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Subcellular localization of p27 and prostate cancer recurrence: automated digital microscopy analysis of tissue microarrays
Authors:Ananthanarayanan Viju  Deaton Ryan J  Amatya Anup  Macias Virgilia  Luther Ed  Kajdacsy-Balla Andre  Gann Peter H
Affiliation:
  • a Department of Pathology, University of Illinois at Chicago, Chicago, IL 60612, USA
  • b Division of Epidemiology and Biostatistics, University of Illinois at Chicago, Chicago, IL 60612, USA
  • c CompuCyte Corporation, Cambridge, MA, USA
  • Abstract:Previous investigations have linked decreased nuclear expression of the cell cycle inhibitor p27 with poor outcome in prostate cancer. However, these reports are inconsistent regarding the magnitude of that association and its independence from other predictors. Moreover, cytoplasmic translocation of p27 has been proposed as a negative prognostic sign. Given the cost and accuracy limitations of manual scoring, particularly of tissue microarrays, we determined if laser-based fluorescence microscopy could provide automated analysis of p27 in both nuclear and cytoplasmic locations and, thus, clarify its significance as a prognostic biomarker. We constructed tissue microarrays covering 202 recurrent cases (rising prostate-specific antigen) and 202 matched controls without recurrence. Quadruplicate tumor samples encompassed 5 slides and 1616 cancer histospots. Cases and controls matched on age, Gleason grade, stage, and hospital. We immunolabeled epithelial cytoplasm with Alexafluor 647, p27 with Alexafluor 488, and nuclei with 4c6-diamidino-2-phenylindole·2HCl. Slides were scanned on an iCys laser scanning cytometer (CompuCyte Corp, Cambridge, MA). Nuclear crowding required a stereological approach--random arrays of circles (phantoms) were layered on images and the content of each phantom was analyzed in scatter plots. Both nuclear and cytoplasmic p27 were significantly lower in cases versus controls (P = .014 and P = .004, respectively). Regression models controlling for matching variables plus prostate-specific antigen showed strong linear trends for increased risk of recurrence with lower p27 in both nucleus and cytoplasm (highest versus lowest quartile; odds ratio, 0.35; P = .006). Manual scoring identified an inverse association between p27 expression and tumor grade but no independent association with recurrence. In conclusion, we developed an automated method for subcellular scoring of p27 without the need to segment individual cells. Our method identified a strong relationship, independent of tumor grade, stage, and prostate-specific antigen, between p27 expression--regardless of subcellular location--and prostate cancer recurrence. This relationship was not observed with manual scoring.
    Keywords:Prostate cancer   Prognosis   p27Kip1   Automated image analysis   Tissue microarray
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