Identifying Robertsonian Translocation Carriers by Microarray-Based DNA Analysis.

OBJECTIVE: To develop a noninvasive prenatal testing improvement that allows identification of Robertsonian translocation carriers. METHODS: Blood samples from 191 subjects, including 7 pregnant and 9 non-pregnant Robertsonian translocation carriers, were analyzed for fetal trisomy and Robertsonian translocation status. Digital Analysis of Selected Regions (DANSR™) assays targeting sequences common to the p arms of 5 acrocentric chromosomes were developed and added to existing DANSR assays. DANSR products were hybridized onto a custom DNA microarray for DNA analysis. The Fetal-Fraction Optimized Risk of Trisomy Evaluation (FORTE™) algorithm measures the fraction of fetal DNA and accounts for both the fetal and maternal fractions in the cell-free DNA sample to determine Robertsonian risk. The expectation in a Robertsonian translocation carrier is that DANSR assays on acrocentric p arms should have a concentration 20% less than that of controls. RESULTS: The FORTE algorithm correctly classified the fetal trisomy status and maternal Robertsonian translocation status in all 191 samples. Sixteen samples had a Robertsonian risk score above 99%, while 175 samples had a Robertsonian risk score below 0.01%. CONCLUSIONS: Robertsonian translocations are the most common chromosomal translocations and can have significant reproductive consequences. A maternal screen for Robertsonian translocation carriers would provide women valuable information regarding the risk of fetal trisomy.

Elevated expression of Fn14 in non-small cell lung cancer correlates with activated EGFR and promotes tumor cell migration and invasion.

Lung cancer is the leading cause of cancer deaths worldwide; approximately 85% of these cancers are non-small cell lung cancer (NSCLC). Patients with NSCLC frequently have tumors harboring somatic mutations in the epidermal growth factor receptor (EGFR) gene that cause constitutive receptor activation. These patients have the best clinical response to EGFR tyrosine kinase inhibitors (TKIs). Herein, we show that fibroblast growth factor-inducible 14 (Fn14; TNFRSF12A) is frequently overexpressed in NSCLC tumors, and Fn14 levels correlate with p-EGFR expression. We also report that NSCLC cell lines that contain EGFR-activating mutations show high levels of Fn14 protein expression. EGFR TKI treatment of EGFR-mutant HCC827 cells decreased Fn14 protein levels, whereas EGF stimulation of EGFR wild-type A549 cells transiently increased Fn14 expression. Furthermore, Fn14 is highly expressed in EGFR-mutant H1975 cells that also contain an EGFR TKI-resistance mutation, and high TKI doses are necessary to reduce Fn14 levels. Constructs encoding EGFRs with activating mutations induced Fn14 expression when expressed in rat lung epithelial cells. We also report that short hairpin RNA-mediated Fn14 knockdown reduced NSCLC cell migration and invasion in vitro. Finally, Fn14 overexpression enhanced NSCLC cell migration and invasion in vitro and increased experimental lung metastases in vivo. Thus, Fn14 may be a novel therapeutic target for patients with NSCLC, in particular for those with EGFR-driven tumors who have either primary or acquired resistance to EGFR TKIs.

Identification of Disease Susceptibility Alleles in the Next Generation Sequencing Era.

The development of next generation sequencing (NGS) technologies has transformed the study of human genetic variation. In less than a decade, NGS has facilitated the discovery of causal mutations in both rare, monogenic diseases and common, heterogeneous disorders, leading to unprecedented improvements in disease diagnosis and treatment strategies. Given the rapid evolution of NGS platforms, it is now possible to analyze whole genomes and exomes quickly and affordably. Further, emerging NGS applications, such as single-cell sequencing, have the power to address specific issues like somatic variation, which is yielding new insights into the role of somatic mutations in cancer and late-onset diseases. Despite limitations associated with current iterations of NGS technologies, the impact of this approach on identifying disease-causing variants has been significant. This chapter provides an overview of several NGS platforms and applications and discusses how these technologies can be used in concert with experimental and computational strategies to identify variants with a causative effect on disease development and progression.

FN14 expression correlates with MET in NSCLC and promotes MET-driven cell invasion.

The five-year survival rate in advanced non-small cell lung cancer (NSCLC) remains below ten percent. The invasive and metastatic nature of NSCLC tumor cells contributes to the high mortality rate, and as such the mechanisms that govern NSCLC metastasis is an active area of investigation. Two surface receptors that influence NSCLC invasion and metastasis are the hepatocyte growth factor receptor (HGFR/MET) and fibroblast growth factor-inducible 14 (FN14). MET protein is over-expressed in NSCLC tumors and associated with poor clinical outcome and metastasis. FN14 protein is also elevated in NSCLC tumors and positively correlates with tumor cell migration and invasion. In this report, we show that MET and FN14 protein expressions are significantly correlated in human primary NSCLC tumors, and the protein levels of MET and FN14 are elevated in metastatic lesions relative to patient-matched primary tumors. In vitro, HGF/MET activation significantly enhances FN14 mRNA and protein expression. Importantly, depletion of FN14 is sufficient to inhibit MET-driven NSCLC tumor cell migration and invasion in vitro. This work suggests that MET and FN14 protein expressions are associated with the invasive and metastatic potential of NSCLC. Receptor-targeted therapeutics for both MET and FN14 are in clinical development, the use of which may mitigate the metastatic potential of NSCLC.

Identification of causal sequence variants of disease in the next generation sequencing era.

Over the last decade, genetic studies have identified numerous associations between single nucleotide polymorphism (SNP) alleles in the human genome and important human diseases. Unfortunately, extending these initial associative findings to identification of the true causal variants that underlie disease susceptibility is usually not a straightforward task. Causal variant identification typically involves searching through sizable regions of genomic DNA in the vicinity of disease-associated SNPs for sequence variants in functional elements including protein coding, regulatory, and structural sequences. Prioritization of these searches is greatly aided by knowledge of the location of functional sequences in the human genome. This chapter briefly reviews several of the common approaches used to functionally annotate the human genome and discusses how this information can be used in concert with the emerging technology of next generation high-throughput sequencing to identify causal variants of human disease.