Purity for clarity: the need for purification of tumor cells in DNA microarray studies

A Bayesian framework to study tumor subclone-specific expression by combining bulk DNA and single-cell RNA sequencing data

Genetic and gene expression heterogeneity is an essential hallmark of many tumors, allowing the cancer to evolve and to develop resistance to treatment. Currently, the most commonly used data types for studying such heterogeneity are bulk tumor/normal whole-genome or whole-exome sequencing (WGS, WES); and single-cell RNA sequencing (scRNA-seq), respectively. However, tools are currently lacking to link genomic tumor subclonality with transcriptomic heterogeneity by integrating genomic and single-cell transcriptomic data collected from the same tumor. To address this gap, we developed scBayes, a Bayesian probabilistic framework that uses tumor subclonal structure inferred from bulk DNA sequencing data to determine the subclonal identity of cells from single-cell gene expression (scRNA-seq) measurements. Grouping together cells representing the same genetically defined tumor subclones allows comparison of gene expression across different subclones, or investigation of gene expression changes within the same subclone across time (i.e., progression, treatment response, or relapse) or space (i.e., at multiple metastatic sites and organs). We used simulated data sets, in silico synthetic data sets, as well as biological data sets generated from cancer samples to extensively characterize and validate the performance of our method, as well as to show improvements over existing methods. We show the validity and utility of our approach by applying it to published data sets and recapitulating the findings, as well as arriving at novel insights into cancer subclonal expression behavior in our own data sets. We further show that our method is applicable to a wide range of single-cell sequencing technologies including single-cell DNA sequencing as well as Smart-seq and 10x Genomics scRNA-seq protocols.

Development of a clinical microarray system for genetic analysis screening

Objectives

Research on the relationship between diseases and genes and the advancement of genetic analysis technologies have made genetic testing in medical care possible. There are various methods for genetic testing, including PCR-based methods and next-generation sequencing; however, screening tests in clinical laboratories are becoming more diverse; therefore, novel measurement systems and equipment are required to meet the needs of each situation. In this study, we aimed to develop a novel microarray-based genetic analysis system that uses a Peltier element to overcome the issues of conventional microarrays, such as the long measurement time and high cost.

Methods

We constructed a microarray system to detect the UDP-glucuronosyltransferase gene polymorphisms UGT1A1*6 and UGT1A1*28 in patients eligible for irinotecan hydrochloride treatment for use in clinical laboratories. To evaluate the performance of the system, the hybridization temperature and reaction time were determined, and the results were compared with those obtained using a conventional hybridization oven.

Results

The hybridization temperature reached its target in 1/27th of the time required by the conventional system. We assessed 111 human clinical samples and found that our results agreed with those obtained using existing methods. The total time for the newly developed device was reduced by 85 min compared to that for existing methods, as the automated DNA microarray eliminates the time that existing methods spend on manual operation.

Conclusions

The surface treatment technology used in our system enables high-density and strong DNA fixation, allowing the construction of a measurement system suitable for clinical applications.

GEOMETRIC STRUCTURE GUIDED MODEL AND ALGORITHMS FOR COMPLETE DECONVOLUTION OF GENE EXPRESSION DATA

Complete deconvolution analysis for bulk RNA-seq data is important and helpful to distinguish whether the differences of disease-associated GEPs (gene expression profiles) in tissues of patients and normal controls are due to changes in cellular composition of tissue samples, or due to GEPs changes in specific cells. One of the major techniques to perform complete deconvolution is nonnegative matrix factorization (NMF), which also has a wide-range of applications in the machine learning community. However, the NMF is a well-known strongly ill-posed problem, so a direct application of NMF to RNA-seq data will suffer severe difficulties in the interpretability of solutions. In this paper, we develop an NMF-based mathematical model and corresponding computational algorithms to improve the solution identifiability of deconvoluting bulk RNA-seq data. In our approach, we combine the biological concept of marker genes with the solvability conditions of the NMF theories, and develop a geometric structures guided optimization model. In this strategy, the geometric structure of bulk tissue data is first explored by the spectral clustering technique. Then, the identified information of marker genes is integrated as solvability constraints, while the overall correlation graph is used as manifold regularization. Both synthetic and biological data are used to validate the proposed model and algorithms, from which solution interpretability and accuracy are significantly improved.

Prediction of prognosis of patients with lung cancer in combination with the immune score

PURPOSE

The host's immune response to malignant tumor is fundamental to tumorigenesis and tumor development. The immune score is currently used to assess prognosis and to guide immunotherapy; however, its association with lung cancer prognosis is not clear.

METHODS

Clinical features and immune score data of lung cancer patients from The Cancer Genome Atlas were obtained to build a clinical prognosis nomogram. The model's accuracy was verified by calibration curves.

RESULTS

In total, 1005 patients with lung cancer were included. Patients were divided into three groups according to low, medium, and high immune scores. Compared with patients in the low immune score group, the disease-free survival (DFS) of patients in medium and high immune score groups was significantly longer; the hazard ratio (HR) and 95% confidence interval (95% CI) were 0.77 [0.60-0.99] and 0.74 [0.60-0.91], respectively. The overall survival (OS) of patients in the medium and high immune score groups was significantly longer than in the low immune score group; the HR and 95% CI were 0.74 [0.57-0.96] and 0.69 [0.55-0.88], respectively. A clinical prediction model was established to predict the survival prognosis. As verified by calibration curves, the model showed good predictive ability, especially for predicting 3-/5-year DFS and OS.

CONCLUSION

Patients with lung cancer with medium and high immune scores had longer DFS and OS than those in low immune score group. Patient prognosis can be effectively predicted by the clinical prediction model combining clinical features and immune score and was consistent with actual clinical outcomes.

Towards multi-omics characterization of tumor heterogeneity: a comprehensive review of statistical and machine learning approaches

The multi-omics molecular characterization of cancer opened a new horizon for our understanding of cancer biology and therapeutic strategies. However, a tumor biopsy comprises diverse types of cells limited not only to cancerous cells but also to tumor microenvironmental cells and adjacent normal cells. This heterogeneity is a major confounding factor that hampers a robust and reproducible bioinformatic analysis for biomarker identification using multi-omics profiles. Besides, the heterogeneity itself has been recognized over the years for its significant prognostic values in some cancer types, thus offering another promising avenue for therapeutic intervention. A number of computational approaches to unravel such heterogeneity from high-throughput molecular profiles of a tumor sample have been proposed, but most of them rely on the data from an individual omics layer. Since the heterogeneity of cells is widely distributed across multi-omics layers, methods based on an individual layer can only partially characterize the heterogeneous admixture of cells. To help facilitate further development of the methodologies that synchronously account for several multi-omics profiles, we wrote a comprehensive review of diverse approaches to characterize tumor heterogeneity based on three different omics layers: genome, epigenome and transcriptome. As a result, this review can be useful for the analysis of multi-omics profiles produced by many large-scale consortia. Contact:sunkim.bioinfo@snu.ac.kr.

Normal tissue content impact on the GBM molecular classification

Molecular classification of glioblastoma has enabled a deeper understanding of the disease. The four-subtype model (including Proneural, Classical, Mesenchymal and Neural) has been replaced by a model that discards the Neural subtype, found to be associated with samples with a high content of normal tissue. These samples can be misclassified preventing biological and clinical insights into the different tumor subtypes from coming to light. In this work, we present a model that tackles both the molecular classification of samples and discrimination of those with a high content of normal cells. We performed a transcriptomic in silico analysis on glioblastoma (GBM) samples (n = 810) and tested different criteria to optimize the number of genes needed for molecular classification. We used gene expression of normal brain samples (n = 555) to design an additional gene signature to detect samples with a high normal tissue content. Microdissection samples of different structures within GBM (n = 122) have been used to validate the final model. Finally, the model was tested in a cohort of 43 patients and confirmed by histology. Based on the expression of 20 genes, our model is able to discriminate samples with a high content of normal tissue and to classify the remaining ones. We have shown that taking into consideration normal cells can prevent errors in the classification and the subsequent misinterpretation of the results. Moreover, considering only samples with a low content of normal cells, we found an association between the complexity of the samples and survival for the three molecular subtypes.

DEBay: A computational tool for deconvolution of quantitative PCR data for estimation of cell type-specific gene expression in a mixed population

The expression of a gene is commonly estimated by quantitative PCR (qPCR) using RNA isolated from a large number of pooled cells. Such pooled samples often have subpopulations of cells with different levels of expression of the target gene. Estimation of gene expression from an ensemble of cells obscures the pattern of expression in different subpopulations. Physical separation of various subpopulations is a demanding task. We have developed a computational tool, Deconvolution of Ensemble through Bayes-approach (DEBay), to estimate cell type-specific gene expression from qPCR data of a mixed population. DEBay estimates Normalized Gene Expression Coefficient (NGEC), which is a relative measure of the expression of the target gene in each cell type in a population. NGEC has a direct algebraic correspondence with the normalized fold change in gene expression measured by qPCR. DEBay can deconvolute both time-dependent and -independent gene expression profiles. It uses the Bayesian method of model selection and parameter estimation. We have evaluated DEBay using synthetic and real experimental data. DEBay is implemented in Python. A GUI of DEBay and its source code are available for download at SourceForge (https://sourceforge.net/projects/debay).

Development of a predictive model for stromal content in prostate cancer samples to improve signature performance

Prostate cancer is heterogeneous in both cellular composition and patient outcome, and development of biomarker signatures to distinguish indolent from aggressive tumours is a high priority. Stroma plays an important role during prostate cancer progression and undergoes histological and transcriptional changes associated with disease. However, identification and validation of stromal markers is limited by a lack of datasets with defined stromal/tumour ratio. We have developed a prostate‐selective signature to estimate the stromal content in cancer samples of mixed cellular composition. We identified stromal‐specific markers from transcriptomic datasets of developmental prostate mesenchyme and prostate cancer stroma. These were experimentally validated in cell lines, datasets of known stromal content, and by immunohistochemistry in tissue samples to verify stromal‐specific expression. Linear models based upon six transcripts were able to infer the stromal content and estimate stromal composition in mixed tissues. The best model had a coefficient of determination R² of 0.67. Application of our stromal content estimation model in various prostate cancer datasets led to improved performance of stromal predictive signatures for disease progression and metastasis. The stromal content of prostate tumours varies considerably; consequently, deconvolution of stromal proportion may yield better results than tumour cell deconvolution. We suggest that adjusting expression data for cell composition will improve stromal signature performance and lead to better prognosis and stratification of men with prostate cancer. © 2019 The Authors. The Journal of Pathology published by John Wiley & Sons Ltd on behalf of Pathological Society of Great Britain and Ireland.

Serum biomarkers identification by iTRAQ and verification by MRM: S100A8/S100A9 levels predict tumor-stroma involvement and prognosis in Glioblastoma

Despite advances in biology and treatment modalities, the prognosis of glioblastoma (GBM) remains poor. Serum reflects disease macroenvironment and thus provides a less invasive means to diagnose and monitor a diseased condition. By employing 4-plex iTRAQ methodology, we identified 40 proteins with differential abundance in GBM sera. The high abundance of serum S100A8/S100A9 was verified by multiple reaction monitoring (MRM). ELISA and MRM-based quantitation showed a significant positive correlation. Further, an integrated investigation using stromal, tumor purity and cell type scores demonstrated an enrichment of myeloid cell lineage in the GBM tumor microenvironment. Transcript levels of S100A8/S100A9 were found to be independent poor prognostic indicators in GBM. Medium levels of pre-operative and three-month post-operative follow-up serum S100A8 levels predicted poor prognosis in GBM patients who lived beyond median survival. In vitro experiments showed that recombinant S100A8/S100A9 proteins promoted integrin signalling dependent glioma cell migration and invasion up to a threshold level of concentrations. Thus, we have discovered GBM serum marker by iTRAQ and verified by MRM. We also demonstrate interplay between tumor micro and macroenvironment and identified S100A8 as a potential marker with diagnostic and prognostic value in GBM.

Laser Capture Microdissection on Frozen Sections for Extraction of High-Quality Nucleic Acids

Many cancers harbor a large fraction of nonmalignant stromal cells intermixed with neoplastic tumor cells. While single-cell transcriptional profiling methods have begun to address the need to distinguish biological programs in different cell types, such methods do not enable the analysis of spatial information available through histopathological examination. Laser capture microdissection offers a means to separate cellular samples based on morphological criteria. We present here an optimized method to retrieve intact RNA from laser capture microdissected tissue samples, using pancreatic ductal adenocarcinoma as an example, in order to separately profile tumor epithelial and stromal compartments. This method may also be applied to nonmalignant tissues to isolate cellular samples from any morphologically identifiable structure.

Whole transcriptome profiling of patient-derived xenograft models as a tool to identify both tumor and stromal specific biomarkers

The tumor microenvironment is emerging as a key regulator of cancer growth and progression, however the exact mechanisms of interaction with the tumor are poorly understood. Whilst the majority of genomic profiling efforts thus far have focused on the tumor, here we investigate RNA-Seq as a hypothesis-free tool to generate independent tumor and stromal biomarkers, and explore tumor-stroma interactions by exploiting the human-murine compartment specificity of patient-derived xenografts (PDX).

Across a pan-cancer cohort of 79 PDX models, we determine that mouse stroma can be separated into distinct clusters, each corresponding to a specific stromal cell type. This implies heterogeneous recruitment of mouse stroma to the xenograft independent of tumor type. We then generate cross-species expression networks to recapitulate a known association between tumor epithelial cells and fibroblast activation, and propose a potentially novel relationship between two hypoxia-associated genes, human MIF and mouse Ddx6. Assessment of disease subtype also reveals MMP12 as a putative stromal marker of triple-negative breast cancer. Finally, we establish that our ability to dissect recruited stroma from trans-differentiated tumor cells is crucial to identifying stem-like poor-prognosis signatures in the tumor compartment.

In conclusion, RNA-Seq is a powerful, cost-effective solution to global analysis of human tumor and mouse stroma simultaneously, providing new insights into mouse stromal heterogeneity and compartment-specific disease markers that are otherwise overlooked by alternative technologies. The study represents the first comprehensive analysis of its kind across multiple PDX models, and supports adoption of the approach in pre-clinical drug efficacy studies, and compartment-specific biomarker discovery.

A 13-gene expression-based radioresistance score highlights the heterogeneity in the response to radiation therapy across HPV-negative HNSCC molecular subtypes

BACKGROUND

Radiotherapy for head and neck squamous cell carcinomas (HNSCC) is associated with a substantial morbidity and inconsistent efficacy. Human papillomavirus (HPV)-positive status is recognized as a marker of increased radiosensitivity. Our goal was to identify molecular markers associated with benefit to radiotherapy in patients with HPV-negative disease.

METHODS

Gene expression profiles from public repositories were downloaded for data mining. Training sets included 421 HPV-negative HNSCC tumors from The Cancer Genome Atlas (TCGA) and 32 HNSCC cell lines with available radiosensitivity data (GSE79368). A radioresistance (RadR) score was computed using the single sample Gene Set Enrichment Analysis tool. The validation sets included two panels of cell lines (NCI-60 and GSE21644) and HPV-negative HNSCC tumor datasets, including 44 (GSE6631), 82 (GSE39366), and 179 (GSE65858) patients, respectively. We finally performed an integrated analysis of the RadR score with known recurrent genomic alterations in HNSCC, patterns of protein expression, biological hallmarks, and patterns of drug sensitivity using TCGA and the E-MTAB-3610 dataset (659 pancancer cell lines, 140 drugs).

RESULTS

We identified 13 genes differentially expressed between tumor and normal head and neck mucosa that were associated with radioresistance in vitro and in patients. The 13-gene expression-based RadR score was associated with recurrence in patients treated with surgery and adjuvant radiotherapy but not with surgery alone. It was significantly different among different molecular subtypes of HPV-negative HNSCC and was significantly lower in the "atypical" molecular subtype. An integrated analysis of RadR score with genomic alterations, protein expression, biological hallmarks and patterns of drug sensitivity showed a significant association with CCND1 amplification, fibronectin expression, seven hallmarks (including epithelial-to-mesenchymal transition and unfolded protein response), and increased sensitivity to elesclomol, an HSP90 inhibitor.

CONCLUSIONS

Our study highlights the clinical relevance of the molecular classification of HNSCC and the RadR score to refine radiation strategies in HPV-negative disease.

Highly efficient and selective isolation of rare tumor cells using a microfluidic chip with wavy-herringbone micro-patterned surfaces

Circulating tumor cells (CTCs) in peripheral blood have been recognized as a general biomarker for diagnosing cancer and providing guidance for personalized treatments. Yet due to their rarity, the challenge for their clinical utility lies in the efficient isolation while avoiding the capture of other non-targeted white blood cells (WBCs). In this paper, a wavy-herringbone (HB) microfluidic chip coated with antibody directly against epithelial cell adhesion molecule (anti-EpCAM) was developed for highly efficient and selective isolation of tumor cells from tumor cell-spiked whole blood samples. By extending the concept of the hallmark HB-Chip in the literature, the wavy-HB chip not only achieves high capture efficiency (up to 85.0%) by micro-vortexes induced by HB structures, but also achieves high purity (up to 39.4%) due to the smooth wavy microstructures. These smooth wavy-HB structures eliminate the ultra-low shear rate regions in the traditional grooved-HB structures that lead to non-specific trapping of cells. Compared with the grooved-HB chip with sharp corners, the wavy-HB chip shows significantly higher purity while maintaining similarly high capture efficiency. Furthermore, the wavy-HB chip has up to 11% higher captured cell viability over the grooved-HB chip. The distributions of tumor cells and WBCs along the grooves and waves are investigated to help understand the mechanisms behind the better performance of the wavy-HB chip. The wavy-HB chip may serve as a promising platform for CTC capture and cancer diagnosis.

Confounding Factors in the Transcriptome Analysis of an In-Vivo Exposure Experiment

Confounding factors

In transcriptomics experimentation, confounding factors frequently exist alongside the intended experimental factors and can severely influence the outcome of a transcriptome analysis. Confounding factors are regularly discussed in methodological literature, but their actual, practical impact on the outcome and interpretation of transcriptomics experiments is, to our knowledge, not documented. For instance, in-vivo experimental factors; like Individual, Sample-Composition and Time-of-Day are potentially formidable confounding factors. To study these confounding factors, we designed an extensive in-vivo transcriptome experiment (n = 264) with UVR exposure of murine skin containing six consecutive samples from each individual mouse (n = 64).

Analysis Approach

Evaluation of the confounding factors: Sample-Composition, Time-of-Day, Handling-Stress, and Individual-Mouse resulted in the identification of many genes that were affected by them. These genes sometimes showed over 30-fold expression differences. The most prominent confounding factor was Sample-Composition caused by mouse-dependent skin composition differences, sampling variation and/or influx/efflux of mobile cells. Although we can only evaluate these effects for known cell type specifically expressed genes in our complex heterogeneous samples, it is clear that the observed variations also affect the cumulative expression levels of many other non-cell-type-specific genes.

ANOVA

ANOVA analysis can only attempt to neutralize the effects of the well-defined confounding factors, such as Individual-Mouse, on the experimental factors UV-Dose and Recovery-Time. Also, by definition, ANOVA only yields reproducible gene-expression differences, but we found that these differences were very small compared to the fold changes induced by the confounding factors, questioning the biological relevance of these ANOVA-detected differences. Furthermore, it turned out that many of the differentially expressed genes found by ANOVA were also present in the gene clusters associated with the confounding factors.

Conclusion

Hence our overall conclusion is that confounding factors have a major impact on the outcome of in-vivo transcriptomics experiments. Thus the set-up, analysis, and interpretation of such experiments should be approached with the utmost prudence.

Hierarchical paracrine interaction of breast cancer associated fibroblasts with cancer cells via hMAPK-microRNAs to drive ER-negative breast cancer phenotype

Multiple juxtacrine and paracrine interactions occur between cancer cells and non-cancer cells of the tumor microenvironment (TME) that direct tumor progression. Cancer Associated Fibroblasts (CAFs) are an integral component of the TME, and the majority of breast tumor stroma is comprised of CAFs. Heterotypic interactions between cancer cells and non-cancer cells of the TME occur via soluble agents, including cytokines, hormones, growth factors, and secreted microRNAs. We previously identified a microRNA signature indicative of hyperactive MAPK signaling (hMAPK-miRNA signature) that significantly associated with reduced recurrence-free and overall survival. Here we report that the hMAPK-miRNA signature associates with a high metric of stromal cell infiltrate, and we investigate the role of microRNAs, particularly hMAPK-microRNAs, secreted by CAFs on estrogen receptor (ER) expression in breast cancer cells. ER-positive MCF-7/ltE2- cells were treated with conditioned media (CM) from CAFs derived from breast cancers of different PAM50 subtypes (CAFBAS, CAFHER2, and CAFLA). CAF CM isolated specifically from ER-negative primary breast tumors led to ER repression in vitro. Nanoparticle tracking analysis and transmission electron microscopy confirmed the presence of CAF-secreted exosomes in CM and the uptake of these exosomes by the ER+ MCF-7/ltE2- cells. Differentially expressed microRNAs in CAF CM as well as in MCF-7/ltE2- cells treated with this CM were identified. Knockdown of miR-221/222 in CAFBAS resulted in knockdown of miR221/222 levels in the conditioned media and the CM from CAFBAS; miR221/222 knockdown rescued ER repression in ER-positive cell lines treated with CAFBAS-CM. Collectively, our results demonstrate that CAF-secreted microRNAs are directly involved in ER-repression, and may contribute to the MAPK-induced ER repression in breast cancer cells.

ISOpureR: an R implementation of a computational purification algorithm of mixed tumour profiles

Background

Tumour samples containing distinct sub-populations of cancer and normal cells present challenges in the development of reproducible biomarkers, as these biomarkers are based on bulk signals from mixed tumour profiles. ISOpure is the only mRNA computational purification method to date that does not require a paired tumour-normal sample, provides a personalized cancer profile for each patient, and has been tested on clinical data. Replacing mixed tumour profiles with ISOpure-preprocessed cancer profiles led to better prognostic gene signatures for lung and prostate cancer.

Results

To simplify the integration of ISOpure into standard R-based bioinformatics analysis pipelines, the algorithm has been implemented as an R package. The ISOpureR package performs analogously to the original code in estimating the fraction of cancer cells and the patient cancer mRNA abundance profile from tumour samples in four cancer datasets.

Conclusions

The ISOpureR package estimates the fraction of cancer cells and personalized patient cancer mRNA abundance profile from a mixed tumour profile. This open-source R implementation enables integration into existing computational pipelines, as well as easy testing, modification and extension of the model.

Electronic supplementary material

The online version of this article (doi:10.1186/s12859-015-0597-x) contains supplementary material, which is available to authorized users.

Inferring tumour purity and stromal and immune cell admixture from expression data

Infiltrating stromal and immune cells form the major fraction of normal cells in tumour tissue and not only perturb the tumour signal in molecular studies but also have an important role in cancer biology. Here we describe ‘Estimation of STromal and Immune cells in MAlignant Tumours using Expression data’ (ESTIMATE)—a method that uses gene expression signatures to infer the fraction of stromal and immune cells in tumour samples. ESTIMATE scores correlate with DNA copy number-based tumour purity across samples from 11 different tumour types, profiled on Agilent, Affymetrix platforms or based on RNA sequencing and available through The Cancer Genome Atlas. The prediction accuracy is further corroborated using 3,809 transcriptional profiles available elsewhere in the public domain. The ESTIMATE method allows consideration of tumour-associated normal cells in genomic and transcriptomic studies. An R-library is available on https://sourceforge.net/projects/estimateproject/.

Tumour biopsies contain contaminating normal cells and these can influence the analysis of tumour samples. In this study, Yoshihara et al. develop an algorithm based on gene expression profiles from The Cancer Genome Atlas to estimate the number of contaminating normal cells in tumour samples.

Molecular profiling of cervical neoplasia

Cervical cancer, a potentially preventable disease, remains the second most common malignancy in women worldwide. Human papillomavirus is the single most important etiological agent in cervical cancer, contributing to neoplastic progression through the action of viral oncoproteins, mainly E6 and E7, which interfere with critical cell cycle pathways, p53 and retinoblastoma. However, evidence suggests that human papillomavirus infection alone is insufficient to induce malignant changes and that other host genetic variations are important in the development of cervical cancer. This article will discuss the latest molecular profiling techniques available and review the published literature relating to their role in the diagnosis and management of cervical dysplasia and cancer. It is hoped that these techniques will allow the detection of novel biomarkers at DNA, RNA, microRNA and protein levels, which may ultimately play a role in facilitating early disease diagnosis and in predicting response to therapies, thus allowing the development of personalized treatment strategies.

Computational deconvolution: extracting cell type-specific information from heterogeneous samples

The quanta unit of the immune system is the cell, yet analyzed samples are often heterogeneous with respect to cell subsets which can mislead result interpretation. Experimentally, researchers face a difficult choice whether to profile heterogeneous samples with the ensuing confounding effects, or a priori focus on a few cell subsets of interest, potentially limiting new discoveries. An attractive alternative solution is to extract cell subset-specific information directly from heterogeneous samples via computational deconvolution techniques, thereby capturing both cell-centered and whole system level context. Such approaches are capable of unraveling novel biology, undetectable otherwise. Here we review the present state of available deconvolution techniques, their advantages and limitations, with a focus on blood expression data and immunological studies in general.

Routine use of microarray-based gene expression profiling to identify patients with low cytogenetic risk acute myeloid leukemia: accurate results can be obtained even with suboptimal samples

Background

Gene expression profiling has shown its ability to identify with high accuracy low cytogenetic risk acute myeloid leukemia such as acute promyelocytic leukemia and leukemias with t(8;21) or inv(16). The aim of this gene expression profiling study was to evaluate to what extent suboptimal samples with low leukemic blast load (range, 2-59%) and/or poor quality control criteria could also be correctly identified.

Methods

Specific signatures were first defined so that all 71 acute promyelocytic leukemia, leukemia with t(8;21) or inv(16)-AML as well as cytogenetically normal acute myeloid leukemia samples with at least 60% blasts and good quality control criteria were correctly classified (training set). The classifiers were then evaluated for their ability to assign to the expected class 111 samples considered as suboptimal because of a low leukemic blast load (n = 101) and/or poor quality control criteria (n = 10) (test set).

Results

With 10-marker classifiers, all training set samples as well as 97 of the 101 test samples with a low blast load, and all 10 samples with poor quality control criteria were correctly classified. Regarding test set samples, the overall error rate of the class prediction was below 4 percent, even though the leukemic blast load was as low as 2%. Sensitivity, specificity, negative and positive predictive values of the class assignments ranged from 91% to 100%. Of note, for acute promyelocytic leukemia and leukemias with t(8;21) or inv(16), the confidence level of the class assignment was influenced by the leukemic blast load.

Conclusion

Gene expression profiling and a supervised method requiring 10-marker classifiers enable the identification of favorable cytogenetic risk acute myeloid leukemia even when samples contain low leukemic blast loads or display poor quality control criterion.

Genome-wide expression analysis of paired diagnosis-relapse samples in ALL indicates involvement of pathways related to DNA replication, cell cycle and DNA repair, independent of immune phenotype

Almost a quarter of pediatric patients with acute lymphoblastic leukemia (ALL) suffer from relapses. The biological mechanisms underlying therapy response and development of relapses have remained unclear. In an attempt to better understand this phenomenon, we have analyzed 41 matched diagnosis-relapse pairs of ALL patients using genome-wide expression arrays (82 arrays) on purified leukemic cells. In roughly half of the patients, very few differences between diagnosis and relapse samples were found ('stable group'), suggesting that mostly extra-leukemic factors (for example, drug distribution, drug metabolism, compliance) contributed to the relapse. Therefore, we focused our further analysis on 20 sample pairs with clear differences in gene expression ('skewed group'), reasoning that these would allow us to better study the biological mechanisms underlying relapsed ALL. After finding the differences between diagnosis and relapse pairs in this group, we identified four major gene clusters corresponding to several pathways associated with changes in cell cycle, DNA replication, recombination and repair, as well as B-cell developmental genes. We also identified cancer genes commonly associated with colon carcinomas and ubiquitination to be upregulated in relapsed ALL. Thus, about half of the relapses are due to the selection or emergence of a clone with deregulated expression of genes involved in pathways that regulate B-cell signaling, development, cell cycle, cellular division and replication.

Microarray study reveals that HIV-1 induces rapid type-I interferon-dependent p53 mRNA up-regulation in human primary CD4+ T cells

Background

Infection with HIV-1 has been shown to alter expression of a large array of host cell genes. However, previous studies aimed at investigating the putative HIV-1-induced modulation of host gene expression have been mostly performed in established human cell lines. To better approximate natural conditions, we monitored gene expression changes in a cell population highly enriched in human primary CD4⁺ T lymphocytes exposed to HIV-1 using commercial oligonucleotide microarrays from Affymetrix.

Results

We report here that HIV-1 influences expression of genes related to many important biological processes such as DNA repair, cellular cycle, RNA metabolism and apoptosis. Notably, expression of the p53 tumor suppressor and genes involved in p53 homeostasis such as GADD34 were up-regulated by HIV-1 at the mRNA level. This observation is distinct from the previously reported p53 phosphorylation and stabilization at the protein level, which precedes HIV-1-induced apoptosis. We present evidence that the HIV-1-mediated increase in p53 gene expression is associated with virus-mediated induction of type-I interferon (i.e. IFN-α and IFN-β).

Conclusion

These observations have important implications for our understanding of HIV-1 pathogenesis, particularly in respect to the virus-induced depletion of CD4⁺ T cells.

Genotypic and gene expression studies in congenital melanocytic nevi: insight into initial steps of melanotumorigenesis

Large congenital melanocytic nevi (CMNs) are said to have a higher propensity to malignant transformation compared with acquired nevi. Thus, they represent a good model for studying initial steps of melanotumorigenesis. We have performed genotypic (karyotype, fluorescence in situ hybridization, and mutational analyses) and differential expression studies on a large cohort of medium (n=3) and large (n=24) CMN. Chromosomal abnormalities were rare and single, a feature probably reflecting the benignity of these lesions. Mutational screening showed a high frequency of NRAS mutations in our series (19/27 cases, 70%), whereas BRAF mutations were less common (4/27 cases, 15%). Differential did not show significant alterations of cellular processes such as cell proliferation, cell migration/invasion, angiogenesis, apoptosis, and immune/inflammatory responses. However, significant downregulation of genes involved in pigmentation and upregulation of genes playing a role in DNA protection were observed. Lastly, our microarrays displayed upregulation of genes mediating chemoresistance in cancer. As alteration of pigmentation mechanisms can trigger oxidative damage, increased expression of genes involved in maintenance of DNA integrity might reflect the ability of nevocytic cells to self-protect against cellular stress. Furthermore, the observed alterations linked to chemoresistance might partially account for the well-known inefficacy of chemotherapy in malignant melanoma.

Negative selection of chronic lymphocytic leukaemia cells using a bifunctional rosette-based antibody cocktail

Background

High purity of tumour samples is a necessity for accurate genetic and expression analysis and is usually achieved by positive selection in chronic lymphocytic leukaemia (CLL).

Results

We adapted a bifunctional rosette-based antibody cocktail for negative selection of B-cells for isolating CLL cells from peripheral blood (PB). PB samples from CLL patients were split into aliquots. One aliquot of each sample was enriched by density gradient centrifugation (DGC), while the other aliquot of each sample was incubated with an antibody cocktail for B-cell enrichment prior to DGC (RS+DGC). The purity of CLL cells after DGC averaged 74.1% (range: 15.9 – 97.4%). Using RS+DGC, the purity averaged 93.8% (range: 80.4 – 99.4%) with 23 of 29 (79%) samples showing CLL purities above 90%. RNA extracted from enriched CLL cells was of appropriately high quality for microarray analysis.

Conclusion

This study confirms the use of a bifunctional rosette-based antibody cocktail as an effective method for the purification of CLL cells from peripheral blood.

Gene expression profiling in sarcomas

Sarcomas are a heterogeneous group of malignant mesenchymal tumors of difficult classification. There is considerable variability in both histological appearance and responsiveness to therapy. Their overall poor clinical prognosis is reflected by the fact that >65% of patients suffering retroperitoneal soft tissue sarcoma die within 5 years [Heslin MJ, et al. Prognostic factors associated with long-term survival for retroperitoneal sarcoma: implications for management. J Clin Oncol 1997;15(8):2832-9]. A greater understanding of the biology of sarcomas is needed in order to increase the potential for identifying new therapeutic targets and strategies. Microarray analysis permits a global approach to gene expression analysis of thousands of genes at the same time and has proven to be useful for further molecular characterization of tumor tissue and cell lines. This article provides a comprehensive review of possible new biomarkers identified in gene expression studies of sarcomas. These markers give new insight into the pathogenesis of sarcomas, such as malignant fibrous histiocytoma [Lee YF, et al. Molecular classification of synovial sarcomas, leiomyosarcomas and malignant fibrous histiocytomas by gene expression profiling. Br J Cancer 2003;88(4):510-5], allow a further subclassifcation of tumors like calponin-positive and calponin-negative leiomyosarcoma, or may help to predict treatment responsiveness and prognosis in patients based on an individual gene expression pattern. In some studies candidate targets for possible new treatment strategies were identified. For instance newly identified markers such as ERBB2 [Allander SV, et al. Expression profiling of synovial sarcoma by cDNA microarrays: association of ERBB2, IGFBP2, and ELF3 with epithelial differentiation. Am J Pathol 2002;161(5):1587-95] and EGFR [Nielsen TO, et al. Molecular characterization of soft tissue tumours: a gene expression study. Lancet 2002;359(9314):1301-7] might lead to the possible therapeutic use of Trastuzumab, Gefitinib or Cetuximab in synovial sarcoma, comparable to the use of tyrosine kinase inhibitor STI (Gleevec) that is the standard treatment today of CD117-positive gastrointestinal stromal tumors.

Consensus guidelines for microarray gene expression analyses in leukemia from three European leukemia networks

A plethora of studies have documented that gene expression profiling using DNA microarrays for various types of hematological malignancies provides novel information, which may have diagnostic and prognostic implications. However, to successfully use microarrays for this purpose, the quality and reproducibility of the whole procedure need to be guaranteed. Critical steps of the method are handling, processing and storage of the leukemic sample, purification of tumor cells (or lack thereof), RNA extraction methods, quality control of RNA, labeling techniques, hybridization, washing, scanning, spot filtering, normalization and initial interpretation, and finally the biostatistical analysis. These items have been extensively discussed and evaluated in different multi-center quality rounds within the three networks, that is, I-BFM-SG, the German Competence Network 'Acute and Chronic Leukemias' and the European LeukemiaNet. Based on the exchange of knowledge and experience between the three networks over the last few years, we have formulated guidelines for performing microarray experiments in leukemia. We confine ourselves to leukemias, but many of these requirements also apply to lymphomas or other clinical samples, including solid tumors.

The effect of oligonucleotide microarray data pre-processing on the analysis of patient-cohort studies

Background

Intensity values measured by Affymetrix microarrays have to be both normalized, to be able to compare different microarrays by removing non-biological variation, and summarized, generating the final probe set expression values. Various pre-processing techniques, such as dChip, GCRMA, RMA and MAS have been developed for this purpose. This study assesses the effect of applying different pre-processing methods on the results of analyses of large Affymetrix datasets. By focusing on practical applications of microarray-based research, this study provides insight into the relevance of pre-processing procedures to biology-oriented researchers.

Results

Using two publicly available datasets, i.e., gene-expression data of 285 patients with Acute Myeloid Leukemia (AML, Affymetrix HG-U133A GeneChip) and 42 samples of tumor tissue of the embryonal central nervous system (CNS, Affymetrix HuGeneFL GeneChip), we tested the effect of the four pre-processing strategies mentioned above, on (1) expression level measurements, (2) detection of differential expression, (3) cluster analysis and (4) classification of samples. In most cases, the effect of pre-processing is relatively small compared to other choices made in an analysis for the AML dataset, but has a more profound effect on the outcome of the CNS dataset. Analyses on individual probe sets, such as testing for differential expression, are affected most; supervised, multivariate analyses such as classification are far less sensitive to pre-processing.

Conclusion

Using two experimental datasets, we show that the choice of pre-processing method is of relatively minor influence on the final analysis outcome of large microarray studies whereas it can have important effects on the results of a smaller study. The data source (platform, tissue homogeneity, RNA quality) is potentially of bigger importance than the choice of pre-processing method.

Maximum significance clustering of oligonucleotide microarrays

MOTIVATION

Affymetrix high-density oligonucleotide microarrays measure the expression of DNA transcripts using probesets, i.e. multiple probes per transcript. Usually, these multiple measurements are transformed into a single probeset expression level before data analysis proceeds; any information on variability is lost. In this paper we demonstrate how individual probe measurements can be used in a statistic for differential expression. Furthermore, we show how this statistic can serve as a criterion for clustering microarrays.

RESULTS

A novel clustering algorithm using this maximum significance criterion is demonstrated to be more efficient with the measured data than competing techniques for dealing with repeated measurements, especially when the sample size is small.