Gene expression-based prediction of pazopanib efficacy in sarcoma

Christoph E. Heilig, Andreas Laßmann, Sadaf S. Mughal, Andreas Mock, Sebastian Pirmann, Veronica Teleanu, Marcus Renner, Carolin Andresen, Bruno C. Köhler, Bogac Aybey, Sebastian Bauer, Jens T. Siveke, Rainer Hamacher, Gunnar Folprecht, Stephan Richter, Evelin Schröck, Christian H. Brandts, Marit Ahrens, Peter Hohenberger, Gerlinde EgererThomas Kindler, Melanie Boerries, Anna L. Illert, Nikolas von Bubnoff, Leonidas Apostolidis, Philipp J. Jost, C. Benedikt Westphalen, Wilko Weichert, Ulrich Keilholz, Frederick Klauschen, Katja Beck, Ulrike Winter, Daniela Richter, Lino Möhrmann, Michael Bitzer, Klaus Schulze-Osthoff, Benedikt Brors, Gunhild Mechtersheimer, Simon Kreutzfeldt, Christoph Heining, Daniel B. Lipka, Albrecht Stenzinger, Richard F. Schlenk, Peter Horak, Hanno Glimm, Daniel Hübschmann, Stefan Fröhling*

*Korrespondierende/r Autor/-in für diese Arbeit


Background: The multi-receptor tyrosine kinase inhibitor pazopanib is approved for the treatment of advanced soft-tissue sarcoma and has also shown activity in other sarcoma subtypes. However, its clinical efficacy is highly variable, and no reliable predictors exist to select patients who are likely to benefit from this drug. Patients and methods: We analysed the molecular profiles and clinical outcomes of patients with pazopanib-treated sarcoma enrolled in a prospective observational study by the German Cancer Consortium, DKTK MASTER, that employs whole-genome/exome sequencing and transcriptome sequencing to inform the care of young adults with advanced cancer across histology and patients with rare cancers. Results: Among 109 patients with available whole-genome/exome sequencing data, there was no correlation between clinical parameters, specific genetic alterations or mutational signatures and clinical outcome. In contrast, the analysis of a subcohort of 62 patients who underwent molecular analysis before pazopanib treatment and had transcriptome sequencing data available showed that mRNA levels of NTRK3 (hazard ratio [HR] = 0.53, p = 0.021), IGF1R (HR = 1.82, p = 0.027) and KDR (HR = 0.50, p = 0.011) were independently associated with progression-free survival (PFS). Based on the expression of these multi-receptor tyrosine kinase genes, i.e. the features NTRK3-high, IGF1R-low and KDR-high, we developed a pazopanib efficacy predictor that stratified patients into three groups with significantly different PFS (p < 0.0001). Application of the pazopanib efficacy predictor to an independent cohort of patients with pazopanib-treated sarcoma from DKTK MASTER (n = 43) confirmed its potential to separate patient groups with significantly different PFS (p = 0.02), whereas no such association was observed in patients with sarcoma from DKTK MASTER (n = 97) or The Cancer Genome Atlas sarcoma cohort (n = 256) who were not treated with pazopanib. Conclusion: A score based on the combined expression of NTRK3, IGF1R and KDR allows the identification of patients with sarcoma and with good, intermediate and poor outcome following pazopanib therapy and warrants prospective investigation as a predictive tool to optimise the use of this drug in the clinic.

ZeitschriftEuropean Journal of Cancer
Seiten (von - bis)107-118
PublikationsstatusVeröffentlicht - 09.2022

Strategische Forschungsbereiche und Zentren

  • Profilbereich: Lübeck Integrated Oncology Network (LION)
  • Zentren: Universitäres Cancer Center Schleswig-Holstein (UCCSH)


Untersuchen Sie die Forschungsthemen von „Gene expression-based prediction of pazopanib efficacy in sarcoma“. Zusammen bilden sie einen einzigartigen Fingerprint.