Expression Concordance of 325 Novel RNA Biomarkers between Data Generated by NanoString nCounter and Affymetrix GeneChip

Gene expression profiling of HPV-associated cervical carcinogenesis in formalin-fixed paraffin-embedded (FFPE) tissues using the NanoString nCounterTM platform

Infection by high-risk human papillomavirus (HPV) causes genetic alterations in host cervical cells with consequent changes in gene expression affecting downstream molecular pathways, leading to the development of cervical cancer. In this exploratory study, we aimed to identify the perturbed cellular pathways during the various stages of cervical carcinogenesis. Total RNA was extracted from three formalin-fixed paraffin-embedded (FFPE) samples each of normal cervix, HPV-infected low-grade squamous intraepithelial lesion (LSIL), high-grade SIL (HSIL) and squamous cell carcinoma (SCC). Gene expression profiling was performed using the 770-gene panel from NanoString nCounter® PanCancer Pathways Panel to identify differentially expressed genes (DEGs) and significantly associated pathways in each stage of cervical cancer development. We identified 121 DEGs involved in cervical carcinogenesis. In the transformation from normal cells to LSIL, the MAPK, transcriptional misregulation and JAK-STAT pathways are implicated, while IL1B may promote inflammation and indirectly activates MMP9, resulting in collagen breakdown and cell migration. The cell cycle - apoptosis pathway with upregulation of E2F1 and MCM2, and DNA repair genes BRCA2-BRIP1 and FANCA are crucial during the progression from LSIL to HSIL. In the final stage of progression to SCC, the cell cycle and signaling pathways, as well as upregulation of c-MYC appear essential. In conclusion, archived FFPE-derived tissue samples are a valuable resource for gene expression profiling. The postulated dysregulated pathways and genes provide a guide of the molecular mechanisms that may be involved in the development of HPV-associated cervical cancer, for further investigation and validation studies.

Molecular subtyping of breast cancer intrinsic taxonomy with oligonucleotide microarray and NanoString nCounter

Breast cancer intrinsic subtypes have been identified based on the transcription of a predefined gene expression (GE) profiles and algorithm (PAM50). This study compared molecular subtyping with oligonucleotide microarray and NanoString nCounter assay. A total of 109 Taiwanese breast cancers (24 with adjacent normal breast tissues) were assayed with Affymetrix Human Genome U133 plus 2.0 microarrays and 144 were assayed with the NanoString nCounter while 64 patients were assayed for both platforms. Subtyping with the nearest centroid (single sample prediction) was performed, and 16 out of 24 (67%) matched normal breasts were categorized as the normal breast-like subtype. For 64 breast cancers assayed for both platforms, 41 (65%, one unclassified by microarray) were predicted with an identical subtype, resulting in a fair Kappa statistic of 0.60. Taking nCounter subtyping as the gold standard, prediction accuracy was 43% (3/7), 81% (13/16), 25% (5/20), and 100% (20/20) for basal-like, HER2-enriched, luminal A and luminal B subtype predicted from microarray GE profiles. Microarray identified more luminal B cases from luminal A subtype predicted by nCounter. It's not uncommon to use microarray for breast cancer molecular subtyping for research. Our study showed that fundamental discrepancy existed between distinct GE assays, and cross platform equivalence should be carefully appraised when molecular subtyping was conducted with oligonucleotide microarray.

Housekeeping genes involved in non-malignant breast phenotypes are widely expressed in multiple cancers and provide novel biomarkers of tumor classification

Clinically relevant biomarkers are useful to determine cancer patients' prognosis and treatments. To discover new putative biomarkers, we performed in silico analysis of a 325-gene panel previously associated with breast epithelial cell biology and clinical outcomes. Sixteen public datasets of microarray samples representing 8 cancer types and a total of 3,663 patients' samples were used for the analyses. Feature selection was used to identify the best subsets of the 325 genes for each classification, and linear discriminant analysis was used to quantify the accuracy of the classifications. A subset of 102 of the 325 genes were found to be housekeeping (HK) genes, and the classifications were repeated using only the 102 HK subset. The 325-gene panel and 102 HK subset were able to distinguish colon, gastric, lung, ovarian, pancreatic, and prostate tumors and leukemia from normal adjacent tissue, and classify disease subtypes of breast and lung cancers and leukemia with 70% or higher accuracy. HK genes have been overlooked as potential biomarkers due to their relative stability. This study describes a set of HK genes as putative biomarkers applicable to multiple cancer types worth following in subsequent validation studies.

Minichromosome Maintenance Complex (MCM) Genes Profiling and MCM2 Protein Expression in Cervical Cancer Development

OBJECTIVE

Minichromosome maintenance complex (MCM) proteins are essential for the process of DNA replication and cell division. This study aimed to evaluate MCM genes expression profiles and MCM2 protein in HPV-associated cervical carcinogenesis.

METHODOLOGY

MCM2, 4, 5 and 7 genes expression profiles were evaluated in three cervical tissue samples each of normal cervix, human papillomavirus (HPV)-infected low grade squamous intraepithelial lesion (LSIL), high grade squamous intraepithelial lesion (HSIL) and squamous cell carcinoma (SCC), using Human Transcriptome Array 2.0 and validated by nCounter® PanCancer Pathway NanoString Array. Immunohistochemical expression of MCM2 protein was semi-quantitatively assessed by histoscore in tissue microarrays containing 9 cases of normal cervix, 10 LSIL, 10 HSIL and 42 cases of SCC.

RESULTS

MCM2, 4, 5 and 7 genes expressions were upregulated with increasing fold change during the progression from LSIL to HSIL and the highest in SCC. MCM2 gene had the highest fold change in SCC compared to normal cervix. Immunohistochemically, MCM2 protein was localised in the nuclei of basal cells of normal cervical epithelium and dysplastic-neoplastic cells of CIN and SCC. There was a significant difference in MCM2 protein expression between the histological groups (P = 0.039), and histoscore was the highest in HSIL compared to normal cervix (P = 0.010).

CONCLUSION

The upregulation of MCM genes expressions in cervical carcinogenesis reaffirms MCM as a proliferative marker in DNA replication pathway, whereby proliferation of dysplastic and cancer cells become increasingly dysregulated and uncontrolled. A strong expression of MCM2 protein in HSIL may aid as a concatenated screening tool in detecting pre-cancerous cervical lesions.