The challenge of gene expression profiling in heterogeneous clinical samples

DECO: decompose heterogeneous population cohorts for patient stratification and discovery of sample biomarkers using omic data profiling

Motivation

Patient and sample diversity is one of the main challenges when dealing with clinical cohorts in biomedical genomics studies. During last decade, several methods have been developed to identify biomarkers assigned to specific individuals or subtypes of samples. However, current methods still fail to discover markers in complex scenarios where heterogeneity or hidden phenotypical factors are present. Here, we propose a method to analyze and understand heterogeneous data avoiding classical normalization approaches of reducing or removing variation.

Results

DEcomposing heterogeneous Cohorts using Omic data profiling (DECO) is a method to find significant association among biological features (biomarkers) and samples (individuals) analyzing large-scale omic data. The method identifies and categorizes biomarkers of specific phenotypic conditions based on a recurrent differential analysis integrated with a non-symmetrical correspondence analysis. DECO integrates both omic data dispersion and predictor–response relationship from non-symmetrical correspondence analysis in a unique statistic (called h-statistic), allowing the identification of closely related sample categories within complex cohorts. The performance is demonstrated using simulated data and five experimental transcriptomic datasets, and comparing to seven other methods. We show DECO greatly enhances the discovery and subtle identification of biomarkers, making it especially suited for deep and accurate patient stratification.

Availability and implementation

DECO is freely available as an R package (including a practical vignette) at Bioconductor repository (http://bioconductor.org/packages/deco/).

Supplementary information

Supplementary data are available at Bioinformatics online.

Increased Fibrogenic Gene Expression in Multifidus Muscles of Patients With Chronic Versus Acute Lumbar Spine Pathology

STUDY DESIGN

Prospective observational study-basic science (Level 1).

OBJECTIVE

The aim of this study was to compare expression of functional groups of genes within the atrophic, myogenic, fibrogenic, adipogenic, and inflammatory pathways between paraspinal muscle biopsies from individuals with acute and chronic lumbar spine pathology.

SUMMARY OF BACKGROUND DATA

Low back pain is a complex and multifactorial condition that affects a majority of the general population annually. Changes in muscle tissue composition (i.e., fatty and fibrotic infiltration) are a common feature in individuals with lumbar spine pathology associated with low back pain, which often results in functional loss. Understanding the molecular underpinnings of these degenerative changes in different phases of disease progression may improve disease prevention and treatment specificity.

METHODS

Intraoperative biopsies of the multifidus muscle were obtained from individuals undergoing surgery for acute (<6-month duration) or chronic (>6-month duration) lumbar spine pathology. Expression of 42 genes related to myogenesis, atrophy, adipogenesis, metabolism, inflammation, and fibrosis were measured in 33 samples (eight acute, 25 chronic) using qPCR, and tissue composition of fat, muscle, and fibrosis was quantified using histology.

RESULTS

We found that tissue composition of the biopsies was heterogeneous, resulting in a trend toward lower RNA yields in biopsies with higher proportions of fat (r <-0.39, P < 0.1). There were no significant differences in gene expression patterns for atrophy (P > 0.635), adipogenesis (P > 0.317), myogenesis (P > 0.320), or inflammatory (P > 0.413) genes after adjusting for the proportion of muscle, fat, and connective tissue. However, in the fibrogenesis pathway, we found significant upregulation of CTGF (P = 0.046), and trends for upregulation of COL1A1 (P = 0.061), and downregulation of MMP1 and MMP9 (P = 0.061) in the chronic group.

CONCLUSION

There is increased fibrogenic gene expression in individuals with chronic disease when compared to acute disease, without significant differences in atrophic, myogenic, adipogenic, or inflammatory pathways, suggesting increased efforts should be made to prevent or reverse fibrogenesis to improve patient function in this population.

LEVEL OF EVIDENCE

N/A.

Bead-based RNA multiplex panels for biomarker detection in oncology samples

Patient stratification, prognosis and disease monitoring are three important aspects of personalized cancer medicine. With traditional serum tumour protein biomarkers showing lack of specificity and sensitivity, and tumour heterogeneity affecting the response to targeted therapy based on tissue biomarkers, the focus has shifted to the use of molecular tumour signatures as specific biomarkers. Multiplex microsphere-based panels are robust and cost-effective, high throughput molecular assays, which can accurately characterize tumours even from small amounts of poor quality nucleic acids. Only few studies have reported the use of microspheres (beads) to quantify RNA expression of targets of interest simultaneously (multiplexing). This review is an overview of the various applications of bead-based RNA panels in molecular oncology, with focus on the Invitrogen™ QuantiGene™ Plex Assay (Thermo Fisher Scientific), and provides a comparison with PCR-based and other methodologies. The advantages of multiplex bead assays are exemplified by the quantification of RNA expression in formalin-fixed, paraffin embedded (FFPE) archival tissue and the simultaneous detection of biomarkers in low input samples, including quantification of markers in microdissected tissue material, to characterise heterogeneous tumour sites within a sample, and by the detection of markers in low numbers of circulating tumour cells.

Heterogeneous muscle gene expression patterns in patients with massive rotator cuff tears

Detrimental changes in the composition and function of rotator cuff (RC) muscles are hallmarks of RC disease progression. Previous studies have demonstrated both atrophic and degenerative muscle loss in advanced RC disease. However, the relationship between gene expression and RC muscle pathology remains poorly defined, in large part due to a lack of studies correlating gene expression to tissue composition. Therefore, the purpose of this study was to determine how tissue composition relates to gene expression in muscle biopsies from patients undergoing reverse shoulder arthroplasty (RSA). Gene expression related to myogenesis, atrophy and cell death, adipogenesis and metabolism, inflammation, and fibrosis was measured in 40 RC muscle biopsies, including 31 biopsies from reverse shoulder arthroplasty (RSA) cases that had available histology data and 9 control biopsies from patients with intact RC tendons. After normalization to reference genes, linear regression was used to identify relationships between gene expression and tissue composition. Hierarchical clustering and principal component analysis (PCA) identified unique clusters, and fold-change analysis was used to determine significant differences in expression between clusters. We found that gene expression profiles were largely dependent on muscle presence, with muscle fraction being the only histological parameter that was significantly correlated to gene expression by linear regression. Similarly, samples with histologically-confirmed muscle distinctly segregated from samples without muscle. However, two sub-groups within the muscle-containing RSA biopsies suggest distinct phases of disease, with one group expressing markers of both atrophy and regeneration, and another group not significantly different from either control biopsies or biopsies lacking muscle. In conclusion, this study provides context for the interpretation of gene expression in heterogeneous and degenerating muscle, and provides further evidence for distinct stages of RC disease in humans.

MiRNA and mRNA Profiling in Systemic Lupus Reveals a Novel Set of Cytokine - Related miRNAs and their Target Genes in Cases With and Without Renal Involvement

BACKGROUND/AIMS

MiRNAs transpire as promising elements in molecular medicine for the identification of new diagnostic, prognostic and targeting therapeutic biomarkers. This study consisted of four steps: First, to investigate one or a group of specific diagnostic miRNAs for Systemic Lupus Erythematosus (SLE) disease in patients with and without renal involvement, second, to identify cytokines genes' expression profiling, third, comparing the profiles with related amounts in the serum and finally, to study target-gene-mediated functional roles of miRNAs, which have been correlated to disease development and progression.

METHODS

In order to use in microarray assays total RNA and miRNAs were isolated from blood and serum samples that were obtained from 16 SLE patients (9 with renal involvement and 7 without renal involvement). Taking coexistence of factors such as hypocomplementemia, positive ANA and anti-DNA into account, obtained data were processed. For each differentially expressed miRNA, potential target genes were predicted by microRNAorg, TargetScan and PITA prediction tools. Obtained mRNA profiling data were interrogated for the target genes. MiRNA and mRNA microarray results were confirmed by QRT-PCR. Finally, the amounts of cytokines were measured by multiplex ELISA method.

RESULTS

The results of study showed that among differentially expressed miRNAs in SLE patients with renal involvement compared to those without renal involvement, hsa-miR-766-3p, may play pivotal roles in PI3K-AKT-mTOR pathway. In addition according to the obtained data it is suggested that blood-borne proinflammatory cytokines such as IL-4, IL-6 and TNF-α alongside with disease stage and severity may contribute to this differential expression of these miRNA which may be leading to insulin resistance. Finally, hsa-miR-621, which was differentially expressed in hypertensive SLE patients without renal involvement and a positive ANA test with its predicted target gene "Kallikrein-related peptidase 9" may play a role in the pathophysiology of hypertension in SLE.

CONCLUSIONS

We reported some human miRNAs which were differentially expressed in SLE patients according to disease activity and renal involvement. Larger studies are necessary to confirm our findings and detect further biomarkers.

Separation of Small DNAs by Gel Electrophoresis in a Fused Silica Capillary Coated with a Negatively Charged Copolymer

Active development of compact analytical instruments suitable for point-of-care testing (POCT) requires optimization of existing methods. To aid the development of capillary gel electrophoresis instruments for POCT, we attempted to separate polymerase chain reaction products (small DNAs) using a short, fused silica capillary coated with an acrylamide (AM)/acrylic acid (AA) copolymer (poly(AM-co-AA)). To realize the high capability of this capillary to separate small DNAs, the magnitude of electroosmotic flow (EOF) was controlled by varying the content of negatively charged AA in the copolymer, which significantly affected the separation ability. At an AA content ≥3.75 mol %, sample DNAs could not be injected into the copolymer-coated capillary owing to strong EOF, whereas a 100 bp DNA ladder sample was successfully separated at an AA content of ≤3.5 mol %, showing that even slight AA content variations impact DNA flow. EOF values measured using a neutral coumarin 334 solution suddenly decreased at an AA content of 3.5 mol % relative to those at an AA content of ≥3.75 mol %. Theoretical plate values revealed that an AA content of 2.75 mol % was optimal for separating ladder DNAs with sizes ≥600 bp. Hence, EOF control achieved by varying the amount of negatively charged AA in the poly(AM-co-AA) coating can promote further development of short capillaries for POCT applications.

IGSA: Individual Gene Sets Analysis, including Enrichment and Clustering

Analysis of gene sets has been widely applied in various high-throughput biological studies. One weakness in the traditional methods is that they neglect the heterogeneity of genes expressions in samples which may lead to the omission of some specific and important gene sets. It is also difficult for them to reflect the severities of disease and provide expression profiles of gene sets for individuals. We developed an application software called IGSA that leverages a powerful analytical capacity in gene sets enrichment and samples clustering. IGSA calculates gene sets expression scores for each sample and takes an accumulating clustering strategy to let the samples gather into the set according to the progress of disease from mild to severe. We focus on gastric, pancreatic and ovarian cancer data sets for the performance of IGSA. We also compared the results of IGSA in KEGG pathways enrichment with David, GSEA, SPIA, ssGSEA and analyzed the results of IGSA clustering and different similarity measurement methods. Notably, IGSA is proved to be more sensitive and specific in finding significant pathways, and can indicate related changes in pathways with the severity of disease. In addition, IGSA provides with significant gene sets profile for each sample.

Translating colorectal cancer genetics into clinically useful biomarkers

Colorectal cancer (CRC) is a major health problem worldwide accounting for over a million deaths annually. While many patients with Stage II and III CRC can be cured with combinations of surgery, radiotherapy and chemotherapy, this is morbid costly treatment and a significant proportion will suffer recurrence and eventually die of CRC. Increased understanding of the molecular pathogenesis of CRC has the potential to identify high risk patients and target therapy more appropriately. Despite increased understanding of the molecular events underlying CRC development, established molecular techniques have only produced a limited number of biomarkers suitable for use in routine clinical practice to predict risk, prognosis and response to treatment. Recent rapid technological developments, however, have made genomic sequencing of CRC more economical and efficient, creating potential for the discovery of genetic biomarkers that have greater diagnostic, prognostic and therapeutic capabilities for the management of CRC. This paper reviews the current understanding of the molecular pathogenesis of CRC, and summarizes molecular biomarkers that surgeons will encounter in current clinical use as well as those under development in clinical and preclinical trials. New molecular technologies are reviewed together with their potential impact on the understanding of the molecular pathogenesis of CRC and their potential clinical utility in classification, diagnosis, prognosis and targeting of therapy.

A Method to Correlate mRNA Expression Datasets Obtained from Fresh Frozen and Formalin-Fixed, Paraffin-Embedded Tissue Samples: A Matter of Thresholds

Background

Gene expression profiling of tumors is a successful tool for the discovery of new cancer biomarkers and potential targets for the development of new therapeutic strategies. Reliable profiling is preferably performed on fresh frozen (FF) tissues in which the quality of nucleic acids is better preserved than in formalin-fixed paraffin-embedded (FFPE) material. However, since snap-freezing of biopsy materials is often not part of daily routine in pathology laboratories, one may have to rely on archival FFPE material. Procedures to retrieve the RNAs from FFPE materials have been developed and therefore, datasets obtained from FFPE and FF materials need to be made compatible to ensure reliable comparisons are possible.

Aim

To develop an efficient method to compare gene expression profiles obtained from FFPE and FF samples using the same platform.

Methods

Twenty-six FFPE-FF sample pairs of the same tumors representing various cancer types, and two FFPE-FF sample pairs of breast cancer cell lines, were included. Total RNA was extracted and gene expression profiling was carried out using Illumina’s Whole-Genome cDNA-mediated Annealing, Selection, extension and Ligation (WG-DASL) V3 arrays, enabling the simultaneous detection of 24,526 mRNA transcripts. A sample exclusion criterion was created based on the expression of 11 stably expressed reference genes. Pearson correlation at the probe level was calculated for paired FFPE-FF, and three cut-off values were chosen. Spearman correlation coefficients between the matched FFPE and FF samples were calculated for three probe lists with varying levels of significance and compared to the correlation based on all measured probes. Unsupervised hierarchical cluster analysis was performed to verify performance of the included probe lists to compare matched FPPE-FF samples.

Results

Twenty-seven FFPE-FF pairs passed the sample exclusion criterion. From the profiles of 27 FFPE and FF matched samples, the best correlating probes were identified for various levels of significance (Pearson P<0.01, n = 1,432; P<0.05, n = 2,530; and P<0.10, n = 3,351 probes). Unsupervised hierarchical clustering of the 27 pairs using the resulting probes yielded 25, 21, and 19 correctly clustered pairs, respectively, compared to 1 pair when all probes were used.

Conclusion

The proposed method enables comparison of gene expression profiles of FFPE and/or FF origin measured on the same platform.

Rapid and highly sensitive detection by a real-time polymerase chain reaction using a chip coated with its reagents

On-site detection by flow-through polymerase chain reaction (PCR) microfluidic systems for rapid and highly sensitive analysis, are significantly desired for bioanalytical and medical research. The conventional continuous-flow PCR chips realized rapid detection, but their sensitivity was very low (10(6) to 10(8) copies μL(-1)). We improved this drawback by coating the chip with a PCR reagents mixture, and succeed to obtain a rapid and highly sensitive detection by using a segment-flow PCR system. In the present work, we developed a portable segment-flow PCR system for practical use. PCR was performed for the uid A gene in E. coli. By real-time segment-flow PCR using coated chips, we realized rapid detection in 8 min and a high sensitivity of 4 cells μL(-1). The sensitivity by the segment-flow PCR chip was the same as that of a conventional thermal cycler. Moreover, the detection speed of our segment-flow PCR chip was 15-times as rapid as that of the conventional thermal cycler.

Clinical significance of epithelial-mesenchymal transition

The concept of epithelial-mesenchymal transition (EMT), a process where cells change their epithelial towards a mesenchymal phenotype, has gained overwhelming attention especially in the cancer research community. Thousands of scientific reports investigated changes in gene, mRNA and protein expression compatible with EMT and their possible correlation with tumor invasion, metastatic spread or patient prognosis; however, up to now, a proof of clinical significance of the concept is still missing. This review, with a main focus on the role of EMT in tumors, will summarize the basic molecular events underlying EMT including the signaling pathways capable of its induction as well as changes in EMT-associated protein expression and will very briefly touch the role of microRNAs in EMT. We then outline protein markers that are used most frequently for the assessment of EMT in research and diagnostic evaluation of tumor specimens and depict the link between EMT, a cancer stem cell (CSC) phenotype and resistance to conventional antineoplastic therapies. Furthermore, we evaluate a possible correlation between EMT marker expression and patient prognosis as well as current therapeutic concepts targeting the EMT process to slow down or prevent metastatic spread of malignant tumors.

Innovations in studying in vivo cell behavior and pharmacology in complex tissues--microvascular endothelial cells in the spotlight

Many studies on the molecular control underlying normal cell behavior and cellular responses to disease stimuli and pharmacological intervention are conducted in single-cell culture systems, while the read-out of cellular engagement in disease and responsiveness to drugs in vivo is often based on overall tissue responses. As the majority of drugs under development aim to specifically interact with molecular targets in subsets of cells in complex tissues, this approach poses a major experimental discrepancy that prevents successful development of new therapeutics. In this review, we address the shortcomings of the use of artificial (single) cell systems and of whole tissue analyses in creating a better understanding of cell engagement in disease and of the true effects of drugs. We focus on microvascular endothelial cells that actively engage in a wide range of physiological and pathological processes. We propose a new strategy in which in vivo molecular control of cells is studied directly in the diseased endothelium instead of at a (far) distance from the site where drugs have to act, thereby accounting for tissue-controlled cell responses. The strategy uses laser microdissection-based enrichment of microvascular endothelium which, when combined with transcriptome and (phospho)proteome analyses, provides a factual view on their status in their complex microenvironment. Combining this with miniaturized sample handling using microfluidic devices enables handling the minute sample input that results from this strategy. The multidisciplinary approach proposed will enable compartmentalized analysis of cell behavior and drug effects in complex tissue to become widely implemented in daily biomedical research and drug development practice.