Chi Song

A combined molecular-pathologic score improves risk stratification of thyroid papillary microcarcinoma.

Thyroid papillary microcarcinoma (TPMC) is an incidentally discovered papillary carcinoma that measures ≤1.0 cm in size. Most TPMCs are indolent, whereas some behave aggressively. The objective of the study was to evaluate whether the combination of v‐raf murine sarcoma viral oncogene homolog B1 (BRAF) mutation and specific histopathologic features allows risk stratification of TPMC.

An R package suite for microarray meta-analysis in quality control, differentially expressed gene analysis and pathway enrichment detection

SUMMARY With the rapid advances and prevalence of high-throughput genomic technologies, integrating information of multiple relevant genomic studies has brought new challenges. Microarray meta-analysis has become a frequently used tool in biomedical research. Little effort, however, has been made to develop a systematic pipeline and user-friendly software. In this article, we present MetaOmics, a suite of three R packages MetaQC, MetaDE and MetaPath, for quality control, differentially expressed gene identification and enriched pathway detection for microarray meta-analysis. MetaQC provides a quantitative and objective tool to assist study inclusion/exclusion criteria for meta-analysis. MetaDE and MetaPath were developed for candidate marker and pathway detection, which provide choices of marker detection, meta-analysis and pathway analysis methods. The system allows flexible input of experimental data, clinical outcome (case-control, multi-class, continuous or survival) and pathway databases. It allows missing values in experimental data and utilizes multi-core parallel computing for fast implementation. It generates informative summary output and visualization plots, operates on different operation systems and can be expanded to include new algorithms or combine different types of genomic data. This software suite provides a comprehensive tool to conveniently implement and compare various genomic meta-analysis pipelines. AVAILABILITY http://www.biostat.pitt.edu/bioinfo/software.htm CONTACT ctseng@pitt.edu SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.

Detecting disease-associated genes with confounding variable adjustment and the impact on genomic meta-analysis: With application to major depressive disorder

BackgroundDetecting candidate markers in transcriptomic studies often encounters difficulties in complex diseases, particularly when overall signals are weak and sample size is small. Covariates including demographic, clinical and technical variables are often confounded with the underlying disease effects, which further hampers accurate biomarker detection. Our motivating example came from an analysis of five microarray studies in major depressive disorder (MDD), a heterogeneous psychiatric illness with mostly uncharacterized genetic mechanisms.ResultsWe applied a random intercept model to account for confounding variables and case-control paired design. A variable selection scheme was developed to determine the effective confounders in each gene. Meta-analysis methods were used to integrate information from five studies and post hoc analyses enhanced biological interpretations. Simulations and application results showed that the adjustment for confounding variables and meta-analysis improved detection of biomarkers and associated pathways.ConclusionsThe proposed framework simultaneously considers correction for confounding variables, selection of effective confounders, random effects from paired design and integration by meta-analysis. The approach improved disease-related biomarker and pathway detection, which greatly enhanced understanding of MDD neurobiology. The statistical framework can be applied to similar experimental design encountered in other complex and heterogeneous diseases.

Statistical Analysis of Electron Transfer Dissociation Pairwise Fragmentation Patterns

Electron transfer dissociation (ETD) is an alternative peptide dissociation method developed in recent years. Compared with the traditional collision induced dissociation (CID) b and y ion formation, ETD generates c and z ions and the backbone cleavage is believed to be less selective. We have reported previously the application of a statistical data mining strategy, K-means clustering, to discover fragmentation patterns for CID, and here we report application of this approach to ETD spectra. We use ETD data sets from digestions with three different proteases. Data analysis shows that selective cleavages do exist for ETD, with the fragmentation patterns affected by protease, charge states, and amino acid residue compositions. It is also noticed that the c(n-1) ion, corresponding to loss of the C-terminal amino acid residue, is statistically strong regardless of the residue at the C-terminus of the peptide, which suggests that the peptide gas phase conformation plays an important role in the dissociation pathways. These patterns provide a basis for mechanism elucidation, spectral prediction, and improvement of ETD peptide identification algorithms.

Biomarker detection in the integration of multiple multi-class genomic studies

MOTIVATION Systematic information integration of multiple-related microarray studies has become an important issue as the technology becomes mature and prevalent in the past decade. The aggregated information provides more robust and accurate biomarker detection. So far, published meta-analysis methods for this purpose mostly consider two-class comparison. Methods for combining multi-class studies and considering expression pattern concordance are rarely explored. RESULTS In this article, we develop three integration methods for biomarker detection in multiple multi-class microarray studies: ANOVA-maxP, min-MCC and OW-min-MCC. We first consider a natural extension of combining P-values from the traditional ANOVA model. Since P-values from ANOVA do not guarantee to reflect the concordant expression pattern information across studies, we propose a multi-class correlation (MCC) measure to specifically seek for biomarkers of concordant inter-class patterns across a pair of studies. For both ANOVA and MCC approaches, we use extreme order statistics to identify biomarkers differentially expressed (DE) in all studies (i.e. ANOVA-maxP and min-MCC). The min-MCC method is further extended to identify biomarkers DE in partial studies by incorporating a recently developed optimally weighted (OW) technique (OW-min-MCC). All methods are evaluated by simulation studies and by three meta-analysis applications to multi-tissue mouse metabolism datasets, multi-condition mouse trauma datasets and multi-malignant-condition human prostate cancer datasets. The results show complementary strength of the three methods for different biological purposes. AVAILABILITY http://www.biostat.pitt.edu/bioinfo/. SUPPLEMENTARY INFORMATION Supplementary data is available at Bioinformatics online.

A genome-wide association study of early spontaneous preterm delivery.

Preterm birth is the leading cause of infant morbidity and mortality. Despite extensive research, the genetic contributions to spontaneous preterm birth (SPTB) are not well understood. Term controls were matched with cases by race/ethnicity, maternal age, and parity prior to recruitment. Genotyping was performed using Affymetrix SNP Array 6.0 assays. Statistical analyses utilized PLINK to compare allele occurrence rates between case and control groups, and incorporated quality control and multiple‐testing adjustments. We analyzed DNA samples from mother–infant pairs from early SPTB cases (200/7–336/7 weeks, 959 women and 979 neonates) and term delivery controls (390/7–416/7 weeks, 960 women and 985 neonates). For validation purposes, we included an independent validation cohort consisting of early SPTB cases (293 mothers and 243 infants) and term controls (200 mothers and 149 infants). Clustering analysis revealed no population stratification. Multiple maternal SNPs were identified with association P‐values between 10 × 10–5 and 10 × 10–6. The most significant maternal SNP was rs17053026 on chromosome 3 with an odds ratio (OR) 0.44 with a P‐value of 1.0 × 10–6. Two neonatal SNPs reached the genome‐wide significance threshold, including rs17527054 on chromosome 6p22 with a P‐value of 2.7 × 10–12 and rs3777722 on chromosome 6q27 with a P‐value of 1.4 × 10–10. However, we could not replicate these findings after adjusting for multiple comparisons in a validation cohort. This is the first report of a genome‐wide case‐control study to identify single nucleotide polymorphisms (SNPs) that correlate with SPTB.

Genome abnormalities precede prostate cancer and predict clinical relapse.

The prediction of prostate cancer clinical outcome remains a major challenge after the diagnosis, even with improved early detection by prostate-specific antigen (PSA) monitoring. To evaluate whether copy number variation (CNV) of the genomes in prostate cancer tumor, in benign prostate tissues adjacent to the tumor (AT), and in the blood of patients with prostate cancer predicts biochemical (PSA) relapse and the kinetics of relapse, 241 samples (104 tumor, 49 matched AT, 85 matched blood, and 3 cell lines) were analyzed using Affymetrix SNP 6.0 chips. By using gene-specific CNV from tumor, the genome model correctly predicted 73% (receiver operating characteristic P = 0.003) cases for relapse and 75% (P < 0.001) cases for short PSA doubling time (PSADT, <4 months). The gene-specific CNV model from AT correctly predicted 67% (P = 0.041) cases for relapse and 77% (P = 0.015) cases for short PSADT. By using median-sized CNV from blood, the genome model correctly predicted 81% (P < 0.001) cases for relapse and 69% (P = 0.001) cases for short PSADT. By using median-sized CNV from tumor, the genome model correctly predicted 75% (P < 0.001) cases for relapse and 80% (P < 0.001) cases for short PSADT. For the first time, our analysis indicates that genomic abnormalities in either benign or malignant tissues are predictive of the clinical outcome of a malignancy.

Ratio adjustment and calibration scheme for gene-wise normalization to enhance microarray inter-study prediction

MOTIVATION Reproducibility analyses of biologically relevant microarray studies have mostly focused on overlap of detected biomarkers or correlation of differential expression evidences across studies. For clinical utility, direct inter-study prediction (i.e. to establish a prediction model in one study and apply to another) for disease diagnosis or prognosis prediction is more important. Normalization plays a key role for such a task. Traditionally, sample-wise normalization has been a standard for inter-array and inter-study normalization. For gene-wise normalization, it has been implemented for intra-study or inter-study predictions in a few papers while its rationale, strategy and effect remain unexplored. RESULTS In this article, we investigate the effect of gene-wise normalization in microarray inter-study prediction. Gene-specific intensity discrepancies across studies are commonly found even after proper sample-wise normalization. We explore the rationale and necessity of gene-wise normalization. We also show that the ratio of sample sizes in normal versus diseased groups can greatly affect the performance of gene-wise normalization and an analytical method is developed to adjust for the imbalanced ratio effect. Both simulation results and applications to three lung cancer and two prostate cancer data sets, considering both binary classification and survival risk predictions, showed significant and robust improvement of the new adjustment. A calibration scheme is developed to apply the ratio-adjusted gene-wise normalization for prospective clinical trials. The number of calibration samples needed is estimated from existing studies and suggested for future applications. The result has important implication to the translational research of microarray as a practical disease diagnosis and prognosis prediction tool.

Assessment of multiple intrauterine gestations from ovarian stimulation (AMIGOS) trial: baseline characteristics

OBJECTIVE To identify baseline characteristics of women with unexplained infertility to determine whether treatment with an aromatase inhibitor will result in a lower rate of multiple gestations than current standard ovulation induction medications. DESIGN Randomized, prospective clinical trial. SETTING Multicenter university-based clinical practices. PATIENT(S) A total of 900 couples with unexplained infertility. INTERVENTION(S) Collection of baseline demographics, blood samples, and ultrasonographic assessments. MAIN OUTCOME MEASURE(S) Demographic, laboratory, imaging, and survey characteristics. RESULT(S) Demographic characteristics of women receiving clomiphene citrate (CC), letrozole, or gonadotropins for ovarian stimulation were very consistent. Their mean age was 32.2 ± 4.4 years and infertility duration was 34.7 ± 25.7 months, with 59% primary infertility. More than one-third of the women were current or past smokers. The mean body mass index (BMI) was 27 and mean antimüllerian hormone level was 2.6; only 11 women (1.3%) had antral follicle counts of <5. Similar observations were identified for hormonal profiles, ultrasound characterization of the ovaries, semen parameters, and quality of life assessments in both male and female partners. CONCLUSION(S) The cause of infertility in the couples recruited to this treatment trial is elusive, as the women were regularly ovulating and had evidence of good ovarian reserve both by basal FSH, antimüllerian hormone levels, and antral follicle counts; the male partners had normal semen parameters. The three treatment groups have common baseline characteristics, thereby providing comparable patient populations for testing the hypothesis that use of letrozole for ovarian stimulation can reduce the rates of multiples from that observed with gonadotropin and CC treatment. CLINICAL TRIAL REGISTRATION NUMBER NCT 01044862.

Module-based prediction approach for robust inter-study predictions in microarray data

MOTIVATION Traditional genomic prediction models based on individual genes suffer from low reproducibility across microarray studies due to the lack of robustness to expression measurement noise and gene missingness when they are matched across platforms. It is common that some of the genes in the prediction model established in a training study cannot be matched to another test study because a different platform is applied. The failure of inter-study predictions has severely hindered the clinical applications of microarray. To overcome the drawbacks of traditional gene-based prediction (GBP) models, we propose a module-based prediction (MBP) strategy via unsupervised gene clustering. RESULTS K-means clustering is used to group genes sharing similar expression profiles into gene modules, and small modules are merged into their nearest neighbors. Conventional univariate or multivariate feature selection procedure is applied and a representative gene from each selected module is identified to construct the final prediction model. As a result, the prediction model is portable to any test study as long as partial genes in each module exist in the test study. We demonstrate that K-means cluster sizes generally follow a multinomial distribution and the failure probability of inter-study prediction due to missing genes is diminished by merging small clusters into their nearest neighbors. By simulation and applications of real datasets in inter-study predictions, we show that the proposed MBP provides slightly improved accuracy while is considerably more robust than traditional GBP. AVAILABILITY http://www.biostat.pitt.edu/bioinfo/ CONTACT ctseng@pitt.edu SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.