Automatic module selection from several microarray gene expression studies.

Independence of genes is commonly but incorrectly assumed in microarray data analysis; rather, genes are activated in co-regulated sets referred to as modules. In this article, we develop an automatic method to define modules common to multiple independent studies. We use an empirical Bayes procedure to estimate a sparse correlation matrix for all studies, identify modules by clustering, and develop an extreme-value-based method to detect so-called scattered genes, which do not belong to any module. The resulting algorithm is very fast and produces accurate modules in simulation studies. Application to real data identifies modules with significant enrichment and results in a huge dimension reduction, which can alleviate the computational burden of further analyses.

Meta-analysis of incomplete microarray studies.

Meta-analysis of microarray studies to produce an overall gene list is relatively straightforward when complete data are available. When some studies lack information-providing only a ranked list of genes, for example-it is common to reduce all studies to ranked lists prior to combining them. Since this entails a loss of information, we consider a hierarchical Bayes approach to meta-analysis using different types of information from different studies: the full data matrix, summary statistics, or ranks. The model uses an informative prior for the parameter of interest to aid the detection of differentially expressed genes. Simulations show that the new approach can give substantial power gains compared with classical meta-analysis and list aggregation methods. A meta-analysis of 11 published studies with different data types identifies genes known to be involved in ovarian cancer and shows significant enrichment.

Meta-analysis of microarray data identifies GAS6 expression as an independent predictor of poor survival in ovarian cancer.

Seeking new biomarkers for epithelial ovarian cancer, the fifth most common cause of death from all cancers in women and the leading cause of death from gynaecological malignancies, we performed a meta-analysis of three independent studies and compared the results in regard to clinicopathological parameters. This analysis revealed that GAS6 was highly expressed in ovarian cancer and therefore was selected as our candidate of choice. GAS6 encodes a secreted protein involved in physiological processes including cell proliferation, chemotaxis, and cell survival. We performed immunohistochemistry on various ovarian cancer tissues and found that GAS6 expression was elevated in tumour tissue samples compared to healthy control samples (P < 0.0001). In addition, GAS6 expression was also higher in tumours from patients with residual disease compared to those without. Our data propose GAS6 as an independent predictor of poor survival, suggesting GAS6, both on the mRNA and on the protein level, as a potential biomarker for ovarian cancer. In clinical practice, the staining of a tumour biopsy for GAS6 may be useful to assess cancer prognosis and/or to monitor disease progression.

MAP kinase phosphatase 1 (MKP-1/DUSP1) is neuroprotective in Huntington's disease via additive effects of JNK and p38 inhibition.

We previously demonstrated that sodium butyrate is neuroprotective in Huntington's disease (HD) mice and that this therapeutic effect is associated with increased expression of mitogen-activated protein kinase/dual-specificity phosphatase 1 (MKP-1/DUSP1). Here we show that enhancing MKP-1 expression is sufficient to achieve neuroprotection in lentiviral models of HD. Wild-type MKP-1 overexpression inhibited apoptosis in primary striatal neurons exposed to an N-terminal fragment of polyglutamine-expanded huntingtin (Htt171-82Q), blocking caspase-3 activation and significantly reducing neuronal cell death. This neuroprotective effect of MKP-1 was demonstrated to be dependent on its enzymatic activity, being ablated by mutation of its phosphatase domain and being attributed to inhibition of specific MAP kinases (MAPKs). Overexpression of MKP-1 prevented the polyglutamine-expanded huntingtin-induced activation of c-Jun N-terminal kinases (JNKs) and p38 MAPKs, whereas extracellular signal-regulated kinase (ERK) 1/2 activation was not altered by either polyglutamine-expanded Htt or MKP-1. Moreover, mutants of MKP-1 that selectively prevented p38 or JNK binding confirmed the important dual contributions of p38 and JNK regulation to MKP-1-mediated neuroprotection. These results demonstrate additive effects of p38 and JNK MAPK inhibition by MKP-1 without consequence to ERK activation in this striatal neuron-based paradigm. MKP-1 also provided neuroprotection in vivo in a lentiviral model of HD neuropathology in rat striatum. Together, these data extend previous evidence that JNK- and p38-mediated pathways contribute to HD pathogenesis and, importantly, show that therapies simultaneously inhibiting both JNK and p38 signaling pathways may lead to improved neuroprotective outcomes.

Serum antiglycan antibody detection of nonmucinous ovarian cancers by using a printed glycan array.

Epithelial ovarian cancer has the highest mortality rate among gynecological cancers. Altered glycosylation is associated with oncogenic transformation producing tumor-associated carbohydrate antigens. We investigated the potential of natural occurring antiglycan antibodies in the diagnosis of ovarian cancer by using printed glycan array. Antiglycan antibodies bound to 203 chemically synthesized printed glycans were detected via biotin-streptavidin fluorescence system in serum of women with normal operative findings (healthy controls; n = 24) and nonmucinous borderline or ovarian cancer of various FIGO stages (n = 33). Data were validated measuring blood group associated di-, tri and tetrasaccharide antigens on known ABO blood groups. Antiglycan antibodies demonstrated high reproducibility (r(c) > 0.9). Cluster analysis identified repetitive patterns of specific core carbohydrate structures: 11 N-linked glycans, 3 O-linked glycans and 2 glycosphingolipids. Biomarker detection revealed 24 glycans including P(1) (Galα1-4Galß1-4GlcNAcß; p < 0.001) significantly discriminating between (low-) malignant tumors and healthy controls. Comparable sensitivity and specificity with tumor marker CA125 was achieved by a panel of multivariate selected and linear combined antiglycan antibody signals (79.2 and 84.8%, respectively). Our findings demonstrate the potential of glycan arrays in the development of a new generation of biomarkers for ovarian cancer.

Comparison of printed glycan array, suspension array and ELISA in the detection of human anti-glycan antibodies.

Anti-glycan antibodies represent a vast and yet insufficiently investigated subpopulation of naturally occurring and adaptive antibodies in humans. Recently, a variety of glycan-based microarrays emerged, allowing high-throughput profiling of a large repertoire of antibodies. As there are no direct approaches for comparison and evaluation of multi-glycan assays we compared three glycan-based immunoassays, namely printed glycan array (PGA), fluorescent microsphere-based suspension array (SA) and ELISA for their efficacy and selectivity in profiling anti-glycan antibodies in a cohort of 48 patients with and without ovarian cancer. The ABO blood group glycan antigens were selected as well recognized ligands for sensitivity and specificity assessments. As another ligand we selected P(1), a member of the P blood group system recently identified by PGA as a potential ovarian cancer biomarker. All three glyco-immunoassays reflected the known ABO blood groups with high performance. In contrast, anti-P(1) antibody binding profiles displayed much lower concordance. Whilst anti-P(1) antibody levels between benign controls and ovarian cancer patients were significantly discriminated using PGA (p=0.004), we got only similar results using SA (p=0.03) but not for ELISA. Our findings demonstrate that whilst assays were largely positively correlated, each presents unique characteristic features and should be validated by an independent patient cohort rather than another array technique. The variety between methods presumably reflects the differences in glycan presentation and the antigen/antibody ratio, assay conditions and detection technique. This indicates that the glycan-antibody interaction of interest has to guide the assay selection.

Environmental enrichment reduces neuronal intranuclear inclusion load but has no effect on messenger RNA expression in a mouse model of Huntington disease.

Huntington disease (HD) is a fatal neurodegenerative disease with no effective treatment. In the R6/1 mouse model of HD, environmental enrichment delays the neurologic phenotype onset and prevents cerebral volume loss by unknown molecular mechanisms. We examined the effects of environmental enrichment on well-characterized neuropathological parameters in a mouse model of HD. We found a trend toward preservation of downregulated neurotransmitter receptors in striatum of environmentally enriched mice and assessed possible enrichment-related modifications in gene expression using microarrays. We observed similar gene expression changes in R6/1 and R6/2 transgenic mice but found no specific changes in enrichment-related microarray expression profiles in either transgenic or wild-type mice. Furthermore, specific corrections in transprotein-induced transcriptional dysregulation in R6/1 mice were not detected by microarray profiling. However, gene-specific analyses suggested that long-term environmental enrichment may beneficially modulate gene expression dysregulation. Finally, environmental enrichment significantly decreased neuronal intranuclear inclusion load, despite unaffected transgene expression levels. Thus, the therapeutic effects of environmental enrichment likely contribute to decreasing aggregated polyglutamine protein levels without exerting strong effects on gene expression.

Comprehensive analysis of Arabidopsis expression level polymorphisms with simple inheritance.

In Arabidopsis thaliana, gene expression level polymorphisms (ELPs) between natural accessions that exhibit simple, single locus inheritance are promising quantitative trait locus (QTL) candidates to explain phenotypic variability. It is assumed that such ELPs overwhelmingly represent regulatory element polymorphisms. However, comprehensive genome-wide analyses linking expression level, regulatory sequence and gene structure variation are missing, preventing definite verification of this assumption. Here, we analyzed ELPs observed between the Eil-0 and Lc-0 accessions. Compared with non-variable controls, 5' regulatory sequence variation in the corresponding genes is indeed increased. However, approximately 42% of all the ELP genes also carry major transcription unit deletions in one parent as revealed by genome tiling arrays, representing a >4-fold enrichment over controls. Within the subset of ELPs with simple inheritance, this proportion is even higher and deletions are generally more severe. Similar results were obtained from analyses of the Bay-0 and Sha accessions, using alternative technical approaches. Collectively, our results suggest that drastic structural changes are a major cause for ELPs with simple inheritance, corroborating experimentally observed indel preponderance in cloned Arabidopsis QTL.

Selecting control genes for RT-QPCR using public microarray data.

BACKGROUND: Gene expression analysis has emerged as a major biological research area, with real-time quantitative reverse transcription PCR (RT-QPCR) being one of the most accurate and widely used techniques for expression profiling of selected genes. In order to obtain results that are comparable across assays, a stable normalization strategy is required. In general, the normalization of PCR measurements between different samples uses one to several control genes (e.g. housekeeping genes), from which a baseline reference level is constructed. Thus, the choice of the control genes is of utmost importance, yet there is not a generally accepted standard technique for screening a large number of candidates and identifying the best ones. RESULTS: We propose a novel approach for scoring and ranking candidate genes for their suitability as control genes. Our approach relies on publicly available microarray data and allows the combination of multiple data sets originating from different platforms and/or representing different pathologies. The use of microarray data allows the screening of tens of thousands of genes, producing very comprehensive lists of candidates. We also provide two lists of candidate control genes: one which is breast cancer-specific and one with more general applicability. Two genes from the breast cancer list which had not been previously used as control genes are identified and validated by RT-QPCR. Open source R functions are available at http://www.isrec.isb-sib.ch/~vpopovic/research/ CONCLUSION: We proposed a new method for identifying candidate control genes for RT-QPCR which was able to rank thousands of genes according to some predefined suitability criteria and we applied it to the case of breast cancer. We also empirically showed that translating the results from microarray to PCR platform was achievable.

Proteogenomic studies in epithelial ovarian cancer: established knowledge and future needs.

There has been a concerted effort over the last decade to improve our understanding of the complex biology of ovarian cancer. A linear growth in published proteogenomic studies has addressed a variety of questions regarding its molecular pathogenesis. A number of genes have been identified by transcriptomic approaches, some of which are being investigated as putative tumor markers (HE4, OPN, Ep-CAM and Mesothelin), whilst others are potential targets for molecular therapeutic approaches (VEGF, IO4, EGFR, MUC1, CLDN4 and SLPI). Proteogenomics has the potential to further change our current characterization and treatment of ovarian cancer. Additional advances will depend on integrated study designs, interdisciplinary collaborations, use of robust high-throughput platforms, as well as uniform guidelines for bioinformatic analyses.

Meta-analysis of gene expression profiles in breast cancer: toward a unified understanding of breast cancer subtyping and prognosis signatures.

INTRODUCTION: Breast cancer subtyping and prognosis have been studied extensively by gene expression profiling, resulting in disparate signatures with little overlap in their constituent genes. Although a previous study demonstrated a prognostic concordance among gene expression signatures, it was limited to only one dataset and did not fully elucidate how the different genes were related to one another nor did it examine the contribution of well-known biological processes of breast cancer tumorigenesis to their prognostic performance. METHOD: To address the above issues and to further validate these initial findings, we performed the largest meta-analysis of publicly available breast cancer gene expression and clinical data, which are comprised of 2,833 breast tumors. Gene coexpression modules of three key biological processes in breast cancer (namely, proliferation, estrogen receptor [ER], and HER2 signaling) were used to dissect the role of constituent genes of nine prognostic signatures. RESULTS: Using a meta-analytical approach, we consolidated the signatures associated with ER signaling, ERBB2 amplification, and proliferation. Previously published expression-based nomenclature of breast cancer 'intrinsic' subtypes can be mapped to the three modules, namely, the ER-/HER2- (basal-like), the HER2+ (HER2-like), and the low- and high-proliferation ER+/HER2- subtypes (luminal A and B). We showed that all nine prognostic signatures exhibited a similar prognostic performance in the entire dataset. Their prognostic abilities are due mostly to the detection of proliferation activity. Although ER- status (basal-like) and ERBB2+ expression status correspond to bad outcome, they seem to act through elevated expression of proliferation genes and thus contain only indirect information about prognosis. Clinical variables measuring the extent of tumor progression, such as tumor size and nodal status, still add independent prognostic information to proliferation genes. CONCLUSION: This meta-analysis unifies various results of previous gene expression studies in breast cancer. It reveals connections between traditional prognostic factors, expression-based subtyping, and prognostic signatures, highlighting the important role of proliferation in breast cancer prognosis.

Mutant huntingtin's effects on striatal gene expression in mice recapitulate changes observed in human Huntington's disease brain and do not differ with mutant huntingtin length or wild-type huntingtin dosage.

To test the hypotheses that mutant huntingtin protein length and wild-type huntingtin dosage have important effects on disease-related transcriptional dysfunction, we compared the changes in mRNA in seven genetic mouse models of Huntington's disease (HD) and postmortem human HD caudate. Transgenic models expressing short N-terminal fragments of mutant huntingtin (R6/1 and R6/2 mice) exhibited the most rapid effects on gene expression, consistent with previous studies. Although changes in the brains of knock-in and full-length transgenic models of HD took longer to appear, 15- and 22-month CHL2(Q150/Q150), 18-month Hdh(Q92/Q92) and 2-year-old YAC128 animals also exhibited significant HD-like mRNA signatures. Whereas it was expected that the expression of full-length huntingtin transprotein might result in unique gene expression changes compared with those caused by the expression of an N-terminal huntingtin fragment, no discernable differences between full-length and fragment models were detected. In addition, very high correlations between the signatures of mice expressing normal levels of wild-type huntingtin and mice in which the wild-type protein is absent suggest a limited effect of the wild-type protein to change basal gene expression or to influence the qualitative disease-related effect of mutant huntingtin. The combined analysis of mouse and human HD transcriptomes provides important temporal and mechanistic insights into the process by which mutant huntingtin kills striatal neurons. In addition, the discovery that several available lines of HD mice faithfully recapitulate the gene expression signature of the human disorder provides a novel aspect of validation with respect to their use in preclinical therapeutic trials.

Modulation of nucleosome dynamics in Huntington's disease.

Transcriptional dysregulation and aberrant chromatin remodeling are central features in the pathology of Huntington's disease (HD). In order to more fully characterize these pathogenic events, an assessment of histone profiles and associated gene changes were performed in transgenic N171-82Q (82Q) and R6/2 HD mice. Analyses revealed significant chromatin modification, resulting in reduced histone acetylation with concomitant increased histone methylation, consistent with findings observed in HD patients. While there are no known interventions that ameliorate or arrest HD progression, DNA/RNA-binding anthracyclines may provide significant therapeutic potential by correcting pathological nucleosome changes and realigning transcription. Two such anthracyclines, chromomycin and mithramycin, improved altered nucleosome homeostasis in HD mice, normalizing the chromatin pattern. There was a significant shift in the balance between methylation and acetylation in treated HD mice to that found in wild-type mice, resulting in greater acetylation of histone H3 at lysine 9 and promoting gene transcription. Gene expression profiling in anthracycline-treated HD mice showed molecular changes that correlate with disease correction, such that a subset of downregulated genes were upregulated with anthracycline treatment. Improved nucleosomal dynamics were concurrent with a significant improvement in the behavioral and neuropathological phenotype observed in HD mice. These data show the ability of anthracycline compounds to rebalance epigenetic histone modification and, as such, may provide the rationale for the design of human clinical trials in HD patients.

Mechanisms regulating the proliferative potential of human CD8+ T lymphocytes overexpressing telomerase.

In human somatic cells, including T lymphocytes, telomeres progressively shorten with each cell division, eventually leading to a state of cellular senescence. Ectopic expression of telomerase results in the extension of their replicative life spans without inducing changes associated with transformation. However, it is yet unknown whether somatic cells that overexpress telomerase are physiologically indistinguishable from normal cells. Using CD8+ T lymphocyte clones overexpressing telomerase, we investigated the molecular mechanisms that regulate T cell proliferation. In this study, we show that early passage T cell clones transduced or not with human telomerase reverse transcriptase displayed identical growth rates upon mitogenic stimulation and no marked global changes in gene expression. Surprisingly, reduced proliferative responses were observed in human telomerase reverse transcriptase-transduced cells with extended life spans. These cells, despite maintaining high expression levels of genes involved in the cell cycle progression, also showed increased expression in several genes found in common with normal aging T lymphocytes. Strikingly, late passage T cells overexpressing telomerase accumulated the cyclin-dependent inhibitors p16Ink4a and p21Cip1 that have largely been associated with in vitro growth arrest. We conclude that alternative growth arrest mechanisms such as those mediated by p16Ink4a and p21Cip1 still remained intact and regulated the growth potential of cells independently of their telomere status.

Partition resampling and extrapolation averaging: approximation methods for quantifying gene expression in large numbers of short oligonucleotide arrays.

MOTIVATION: Studies of gene expression using high-density short oligonucleotide arrays have become a standard in a variety of biological contexts. Of the expression measures that have been proposed to quantify expression in these arrays, multi-chip-based measures have been shown to perform well. As gene expression studies increase in size, however, utilizing multi-chip expression measures is more challenging in terms of computing memory requirements and time. RESULTS: A strategic alternative to exact multi-chip quantification on a full large chip set is to approximate expression values based on subsets of chips. This paper introduces an extrapolation method, Extrapolation Averaging (EA), and a resampling method, Partition Resampling (PR), to approximate expression in large studies. An examination of properties indicates that subset-based methods can perform well compared with exact expression quantification. The focus is on short oligonucleotide chips, but the same ideas apply equally well to any array type for which expression is quantified using an entire set of arrays, rather than for only a single array at a time. AVAILABILITY: Software implementing Partition Resampling and Extrapolation Averaging is under development as an R package for the BioConductor project.

Interaction of bifidobacteria with Caco-2 cells-adhesion and impact on expression profiles.

The aim of the present study was to study different strains of bifidobacteria for adhesion to Caco-2 intestinal epithelial cells (IECs) and to test for the mRNA response of these cells following interaction with bifidobacteria. Adhesion was tested at different pH conditions using model epithelia consisting of transwell cultures of fully differentiated Caco-2 cells. Microarrays were used to characterize changes in global expression profiles of Caco-2 cells co-cultured with peripheral blood mononuclear cells (PBMCs) and challenged with non-pathogenic Escherichia coli D2241 or four different strains of bifidobacteria. Furthermore, cytokine mRNA of IECs in responses to challenge with Bifidobacterium bifidum S17 or E. coli D2241 was tested in PBMC-sensitised Caco-2 cells using RT-PCR. Bifidobacteria showed strain-specific adhesion to Caco-2. Shift of apical pH from 7 to 4.5 resulted in strain-specific changes of adhesion. Global expression profiles of PBMC-sensitised Caco-2 cells revealed differential expression of a significant number of genes only after challenge with E. coli D2241 while cells were essentially unresponsive to challenge with four strains of bifidobacteria showing different adhesion properties. Using a RT-PCR approach, in the same system a similar differential expression after challenge with E. coli D2241 or B. bifidum S17 was observed for various immune markers. The presented results suggest that Caco-2 cells might be specifically unresponsive to challenge with bifidobacteria irrespective of the level of adhesion.

Assessment of the relationship between pre-chip and post-chip quality measures for Affymetrix GeneChip expression data.

BACKGROUND: Gene expression microarray experiments are expensive to conduct and guidelines for acceptable quality control at intermediate steps before and after the samples are hybridised to chips are vague. We conducted an experiment hybridising RNA from human brain to 117 U133A Affymetrix GeneChips and used these data to explore the relationship between 4 pre-chip variables and 22 post-chip outcomes and quality control measures. RESULTS: We found that the pre-chip variables were significantly correlated with each other but that this correlation was strongest between measures of RNA quality and cRNA yield. Post-mortem interval was negatively correlated with these variables. Four principal components, reflecting array outliers, array adjustment, hybridisation noise and RNA integrity, explain about 75% of the total post-chip measure variability. Two significant canonical correlations existed between the pre-chip and post-chip variables, derived from MAS 5.0, dChip and the Bioconductor packages affy and affyPLM. The strongest (CANCOR 0.838, p < 0.0001) correlated RNA integrity and yield with post chip quality control (QC) measures indexing 3'/5' RNA ratios, bias or scaling of the chip and scaling of the variability of the signal across the chip. Post-mortem interval was relatively unimportant. We also found that the RNA integrity number (RIN) could be moderately well predicted by post-chip measures B_ACTIN35, GAPDH35 and SF. CONCLUSION: We have found that the post-chip variables having the strongest association with quantities measurable before hybridisation are those reflecting RNA integrity. Other aspects of quality, such as noise measures (reflecting the execution of the assay) or measures reflecting data quality (outlier status and array adjustment variables) are not well predicted by the variables we were able to determine ahead of time. There could be other variables measurable pre-hybridisation which may be better associated with expression data quality measures. Uncovering such connections could create savings on costly microarray experiments by eliminating poor samples before hybridisation.

A Laplace mixture model for identification of differential expression in microarray experiments.

Microarrays have become an important tool for studying the molecular basis of complex disease traits and fundamental biological processes. A common purpose of microarray experiments is the detection of genes that are differentially expressed under two conditions, such as treatment versus control or wild type versus knockout. We introduce a Laplace mixture model as a long-tailed alternative to the normal distribution when identifying differentially expressed genes in microarray experiments, and provide an extension to asymmetric over- or underexpression. This model permits greater flexibility than models in current use as it has the potential, at least with sufficient data, to accommodate both whole genome and restricted coverage arrays. We also propose likelihood approaches to hyperparameter estimation which are equally applicable in the Normal mixture case. The Laplace model appears to give some improvement in fit to data, though simulation studies show that our method performs similarly to several other statistical approaches to the problem of identification of differential expression.

Regional and cellular gene expression changes in human Huntington's disease brain.

Huntington's disease (HD) pathology is well understood at a histological level but a comprehensive molecular analysis of the effect of the disease in the human brain has not previously been available. To elucidate the molecular phenotype of HD on a genome-wide scale, we compared mRNA profiles from 44 human HD brains with those from 36 unaffected controls using microarray analysis. Four brain regions were analyzed: caudate nucleus, cerebellum, prefrontal association cortex [Brodmann's area 9 (BA9)] and motor cortex [Brodmann's area 4 (BA4)]. The greatest number and magnitude of differentially expressed mRNAs were detected in the caudate nucleus, followed by motor cortex, then cerebellum. Thus, the molecular phenotype of HD generally parallels established neuropathology. Surprisingly, no mRNA changes were detected in prefrontal association cortex, thereby revealing subtleties of pathology not previously disclosed by histological methods. To establish that the observed changes were not simply the result of cell loss, we examined mRNA levels in laser-capture microdissected neurons from Grade 1 HD caudate compared to control. These analyses confirmed changes in expression seen in tissue homogenates; we thus conclude that mRNA changes are not attributable to cell loss alone. These data from bona fide HD brains comprise an important reference for hypotheses related to HD and other neurodegenerative diseases.

Reliable gene expression measurements from degraded RNA by quantitative real-time PCR depend on short amplicons and a proper normalization.

Quantitative reverse transcriptase real-time PCR (QRT-PCR) is a robust method to quantitate RNA abundance. The procedure is highly sensitive and reproducible as long as the initial RNA is intact. However, breaks in the RNA due to chemical or enzymatic cleavage may reduce the number of RNA molecules that contain intact amplicons. As a consequence, the number of molecules available for amplification decreases. We determined the relation between RNA fragmentation and threshold values (Ct values) in subsequent QRT-PCR for four genes in an experimental model of intact and partially hydrolyzed RNA derived from a cell line and we describe the relation between RNA integrity, amplicon size and Ct values in this biologically homogenous system. We demonstrate that degradation-related shifts of Ct values can be compensated by calculating delta Ct values between test genes and the mean values of several control genes. These delta Ct values are less sensitive to fragmentation of the RNA and are unaffected by varying amounts of input RNA. The feasibility of the procedure was demonstrated by comparing Ct values from a larger panel of genes in intact and in partially degraded RNA. We compared Ct values from intact RNA derived from well-preserved tumor material and from fragmented RNA derived from formalin-fixed, paraffin-embedded (FFPE) samples of the same tumors. We demonstrate that the relative abundance of gene expression can be based on FFPE material even when the amount of RNA in the sample and the extent of fragmentation are not known.