Macrophages are key contributors to vascularization, but the mechanisms behind their actions are not understood. Here, we show that diverse macrophage phenotypes have distinct effects on endothelial cell behavior, with resulting effects on vascularization of engineered tissues. In Transwell coculture, proinflammatory M1 macrophages caused endothelial cells to up-regulate genes associated with sprouting angiogenesis, whereas prohealing (M2a), proremodeling (M2c), and anti-inflammatory (M2f) macrophages promoted up-regulation of genes associated with pericyte cell differentiation. In 3D tissue-engineered human blood vessel networks in vitro, short-term exposure (1 day) to M1 macrophages increased vessel formation, while long-term exposure (3 days) caused regression. When human tissue-engineered blood vessel networks were implanted into athymic mice, macrophages expressing markers of both M1 and M2 phenotypes wrapped around and bridged adjacent vessels and formed vessel-like structures themselves. Last, depletion of host macrophages inhibited remodeling of engineered vessels, infiltration of host vessels, and anastomosis with host vessels.

Monocyte-derived macrophages orchestrate tissue regeneration by homing to sites of injury, phagocytosing pathological debris, and stimulating other cell types to repair the tissue. Accordingly, monocytes have been investigated as a translational and potent source for cell therapy, but their utility has been hampered by their rapid acquisition of a pro-inflammatory phenotype in response to the inflammatory injury microenvironment. To overcome this problem, we designed a cell therapy strategy where monocytes are exogenously reprogrammed by intracellularly loading the cells with biodegradable microparticles containing an anti-inflammatory drug in order to modulate and maintain an anti-inflammatory phenotype over time. To test this concept, poly(lactic-co-glycolic) acid microparticles were loaded with the anti-inflammatory drug dexamethasone (Dex) and administered to primary human monocytes for four hours to facilitate phagocytic uptake. After removal of non-phagocytosed microparticles, microparticle-loaded monocytes differentiated into macrophages and stored the microparticles intracellularly for several weeks in vitro, releasing drug into the extracellular environment over time. Cells loaded with intracellular Dex microparticles showed decreased expression and secretion of inflammatory factors even in the presence of pro-inflammatory stimuli up to 7 days after microparticle uptake compared to untreated cells or cells loaded with blank microparticles, without interfering with phagocytosis of tissue debris. This study represents a new strategy for long-term maintenance of anti-inflammatory macrophage phenotype using a translational monocyte-based cell therapy strategy without the use of genetic modification. Because of the ubiquitous nature of monocyte-*

Purpose: De-identification of cancer imaging data is vitally important for data sharing and the advancement of research, however it is a time consuming and complex process that limits access to new cancer data sets such as those shared through NCI's Imaging Data Commons (IDC), built on the Google Cloud Platform (GCP). Our research demonstrates how this process can be automated using GCP-native services. Methods: We configured the Medical Image De-Identification (MIDI) pipeline to automate de-identification of cancer imaging data. De-identification is performed using an alpha release of GCP’s Healthcare API which was configured to scrub all Protected Health Information (PHI) from both Digital Imaging and Communications in Medicine (DICOM) headers and burnt-in text in pixel data. A dataset containing 216 patients and 23,921 images was prepared to test the de-identification algorithm by placing synthetic PHI in both DICOM headers and pixel data. The synthetic data matched real data seen during curation at The Cancer Imaging Archive (TCIA) and included data difficult for an algorithm to detect. Accuracy of the MIDI pipeline was measured against TCIA’s standard tools and procedures for de-identification. Measures included correct detection of all PHI data and correct action taken (e.g., remove, encrypt, or otherwise obscure). Throughput was also measured. Results: Throughput was measured at 22.0 images per second over 10 runs. The MIDI pipeline’s accuracy for DICOM headers was 98.7%, accurately detecting dates, addresses, phone numbers, unique identifiers, names, and other common PHI. The most common PHI failed to remove were special cases that included uncommon names or names with symbols, dates in string data types that were mistaken for other IDs, patient IDs, and abbreviated institution names. Private Creator data elements were consistently failed to be retained. These errors were due to options not currently available, and algorithms not trained on specific PHI, such as abbreviated institution names. UIDs were correctly replaced. PHI burnt-in the pixel data was successfully detected and removed, with one false positive. Conclusion: We demonstrate the current capability and performance of automated cancer image de-identification. Our results show that while full automation is within grasp, a semi-automated pipeline is now feasible. A human expert in the loop can be used for final verification. This will lead to a much-needed acceleration of image de-identification, to handle the rapidly growing volume of data and provide rapid timely access in support of cancer research. Future work will focus on including pre- and post-processing tools to aid the human expert in the loop, such as identifying and flagging questionable images for manual review. These tools will also be used to catch the errors mentioned in results. Citation Format: Benjamin P. Kopchick, Laura K. Opsahl-Ong, Qinyan Pan, Michael W. Rutherford, Ulrike Wagner, Bhavani S. Singh, Scott Gustafson, Fred W. Prior, David A. Clunie, Juergen A. Klenk, Keyvan Farahani. Accelerating de-identification of images with cloud services to support data sharing in cancer research. [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 6579.

Monocyte-derived macrophages orchestrate tissue regeneration by homing to sites of injury, phagocytosing pathological debris, and stimulating other cell types to repair the tissue. Accordingly, monocytes have been investigated as a translational and potent source for cell therapy, but their utility has been hampered by their rapid acquisition of a pro-inflammatory phenotype in response to the inflammatory injury microenvironment. To overcome this problem, we designed a cell therapy strategy where we collect and exogenously reprogram monocytes by intracellularly loading the cells with biodegradable microparticles containing an anti-inflammatory drug in order to modulate and maintain an anti-inflammatory phenotype over time. To test this concept, poly(lactic-co-glycolic) acid microparticles were loaded with the anti-inflammatory drug dexamethasone (Dex) and administered to primary human monocytes for four hours to facilitate phagocytic uptake. After removal of non-phagocytosed microparticles, microparticle-loaded monocytes differentiated into macrophages and stored the microparticles intracellularly for several weeks in vitro, releasing drug into the extracellular environment over time. Cells loaded with intracellular Dex microparticles showed decreased expression and secretion of inflammatory factors even in the presence of pro-inflammatory stimuli up to 7 days after microparticle uptake compared to untreated cells or cells loaded with blank microparticles. This study represents a new strategy for long-term maintenance of anti-inflammatory macrophage phenotype using a translational monocyte-based cell therapy strategy without the use of genetic modification. Because of the ubiquitous nature of monocytederived macrophage involvement in pathology and regeneration, this strategy holds potential as a treatment for a vast number of diseases and disorders. RESULTS Microparticle Characteristics and Intracellular StabilityPoly(lactic-co-glycolic) acid (PLGA) microparticles were fabricated with dexamethasone (Dex) or with tetramethylrhodamine (TRITC) as a fluorescent model drug. Microparticles ranged in diameter from 0.98 to 2.05 µm with polydispersity indices between 0.08 and 0.28 ( Fig. 2A).Microparticles were administered to primary human monocytes for 4 hours followed by removal of non-phagocytosed microparticles. Thereafter, monocytes were cultured in macrophage colony stimulating factor (MCSF)-containing media to induce differentiation into macrophages and were imaged at regular intervals (Fig. 2B). Cell area increased over time for both untreated and microparticle-loaded cells, indicating that that monocyte-to-macrophage differentiation was not hindered by intracellular microparticle loading (Fig. 2C). Intracellular fluorescent microparticles were detected for up to 16 days in vitro (detection and quantification methods outlined in Sup. Fig. 1). Interestingly, the number of microparticles ( Fig. 2D) and their intensity per cell ( Fig. 2E) increased in the first three days, which may be due to cell death, phagocytosi...

Purpose: The volume of data produced by the biomedical research community continues to grow rapidly. Efficient collection, curation, and sharing of these data are expected to enable researchers to synthesize larger datasets and apply novel methods to discover new patterns for diagnosis, treatment, and care of disease. Consequently, data platforms that support these functionalities have become an important instrument for the biomedical research community. The National Cancer Institute’s (NCI’s) Cancer Research Data Commons (CRDC) is a data platform for the cancer research community that provides cloud-based, secure storage and analytic tools for cancer data, including genomics, proteomics, imaging, and clinical trial data. CRDC has been funded by multiple sources, including the Beau Biden Cancer Moonshot program. Achieving long-term sustainability of the CRDC program is the focus of our study. Methods: We applied a Financial Operations (FinOps) framework to map all costs to functionalities provided by the CRDC, including processes such as data intake, storage, compute, user support, and project management. We established a comprehensive financial baseline for the operations of the CRDC, identified opportunities for optimization across multiple dimensions (people, processes, technology), projected future costs based on trends for cancer data, and evaluated sustainability under defined funding scenarios. Results: We identified 15 recommendations for optimization of the CRDC, including automation and centralization of core services (e.g., intake, curation, indexing), optimization of storage (e.g., compression, archiving), and harmonization of common services across the CRDC components (e.g., common architecture, governance). If implemented, we could demonstrate that these recommendations offer a path to long-term sustainability of the CRDC program under a variety of funding scenarios. Conclusion: Data platforms such as the CRDC will see rapid increases in operational costs due to the growing data volumes and demand from researchers. Optimizing governance and operational frameworks will result in efficiency gains that can ensure long-term sustainability. Taking these steps will be critical to provide the necessary infrastructure to advance the field of data-driven biomedical research. Citation Format: Juergen A. Klenk, Angela Maggio, Bhavani S. Singh, Erin N. Byrne, Erika Kim, Tanja M. Davidsen. A path to sustainability for the National Cancer Institute's Cancer Research Data Commons. [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 6582.