Musashi 2 (MSI2) expression as an independent prognostic biomarker in non-small cell lung cancer (NSCLC).

BACKGROUND: Musashi-2 (MSI2) is a member of RNA-binding protein family that regulates mRNA translation of numerous intracellular targets and influences maintenance of stem cell identity. This study assessed MSI2 as a potential clinical biomarker in non-small cell lung cancer (NSCLC). METHODS: The current study included 40 patients with NSCLC, of whom one presented with stage 1, 14 presented with stage II, 15 presented with stage III, and 10 patients had stage IV. All patients received standard of care treatments. All patient samples were obtained before treatment started. We used immunohistochemical (IHC) approach to measure MSI2 protein expression in matching specimens of normal lung versus tumor tissues, and primary versus metastatic tumors, followed by correlative analysis in relation to clinical outcomes. In parallel, clinical correlative analysis of MSI2 mRNA expression was performed in silico using publicly available datasets (TCGA/ICGC and KM plots). RESULTS: MSI2 protein expression in patient samples was significantly elevated in NSCLC primary tumors versus normal lung tissue (P=0.03). MSI2 elevated expression positively correlated with a decreased progression free survival (PFS) (P=0.026) combined for all stages and with overall survival (OS) at stage IV (P=0.013). Elevated MSI2 expression on RNA level was confirmed in primary tumor versus normal tissue samples in TCGA dataset (P<0.0001), and positively correlated with decreased OS (P=0.02). No correlation was observed between MSI2 expression and age, sex, smoking, and treatment type. CONCLUSIONS: Elevated MSI2 expression in primary NSCLC tumors is associated with poor prognosis and can be used as a novel potential prognostic biomarker in NSCLC patients. Future studies in an extended patient cohort are warranted.

Massive Parallel Sequencing for Diagnostic Genetic Testing of BRCA Genes--a Single Center Experience.

The aim of this study was to implement massive parallel sequencing (MPS) technology in clinical genetics testing. We developed and tested an amplicon-based method for resequencing the BRCA1 and BRCA2 genes on an Illumina MiSeq to identify disease-causing mutations in patients with hereditary breast or ovarian cancer (HBOC). The coding regions of BRCA1 and BRCA2 were resequenced in 96 HBOC patient DNA samples obtained from different sample types: peripheral blood leukocytes, whole blood drops dried on paper, and buccal wash epithelia. A total of 16 random DNA samples were characterized using standard Sanger sequencing and applied to optimize the variant calling process and evaluate the accuracy of the MPS-method. The best bioinformatics workflow included the filtration of variants using GATK with the following cut-offs: variant frequency >14%, coverage (>25x) and presence in both the forward and reverse reads. The MPS method had 100% sensitivity and 94.4% specificity. Similar accuracy levels were achieved for DNA obtained from the different sample types. The workflow presented herein requires low amounts of DNA samples (170 ng) and is cost-effective due to the elimination of DNA and PCR product normalization steps.