Filippo Fraggetta, Elena Guerini‐Rocco, Giulia Pasello, Giuseppe Perrone, Francesco Pepe, Simona Vatrano, Sandro Pignata, Carmine Pinto, Giancarlo Pruneri, Antonio Russo, Héctor Soto Parrà, S. Vallone, Antonio Marchetti, Giancarlo Troncone, Silvia Novello
Abstract
Molecular biomarker testing is increasingly becoming standard of care for advanced non-small cell lung cancer (NSCLC). Tissue and liquid biopsy-based next-generation sequencing (NGS) is now highly recommended and has become an integral part of the routine management of advanced NSCLC patients. This highly sensitive approach can simultaneously and efficiently detect multiple biomarkers even in scant samples. However full optimization of NGS in clinical practice requires accurate reporting and interpretation of NGS findings. Indeed, as the number of NSCLC biomarkers continues to grow, clinical reporting of NGS data is becoming increasingly complex. In this scenario, achieving standardization, simplification, and improved readability of NGS reports is key to ensuring timely and appropriate treatment decisions. In an effort to address the complexity and lengthy reporting of NGS mutation results, an Italian group of 14 healthcare professionals involved in NSCLC management convened in 2023 to address the content, structure, and ease-of-use of NGS reporting practices and proposed a standard report template for clinical use This article presents the key discussion points addressed by the Italian working group and describes the essential elements of the report template. • Next generation sequencing has revolutionized predictive molecular pathology and precision oncology. • Concise and thorough clinical reports are key to sharing information between molecular pathologists and oncologist. • Multidisciplinary team discussions are essential to decipher and leverage predictive molecular pathology results.