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Cytogenomic microarray analysis (CMA) offers high resolution, genome-wide copy number information and is widely used in clinical laboratories for diagnosis of constitutional abnormalities. The Cancer Genomics Consortium (CGC) conducted a multiplatform, multicenter clinical validation project to compare the reliability and inter- and intralaboratory reproducibility of this technology for clinical oncology applications. Four specimen types were processed on three different microarray platforms-from Affymetrix, Agilent, and Illumina. Each microarray platform was employed at two independent test sites. The results were compared in a blinded manner with current standard methods, including karyotype, FISH, or morphology. Twenty-nine chronic lymphocytic leukemia blood, 34 myelodysplastic syndrome bone marrow, and 30 fresh frozen renal epithelial tumor samples were assessed by all six laboratories. Thirty formalin fixed paraffin embedded renal tumor samples were analyzed at the Affymetrix and Agilent test sites only. All study samples were initial diagnostic samples. Array data were analyzed at each participating site and were submitted to caArray for central analysis. Laboratory interpretive results were submitted to the central analysis team for comparison with the standard-of-care assays and for calculation of intraplatform reproducibility and cross-platform concordance. The results demonstrated that the three microarray platforms 1) detect clinically actionable genomic changes in cancer compatible to standard-of-care methods; 2) further define cytogenetic aberrations; 3) identify submicroscopic alterations and loss of heterozygosity (LOH); and 4) yield consistent results within and between laboratories. Based on this study, the CGC concludes that CMA is a sensitive and reliable technique for copy number and LOH assessment that may be used for clinical oncology genomic analysis., (Copyright © 2015 Elsevier Inc. All rights reserved.)

Metastatic tumors with an uncertain primary site can be a difficult clinical problem. In tens of thousands of patients every year, no confident diagnosis is ever issued, making standard-of-care treatment impossible. Gene expression profiling (GEP) tests currently available to analyze these difficult-to-diagnose tumors have never been directly compared with the diagnostic standard of care, immunochemistry (IHC). This prospectively conducted, blinded, multicenter study compares the diagnostic accuracy of GEP with IHC in identifying the primary site of 157 formalin-fixed paraffin-embedded specimens from metastatic tumors with known primaries, representing the 15 tissues on the GEP test panel. Four pathologists rendered diagnoses by selecting from 84 stains in 2 rounds. GEP was performed using the Pathwork Tissue of Origin Test. Overall, GEP accurately identified 89% of specimens, compared with 83% accuracy using IHC (P=0.013). In the subset of 33 poorly differentiated and undifferentiated carcinomas, GEP accuracy exceeded that of IHC (91% to 71%, P=0.023). In specimens for which pathologists rendered their final diagnosis with a single round of stains, both IHC and GEP exceeded 90% accuracy. However, when the diagnosis required a second round, IHC significantly underperformed GEP (67% to 83%, P<0.001). GEP has been validated as accurate in diagnosing the primary site in metastatic tumors. The Pathwork Tissue of Origin Test used in this study was significantly more accurate than IHC when used to identify the primary site, with the most pronounced superiority observed in specimens that required a second round of stains and in poorly differentiated and undifferentiated metastatic carcinomas.

Background: Chromosomal imbalances are commonly seen in cancer and inherited genetic diseases. These imbalances may assist in the diagnosis, prognosis, and/or therapeutic management of certain neoplasms. Several methods for detecting chromosomal imbalances, such as, fluorescent in situ hybridization, array comparative genomic hybridization, and single nucleotide polymorphism (SNP) arrays have proven useful in formalin-fixed paraffin-embedded (FFPE) tissues. Here, we report the performance and reproducibility of virtual karyotyping of FFPE tissues with Affymetrix SNP arrays., Methods: Virtual karyotypes from 442 FFPE tumor samples were generated using the Affymetrix GeneChip Mapping 10K Xba 2.0 and/or 250K Nsp SNP mapping arrays. Samples ranged from a few weeks to 17 years in archival storage. Virtual karyotypes were assessed for copy number changes, loss of heterozygosity, and acquired uniparental disomy., Results: Overall, 75.3% of samples produced interpretable virtual karyotypes with the 10K arrays and 76.7% in the 250K arrays. Parameters for the selection of samples for hybridization were determined, which increased the success rate in both platforms to 81.3 and 92.6%, respectively. FFPE virtual karyotypes generated with both 10K Xba 2.0 and 250K Nsp arrays showed 100% concordance in intralaboratory and interlaboratory reproducibility studies. Samples older than 7 years showed decreased performance., Conclusions: SNP arrays are a reliable, reproducible, and robust platform for the virtual karyotyping of FFPE tumor tissues with performance characteristics adequate for clinical application. Parameters that most significantly affected sample performance were sample age and storage conditions.

Clinical workup of metastatic malignancies of unknown origin is an arduous and expensive process, which is reported to be unsuccessful in up to 30% of cases. Global gene expression-based molecular testing may offer accurate classification of metastatic tumors in which a primary site has not been identified. Recently, the US FDA cleared the Pathwork((R)) tissue-of-origin test, which is a gene expression microarray-based test that quantifies the molecular similarity of tumor specimens to 15 known tissue types. A blinded, multicenter validation on poorly differentiated and undifferentiated tumors showed 87.8% sensitivity and 99.4% specificity in frozen tissue samples. The availability of ancillary gene expression-based molecular tests for tissue of origin determination represents a milestone in cancer patient management as part of the personalized medicine revolution.

Purpose: Malignancies found in unexpected locations or with poorly differentiated morphologies can pose a significant challenge for tissue of origin determination. Current histologic and imaging techniques fail to yield definitive identification of the tissue of origin in a significant number of cases. The aim of this study was to validate a predefined 1,550-gene expression profile for this purpose., Methods: Four institutions processed 547 frozen specimens representing 15 tissues of origin using oligonucleotide microarrays. Half of the specimens were metastatic tumors, with the remainder being poorly differentiated and undifferentiated primary cancers chosen to resemble those that present as a clinical challenge., Results: In this blinded multicenter validation study the 1,550-gene expression profile was highly informative in tissue determination. The study found overall sensitivity (positive percent agreement with reference diagnosis) of 87.8% (95% CI, 84.7% to 90.4%) and overall specificity (negative percent agreement with reference diagnosis) of 99.4% (95% CI, 98.3% to 99.9%). Performance within the subgroup of metastatic tumors (n = 258) was found to be slightly lower than that of the poorly differentiated and undifferentiated primary tumor subgroup, 84.5% and 90.7%, respectively (P = .04). Differences between individual laboratories were not statistically significant., Conclusion: This study represents the first adequately sized, multicenter validation of a gene-expression profile for tissue of origin determination restricted to poorly differentiated and undifferentiated primary cancers and metastatic tumors. These results indicate that this profile should be a valuable addition or alternative to currently available diagnostic methods for the evaluation of uncertain primary cancers.

Clinical workup of metastatic malignancies of unknown origin is often arduous and expensive and is reported to be unsuccessful in 30 to 60% of cases. Accurate classification of uncertain primary cancers may improve with microarray-based gene expression testing. We evaluated the analytical performance characteristics of the Pathwork tissue of origin test, which uses expression signals from 1668 probe sets in a gene expression microarray, to quantify the similarity of tumor specimens to 15 known tissues of origin. Sixty archived tissue specimens from poorly and undifferentiated tumors (metastatic and primary) were analyzed at four laboratories representing a wide range of preanalytical conditions (eg, personnel, reagents, instrumentation, and protocols). Cross-laboratory comparisons showed highly reproducible results between laboratories, with correlation coefficients between 0.95 to 0.97 for measurements of similarity scores, and an average 93.8% overall concordance between laboratories in terms of final tissue calls. Bland-Altman plots (mean coefficients of reproducibility of 32.48+/-3.97) and kappa statistics (kappa >0.86) also indicated a high level of agreement between laboratories. We conclude that the Pathwork tissue of origin test is a robust assay that produces consistent results in diverse laboratory conditions reflecting the preanalytical variations found in the everyday clinical practice of molecular diagnostics laboratories.