Gene therapy for cystic fibrosis (CF) is making encouraging progress into clinical trials. However, further improvements in transduction efficiency are desired. To develop a novel gene transfer vector that is improved and truly effective for CF gene therapy, a simian immunodeficiency virus (SIV) was pseudotyped with envelope proteins from Sendai virus (SeV), which is known to efficiently transduce unconditioned airway epithelial cells from the apical side. This novel vector was evaluated in mice in vivo and in vitro directed toward CF gene therapy. Here, we show that (i) we can produce relevant titers of an SIV vector pseudotyped with SeV envelope proteins for in vivo use, (ii) this vector can transduce the respiratory epithelium of the murine nose in vivo at levels that may be relevant for clinical benefit in CF, (iii) this can be achieved in a single formulation, and without the need for preconditioning, (iv) expression can last for 15 months, (v) readministration is feasible, (vi) the vector can transduce human air-liquid interface (ALI) cultures, and (vii) functional CF transmembrane conductance regulator (CFTR) chloride channels can be generated in vitro. Our data suggest that this lentiviral vector may provide a step change in airway transduction efficiency relevant to a clinical programme of gene therapy for CF.
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http://dx.doi.org/10.1038/mt.2010.13 | DOI Listing |
Brief Bioinform
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School of Electrical Engineering and Computer Science, Gwangju Institute of Science and Technology (GIST), Buk-gu, Gwangju 61005, Republic of Korea.
Combination therapies have emerged as a promising approach for treating complex diseases, particularly cancer. However, predicting the efficacy and safety profiles of these therapies remains a significant challenge, primarily because of the complex interactions among drugs and their wide-ranging effects. To address this issue, we introduce DD-PRiSM (Decomposition of Drug-Pair Response into Synergy and Monotherapy effect), a deep-learning pipeline that predicts the effects of combination therapy.
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Bioinformatics and computational systems biology of cancer, Institut Curie, Inserm U900, PSL Research University, Paris, France.
Immunotherapy is improving the survival of patients with metastatic non-small cell lung cancer (NSCLC), yet reliable biomarkers are needed to identify responders prospectively and optimize patient care. In this study, we explore the benefits of multimodal approaches to predict immunotherapy outcome using multiple machine learning algorithms and integration strategies. We analyze baseline multimodal data from a cohort of 317 metastatic NSCLC patients treated with first-line immunotherapy, including positron emission tomography images, digitized pathological slides, bulk transcriptomic profiles, and clinical information.
View Article and Find Full Text PDFWiley Interdiscip Rev Nanomed Nanobiotechnol
January 2025
School of Pharmacy and Waterloo Institute of Nanotechnology, University of Waterloo, 200 University Avenue West, Waterloo, Ontario, Canada.
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Cancer Science Institute of Singapore, National University of Singapore, Singapore, Singapore.
Background: Radiotherapy is the primary treatment modality for most head and neck cancers (HNCs). Despite the addition of chemotherapy to radiotherapy to enhance its tumoricidal effects, almost a third of HNC patients suffer from locoregional relapses. Salvage therapy options for such recurrences are limited and often suboptimal, partly owing to divergent tumor and microenvironmental factors underpinning radioresistance.
View Article and Find Full Text PDFBreast Cancer Res
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School of Electronic Engineering and Computer Science, Queen Mary University of London, London, UK.
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