Ex vivo tissue modelling informs drug selection for rare cancers

• Published in: International journal of cancer 2024-04, Vol.154 (7), p.1158-1163
• Main Authors: Lee, Jenny H., Ming, Zizhen, Cheung, Veronica K. Y., Pedersen, Bernadette, Wykes, James J., Palme, Carsten E., Clark, Jonathan J., Gupta, Ruta, Rizos, Helen
• Format: Article
• Language: English
• Subjects: Cancer; Clinical outcomes; Disease resistance; ex vivo models; Humans; Mutation; Patients; Precision medicine; precision oncology; Proto-Oncogene Proteins B-raf - genetics; Targeted cancer therapy; targeted therapy; Therapeutic targets; Thyroid Neoplasms - drug therapy; Thyroid Neoplasms - genetics; Thyroid Neoplasms - pathology; Treatment resistance; treatment response; Tumors
• ISSN: 0020-7136; 1097-0215
• DOI: 10.1002/ijc.34802

The identification and therapeutic targeting of actionable gene mutations across many cancer types has resulted in improved response rates in a minority of patients. The identification of actionable mutations is usually not sufficient to ensure complete nor durable responses, and in rare cancers, where no therapeutic standard of care exists, precision medicine indications are often based on pan‐cancer data. The inclusion of functional data, however, can provide evidence of oncogene dependence and guide treatment selection based on tumour genetic data. We applied an ex vivo cancer explant modelling approach, that can be embedded in routine clinical care and allows for pathological review within 10 days of tissue collection. We now report that ex vivo tissue modelling provided accurate longitudinal response data in a patient with BRAFV600E‐mutant papillary thyroid tumour with squamous differentiation. The ex vivo model guided treatment selection for this patient and confirmed treatment resistance when the patient's disease progressed after 8 months of treatment. What's new? Treatment selection driven by tumour‐specific mutations is associated with highly variable response rates. Hence, a new approach is needed to guide treatment decisions, especially in rare cancers that lack a therapeutic standard of care. Here, the authors explored the inclusion of functional data in an explant modelling approach as a means of informing treatment selection in a patient with BRAFV600E‐mutant squamous cell thyroid carcinoma. The approach provided data that matched therapeutic responses and confirmed treatment resistance upon disease progression. The results highlight the utility of functional models in supporting genomic data and guiding treatment selection for cancers with actionable mutations.