Multimodal platform for assessing drug distribution and response in clinical trials

Author:

Lopez Begoña G C1,Kohale Ishwar N23,Du Ziming4,Korsunsky Ilya5678,Abdelmoula Walid M1,Dai Yang4,Stopka Sylwia A19,Gaglia Giorgio4,Randall Elizabeth C9,Regan Michael S1,Basu Sankha S4,Clark Amanda R1,Marin Bianca-Maria10,Mladek Ann C10,Burgenske Danielle M10,Agar Jeffrey N11,Supko Jeffrey G12,Grossman Stuart A13ORCID,Nabors Louis B14,Raychaudhuri Soumya5678,Ligon Keith L4,Wen Patrick Y15,Alexander Brian16,Lee Eudocia Q15,Santagata Sandro4ORCID,Sarkaria Jann10,White Forest M2317,Agar Nathalie Y R1918

Affiliation:

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

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.

Funder

Adult Brain Tumor Consortium

National Institutes of Health

Dana-Farber Cancer Institute PLGA Fund

Ferenc Jolesz National Center

Publisher

Oxford University Press (OUP)

Subject

Cancer Research,Neurology (clinical),Oncology

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