Revisiting Concurrent Radiation Therapy, Temozolomide, and the Histone Deacetylase Inhibitor Valproic Acid for Patients with Glioblastoma-Proteomic Alteration and Comparison Analysis with the Standard-of-Care Chemoirradiation.

BACKGROUND: Glioblastoma (GBM) is the most common brain tumor with an overall survival (OS) of less than 30% at two years. Valproic acid (VPA) demonstrated survival benefits documented in retrospective and prospective trials, when used in combination with chemo-radiotherapy (CRT). PURPOSE: The primary goal of this study was to examine if the differential alteration in proteomic expression pre vs. post-completion of concurrent chemoirradiation (CRT) is present with the addition of VPA as compared to standard-of-care CRT. The second goal was to explore the associations between the proteomic alterations in response to VPA/RT/TMZ correlated to patient outcomes. The third goal was to use the proteomic profile to determine the mechanism of action of VPA in this setting. MATERIALS AND METHODS: Serum obtained pre- and post-CRT was analyzed using an aptamer-based SOMAScan® proteomic assay. Twenty-nine patients received CRT plus VPA, and 53 patients received CRT alone. Clinical data were obtained via a database and chart review. Tests for differences in protein expression changes between radiation therapy (RT) with or without VPA were conducted for individual proteins using two-sided t-tests, considering p-values of <0.05 as significant. Adjustment for age, sex, and other clinical covariates and hierarchical clustering of significant differentially expressed proteins was carried out, and Gene Set Enrichment analyses were performed using the Hallmark gene sets. Univariate Cox proportional hazards models were used to test the individual protein expression changes for an association with survival. The lasso Cox regression method and 10-fold cross-validation were employed to test the combinations of expression changes of proteins that could predict survival. Predictiveness curves were plotted for significant proteins for VPA response (p-value < 0.005) to show the survival probability vs. the protein expression percentiles. RESULTS: A total of 124 proteins were identified pre- vs. post-CRT that were differentially expressed between the cohorts who received CRT plus VPA and those who received CRT alone. Clinical factors did not confound the results, and distinct proteomic clustering in the VPA-treated population was identified. Time-dependent ROC curves for OS and PFS for landmark times of 20 months and 6 months, respectively, revealed AUC of 0.531, 0.756, 0.774 for OS and 0.535, 0.723, 0.806 for PFS for protein expression, clinical factors, and the combination of protein expression and clinical factors, respectively, indicating that the proteome can provide additional survival risk discrimination to that already provided by the standard clinical factors with a greater impact on PFS. Several proteins of interest were identified. Alterations in GALNT14 (increased) and CCL17 (decreased) (p = 0.003 and 0.003, respectively, FDR 0.198 for both) were associated with an improvement in both OS and PFS. The pre-CRT protein expression revealed 480 proteins predictive for OS and 212 for PFS (p < 0.05), of which 112 overlapped between OS and PFS. However, FDR-adjusted p values were high, with OS (the smallest p value of 0.586) and PFS (the smallest p value of 0.998). The protein PLCD3 had the lowest p-value (p = 0.002 and 0.0004 for OS and PFS, respectively), and its elevation prior to CRT predicted superior OS and PFS with VPA administration. Cancer hallmark genesets associated with proteomic alteration observed with the administration of VPA aligned with known signal transduction pathways of this agent in malignancy and non-malignancy settings, and GBM signaling, and included epithelial-mesenchymal transition, hedgehog signaling, Il6/JAK/STAT3, coagulation, NOTCH, apical junction, xenobiotic metabolism, and complement signaling. CONCLUSIONS: Differential alteration in proteomic expression pre- vs. post-completion of concurrent chemoirradiation (CRT) is present with the addition of VPA. Using pre- vs. post-data, prognostic proteins emerged in the analysis. Using pre-CRT data, potentially predictive proteins were identified. The protein signals and hallmark gene sets associated with the alteration in the proteome identified between patients who received VPA and those who did not, align with known biological mechanisms of action of VPA and may allow for the identification of novel biomarkers associated with outcomes that can help advance the study of VPA in future prospective trials.

Discovery of pathway-independent protein signatures associated with clinical outcome in human cancer cohorts.

Proteomic data provide a direct readout of protein function, thus constituting an information-rich resource for prognostic and predictive modeling. However, protein array data may not fully capture pathway activity due to the limited number of molecules and incomplete pathway coverage compared to other high-throughput technologies. For the present study, our aim was to improve clinical outcome prediction compared to published pathway-dependent prognostic signatures for The Cancer Genome Atlas (TCGA) cohorts using the least absolute shrinkage and selection operator (LASSO). RPPA data is particularly well-suited to the LASSO due to the relatively low number of predictors compared to larger genomic data matrices. Our approach selected predictors regardless of their pathway membership and optimally combined their RPPA measurements into a weighted risk score. Performance was assessed and compared to that of the published signatures using two unbiased approaches: 1) 10 iterations of threefold cross-validation for unbiased estimation of hazard ratio and difference in 5-year survival (by Kaplan-Meier method) between predictor-defined high and low risk groups; and 2) a permutation test to evaluate the statistical significance of the cross-validated log-rank statistic. Here, we demonstrate strong stratification of 445 renal clear cell carcinoma tumors from The Cancer Genome Atlas (TCGA) into high and low risk groups using LASSO regression on RPPA data. Median cross-validated difference in 5-year overall survival was 32.8%, compared to 25.2% using a published receptor tyrosine kinase (RTK) prognostic signature (median hazard ratios of 3.3 and 2.4, respectively). Applicability and performance of our approach was demonstrated in three additional TCGA cohorts: ovarian serous cystadenocarcinoma (OVCA), sarcoma (SARC), and cutaneous melanoma (SKCM). The data-driven LASSO-based approach is versatile and well-suited for discovery of new protein/disease associations.

TPWshiny: an interactive R/Shiny app to explore cell line transcriptional responses to anti-cancer drugs.

SUMMARY: The NCI Transcriptional Pharmacodynamics Workbench (NCI TPW) is an extensive compilation of directly measured transcriptional responses to anti-cancer agents across the well-characterized NCI-60 cancer cell lines. The NCI TPW data are publicly available through a web interface that allows limited user interaction with the data. We developed 'TPWshiny' as a standalone, easy to install, R application to facilitate more interactive data exploration.With no programming skills required, TPWshiny provides an intuitive and comprehensive graphical interface to help researchers understand the response of tumor cell lines to 15 therapeutic agents. The data are presented in interactive scatter plots, heatmaps, time series and Venn diagrams. Data can be queried by drug concentration, time point, gene and tissue type. Researchers can download the data for further analysis. AVAILABILITY AND IMPLEMENTATION: Users can download the ready-to-use, self-extracting package for Windows or macOS, and R source code from the project website (https://brb.nci.nih.gov/TPWshiny/). TPWshiny documentation and additional information can be found on the project website.

TPM, FPKM, or Normalized Counts? A Comparative Study of Quantification Measures for the Analysis of RNA-seq Data from the NCI Patient-Derived Models Repository.

BACKGROUND: In order to correctly decode phenotypic information from RNA-sequencing (RNA-seq) data, careful selection of the RNA-seq quantification measure is critical for inter-sample comparisons and for downstream analyses, such as differential gene expression between two or more conditions. Several methods have been proposed and continue to be used. However, a consensus has not been reached regarding the best gene expression quantification method for RNA-seq data analysis. METHODS: In the present study, we used replicate samples from each of 20 patient-derived xenograft (PDX) models spanning 15 tumor types, for a total of 61 human tumor xenograft samples available through the NCI patient-derived model repository (PDMR). We compared the reproducibility across replicate samples based on TPM (transcripts per million), FPKM (fragments per kilobase of transcript per million fragments mapped), and normalized counts using coefficient of variation, intraclass correlation coefficient, and cluster analysis. RESULTS: Our results revealed that hierarchical clustering on normalized count data tended to group replicate samples from the same PDX model together more accurately than TPM and FPKM data. Furthermore, normalized count data were observed to have the lowest median coefficient of variation (CV), and highest intraclass correlation (ICC) values across all replicate samples from the same model and for the same gene across all PDX models compared to TPM and FPKM data. CONCLUSION: We provided compelling evidence for a preferred quantification measure to conduct downstream analyses of PDX RNA-seq data. To our knowledge, this is the first comparative study of RNA-seq data quantification measures conducted on PDX models, which are known to be inherently more variable than cell line models. Our findings are consistent with what others have shown for human tumors and cell lines and add further support to the thesis that normalized counts are the best choice for the analysis of RNA-seq data across samples.

Molecular Profiling-Based Assignment of Cancer Therapy (NCI-MPACT): A Randomized Multicenter Phase II Trial.

This trial assessed the utility of applying tumor DNA sequencing to treatment selection for patients with advanced, refractory cancer and somatic mutations in one of four signaling pathways by comparing the efficacy of four study regimens that were either matched to the patient's aberrant pathway (experimental arm) or not matched to that pathway (control arm). MATERIALS AND METHODS: Adult patients with an actionable mutation of interest were randomly assigned 2:1 to receive either (1) a study regimen identified to target the aberrant pathway found in their tumor (veliparib with temozolomide or adavosertib with carboplatin [DNA repair pathway], everolimus [PI3K pathway], or trametinib [RAS/RAF/MEK pathway]), or (2) one of the same four regimens, but chosen from among those not targeting that pathway. RESULTS: Among 49 patients treated in the experimental arm, the objective response rate was 2% (95% CI, 0% to 10.9%). One of 20 patients (5%) in the experimental trametinib cohort had a partial response. There were no responses in the other cohorts. Although patients and physicians were blinded to the sequencing and random assignment results, a higher pretreatment dropout rate was observed in the control arm (22%) compared with the experimental arm (6%; P = .038), suggesting that some patients may have had prior tumor mutation profiling performed that led to a lack of participation in the control arm. CONCLUSION: Further investigation, better annotation of predictive biomarkers, and the development of more effective agents are necessary to inform treatment decisions in an era of precision cancer medicine. Increasing prevalence of tumor mutation profiling and preference for targeted therapy make it difficult to use a randomized phase II design to evaluate targeted therapy efficacy in an advanced disease setting.

Bioinformatics Tools and Resources for Cancer Immunotherapy Study.

In recent years, cancer immunotherapy has emerged as a highly promising approach to treat patients with cancer, as the patient's own immune system is harnessed to attack cancer cells. However, the application of these approaches is still limited to a minority of patients with cancer and it is difficult to predict which patients will derive the greatest clinical benefit.One of the challenges faced by the biomedical community in the search of more effective biomarkers is the fact that translational research efforts involve collecting and accessing data at many different levels: from the type of material examined (e.g., cell line, animal models, clinical samples) to multiple data type (e.g., pharmacodynamic markers, genetic sequencing data) to the scale of a study (e.g., small preclinical study, moderate retrospective study on stored specimen sets, clinical trials with large cohorts).This chapter reviews several publicly available bioinformatics tools and data resources for high throughput molecular analyses applied to a range of data types, including those generated from microarray, whole-exome sequencing (WES), RNA-seq, DNA copy number, and DNA methylation assays, that are extensively used for integrative multidimensional data analysis and visualization.

The NCI Transcriptional Pharmacodynamics Workbench: A Tool to Examine Dynamic Expression Profiling of Therapeutic Response in the NCI-60 Cell Line Panel.

: The intracellular effects and overall efficacies of anticancer therapies can vary significantly by tumor type. To identify patterns of drug-induced gene modulation that occur in different cancer cell types, we measured gene-expression changes across the NCI-60 cell line panel after exposure to 15 anticancer agents. The results were integrated into a combined database and set of interactive analysis tools, designated the NCI Transcriptional Pharmacodynamics Workbench (NCI TPW), that allows exploration of gene-expression modulation by molecular pathway, drug target, and association with drug sensitivity. We identified common transcriptional responses across agents and cell types and uncovered gene-expression changes associated with drug sensitivity. We also demonstrated the value of this tool for investigating clinically relevant molecular hypotheses and identifying candidate biomarkers of drug activity. The NCI TPW, publicly available at https://tpwb.nci.nih.gov, provides a comprehensive resource to facilitate understanding of tumor cell characteristics that define sensitivity to commonly used anticancer drugs. SIGNIFICANCE: The NCI Transcriptional Pharmacodynamics Workbench represents the most extensive compilation to date of directly measured longitudinal transcriptional responses to anticancer agents across a thoroughly characterized ensemble of cancer cell lines.

OpenGeneMed: a portable, flexible and customizable informatics hub for the coordination of next-generation sequencing studies in support of precision medicine trials.

Trials involving genomic-driven treatment selection require the coordination of many teams interacting with a great variety of information. The need of better informatics support to manage this complex set of operations motivated the creation of OpenGeneMed. OpenGeneMed is a stand-alone and customizable version of GeneMed (Zhao et al. GeneMed: an informatics hub for the coordination of next-generation sequencing studies that support precision oncology clinical trials. Cancer Inform 2015;14(Suppl 2):45), a web-based interface developed for the National Cancer Institute Molecular Profiling-based Assignment of Cancer Therapy (NCI-MPACT) clinical trial coordinated by the NIH. OpenGeneMed streamlines clinical trial management and it can be used by clinicians, lab personnel, statisticians and researchers as a communication hub. It automates the annotation of genomic variants identified by sequencing tumor DNA, classifies the actionable mutations according to customizable rules and facilitates quality control in reviewing variants. The system generates summarized reports with detected genomic alterations that a treatment review team can use for treatment assignment. OpenGeneMed allows collaboration to happen seamlessly along the clinical pipeline; it helps reduce errors made transferring data between groups and facilitates clear documentation along the pipeline. OpenGeneMed is distributed as a stand-alone virtual machine, ready for deployment and use from a web browser; its code is customizable to address specific needs of different clinical trials and research teams. Examples on how to change the code are provided in the technical documentation distributed with the virtual machine. In summary, OpenGeneMed offers an initial set of features inspired by our experience with GeneMed, a system that has been proven to be efficient and successful for coordinating the application of next-generation sequencing in the NCI-MPACT trial.

Doubly bonded E13═P and B═E15 molecules and their reactions with H2, acetonitrile, benzophenone, and 2,3-dimethylbutadiene.

The bonding properties and the potential energy surfaces for the chemical reactions of doubly bonded compounds that have the >E13═E15< pattern are studied using density functional theory (M06-2X/Def2-SVPD). Nine molecules, >E13═P< (E13 = B, Al, Ga, In, and Tl) and >B═E15< (E15 = N, P, As, Sb, and Bi), are used as model reactants in this work. Four types of chemical reactions, H2 addition, acetonitrile, benzophenone [2 + 2] cycloadditions, and dimethylbutadiene [4 + 2] cycloaddition, are used to study the chemical reactivity of these inorganic, ethylene-like molecules. The results of these theoretical analyses show that only the >B═P< molecule has a weak B═P double bond, while the >Al═P< , >Ga═P< , >In═P< , >Tl═P< , >B═N< , >B═As<, >B═Sb<, and >B═Bi< compounds are best described as having a strong single σ bond, instead of a traditional p-p π bond. The theoretical results also show that the singlet-triplet energy gap can be used to determine the relative reactivity of these doubly bonded molecules. According to these theoretical investigations, it is predicted that the order of reactivity is as follows: B═P > Al═P > Ga═P > In═P > Tl═P and B═N ≪ B═P < B═As < B═Sb < B═Bi. The conclusions drawn are consistent with the available experimental observations.

GeneMed: An Informatics Hub for the Coordination of Next-Generation Sequencing Studies that Support Precision Oncology Clinical Trials.

We have developed an informatics system, GeneMed, for the National Cancer Institute (NCI) molecular profiling-based assignment of cancer therapy (MPACT) clinical trial (NCT01827384) being conducted in the National Institutes of Health (NIH) Clinical Center. This trial is one of the first to use a randomized design to examine whether assigning treatment based on genomic tumor screening can improve the rate and duration of response in patients with advanced solid tumors. An analytically validated next-generation sequencing (NGS) assay is applied to DNA from patients' tumors to identify mutations in a panel of genes that are thought likely to affect the utility of targeted therapies available for use in the clinical trial. The patients are randomized to a treatment selected to target a somatic mutation in the tumor or with a control treatment. The GeneMed system streamlines the workflow of the clinical trial and serves as a communications hub among the sequencing lab, the treatment selection team, and clinical personnel. It automates the annotation of the genomic variants identified by sequencing, predicts the functional impact of mutations, identifies the actionable mutations, and facilitates quality control by the molecular characterization lab in the review of variants. The GeneMed system collects baseline information about the patients from the clinic team to determine eligibility for the panel of drugs available. The system performs randomized treatment assignments under the oversight of a supervising treatment selection team and generates a patient report containing detected genomic alterations. NCI is planning to expand the MPACT trial to multiple cancer centers soon. In summary, the GeneMed system has been proven to be an efficient and successful informatics hub for coordinating the reliable application of NGS to precision medicine studies.

Quantum chemistry study on internal conversion of diphenyldibenzofulvene in solid phase.

We investigate the nonradiative decay process of diphenyldibenzofulvene (DPDBF) in solid phase by using the quantum chemistry methods. To carry out the nonradiative rate constant calculation, we construct a solid phase model based on the ONIOM method. The geometry of the DPDBF molecule is optimized for the ground state by DFT and the first excited state by TD-DFT, and the corresponding vibrational frequencies and normal coordinates are computed. Under displaced-distorted harmonic oscillator potential approximation, Huang-Rhys factors are obtained. Vibronic coupling constants are calculated as a function of the normal mode based on Domcke's scheme. We find that vibronic coupling constants of 12 modes with large reorganization energies are of similar order, and if this result is still valid for other modes, the internal conversion rate would be determined by high frequency modes because they have a significant nuclear factor that is related to Franck-Condon overlap intergrals. We also find that geometrical changes are suppressed due to the stacking effect, which yields small Huang-Rhys values in the solid phase.

Using cross-validation to evaluate predictive accuracy of survival risk classifiers based on high-dimensional data.

Developments in whole genome biotechnology have stimulated statistical focus on prediction methods. We review here methodology for classifying patients into survival risk groups and for using cross-validation to evaluate such classifications. Measures of discrimination for survival risk models include separation of survival curves, time-dependent ROC curves and Harrell's concordance index. For high-dimensional data applications, however, computing these measures as re-substitution statistics on the same data used for model development results in highly biased estimates. Most developments in methodology for survival risk modeling with high-dimensional data have utilized separate test data sets for model evaluation. Cross-validation has sometimes been used for optimization of tuning parameters. In many applications, however, the data available are too limited for effective division into training and test sets and consequently authors have often either reported re-substitution statistics or analyzed their data using binary classification methods in order to utilize familiar cross-validation. In this article we have tried to indicate how to utilize cross-validation for the evaluation of survival risk models; specifically how to compute cross-validated estimates of survival distributions for predicted risk groups and how to compute cross-validated time-dependent ROC curves. We have also discussed evaluation of the statistical significance of a survival risk model and evaluation of whether high-dimensional genomic data adds predictive accuracy to a model based on standard covariates alone.

A theoretical study on the spectroscopy and the radiative and non-radiative relaxation rate constants of the S(0)(1)A(1)-S(1)(1)A(2) vibronic transitions of formaldehyde.

We have carried out a close examination on the mathematical treatments and the first-principle computations concerning the vibronic transitions between the S(0)(1)A(1) and the S(1)(1)A(2) states of formaldehyde. The simulation of absorption spectrum was presented with peak intensities calculated according to vibronic-coupled transition dipole moments and Franck-Condon factors. The radiative and non-radiative transition rate constants from the excited to the ground states were calculated with formulas based on Fermi's golden rule. It is concluded that our simulated absorption spectrum between 300 and 360 nm, as well as the estimated relaxation rate constants, showed good agreements with experimental reports.

Identification of an integrated SV40 T/t-antigen cancer signature in aggressive human breast, prostate, and lung carcinomas with poor prognosis.

Understanding the genetic architecture of cancer pathways that distinguishes subsets of human cancer is critical to developing new therapies that better target tumors based on their molecular expression profiles. In this study, we identify an integrated gene signature from multiple transgenic models of epithelial cancers intrinsic to the functions of the Simian virus 40 T/t-antigens that is associated with the biological behavior and prognosis for several human epithelial tumors. This genetic signature, composed primarily of genes regulating cell replication, proliferation, DNA repair, and apoptosis, is not a general cancer signature. Rather, it is uniquely activated primarily in tumors with aberrant p53, Rb, or BRCA1 expression but not in tumors initiated through the overexpression of myc, ras, her2/neu, or polyoma middle T oncogenes. Importantly, human breast, lung, and prostate tumors expressing this set of genes represent subsets of tumors with the most aggressive phenotype and with poor prognosis. The T/t-antigen signature is highly predictive of human breast cancer prognosis. Because this class of epithelial tumors is generally intractable to currently existing standard therapies, this genetic signature identifies potential targets for novel therapies directed against these lethal forms of cancer. Because these genetic targets have been discovered using mammary, prostate, and lung T/t-antigen mouse cancer models, these models are rationale candidates for use in preclinical testing of therapies focused on these biologically important targets.

An investigation of two multivariate permutation methods for controlling the false discovery proportion.

Identifying genes that are differentially expressed between classes of samples is an important objective of many microarray experiments. Because of the thousands of genes typically considered, there is a tension between identifying as many of the truly differentially expressed genes as possible, but not too many genes that are not really differentially expressed (false discoveries). Controlling the proportion of identified genes that are false discoveries, the false discovery proportion (FDP), is a goal of interest. In this paper, two multivariate permutation methods are investigated for controlling the FDP. One is based on a multivariate permutation testing (MPT) method that probabilistically controls the number of false discoveries, and the other is based on the Significance Analysis of Microarrays (SAM) procedure that provides an estimate of the FDP. Both methods account for the correlations among the genes. We find the ability of the methods to control the proportion of false discoveries varies substantially depending on the implementation characteristics. For example, for both methods one can proceed from the most significant gene to the least significant gene until the estimated FDP is just above the targeted level ('top-down' approach), or from the least significant gene to the most significant gene until the estimated FDP is just below the targeted level ('bottom-up' approach). We find that the top-down MPT-based method probabilistically controls the FDP, whereas our implementation of the top-down SAM-based method does not. Bottom-up MPT-based or SAM-based methods can result in poor control of the FDP.

Analysis of gene expression data using BRB-ArrayTools.

BRB-ArrayTools is an integrated software system for the comprehensive analysis of DNA microarray experiments. It was developed by professional biostatisticians experienced in the design and analysis of DNA microarray studies and incorporates methods developed by leading statistical laboratories. The software is designed for use by biomedical scientists who wish to have access to state-of-the-art statistical methods for the analysis of gene expression data and to receive training in the statistical analysis of high dimensional data. The software provides the most extensive set of tools available for predictive classifier development and complete cross-validation. It offers extensive links to genomic websites for gene annotation and analysis tools for pathway analysis. An archive of over 100 datasets of published microarray data with associated clinical data is provided and BRB-ArrayTools automatically imports data from the Gene Expression Omnibus public archive at the National Center for Biotechnology Information.

Histological staining methods preparatory to laser capture microdissection significantly affect the integrity of the cellular RNA.

BACKGROUND: Gene expression profiling by microarray analysis of cells enriched by laser capture microdissection (LCM) faces several technical challenges. Frozen sections yield higher quality RNA than paraffin-imbedded sections, but even with frozen sections, the staining methods used for histological identification of cells of interest could still damage the mRNA in the cells. To study the contribution of staining methods to degradation of results from gene expression profiling of LCM samples, we subjected pellets of the mouse plasma cell tumor cell line TEPC 1165 to direct RNA extraction and to parallel frozen sectioning for LCM and subsequent RNA extraction. We used microarray hybridization analysis to compare gene expression profiles of RNA from cell pellets with gene expression profiles of RNA from frozen sections that had been stained with hematoxylin and eosin (H&E), Nissl Stain (NS), and for immunofluorescence (IF) as well as with the plasma cell-revealing methyl green pyronin (MGP) stain. All RNAs were amplified with two rounds of T7-based in vitro transcription and analyzed by two-color expression analysis on 10-K cDNA microarrays. RESULTS: The MGP-stained samples showed the least introduction of mRNA loss, followed by H&E and immunofluorescence. Nissl staining was significantly more detrimental to gene expression profiles, presumably owing to an aqueous step in which RNA may have been damaged by endogenous or exogenous RNAases. CONCLUSION: RNA damage can occur during the staining steps preparatory to laser capture microdissection, with the consequence of loss of representation of certain genes in microarray hybridization analysis. Inclusion of RNAase inhibitor in aqueous staining solutions appears to be important in protecting RNA from loss of gene transcripts.

An adaptive method for cDNA microarray normalization.

BACKGROUND: Normalization is a critical step in analysis of gene expression profiles. For dual-labeled arrays, global normalization assumes that the majority of the genes on the array are non-differentially expressed between the two channels and that the number of over-expressed genes approximately equals the number of under-expressed genes. These assumptions can be inappropriate for custom arrays or arrays in which the reference RNA is very different from the experimental samples. RESULTS: We propose a mixture model based normalization method that adaptively identifies non-differentially expressed genes and thereby substantially improves normalization for dual-labeled arrays in settings where the assumptions of global normalization are problematic. The new method is evaluated using both simulated and real data. CONCLUSIONS: The new normalization method is effective for general microarray platforms when samples with very different expression profile are co-hybridized and for custom arrays where the majority of genes are likely to be differentially expressed.

Methods for assessing reproducibility of clustering patterns observed in analyses of microarray data.

MOTIVATION: Recent technological advances such as cDNA microarray technology have made it possible to simultaneously interrogate thousands of genes in a biological specimen. A cDNA microarray experiment produces a gene expression 'profile'. Often interest lies in discovering novel subgroupings, or 'clusters', of specimens based on their profiles, for example identification of new tumor taxonomies. Cluster analysis techniques such as hierarchical clustering and self-organizing maps have frequently been used for investigating structure in microarray data. However, clustering algorithms always detect clusters, even on random data, and it is easy to misinterpret the results without some objective measure of the reproducibility of the clusters. RESULTS: We present statistical methods for testing for overall clustering of gene expression profiles, and we define easily interpretable measures of cluster-specific reproducibility that facilitate understanding of the clustering structure. We apply these methods to elucidate structure in cDNA microarray gene expression profiles obtained on melanoma tumors and on prostate specimens.