title: Cell-based quantification of molecular biomarkers in histopathology specimens.
abstract: OBJECTIVE
  To investigate the use of a computer-assisted technology for objective, cell-based quantification of molecular biomarkers in specified cell types in histopathology specimens, with the aim of advancing current visual estimation and pixel-level (rather than cell-based) quantification methods.
  
  
  RESULTS
  Tissue specimens were multiplex-immunostained to reveal cell structures, cell type markers, and analytes, and imaged with multispectral microscopy. The image data were processed with novel software that automatically delineates and types each cell in the field, measures morphological features, and quantifies analytes in different subcellular compartments of specified cells.The methodology was validated with the use of cell blocks composed of differentially labelled cultured cells mixed in known proportions, and evaluated on human breast carcinoma specimens for quantifying human epidermal growth factor receptor 2, estrogen receptor, progesterone receptor, Ki67, phospho-extracellular signal-related kinase, and phospho-S6. Automated cell-level analyses closely matched human assessments, but, predictably, differed from pixel-level analyses of the same images.
  
  
  CONCLUSIONS
  Our method reveals the type, distribution, morphology and biomarker state of each cell in the field, and allows multiple biomarkers to be quantified over specified cell types, regardless of their abundance. It is ideal for studying specimens from patients in clinical trials of targeted therapeutic agents, for investigating minority stromal cell subpopulations, and for phenotypic characterization to personalize therapy and prognosis.
authors.affiliation: Department of Electrical, Computer and Systems Engineering, Rensselaer Polytechnic Institute, Troy, NY, USA.
authors.forename: Yousef
authors.initials: Y
authors.lastname: Al-Kofahi
authors.forename: Wiem
authors.initials: W
authors.lastname: Lassoued
authors.forename: Kedar
authors.initials: K
authors.lastname: Grama
authors.forename: Sumit K
authors.initials: SK
authors.lastname: Nath
authors.forename: Jianliang
authors.initials: J
authors.lastname: Zhu
authors.forename: Ridha
authors.initials: R
authors.lastname: Oueslati
authors.forename: Michael
authors.initials: M
authors.lastname: Feldman
authors.forename: William M F
authors.initials: WM
authors.lastname: Lee
authors.forename: Badrinath
authors.initials: B
authors.lastname: Roysam
mesh_terms: D057205:Automation, Laboratory / Q000379:methods; D015415:Biomarkers / Q000378:metabolism*; D014408:Biomarkers, Tumor / Q000378:metabolism; D001943:Breast Neoplasms / Q000378:metabolism / Q000473:pathology; D002462:Cell Membrane / Q000378:metabolism / Q000473:pathology; D002467:Cell Nucleus / Q000378:metabolism / Q000473:pathology; D003593:Cytoplasm / Q000378:metabolism / Q000473:pathology; D003936:Diagnosis, Computer-Assisted / Q000379:methods; D048049:Extracellular Signal-Regulated MAP Kinases / Q000378:metabolism; D005260:Female; D006652:Histological Techniques / Q000379:methods; D006801:Humans; D007091:Image Processing, Computer-Assisted; D007150:Immunohistochemistry / Q000379:methods*; D007633:Keratins / Q000378:metabolism; D019394:Ki-67 Antigen / Q000378:metabolism; D011960:Receptors, Estrogen / Q000378:metabolism; D011980:Receptors, Progesterone / Q000378:metabolism
chemical_list: D015415:Biomarkers; D014408:Biomarkers, Tumor; D019394:Ki-67 Antigen; D011960:Receptors, Estrogen; D011980:Receptors, Progesterone; D007633:Keratins; D048049:Extracellular Signal-Regulated MAP Kinases