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AUTHOR Sarti, Mattia and Parlani, Maria and Diaz-Gomez, Luis and Mikos, Antonios G. and Cerveri, Pietro and Casarin, Stefano and Dondossola, Eleonora
Title Deep Learning for Automated Analysis of Cellular and Extracellular Components of the Foreign Body Response in Multiphoton Microscopy Images [Abstract]
Year 2022
Journal/Proceedings Frontiers in Bioengineering and Biotechnology
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Abstract
The Foreign body response (FBR) is a major unresolved challenge that compromises medical implant integration and function by inflammation and fibrotic encapsulation. Mice implanted with polymeric scaffolds coupled to intravital non-linear multiphoton microscopy acquisition enable multiparametric, longitudinal investigation of the FBR evolution and interference strategies. However, follow-up analyses based on visual localization and manual segmentation are extremely time-consuming, subject to human error, and do not allow for automated parameter extraction. We developed an integrated computational pipeline based on an innovative and versatile variant of the U-Net neural network to segment and quantify cellular and extracellular structures of interest, which is maintained across different objectives without impairing accuracy. This software for automatically detecting the elements of the FBR shows promise to unravel the complexity of this pathophysiological process.
AUTHOR Bello, Thomas and Paindelli, Claudia and Diaz-Gomez, Luis A. and Melchiorri, Anthony and Mikos, Antonios G. and Nelson, Peter S. and Dondossola, Eleonora and Gujral, Taranjit S.
Title Computational modeling identifies multitargeted kinase inhibitors as effective therapies for metastatic, castration-resistant prostate cancer [Abstract]
Year 2021
Journal/Proceedings Proceedings of the National Academy of Sciences
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Abstract
Metastatic, castration-resistant prostate cancer (mCRPC) is an advanced prostate cancer with limited therapeutic options and poor patient outcomes. To investigate whether multitargeted kinase inhibitors (KIs) represent an opportunity for mCRPC drug development, we applied machine learning{textendash}based functional screening and identified two KIs, PP121 and SC-1, which demonstrated strong suppression of CRPC growth in vitro and in vivo. Furthermore, we show the marked ability of these KIs to improve on standard-of-care chemotherapy in both tumor response and survival, suggesting that combining multitargeted KIs with chemotherapy represents a promising avenue for mCRPC treatment. Overall, our findings demonstrate the application of a multidisciplinary strategy that blends bench science with machine-learning approaches for rapidly identifying KIs that result in desired phenotypic effects.Castration-resistant prostate cancer (CRPC) is an advanced subtype of prostate cancer with limited therapeutic options. Here, we applied a systems-based modeling approach called kinome regularization (KiR) to identify multitargeted kinase inhibitors (KIs) that abrogate CRPC growth. Two predicted KIs, PP121 and SC-1, suppressed CRPC growth in two-dimensional in vitro experiments and in vivo subcutaneous xenografts. An ex vivo bone mimetic environment and in vivo tibia xenografts revealed resistance to these KIs in bone. Combining PP121 or SC-1 with docetaxel, standard-of-care chemotherapy for late-stage CRPC, significantly reduced tibia tumor growth in vivo, decreased growth factor signaling, and vastly extended overall survival, compared to either docetaxel monotherapy. These results highlight the utility of computational modeling in forming physiologically relevant predictions and provide evidence for the role of multitargeted KIs as chemosensitizers for late-stage, metastatic CRPC.All study data are included in the article and/or supporting information.
AUTHOR Paindelli, Claudia and Casarin, Stefano and Wang, Feng and Diaz-Gomez, Luis and Zhang, Jianhua and Mikos, Antonios G. and Logothetis, Christopher J. and Friedl, Peter and Dondossola, Eleonora
Title Enhancing Radium 223 treatment efficacy by anti-beta 1 integrin targeting [Abstract]
Year 2021
Journal/Proceedings Journal of Nuclear Medicine
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Abstract
Radium 223 (223Ra) is an α-emitter approved for the treatment of bone metastatic prostate cancer (PCa), which exerts direct cytotoxicity towards PCa cells near the bone interface, whereas cells positioned in the core respond poorly, due to short α-particle penetrance. β1 integrin (β1I) interference has been shown to increase radiosensitivity and significantly enhance external beam radiation efficiency. We hypothesized that targeting β1I would improve 223Ra outcome. We tested the effect of combining 223Ra and anti-β1I antibody treatment in PC3 and C4-2B PCa cell models expressing high and low β1I levels, respectively. In vivo tumor growth was evaluated through bioluminescence. Cellular and molecular determinants of response were analyzed by ex vivo three-dimensional imaging of bone lesions, proteomic analysis and further confirmed by computational modeling and in vitro functional analysis in tissue-engineered bone mimetic systems. Interference with β1I combined with 223Ra reduced PC3 cell growth in bone and significantly improved overall mouse survival, while no change was achieved in C4-2B tumors. Anti-β1I treatment decreased PC3 tumor cell mitosis index and spatially expanded 223Ra lethal effects two-fold, in vivo and in silico. Regression was paralleled by decreased expression of radio-resistance mediators. Targeting β1I significantly improves 223Ra outcome and points towards combinatorial application in PCa tumors with high β1I expression.