Xiaofeng Zhou

RNA Profiling of Cell-free Saliva Using Microarray Technology:

Saliva, like other bodily fluids, has been used to monitor human health and disease. This study tests the hypothesis that informative human mRNA exists in cell-free saliva. If present, salivary mRNA may provide potential biomarkers to identify populations and patients at high risk for oral and systemic diseases. Unstimulated saliva was collected from ten normal subjects. RNA was isolated from the cell-free saliva supernatant and linearly amplified. High-density oligonucleotide microarrays were used to profile salivary mRNA. The results demonstrated that there are thousands of human mRNAs in cell-free saliva. Quantitative PCR (Q-PCR) analysis confirmed the present of mRNA identified by our microarray study. A reference database was generated based on the mRNA profiles in normal saliva. Our finding proposes a novel clinical approach to salivary diagnostics, Salivary Transcriptome Diagnostics (STD), for potential applications in disease diagnostics as well as normal health surveillance.

Concurrent analysis of loss of heterozygosity (LOH) and copy number abnormality (CNA) for oral premalignancy progression using the Affymetrix 10K SNP mapping array

Like most human cancers, oral squamous cell carcinoma (SCC) is characterized by genetic instabilities. In this study, a single platform (Affymetrix 10K SNP mapping array) was used to generate both loss of heterozygosity (LOH) and DNA copy number abnormality (CNA) read-outs for precise and high-resolution genetic alteration profiles. As a proof of principle, we performed this concordant analysis on a panel of deletion and trisomy cell lines with known chromosomal alterations and the precise LOH and CNA regions were detected as expected. Using a previously described oral SCC progression model system, we identified a set of genomic regions that may be associated with the malignancy progression, including chromosome regions 3pter–3p35.3, 3p14.1–3p13, 11p, 11q14.3–11q22.2, and 11q13.5–11q14.1. These data show that it is feasible to utilize high-density SNP arrays to generate concordant LOH and CNA profiles at high resolution.

Identification of domains of c-Jun mediating androgen receptor transactivation

The proto-oncoprotein c-Jun, when complexed with c-Fos, forms the climeric complex identified as AP-1 which regulates transcription directly by binding to AP-1-responsive genes. We have previously reported an indirect mechanism by which c-Jun is able to regulate transcription by stimulating androgen receptor transactivation in the absence of c-Fos or any apparent DNA binding. A series of c-Jun mutants were tested in order to characterize the domains of c-Jun responsible for this effect. The studies reported here indicate that a functional bZIP region and a portion of the N-terminal activation functions is necessary for c-Jun stimulation of androgen receptor transactivation. Testing c-Jun/v-Jun chimeras, we show that v-Jun is unable to stimulate androgen receptor transactivation and the effect is dependent on the c-Jun activation functions. c-Jun exhibits a bell-shaped activity on androgen receptor-mediated transactivation which appears to be distinct from c-Juns transactivation ability. A c-Jun mutant deficient in transactivation is able to stimulate androgen receptor activity. These results indicate that c-Juns transactivation ability can be separated from c-Juns ability to stimulate the androgen receptor transactivation.

Detection of DNA copy number abnormality by microarray expression analysis

Gene copy-number abnormalities (CNAs) are characteristic of solid tumors and are found in association with developmental abnormalities and/or mental retardation. The ultimate impact of CNAs is exerted by the altered expression of encoded genes. We have utilized high-density oligonucleotide arrays from Affymetrix to identify DNA CNAs via their impact on mRNA expression levels. In these studies, we have used three different trisomic cell lines (trisomyxa09, trisomyxa018, trisomyxa021) as models of CNAs and have compared mRNA expression in those trisomic cells with that observed in diploid cell lines of matched tissue origin. Our data clearly show that genes from CNA chromosome regions are substantially over-represented (P<0.000001 by chi-square analysis) in the differentially expressed subset from comparisons of all three trisomic cell lines with normal matching cells. In addition, we have been able to detect the origin of the duplication by a statistical scan for over-expressed genes. These data show that microarray detection of differential mRNA expression can be used to identify significant DNA CNAs.

Allelic imbalance analysis of oral tongue squamous cell carcinoma by high-density single nucleotide polymorphism arrays using whole-genome amplified DNA

Multiple displacement-based whole-genome DNA amplification is a promising tool to obtain sufficient DNA from small tissue specimens for various genetic analyses, such as SNP array-based analysis. Using Affymetrix 10xa0K and 100xa0K SNP mapping array, we evaluated the performance of the Phi29 DNA polymerase-based genome amplification. Greater than 99% concordance in genotyping calls were achieved between amplified and non-amplified DNAs for both arrays. By utilizing the Affymetrix GeneChip Chromosome Copy Number Tool, the allelic imbalance profiles for the advanced stage oral tongue squamous cell carcinoma (OTSCC) were generated based on 10xa0K and 100xa0K SNP mapping array results. The results from these two array platforms agree closely, but more precise allelic imbalance patterns can be revealed from the 100xa0K SNP mapping array data. Furthermore, our data suggested a frequent loss at 3p11–p12 for advanced stage OTSCC.

Identification of discrete chromosomal deletion by binary recursive partitioning of microarray differential expression data

DNA copy number abnormalities (CNA) are characteristic of tumours, and are also found in association with congenital anomalies and mental retardation. The ultimate impact of copy number abnormalities is manifested by the altered expression of the encoded genes. We previously developed a statistical method for the detection of simple chromosomal amplification using microarray expression data. In this study, we significantly advanced those analytical techniques to allow detection of localised chromosomal deletions based on differential gene expression data. Using three cell lines with known chromosomal deletions as model system, mRNA expression in those cells was compared with that observed in diploid cell lines of matched tissue origin. Results show that genes from deleted chromosomal regions are substantially over-represented (p<0.000001 by χ2) among genes identified as underexpressed in deletion cell lines relative to normal matching cells. Using a likelihood based statistical model, we were able to identify the breakpoint of the chromosomal deletion and match with the karyotype data in each cell line. In one such cell line, our analyses refined a previously identified 10p chromosomal deletion region. The deletion region was mapped to between 10p14 and 10p12, which was further confirmed by subtelomeric fluorescence in situ hybridisation. These data show that microarray differential expression data can be used to detect and map the boundaries of submicroscopic chromosomal deletions.

Advancement in characterization of genomic alterations for improved diagnosis, treatment and prognostics in cancer

Most human cancers are characterized by genetic instabilities. These instabilities manifest themselves as a series of genetic alterations, including discrete mutations and chromosomal aberrations. With the human genome deciphered, high-throughput technologies are rapidly advancing the field to generate genome-wide gene expression and mutation profiles that are highly correlative of biologic and disease phenotypes. While recent advancement in comprehensive genomic characterization presents an unprecedented opportunity for advancing the treatment of cancer, there are still many challenges that need to be overcome before we can fully utilize genomic markers and targets for cancer prediction, diagnostics, treatment and prognostics. This review describes recent advances in comprehensive genomic characterization at the DNA level, and considers some of the challenges that remain for defining the precise genomic portrait of tumors. Potential solutions that may help overcome these challenges are also offered.

Single Nucleotide Polymorphism Mapping Array Assay

Single nucleotide polymorphisms (SNPs) are the most frequent form of DNA variation present in the human genome, and millions of SNPs have been identified http://www.ncbi.nlm.nih.gov/SNP/). Because of their abundance, even spacing, and stability across the genome, SNPs have significant advantages over other genetic markers (such as restriction fragment length polymorphisms and microsatellite markers) as a basis for high-resolution whole genome allelotyping. SNP scoring is easily automated and high-density oligonucleotide arrays have recently been generated to support large-scale high throughput SNP analysis. High-density SNP allele arrays have improved significantly and it is now possible to genotype hundreds of thousands SNP markers using a single SNP array. In this chapter, we will provide a detailed experimental protocol of Affymetrix GeneChip SNP Mapping Array-based whole genome SNP genotyping assay.

2 - Methodology of Microarray Data Analysis

A variety of data analysis tools has been developed to accommodate the various applications for microarray analysis. This chapter discusses some common analytical strategies for expression analysis, which can be potentially adapted for most microarray applications. The major steps involved in microarray data analysis are (1) microarray image acquisition and raw data generation, (2) data normalization and transformation, (3) classification and exploratory data analysis, and (4) post-analysis follow-up and validation. The first step, microarray image acquisition and raw data generation, is heavily platform dependent. Regardless of the approach chosen, the arrays are scanned after hybridization. Independent grayscale images, typically 16 bit tagged information file format files (tiff), are generated for each sample to be analyzed. Image analysis software is then used to identify arrayed spots and measure the relative fluorescence intensities for each element. There are many commercial and freely available software packages for image quantitation. Although there are differences between various imaging software, most give high quality, reproducible measures of hybridization intensities.