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Updated: Aug 6, 2026

Technical Demonstration of Whole Genome Array Comparative Genomic Hybridization
Published on: August 5, 2008
The Critical Role of Preanalytical Factors in Validating Cytology Specimens for Hybrid-Capture Next-Generation
Holly Atwell1, Sinchita Roy-Chowdhuri2, Rana Seyedjafari1
1Molecular Diagnostic Laboratory, Division of Pathology and Laboratory Medicine, The University of Texas MD Anderson Cancer Center, Houston, Texas.
Abstract:
Cytologic specimens, often the only available sample, are increasingly relied upon for comprehensive genomic profiling in patients with advanced malignancies, necessitating detailed characterization of next-generation sequencing (NGS) assay performance. Whether quality thresholds based predominantly on formalin-fixed, paraffin-embedded (FFPE) tissue can be directly applied to cytologic preparations remains uncertain. This study retrospectively analyzed 10,900 unique specimens sequenced on a custom NGS panel, including a focused subset of 455 consecutive cytologic specimens and 2759 FFPE surgical pathology controls. Specimen types included Diff-Quik-stained smears, Papanicolaou-stained smears, cell-block sections, and FFPE tissue. Sequencing quality was assessed by the percentage of targeted bases achieving ≥100× coverage. Cell blocks performed comparably to FFPE tissue across all quality metrics (median percentage target coverage ≥98%). Cytologic smears yielded DNA of adequate quantity and quality for sequencing but demonstrated reduced target coverage uniformity (median percentage target coverage 74.7% for Papanicolaou-stained smears and 89.2% for combined preparations), increased GC bias, and elevated mean absolute pairwise difference values relative to FFPE controls (P < 2.2 × 10-16). These differences were driven by intrinsic physicochemical properties of smear preparation and staining rather than by DNA input concentration or tumor purity. Although cytologic smears yield DNA suitable for hybrid-capture NGS, specimen-type-specific validation criteria and cytology-aware normalization strategies are essential for maximizing clinical yield and reliability.
