Effectiveness of a Molecular Tumor Board in Making Personalized Treatment Recommendations for Patients with Advanced Cancer
Year: 2026
Type: preprint
Study design: Observational Study
Abstract: ABSTRACT BACKGROUND Next-generation sequencing (NGS) and the development of targeted therapies have revolutionized cancer treatment. Molecular Tumour Boards (MTB) serve as collaborative platforms to integrate individual clinical and molecular information to make recommendations for personalized treatment. METHOD This is a single-center, retrospective cohort study of patients presented at the Jewish General Hospital (JGH) MTB between July 2018 and Dec 2022. Clinical, molecular/pathologic data, MTB recommendations, and treatment details were collected. Treatment response was determined by radiological evaluation and physician clinical assessment. Fisher’s exact test and Log-rank tests were used to assess differences between groups. RESULTS A total of 328 patients were presented to the MTB, including 23 with repeat presentations, resulting in 355 case reviews. Tumour-agnostic biomarkers were detected in 18.9% of cases, most commonly high tumour mutational burden (TMB, 9.5%) and HER2 amplification (4.3%). The most frequent actionable mutations were PIK3CA (19.8%) and PTEN (13.1%), often leading to recommendations for mTOR inhibitor therapy (26.0%). Overall, therapeutic recommendations were issued in 84.5% (300/355) of cases, and implemented in 43.3% (130/300). Patients who received MTB-recommended therapies achieved significantly higher objective response rates (ORR) (14.2% vs. 4.2%; P = 0.047), longer real-world progression-free survival (rwPFS) (5.6 vs. 3.5 months; HR 0.57, 95% CI 0.390–0.828; P = 0.003), and improved median overall survival (OS) (12.8 vs. 5.2 months; HR 0.60, 95% CI 0.452–0.793; P < 0.001) compared with those treated without MTB guidance. CONCLUSION MTB is effective in identifying biomarker-matched therapeutic recommendations for advanced malignancies where limited standard-of-care treatment option is available.
Source: medRxiv
Authors: Charles V. Rajadurai, Drew Harrigan, Josiane Lafleur, Soumaya Labidi, Francois Panet, Islam E. Elkholi, Andreas I. Papadakis, Aline Mamo, Adriana Aguilar-Mahecha, Susie Lau, Petr Kavan, Alan Spatz, Gerald Batist, Jennifer Friedmann, Parvaneh Fallah, Cristiano Ferrario, Mark Basik, April A. N. Rose
Language: English
Subfield: Cancer Research
Domain: Life Sciences
Open Access status: green
OpenAlex ID: W7215797368