Context: Molecular Tumor Boards (MTBs) are multidisciplinary meetings of specialists dedicated to analyzing biomarker test results to provide personalized treatment recommendations. However, global disparities in the successful implementation of MTBs exist, driven by unequal access to molecular diagnostics and supportive multidimensional expertise.
Objective: To establish recommendations for MTB implementation, outline practical frameworks for their operation, and address disparities in expertise and resources between new and established MTBs.
Design: A modified Delphi method involved 37 international experts in three survey rounds and online meetings, with consensus defined as ≥75% agreement.
Results: The panel identified a molecular biologist or pathologist with expertise in molecular diagnostics and tumor-specific medical oncologists as indispensable MTB members. Case selection should reflect institutional expertise and volume, with newer MTBs reviewing less selected cases to gain experience. Regular meetings are advised to avoid delays beyond 14 days from result availability to discussion. Reporting should be standardized to include clinicopathologic data (tumor characteristics, treatment history), biomarker findings (testing results, sample details), and recommendations (treatment, retesting, genetic counseling). Treatment options should be ranked by the level of evidence for actionability and may include options not available locally. Performance evaluation should consider changes in patient management based on MTB input and matched therapy rates.
Conclusions: These MTB consensus recommendations are applicable across tumor types, despite being developed by lung cancer and molecular specialists and initiated by the International Association for the Study of Lung Cancer (IASLC). They provide a structured framework for MTB implementation, report standardization, case selection, and quality assessment, aiming to standardize practice and address gaps in expertise for personalized cancer care.
Keywords: ESCAT; OncoKB; artificial intelligence; biomarker; molecular tumor board; next-generation sequencing.
Copyright © 2025. Published by Elsevier Inc.