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Developing a growing cancer profile database based on quantitative analysis of protein biomarkers in formalin-fixed paraffin-embedded specimens.

Over a century of clinical practice has led to the accumulation of millions of archived formalin fixed paraffin embedded (FFPE) cancer specimens with detailed medical records worldwide. Absolute quantitation of clinical protein biomarkers in these FFPE specimens allows individual specimens to be pro...

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Publicado en:Future Oncology Vol. 16; no. 31; pp. 2471 - 2475
Autores principales: Zhang, Jiandi, Yang, Maozhou
Formato: letter Journal Article
Publicado: Taylor & Francis Ltd Oct2020
Acceso en línea:Ver este registro en EBSCOhost
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      dt: Oct2020
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      pub: Taylor & Francis Ltd
      place: Philadelphia, Pennsylvania
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        atl: Developing a growing cancer profile database based on quantitative analysis of protein biomarkers in formalin-fixed paraffin-embedded specimens.
      aug:
        au:
          Zhang, Jiandi
          Yang, Maozhou
        affil: Quanticision Diagnostics, Inc., 400 Park Offices Drive, Room 110, Research Triangle Park, NC 27709, USA
      sug:
        subj:
          Resource Databases
          Neoplasms Metabolism
          Proteomics Methods
          Proteomics
          Neoplasms Diagnosis
          Tissue Fixation
          Neoplasms Etiology
          Cytological Techniques
          Paraffin Embedding
          Scales
      ab: Over a century of clinical practice has led to the accumulation of millions of archived formalin fixed paraffin embedded (FFPE) cancer specimens with detailed medical records worldwide. Absolute quantitation of clinical protein biomarkers in these FFPE specimens allows individual specimens to be profiled at the population level, with the absolute nature of the measurements enabling the continuous processing of archived FFPE specimens over the time. A continuously growing cancer patient profile database is proposed here to support "big data" profiling of these protein biomarkers alone or in combination, enabling next-generation retrospective-prospective analytics into the field of clinical diagnostics.
      pubtype: Academic Journal
      doctype:
        letter
        Journal Article
      ougenre: Article
    language: English
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