Circulating tumor cells (CTCs) have potential utility as a surrogate biomarker of tumor biology via a liquid biopsy. The aim of this study was to evaluate if the nCounter NanoString assay could be used for accurate gene expression profiling of CTCs using the PAM50 research-use-only CodeSet. Analysis was performed on CTCs isolated by the ANGLE Parsortix system from healthy blood spiked with the breast cancer cell lines Hs578T, SkBr3, MDA-MB-231 or MCF7. Using cell lines as gold standard positive controls and Parsortix processed blood without spiking (unspiked) as negative controls, we found an average of 12 significantly differentially expressed genes among spiked samples versus unspiked controls. We validated our findings with the NanoStringDiff differential expression statistical method. The NanoString recommended targeted pre-amplification introduced false positive results due to pre-amplification bias, and the amplification of non-cancer genes from normal leukocytes confounded gene expression profiling of CTCs. Pre-amplification bias is a concern for other similar assays that may be used as discovery tools or target validation of transcripts of interest in gene expression profiling of CTCs. We recommend the use of an unspiked negative control when evaluating CTC technologies regarding gene expression profiling. Given that the molecular profiling of CTCs as a liquid biopsy may have clinical ramifications for potential treatment selection in future clinical trials, our study emphasizes cautious consideration of pre-analytical variables such as amplification bias in the context of liquid biopsy studies.

Whole exome sequencing (WES), targeted gene panel sequencing and single nucleotide polymorphism (SNP) arrays are increasingly used for the identification of actionable alterations that are critical to cancer care. Here, we compared The Cancer Genome Atlas (TCGA) and the Genomics Evidence Neoplasia Information Exchange (GENIE) breast cancer genomic datasets (array and next generation sequencing (NGS) data) in detecting genomic alterations in clinically relevant genes. We performed an in silico analysis to determine the concordance in the frequencies of actionable mutations and copy number alterations/aberrations (CNAs) in the two most common breast cancer histologies, invasive lobular and invasive ductal carcinoma. We found that targeted sequencing identified a larger number of mutational hotspots and clinically significant amplifications that would have been missed by WES and SNP arrays in many actionable genes such as PIK3CA, EGFR, AKT3, FGFR1, ERBB2, ERBB3 and ESR1. The striking differences between the number of mutational hotspots and CNAs generated from these platforms highlight a number of factors that should be considered in the interpretation of array and NGS-based genomic data for precision medicine. Targeted panel sequencing was preferable to WES to define the full spectrum of somatic mutations present in a tumor.