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  • The Company of Biologists  (3)
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  • The Company of Biologists  (3)
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  • 1
    Online Resource
    Online Resource
    The Company of Biologists ; 2016
    In:  Disease Models & Mechanisms Vol. 9, No. 5 ( 2016-05-01), p. 483-485
    In: Disease Models & Mechanisms, The Company of Biologists, Vol. 9, No. 5 ( 2016-05-01), p. 483-485
    Abstract: The largely untapped potential of big data analytics is a feeding frenzy that has been fueled by the production of many next-generation-sequencing-based data sets that are seeking to answer long-held questions about the biology of human diseases. Although these approaches are likely to be a powerful means of revealing new biological insights, there are a number of substantial challenges that currently hamper efforts to harness the power of big data. This Editorial outlines several such challenges as a means of illustrating that the path to big data revelations is paved with perils that the scientific community must overcome to pursue this important quest.
    Type of Medium: Online Resource
    ISSN: 1754-8411 , 1754-8403
    Language: English
    Publisher: The Company of Biologists
    Publication Date: 2016
    detail.hit.zdb_id: 2451104-3
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  • 2
    In: Disease Models & Mechanisms, The Company of Biologists, ( 2016-01-01)
    Abstract: Targeted therapies against basal-like breast tumors, which are typically ‘triple negative breast cancers (TNBC)’, remain an important unmet clinical need. Somatic TP53 mutations are the most common genetic event in basal-like and TNBC breast tumors. To identify additional drivers and possible drug targets of this subtype, a comparative study between human and murine tumors was performed utilizing a murine Trp53null mammary transplant tumor model. We show that two subsets of murine Trp53null mammary transplant tumors resemble aspects of the human basal-like subtype. DNA microarray, whole genome, and exome-based sequencing approaches were used to interrogate the secondary genetic aberrations of these tumors, which were then compared to human basal-like tumors to identify conserved somatic genetic features. DNA copy number variation produced the largest number of conserved candidate personalized drug targets. These candidates were filtered using a DNA-RNA Pearson correlation cutoff and a requirement that the gene was deemed essential in at least 5% of human breast cancer cell lines from a RNA-mediated interference screen database. Five potential personalized drug target genes were identified which were spontaneously amplified loci in both murine and human basal-like tumors: Cul4a, Lamp1, Met, Pnpla6, and Tubgcp3. As a proof of concept, inhibition of Met using Crizotinib caused Met amplified murine tumors to initially undergo complete regression. This study identifies Met as a promising drug target in a subset of murine Trp53null tumors, thus identifying a potential shared driver with a subset of human basal-like breast cancer. Our results also highlight the importance of comparative genomic studies for discovering personalized drug targets and for providing a preclinical model for further investigations of key tumor signaling pathways.
    Type of Medium: Online Resource
    ISSN: 1754-8411 , 1754-8403
    Language: English
    Publisher: The Company of Biologists
    Publication Date: 2016
    detail.hit.zdb_id: 2451104-3
    Library Location Call Number Volume/Issue/Year Availability
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  • 3
    Online Resource
    Online Resource
    The Company of Biologists ; 2021
    In:  Disease Models & Mechanisms Vol. 14, No. 12 ( 2021-12-01)
    In: Disease Models & Mechanisms, The Company of Biologists, Vol. 14, No. 12 ( 2021-12-01)
    Abstract: Summary: We provide an Editorial perspective on approaches to improve ethnic representation in the human genome reference sequence, enabling its widespread use in genomic studies and precision medicine to benefit all peoples.
    Type of Medium: Online Resource
    ISSN: 1754-8403 , 1754-8411
    Language: English
    Publisher: The Company of Biologists
    Publication Date: 2021
    detail.hit.zdb_id: 2451104-3
    Library Location Call Number Volume/Issue/Year Availability
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