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Tissue microarray sampling strategy for prostate cancer biomarker analysis.

Mark A Rubin1, Rodney Dunn, Myla Strawderman

  • 1Department of Pathology, University of Michigan Comprehensive Cancer Center, Ann Arbor, Michigan 48109-0054, USA. marubin@umich.edu

The American Journal of Surgical Pathology
|February 23, 2002
PubMed
Summary
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Optimizing tissue microarrays (TMA) for prostate cancer research requires 3-4 cores to accurately represent Ki-67 expression and predict patient outcomes, ensuring reliable biomarker studies.

Area of Science:

  • Oncology
  • Pathology
  • Biomarker Discovery

Background:

  • Tissue microarrays (TMA) enable high-throughput protein expression profiling but face challenges with heterogeneous tumors like prostate cancer.
  • Accurate tumor sampling is crucial for reliable biomarker studies in clinical settings.

Purpose of the Study:

  • To optimize and validate a tumor sampling strategy for a prostate cancer outcomes TMA.
  • To determine the optimal number of tissue cores for accurately assessing Ki-67 expression and predicting patient outcomes.

Main Methods:

  • Prostate cancer proliferation was assessed using Ki-67 immunohistochemistry on standard slides and TMA cores.
  • Bootstrap resampling and Cox proportional hazards analysis were employed to evaluate sampling strategies.
  • A prostate cancer outcomes TMA with 10 replicate samples from 88 cases was constructed for validation.

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Main Results:

  • Three TMA cores were found to optimally represent Ki-67 expression compared to standard slides, minimizing variability.
  • Four cores in the TMA model were optimal for predicting time to prostate-specific antigen (PSA) recurrence.
  • Ki-67 expression, preoperative PSA, and surgical margin status were significant predictors of PSA recurrence.

Conclusions:

  • A sampling strategy of 3-4 cores per patient is optimal for prostate cancer outcome TMAs.
  • Fewer than 3 cores may lead to inaccurate protein expression representation, while more than 4 offer diminishing returns.
  • This optimized TMA strategy can facilitate the evaluation of other prostate cancer biomarkers.