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  • Oxford University Press (OUP)  (20)
  • 1
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    IMMU-01. MYELOID-DERIVED SUPPRESSOR CELL HETEROGENEITY DRIVES GLIOBLASTOMA PROGRESSION IN A SEX-DEPENDENT MANNER
    Oxford University Press (OUP) ; 2019
    In:  Neuro-Oncology Vol. 21, No. Supplement_6 ( 2019-11-11), p. vi118-vi118
    Details
    In: Neuro-Oncology, Oxford University Press (OUP), Vol. 21, No. Supplement_6 ( 2019-11-11), p. vi118-vi118
    Abstract: An immunosuppressive tumor microenvironment is a major factor facilitating glioblastoma (GBM) progression and therapeutic resistance. Immunotherapies have had variable success in improving the outcome of GBM patients, suggesting that there is a need to gain insight into the mechanisms of immunosuppression. Our group previously demonstrated that myeloid-derived suppressor cells (MDSCs) expand in GBM patients and infiltrate tumors, where they suppress the activity of cytotoxic cells. However, the mechanisms by which individual MDSC subsets promote tumorigenesis remain understudied. Using the syngeneic mouse glioma models GL261, CT-2A and SB28, we show that monocytic MDSCs (mMDSCs) are prevalent in tumors and that their frequency is significantly higher in males, who constitute 60% of GBM patients and have a worse prognosis than females. mMDSC abundance was further associated with poor survival, and male mice reached morbidity endpoint earlier. Consistent with preclinical observations, male GBM patient specimens had significantly more IBA+CD204+ immunosuppressive myeloid cells compared to female GBM tissue. In contrast, female tumor-bearing mice had a two-fold increase in circulating granulocytic MDSC (gMDSC) frequency, while this population remained unchanged in males. Female-to-male bone marrow chimeras demonstrated that intrinsic discrepancies in immune cell characteristics drive the sex differences in survival. Consistent with the differential MDSC accumulation pattern, targeting gMDSCs with anti-Ly6G neutralizing antibodies extended the lifespan of female mice without providing a survival advantage to males. However, mMDSCs were protected from the anti-Ly6C depletion strategy due to their systemic and local proliferation, as indicated by ex vivo Ki-67 staining and subsequently confirmed by gene expression analysis. Drug-prediction algorithms using the differential RNA sequencing profiles demonstrated that mMDSCs can be targeted by chemotherapeutics, while immunomodulatory drugs are effective against gMDSCs. Collectively, these findings indicate that MDSC subset variation might represent a therapeutic opportunity for improved therapeutic efficacy of immunotherapies while accounting for sex as a biological variable.
    Type of Medium: Online Resource
    ISSN: 1522-8517 , 1523-5866
    URL: Article
    DOI: 10.1093/neuonc/noz175.495
    Language: English
    Publisher: Oxford University Press (OUP)
    Publication Date: 2019
    detail.hit.zdb_id: 2094060-9
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  • 2
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    Pharmacogenomics meets precision cardio-oncology: is there synergistic potential?
    Oxford University Press (OUP) ; 2020
    In:  Human Molecular Genetics Vol. 29, No. R2 ( 2020-10-20), p. R177-R185
    Details
    In: Human Molecular Genetics, Oxford University Press (OUP), Vol. 29, No. R2 ( 2020-10-20), p. R177-R185
    Abstract: An individual’s inherited genetic makeup and acquired genomic variants may account for a significant portion of observable variability in therapy efficacy and toxicity. Pharmacogenomics (PGx) is the concept that treatments can be modified to account for these differences to increase chances of therapeutic efficacy while minimizing risk of adverse effects. This is particularly applicable to oncology in which treatment may be multimodal. Each tumor type has a unique genomic signature that lends to inclusion of targeted therapy but may be associated with cumulative toxicity, such as cardiotoxicity, and can impact quality of life. A greater understanding of therapeutic agents impacted by PGx and subsequent implementation has the potential to improve outcomes and reduce risk of drug-induced adverse effects.
    Type of Medium: Online Resource
    ISSN: 0964-6906 , 1460-2083
    URL: Article
    DOI: 10.1093/hmg/ddaa134
    RVK:
    XA 10000
    Language: English
    Publisher: Oxford University Press (OUP)
    Publication Date: 2020
    detail.hit.zdb_id: 1474816-2
    SSG: 12
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  • 3
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    Human Molecular Genetics Review Issue 2022
    Oxford University Press (OUP) ; 2022
    In:  Human Molecular Genetics Vol. 31, No. R1 ( 2022-10-20), p. R1-R3
    Details
    In: Human Molecular Genetics, Oxford University Press (OUP), Vol. 31, No. R1 ( 2022-10-20), p. R1-R3
    Type of Medium: Online Resource
    ISSN: 0964-6906 , 1460-2083
    URL: Article
    DOI: 10.1093/hmg/ddac219
    RVK:
    XA 10000
    Language: English
    Publisher: Oxford University Press (OUP)
    Publication Date: 2022
    detail.hit.zdb_id: 1474816-2
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  • 4
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    Entropy-based consensus clustering for patient stratification
    Oxford University Press (OUP) ; 2017
    In:  Bioinformatics Vol. 33, No. 17 ( 2017-09-01), p. 2691-2698
    Details
    In: Bioinformatics, Oxford University Press (OUP), Vol. 33, No. 17 ( 2017-09-01), p. 2691-2698
    Abstract: Patient stratification or disease subtyping is crucial for precision medicine and personalized treatment of complex diseases. The increasing availability of high-throughput molecular data provides a great opportunity for patient stratification. Many clustering methods have been employed to tackle this problem in a purely data-driven manner. Yet, existing methods leveraging high-throughput molecular data often suffers from various limitations, e.g. noise, data heterogeneity, high dimensionality or poor interpretability. Results Here we introduced an Entropy-based Consensus Clustering (ECC) method that overcomes those limitations all together. Our ECC method employs an entropy-based utility function to fuse many basic partitions to a consensus one that agrees with the basic ones as much as possible. Maximizing the utility function in ECC has a much more meaningful interpretation than any other consensus clustering methods. Moreover, we exactly map the complex utility maximization problem to the classic K-means clustering problem, which can then be efficiently solved with linear time and space complexity. Our ECC method can also naturally integrate multiple molecular data types measured from the same set of subjects, and easily handle missing values without any imputation. We applied ECC to 110 synthetic and 48 real datasets, including 35 cancer gene expression benchmark datasets and 13 cancer types with four molecular data types from The Cancer Genome Atlas. We found that ECC shows superior performance against existing clustering methods. Our results clearly demonstrate the power of ECC in clinically relevant patient stratification. Availability and implementation The Matlab package is available at http://scholar.harvard.edu/yyl/ecc. Supplementary information Supplementary data are available at Bioinformatics online.
    Type of Medium: Online Resource
    ISSN: 1367-4803 , 1367-4811
    URL: Article
    DOI: 10.1093/bioinformatics/btx167
    Language: English
    Publisher: Oxford University Press (OUP)
    Publication Date: 2017
    detail.hit.zdb_id: 1468345-3
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  • 5
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    deepDR: a network-based deep learning approach to in silico drug repositioning
    Oxford University Press (OUP) ; 2019
    In:  Bioinformatics Vol. 35, No. 24 ( 2019-12-15), p. 5191-5198
    Details
    In: Bioinformatics, Oxford University Press (OUP), Vol. 35, No. 24 ( 2019-12-15), p. 5191-5198
    Abstract: Traditional drug discovery and development are often time-consuming and high risk. Repurposing/repositioning of approved drugs offers a relatively low-cost and high-efficiency approach toward rapid development of efficacious treatments. The emergence of large-scale, heterogeneous biological networks has offered unprecedented opportunities for developing in silico drug repositioning approaches. However, capturing highly non-linear, heterogeneous network structures by most existing approaches for drug repositioning has been challenging. Results In this study, we developed a network-based deep-learning approach, termed deepDR, for in silico drug repurposing by integrating 10 networks: one drug–disease, one drug-side-effect, one drug–target and seven drug–drug networks. Specifically, deepDR learns high-level features of drugs from the heterogeneous networks by a multi-modal deep autoencoder. Then the learned low-dimensional representation of drugs together with clinically reported drug–disease pairs are encoded and decoded collectively via a variational autoencoder to infer candidates for approved drugs for which they were not originally approved. We found that deepDR revealed high performance [the area under receiver operating characteristic curve (AUROC) = 0.908], outperforming conventional network-based or machine learning-based approaches. Importantly, deepDR-predicted drug–disease associations were validated by the ClinicalTrials.gov database (AUROC = 0.826) and we showcased several novel deepDR-predicted approved drugs for Alzheimer’s disease (e.g. risperidone and aripiprazole) and Parkinson’s disease (e.g. methylphenidate and pergolide). Availability and implementation Source code and data can be downloaded from https://github.com/ChengF-Lab/deepDR Supplementary information Supplementary data are available online at Bioinformatics.
    Type of Medium: Online Resource
    ISSN: 1367-4803 , 1367-4811
    URL: Article
    DOI: 10.1093/bioinformatics/btz418
    Language: English
    Publisher: Oxford University Press (OUP)
    Publication Date: 2019
    detail.hit.zdb_id: 1468345-3
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  • 6
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    A network-based deep learning methodology for stratification of tumor mutations
    Oxford University Press (OUP) ; 2021
    In:  Bioinformatics Vol. 37, No. 1 ( 2021-04-09), p. 82-88
    Details
    In: Bioinformatics, Oxford University Press (OUP), Vol. 37, No. 1 ( 2021-04-09), p. 82-88
    Abstract: Tumor stratification has a wide range of biomedical and clinical applications, including diagnosis, prognosis and personalized treatment. However, cancer is always driven by the combination of mutated genes, which are highly heterogeneous across patients. Accurately subdividing the tumors into subtypes is challenging. Results We developed a network-embedding based stratification (NES) methodology to identify clinically relevant patient subtypes from large-scale patients’ somatic mutation profiles. The central hypothesis of NES is that two tumors would be classified into the same subtypes if their somatic mutated genes located in the similar network regions of the human interactome. We encoded the genes on the human protein–protein interactome with a network embedding approach and constructed the patients’ vectors by integrating the somatic mutation profiles of 7344 tumor exomes across 15 cancer types. We firstly adopted the lightGBM classification algorithm to train the patients’ vectors. The AUC value is around 0.89 in the prediction of the patient’s cancer type and around 0.78 in the prediction of the tumor stage within a specific cancer type. The high classification accuracy suggests that network embedding-based patients’ features are reliable for dividing the patients. We conclude that we can cluster patients with a specific cancer type into several subtypes by using an unsupervised clustering algorithm to learn the patients’ vectors. Among the 15 cancer types, the new patient clusters (subtypes) identified by the NES are significantly correlated with patient survival across 12 cancer types. In summary, this study offers a powerful network-based deep learning methodology for personalized cancer medicine. Availability and implementation Source code and data can be downloaded from https://github.com/ChengF-Lab/NES. Supplementary information Supplementary data are available at Bioinformatics online.
    Type of Medium: Online Resource
    ISSN: 1367-4803 , 1367-4811
    URL: Article
    DOI: 10.1093/bioinformatics/btaa1099
    Language: English
    Publisher: Oxford University Press (OUP)
    Publication Date: 2021
    detail.hit.zdb_id: 1468345-3
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  • 7
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    Network-based prediction of drug–target interactions using an arbitrary-order proximity embedded deep forest
    Oxford University Press (OUP) ; 2020
    In:  Bioinformatics Vol. 36, No. 9 ( 2020-05-01), p. 2805-2812
    Details
    In: Bioinformatics, Oxford University Press (OUP), Vol. 36, No. 9 ( 2020-05-01), p. 2805-2812
    Abstract: Systematic identification of molecular targets among known drugs plays an essential role in drug repurposing and understanding of their unexpected side effects. Computational approaches for prediction of drug–target interactions (DTIs) are highly desired in comparison to traditional experimental assays. Furthermore, recent advances of multiomics technologies and systems biology approaches have generated large-scale heterogeneous, biological networks, which offer unexpected opportunities for network-based identification of new molecular targets among known drugs. Results In this study, we present a network-based computational framework, termed AOPEDF, an arbitrary-order proximity embedded deep forest approach, for prediction of DTIs. AOPEDF learns a low-dimensional vector representation of features that preserve arbitrary-order proximity from a highly integrated, heterogeneous biological network connecting drugs, targets (proteins) and diseases. In total, we construct a heterogeneous network by uniquely integrating 15 networks covering chemical, genomic, phenotypic and network profiles among drugs, proteins/targets and diseases. Then, we build a cascade deep forest classifier to infer new DTIs. Via systematic performance evaluation, AOPEDF achieves high accuracy in identifying molecular targets among known drugs on two external validation sets collected from DrugCentral [area under the receiver operating characteristic curve (AUROC) = 0.868] and ChEMBL (AUROC = 0.768) databases, outperforming several state-of-the-art methods. In a case study, we showcase that multiple molecular targets predicted by AOPEDF are associated with mechanism-of-action of substance abuse disorder for several marketed drugs (such as aripiprazole, risperidone and haloperidol). Availability and implementation Source code and data can be downloaded from https://github.com/ChengF-Lab/AOPEDF. Supplementary information Supplementary data are available at Bioinformatics online.
    Type of Medium: Online Resource
    ISSN: 1367-4803 , 1367-4811
    URL: Article
    DOI: 10.1093/bioinformatics/btaa010
    Language: English
    Publisher: Oxford University Press (OUP)
    Publication Date: 2020
    detail.hit.zdb_id: 1468345-3
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  • 8
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    Machine learning-based prediction of drug–drug interactions by integrating drug phenotypic, therapeutic, chemical, and genomic properties
    Oxford University Press (OUP) ; 2014
    In:  Journal of the American Medical Informatics Association Vol. 21, No. e2 ( 2014-10), p. e278-e286
    Details
    In: Journal of the American Medical Informatics Association, Oxford University Press (OUP), Vol. 21, No. e2 ( 2014-10), p. e278-e286
    Type of Medium: Online Resource
    ISSN: 1527-974X , 1067-5027
    URL: Article
    DOI: 10.1136/amiajnl-2013-002512
    Language: English
    Publisher: Oxford University Press (OUP)
    Publication Date: 2014
    detail.hit.zdb_id: 2018371-9
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  • 9
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    Studying Tumorigenesis through Network Evolution and Somatic Mutational Perturbations in the Cancer Interactome
    Oxford University Press (OUP) ; 2014
    In:  Molecular Biology and Evolution Vol. 31, No. 8 ( 2014-08), p. 2156-2169
    Details
    In: Molecular Biology and Evolution, Oxford University Press (OUP), Vol. 31, No. 8 ( 2014-08), p. 2156-2169
    Type of Medium: Online Resource
    ISSN: 1537-1719 , 0737-4038
    URL: Article
    DOI: 10.1093/molbev/msu167
    Language: English
    Publisher: Oxford University Press (OUP)
    Publication Date: 2014
    detail.hit.zdb_id: 2024221-9
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  • 10
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    Quantitative trait locus (xQTL) approaches identify risk genes and drug targets from human non-coding genomes
    Oxford University Press (OUP) ; 2022
    In:  Human Molecular Genetics Vol. 31, No. R1 ( 2022-10-20), p. R105-R113
    Details
    In: Human Molecular Genetics, Oxford University Press (OUP), Vol. 31, No. R1 ( 2022-10-20), p. R105-R113
    Abstract: Advances and reduction of costs in various sequencing technologies allow for a closer look at variations present in the non-coding regions of the human genome. Correlating non-coding variants with large-scale multi-omic data holds the promise not only of a better understanding of likely causal connections between non-coding DNA and expression of traits but also identifying potential disease-modifying medicines. Genome–phenome association studies have created large datasets of DNA variants that are associated with multiple traits or diseases, such as Alzheimer’s disease; yet, the functional consequences of variants, in particular of non-coding variants, remain largely unknown. Recent advances in functional genomics and computational approaches have led to the identification of potential roles of DNA variants, such as various quantitative trait locus (xQTL) techniques. Multi-omic assays and analytic approaches toward xQTL have identified links between genetic loci and human transcriptomic, epigenomic, proteomic and metabolomic data. In this review, we first discuss the recent development of xQTL from multi-omic findings. We then highlight multimodal analysis of xQTL and genetic data for identification of risk genes and drug targets using Alzheimer’s disease as an example. We finally discuss challenges and future research directions (e.g. artificial intelligence) for annotation of non-coding variants in complex diseases.
    Type of Medium: Online Resource
    ISSN: 0964-6906 , 1460-2083
    URL: Article
    DOI: 10.1093/hmg/ddac208
    RVK:
    XA 10000
    Language: English
    Publisher: Oxford University Press (OUP)
    Publication Date: 2022
    detail.hit.zdb_id: 1474816-2
    SSG: 12
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