Xiaotu Ma

CGI: a new approach for prioritizing genes by combining gene expression and protein--protein interaction data

MOTIVATION Identifying candidate genes associated with a given phenotype or trait is an important problem in biological and biomedical studies. Prioritizing genes based on the accumulated information from several data sources is of fundamental importance. Several integrative methods have been developed when a set of candidate genes for the phenotype is available. However, how to prioritize genes for phenotypes when no candidates are available is still a challenging problem. RESULTS We develop a new method for prioritizing genes associated with a phenotype by Combining Gene expression and protein Interaction data (CGI). The method is applied to yeast gene expression data sets in combination with protein interaction data sets of varying reliability. We found that our method outperforms the intuitive prioritizing method of using either gene expression data or protein interaction data only and a recent gene ranking algorithm GeneRank. We then apply our method to prioritize genes for Alzheimers disease. AVAILABILITY The code in this paper is available upon request.

DNA methylation data analysis and its application to cancer research

With the rapid development of genome-wide high-throughput technologies, including expression arrays, SNP arrays and next-generation sequencing platforms, enormous amounts of molecular data have been generated and deposited in the public domain. The application of computational approaches is required to yield biological insights from this enormous, ever-growing resource. A particularly interesting subset of these resources is related to epigenetic regulation, with DNA methylation being the most abundant data type. In this paper, we will focus on the analysis of DNA methylation data and its application to cancer studies. We first briefly review the molecular techniques that generate such data, much of which has been obtained with the use of the most recent version of Infinium HumanMethylation450 BeadChip(®) technology (Illumina, CA, USA). We describe the coverage of the methylome by this technique. Several examples of data mining are provided. However, it should be understood that reliance on a single aspect of epigenetics has its limitations. In the not too distant future, these defects may be rectified, providing scientists with previously unavailable opportunities to explore in detail the role of epigenetics in cancer and other disease states.

A highly efficient and effective motif discovery method for ChIP-seq/ChIP-chip data using positional information

Identification of DNA motifs from ChIP-seq/ChIP-chip [chromatin immunoprecipitation (ChIP)] data is a powerful method for understanding the transcriptional regulatory network. However, most established methods are designed for small sample sizes and are inefficient for ChIP data. Here we propose a new k-mer occurrence model to reflect the fact that functional DNA k-mers often cluster around ChIP peak summits. With this model, we introduced a new measure to discover functional k-mers. Using simulation, we demonstrated that our method is more robust against noises in ChIP data than available methods. A novel word clustering method is also implemented to group similar k-mers into position weight matrices (PWMs). Our method was applied to a diverse set of ChIP experiments to demonstrate its high sensitivity and specificity. Importantly, our method is much faster than several other methods for large sample sizes. Thus, we have developed an efficient and effective motif discovery method for ChIP experiments.

CDKN2A/p16 Inactivation Mechanisms and Their Relationship to Smoke Exposure and Molecular Features in Non–Small-Cell Lung Cancer

Introduction: CDKN2A (p16) inactivation is common in lung cancer and occurs via homozygous deletions, methylation of promoter region, or point mutations. Although p16 promoter methylation has been linked to KRAS mutation and smoking, the associations between p16 inactivation mechanisms and other common genetic mutations and smoking status are still controversial or unknown. Methods: We determined all three p16 inactivation mechanisms with the use of multiple methodologies for genomic status, methylation, RNA, and protein expression, and correlated them with EGFR, KRAS, STK11 mutations and smoking status in 40 cell lines and 45 tumor samples of primary non–small-cell lung carcinoma. We also performed meta-analyses to investigate the impact of smoke exposure on p16 inactivation. Results: p16 inactivation was the major mechanism of RB pathway perturbation in non–small-cell lung carcinoma, with homozygous deletion being the most frequent method, followed by methylation and the rarer point mutations. Inactivating mechanisms were tightly correlated with loss of mRNA and protein expression. p16 inactivation occurred at comparable frequencies regardless of mutational status of EGFR, KRAS, and STK11, however, the major inactivation mechanism of p16 varied. p16 methylation was linked to KRAS mutation but was mutually exclusive with EGFR mutation. Cell lines and tumor samples demonstrated similar results. Our meta-analyses confirmed a modest positive association between p16 promoter methylation and smoking. Conclusion: Our results confirm that all the inactivation mechanisms are truly associated with loss of gene product and identify specific associations between p16 inactivation mechanisms and other genetic changes and smoking status.

A Search for Novel Cancer/Testis Antigens in Lung Cancer Identifies VCX/Y Genes, Expanding the Repertoire of Potential Immunotherapeutic Targets

Cancer/testis (CT) antigens are potential immunotherapeutic targets in cancer. However, the expression of particular antigens is limited to a subset of tumors of a given type. Thus, there is a need to identify antigens with complementary expression patterns for effective therapeutic intervention. In this study, we searched for genes that were distinctly expressed at a higher level in lung tumor tissue and the testes compared with other nontumor tissues and identified members of the VCX/Y gene family as novel CT antigens. VCX3A, a member of the VCX/Y gene family, was expressed at the protein level in approximately 20% of lung adenocarcinomas and 35% of squamous cell carcinomas, but not expressed in normal lung tissues. Among CT antigens with concordant mRNA and protein expression levels, four CT antigens, XAGE1, VCX, IL13RA2, and SYCE1, were expressed, alone or in combination, in about 80% of lung adenocarcinoma tumors. The CT antigen VCX/Y gene family broadens the spectrum of CT antigens expressed in lung adenocarcinomas for clinical applications.

Understanding gene essentiality by finely characterizing hubs in the yeast protein interaction network.

The centrality-lethality rule, i.e., high-degree proteins or hubs tend to be more essential than low-degree proteins in the yeast protein interaction network, reveals that a proteins central position indicates its important function, but whether and why hubs tend to be more essential have been heavily debated. Here, we integrated gene expression and functional module data to classify hubs into four types: non-co-expressed non-co-cluster hubs, non-co-expressed co-cluster hubs, co-expressed non-co-cluster hubs and co-expressed co-cluster hubs. We found that all the four hub types are more essential than non-hubs, but they also show different enrichments in essential proteins. Non-co-expressed non-co-cluster hubs play key role in organizing different modules formed by the other three hub types, but they are less important to the survival of the yeast cell. Among the four hub types, co-expressed co-cluster hubs, which likely correspond to the core components of stable protein complexes, are the most essential. These results demonstrated that our classification of hubs into four types could better improve the understanding of gene essentiality.

Cancer-Specific Production of N-Acetylaspartate via NAT8L Overexpression in Non–Small Cell Lung Cancer and Its Potential as a Circulating Biomarker

In order to identify new cancer-associated metabolites that may be useful for early detection of lung cancer, we performed a global metabolite profiling of a non–small cell lung cancer (NSCLC) line and immortalized normal lung epithelial cells from the same patient. Among several metabolites with significant cancer/normal differences, we identified a unique metabolic compound, N-acetylaspartate (NAA), in cancer cells—undetectable in normal lung epithelium. NAAs cancer-specific detection was validated in additional cancer and control lung cells as well as selected NSCLC patient tumors and control tissues. NAAs cancer specificity was further supported in our analysis of NAA synthetase (gene symbol: NAT8L) gene expression levels in The Cancer Genome Atlas: elevated NAT8L expression in approximately 40% of adenocarcinoma and squamous cell carcinoma cases (N = 577), with minimal expression in all nonmalignant lung tissues (N = 74). We then showed that NAT8L is functionally involved in NAA production of NSCLC cells through siRNA-mediated suppression of NAT8L, which caused selective reduction of intracellular and secreted NAA. Our cell culture experiments also indicated that NAA biosynthesis in NSCLC cells depends on glutamine availability. For preliminary evaluation of NAAs clinical potential as a circulating biomarker, we developed a sensitive NAA blood assay and found that NAA blood levels were elevated in 46% of NSCLC patients (N = 13) in comparison with age-matched healthy controls (N = 21) among individuals aged 55 years or younger. Taken together, these results indicate that NAA is produced specifically in NSCLC tumors through NAT8L overexpression, and its extracellular secretion can be detected in blood. Cancer Prev Res; 9(1); 43–52. ©2015 AACR.

ITPKA Gene Body Methylation Regulates Gene Expression and Serves as an Early Diagnostic Marker in Lung and Other Cancers

Introduction: Despite recent advances in cancer therapy, the overall 5‐year survival rate of patients with lung cancer remains low. The aim of our study was to search for novel markers for early diagnosis in patients with lung cancer. Methods: Complementary DNA microarray analysis was performed in primary lung adenocarcinomas and cell lines to search for differentially expressed genes, followed by in vivo and in vitro tumorigenic assays to characterize the oncogenic potential of the candidate genes. Gene body methylation was analyzed by 450K methylation array, bisulfite sequencing, and quantitative methylation‐specific polymerase chain reaction assays. In silico analysis of The Cancer Genome Atlas data set was also performed. Results: Inositol‐trisphosphate 3‐kinase A gene (ITPKA), a kinase with limited tissue distribution, was identified as a potential oncogene. We showed that ITPKA expression is up‐regulated in many forms of cancers, including lung and breast cancers, and that overexpressed ITPKA contributes to tumorigenesis. We also demonstrated that ITPKA expression is regulated by epigenetic DNA methylation of ITPKA gene body through modulation of the binding of SP1 transcription factor to the ITPKA promoter. ITPKA gene body displayed low or absent levels of methylation in most normal tissue but was significantly methylated in malignant tumors. In lung cancer, ITPKA gene body methylation first appeared at the in situ carcinoma stage and progressively increased after invasion. Conclusions: ITPKA is a potential oncogene that it is overexpressed in most tumors, and its overexpression promotes tumorigenesis. ITPKA gene body methylation regulates its expression and thus serves as a novel and potential biomarker for early cancer detection.

Validation of SCT Methylation As a Hallmark Biomarker for Lung Cancers

Introduction: The human secretin gene (SCT) encodes secretin, a hormone with limited tissue distribution. Analysis of the 450k methylation array data in The Cancer Genome Atlas (TCGA) indicated that the SCT promoter region is differentially hypermethylated in lung cancer. Our purpose was to validate SCT methylation as a potential biomarker for lung cancer. Methods: We analyzed data from TCGA and developed and applied SCT‐specific bisulfite DNA sequencing and quantitative methylation‐specific polymerase chain reaction assays. Results: The analyses of TCGA 450K data for 801 samples showed that SCT hypermethylation has an area under the curve (AUC) value greater than 0.98 that can be used to distinguish lung adenocarcinomas or squamous cell carcinomas from nonmalignant lung tissue. Bisulfite sequencing of lung cancer cell lines and normal blood cells allowed us to confirm that SCT methylation is highly discriminative. By applying a quantitative methylation‐specific polymerase chain reaction assay, we found that SCT hypermethylation is frequently detected in all major subtypes of malignant non–small cell lung cancer (AUC = 0.92, n = 108) and small cell lung cancer (AUC = 0.93, n = 40) but is less frequent in lung carcinoids (AUC = 0.54, n = 20). SCT hypermethylation appeared in samples of lung carcinoma in situ during multistage pathogenesis and increased in invasive samples. Further analyses of TCGA 450k data showed that SCT hypermethylation is highly discriminative in most other types of malignant tumors but less frequent in low‐grade malignant tumors. The only normal tissue with a high level of methylation was the placenta. Conclusions: Our findings demonstrated that SCT methylation is a highly discriminative biomarker for lung and other malignant tumors, is less frequent in low‐grade malignant tumors (including lung carcinoids), and appears at the carcinoma in situ stage.

Identification of Breast Cancer DNA Methylation Markers Optimized for Fine-Needle Aspiration Samples

Background: Random periareolar fine-needle aspiration (RP-FNA) is increasingly used in trials of breast cancer prevention for biomarker assessments. DNA methylation markers may have value as surrogate endpoint biomarkers, but this requires identification of biologically relevant markers suitable for paucicellular, lymphocyte-contaminated clinical samples. Methods: Unbiased whole-genome 5-aza-2′-deoxycytidine (5AZA)–induced gene expression assays, followed by several phases of qualitative and quantitative methylation-specific PCR (MSP) testing, were used to identify novel breast cancer DNA methylation markers optimized for clinical FNA samples. Results: The initial 5AZA experiment identified 453 genes whose expression was potentially regulated by promoter region methylation. Informatics filters excluded 273 genes unlikely to yield useful DNA methylation markers. MSP assays were designed for 271 of the remaining genes and, ultimately, 33 genes were identified that were differentially methylated in clinical breast cancer samples, as compared with benign RP-FNA samples, and never methylated in lymphocytes. A subset of these markers was validated by quantitative multiplex MSP in extended clinical sample sets. Using a novel permutation method for analysis of quantitative methylation data, PSAT1, GNE, CPNE8, and CXCL14 were found to correlate strongly with specific clinical and pathologic features of breast cancer. In general, our approach identified markers methylated in a smaller subpopulation of tumor cells than those identified in published methylation array studies. Conclusions: Clinically relevant DNA methylation markers were identified using a 5AZA-induced gene expression approach. Impact: These breast cancer-relevant, FNA-optimized DNA methylation markers may have value as surrogate endpoint biomarkers in RP-FNA studies. Cancer Epidemiol Biomarkers Prev; 22(12); 2212–21. ©2013 AACR.