Search PubMedSearch

Biomedical subjects

Josep Tabernero

Publications and source records attributed to Josep Tabernero.

3 recordsLinked to original sources

Application of a Translational Research Platform to Unveil Efficacy Signals and Mechanisms of Resistance of FGFR Inhibitors in Multiple FGFR-Altered Solid Tumors.

PURPOSE: The predictive value of fibroblast growth factor receptor (FGFR) amplifications (amp) and the role of FGFR mutations (mut) beyond known activating variants remain unclear. We aimed to establish a translational research platform to characterize FGFR alterations (alt) and explore their potential as predictive biomarkers for FGFR-targeted agents. EXPERIMENTAL DESIGN: This ambispective study included a retrospective analysis of patients with FGFR-alt tumors treated with selective FGFR inhibitors (FGFRi) and a prospective collection of longitudinal tumor samples. Patient-derived xenografts (PDX) were generated to investigate FGFRi mechanisms of action and resistance. Molecular characterization included genomic, transcriptomic, proteomic, and functional analyses using the Functional Annotation for Cancer Treatment (FACT) assay. RESULTS: Among 36 retrospectively analyzed patients, clinical benefit from FGFRis was observed in cases with FGFR mRNA overexpression or FGFR2/11q co-amp, but no association was found with the amplification levels. In archival tumor samples, exploratory proteomic analysis showed FGFR1-4 protein expression in 78% of FGFR1/2-amp tumors detected by fluorescence in situ hybridization. RNA sequencing identified a higher prevalence of FGFR mRNA overexpression than proteomic analysis. Among patients harboring FGFR-mut, only one bladder cancer with an FGFR3-mut S249C derived benefit. FACT assay supported the functional activity of selected variants, including FGFR3 T689M, and suggested potential resistance mechanisms involving PI3K/PTEN and MAPK pathway co-alterations. A prospective FGFR-alt PDX biorepository enabled exploratory biomarker analyses, supporting the hypothesis that FGFR1-4 mRNA expression may better reflect FGFR dependency than genomic alterations alone. CONCLUSIONS: These findings highlight the complexity of FGFR-driven oncogenesis and support integrative molecular approaches to refine patient selection for FGFR-targeted therapies.

Humans

Worldwide Innovative Network (WIN) Consortium in Personalized Cancer Medicine: Bringing next-generation precision oncology to patients.

The human genome project ushered in a genomic medicine era that was largely unimaginable three decades ago. Discoveries of druggable cancer drivers enabled biomarker-driven gene- and immune-targeted therapy and transformed cancer treatment. Minimizing treatment not expected to benefit, and toxicity-including financial and time-are important goals of modern oncology. The Worldwide Innovative Network (WIN) Consortium in Personalized Cancer Medicine founded by Drs. John Mendelsohn and Thomas Tursz provided a vision for innovation, collaboration and global impact in precision oncology. Through pursuit of transcriptomic signatures, artificial intelligence (AI) algorithms, global precision cancer medicine clinical trials and input from an international Molecular Tumor Board (MTB), WIN has led the way in demonstrating patient benefit from precision-therapeutics through N-of-1 molecularly-driven studies. WIN Next-Generation Precision Oncology (WINGPO) trials are being developed in the neoadjuvant, adjuvant or metastatic settings, incorporate real-world data, digital pathology, and advanced algorithms to guide MTB prioritization of therapy combinations for a diverse global population. WIN has pursued combinations that target multiple drivers/hallmarks of cancer in individual patients. WIN continues to be impactful through collaboration with industry, government, sponsors, funders, academic and community centers, patient advocates, and other stakeholders to tackle challenges including drug access, costs, regulatory barriers, and patient support. WIN's collaborative next generation of precision oncology trials will guide treatment selection for patients with advanced cancers through MTB and AI algorithms based on serial liquid and tissue biopsies and exploratory omics including transcriptomics, proteomics, metabolomics and functional precision medicine. Our vision is to accelerate the future of precision oncology care.

Humans