139 Background: PD-(L)1 immunotherapy is effective in multiple tumors, including NSCLC and melanoma, but tumor PD-L1 IHC correlates only moderately with treatment outcome. This study aims to assess 1) safety of 18F-BMS-986192 (18F-PD-L1) in human, 2) PD-L1 quantification in tumors using 18F-PD-L1 PET, 3) PD-L1 PET correlation with IHC and treatment outcome, and 4) intra and inter subject tracer uptake variability. Methods: Pts with NSCLC (N = 10) and melanoma (N = 3) were included. At baseline, pts received a static or multiphase dynamic whole body PET scan after injecting 200 MBq 18F-BMS-986192. For NSCLC pts, (1) SUV(max, peak and mean) were measured for each delineable tumor (N = 32, 1-7 tumors/pt), (2) PD-L1 IHC (28.8 assay) was performed on the biopsy, and (3) response to Nivolumab therapy assessed by RECIST 1.1. Intra and inter subject variability and intraclass correlation were calculated using SUVs of all assessed tumors. Equal variance for PD-L1 status was evaluated by a Levene’s test. Four (3 female) pts underwent dosimetry study (ICRP 60). Results: No AEs related to radiotracer was observed. Dosimetry study demonstrated whole body exposure of 30 mGy at dose > 1400 MBq. Biodistribution among pts is comparable. PD-L1 IHC from 13 biopsied lesions were evaluated, 5 < 1%, 4 ≥1%, and 4 ≥50%. Tumor tracer uptake was measured in NSCLC pts and categorized by PDL-1 IHC as ≥50% or < 50%. Clinical trial information: 2015-004760-11. Tumor SUVs did not correlate with RECIST 1.1 assessment. Lesion heterogeneity was reflected in both inter and intra pt variability (CVinter = 41%, CVintra = 53%, ICC = 0.41 for SUVpeak). Levene’s test showed no significance in variability between the two PD-L1 categories. Conclusions: PET-imaging with 18F-BMS-986192 is safe and feasible in pts with NSCLC and melanoma. Pts with higher PD-L1 PET SUV have higher PD-L1 by IHC. Intra pt variability is similar to inter pt variability. With limited number of pts, no clear correlation of PET PD-L1 and tumor response is observed. A prospective study with this tracer is underway to further investigate 18F-BMS-986192 in understanding of PD-L1 expression.[Table: see text]
Efficient data sharing is hampered by an array of organizational, ethical, behavioral, and technical challenges, slowing research progress and reducing the utility of data generated by clinical research studies on neurodegenerative diseases. There is a particular need to address differences between public and private sector environments for research and data sharing, which have varying standards, expectations, motivations, and interests. The Neuronet data sharing Working Group was set up to understand the existing barriers to data sharing in public-private partnership projects, and to provide guidance to overcome these barriers, by convening data sharing experts from diverse projects in the IMI neurodegeneration portfolio. In this policy and practice review, we outline the challenges and learnings of the WG, providing the neurodegeneration community with examples of good practices and recommendations on how to overcome obstacles to data sharing. These obstacles span organizational issues linked to the unique structure of cross-sectoral, collaborative research initiatives, to technical issues that affect the storage, structure and annotations of individual datasets. We also identify sociotechnical hurdles, such as academic recognition and reward systems that disincentivise data sharing, and legal challenges linked to heightened perceptions of data privacy risk, compounded by a lack of clear guidance on GDPR compliance mechanisms for public-private research. Focusing on real-world, neuroimaging and digital biomarker data, we highlight particular challenges and learnings for data sharing, such as data management planning, development of ethical codes of conduct, and harmonization of protocols and curation processes. Cross-cutting solutions and enablers include the principles of transparency, standardization and co-design – from open, accessible metadata catalogs that enhance findability of data, to measures that increase visibility and trust in data reuse.
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