Begoña G C Lopez1, Ishwar N Kohale2,3, Ziming Du4, Ilya Korsunsky5,6,7,8, Walid M Abdelmoula1, Yang Dai4, Sylwia A Stopka1,9, Giorgio Gaglia4, Elizabeth C Randall9, Michael S Regan1, Sankha S Basu4, Amanda R Clark1, Bianca-Maria Marin10, Ann C Mladek10, Danielle M Burgenske10, Jeffrey N Agar11, Jeffrey G Supko12, Stuart A Grossman13, Louis B Nabors14, Soumya Raychaudhuri5,6,7,8, Keith L Ligon4, Patrick Y Wen15, Brian Alexander16, Eudocia Q Lee15, Sandro Santagata4, Jann Sarkaria10, Forest M White2,3,17, Nathalie Y R Agar1,9,18. 1. Department of Neurosurgery, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts, USA. 2. Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA. 3. Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA. 4. Department of Pathology, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts, USA. 5. Center for Data Sciences, Brigham and Women's Hospital, Boston, Massachusetts, USA. 6. Divisions of Genetics and Rheumatology, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, Massachusetts, USA. 7. Department of Biomedical Informatics, Harvard Medical School, Boston, Massachusetts, USA. 8. Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA. 9. Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts, USA. 10. Department of Radiation Oncology, Mayo Clinic, Rochester, Minnesota, USA. 11. Department of Chemistry and Chemical Biology, Northeastern University, Boston, Massachusetts, USA. 12. Massachusetts General Hospital Cancer Center, Harvard Medical School, Boston, Massachusetts, USA. 13. Brain Cancer Program, Johns Hopkins Hospital, Baltimore, Maryland, USA. 14. University of Alabama at Birmingham, Birmingham, Alabama, USA. 15. Center for Neuro-Oncology, Dana-Farber Cancer Institute, Boston, Massachusetts, USA. 16. Department of Radiation Oncology, Center for Neuro-Oncology, Dana-Farber Cancer Institute, Boston, Massachusetts, USA. 17. Center for Precision Cancer Medicine, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA. 18. Department of Cancer Biology, Dana-Farber Cancer Institute, Harvard Medical School, Boston, Massachusetts, USA.
Abstract
BACKGROUND: Response to targeted therapy varies between patients for largely unknown reasons. Here, we developed and applied an integrative platform using mass spectrometry imaging (MSI), phosphoproteomics, and multiplexed tissue imaging for mapping drug distribution, target engagement, and adaptive response to gain insights into heterogeneous response to therapy. METHODS: Patient-derived xenograft (PDX) lines of glioblastoma were treated with adavosertib, a Wee1 inhibitor, and tissue drug distribution was measured with MALDI-MSI. Phosphoproteomics was measured in the same tumors to identify biomarkers of drug target engagement and cellular adaptive response. Multiplexed tissue imaging was performed on sister sections to evaluate spatial co-localization of drug and cellular response. The integrated platform was then applied on clinical specimens from glioblastoma patients enrolled in the phase 1 clinical trial. RESULTS: PDX tumors exposed to different doses of adavosertib revealed intra- and inter-tumoral heterogeneity of drug distribution and integration of the heterogeneous drug distribution with phosphoproteomics and multiplexed tissue imaging revealed new markers of molecular response to adavosertib. Analysis of paired clinical specimens from patients enrolled in the phase 1 clinical trial informed the translational potential of the identified biomarkers in studying patient's response to adavosertib. CONCLUSIONS: The multimodal platform identified a signature of drug efficacy and patient-specific adaptive responses applicable to preclinical and clinical drug development. The information generated by the approach may inform mechanisms of success and failure in future early phase clinical trials, providing information for optimizing clinical trial design and guiding future application into clinical practice.
BACKGROUND: Response to targeted therapy varies between patients for largely unknown reasons. Here, we developed and applied an integrative platform using mass spectrometry imaging (MSI), phosphoproteomics, and multiplexed tissue imaging for mapping drug distribution, target engagement, and adaptive response to gain insights into heterogeneous response to therapy. METHODS: Patient-derived xenograft (PDX) lines of glioblastoma were treated with adavosertib, a Wee1 inhibitor, and tissue drug distribution was measured with MALDI-MSI. Phosphoproteomics was measured in the same tumors to identify biomarkers of drug target engagement and cellular adaptive response. Multiplexed tissue imaging was performed on sister sections to evaluate spatial co-localization of drug and cellular response. The integrated platform was then applied on clinical specimens from glioblastoma patients enrolled in the phase 1 clinical trial. RESULTS: PDX tumors exposed to different doses of adavosertib revealed intra- and inter-tumoral heterogeneity of drug distribution and integration of the heterogeneous drug distribution with phosphoproteomics and multiplexed tissue imaging revealed new markers of molecular response to adavosertib. Analysis of paired clinical specimens from patients enrolled in the phase 1 clinical trial informed the translational potential of the identified biomarkers in studying patient's response to adavosertib. CONCLUSIONS: The multimodal platform identified a signature of drug efficacy and patient-specific adaptive responses applicable to preclinical and clinical drug development. The information generated by the approach may inform mechanisms of success and failure in future early phase clinical trials, providing information for optimizing clinical trial design and guiding future application into clinical practice.
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