A Hidden Markov Model to estimate population mixture and allelic copy-numbers in cancers using Affymetrix SNP arrays

<p>Abstract</p> <p>Background</p> <p>Affymetrix SNP arrays can interrogate thousands of SNPs at the same time. This allows us to look at the genomic content of cancer cells and to investigate the underlying events leading to cancer. Genomic copy-numbers are today routinely derived from SNP array data, but the proposed algorithms for this task most often disregard the genotype information available from germline cells in paired germline-tumour samples. Including this information may deepen our understanding of the "true" biological situation e.g. by enabling analysis of allele specific copy-numbers. Here we rely on matched germline-tumour samples and have developed a Hidden Markov Model (HMM) to estimate allelic copy-number changes in tumour cells. Further with this approach we are able to estimate the proportion of normal cells in the tumour (mixture proportion).</p> <p>Results</p> <p>We show that our method is able to recover the underlying copy-number changes in simulated data sets with high accuracy (above 97.71%). Moreover, although the known copy-numbers could be well recovered in simulated cancer samples with more than 70% cancer cells (and less than 30% normal cells), we demonstrate that including the mixture proportion in the HMM increases the accuracy of the method. Finally, the method is tested on HapMap samples and on bladder and prostate cancer samples.</p> <p>Conclusion</p> <p>The HMM method developed here uses the genotype calls of germline DNA and the allelic SNP intensities from the tumour DNA to estimate allelic copy-numbers (including changes) in the tumour. It differentiates between different events like uniparental disomy and allelic imbalances. Moreover, the HMM can estimate the mixture proportion, and thus inform about the purity of the tumour sample.</p

Stage-associated overexpression of the ubiquitin-like protein, ISG15, in bladder cancer

Bladder cancer is among the most prevalent malignancies, and is characterised by frequent tumour recurrences and localised inflammation, which may promote tissue invasion and metastasis. Microarray analysis was used to compare gene expression in normal bladder urothelium with that in tumours at different stages of progression. The innate immune response gene, interferon-stimulated gene 15 kDa (ISG15, GIP2), was highly expressed at all stages of bladder cancer as compared to normal urothelium. Western blotting revealed a tumour-associated expression of ISG15 protein. ISG15 exhibited a stage-associated expression, with significantly (P<0.05) higher levels of ISG15 protein in muscle-invasive T2–T4 tumours, compared with normal urothelium. Although ISG15 is involved in the primary immune response, ISG15 expression did not correlate with bladder inflammation. However, immunohistochemical staining revealed expression of ISG15 protein in both cancer cells and stromal immune cells. Interestingly, a significant fraction of ISG15 protein was localised to the nuclei of tumour cells, whereas no nuclear ISG15 staining was observed in ISG15-positive stromal cells. Taken together, our findings identify ISG15 as a novel component of bladder cancer-associated gene expression

The Use of Molecular Analyses in Voided Urine for the Assessment of Patients with Hematuria

Introduction:Patients presenting with painless hematuria form a large part of the urological patient population. In many cases, especially in younger patients, the cause of hematuria is harmless. Nonetheless, hematuria could be a symptom of malignant disease and hence most patients will be subject to cystoscopy. In this study, we aimed to develop a prediction model based on methylation markers in combination with clinical variables, in order to stratify patients with high risk for bladder cancer.Material and Methods:Patients (n=169) presenting with painless hematuria were included. 54 patients were diagnosed with bladder cancer. In the remaining 115 patients, the cause of hematuria was non-malignant. Urine samples were collected prior to cystoscopy. Urine DNA was analyzed for methylation of OSR1, SIM2, OTX1, MEIS1 and ONECUT2. Methylation percentages were calculated and were combined with clinical variables into a logistic regression model.Results:Logistic regression analysis based on the five methylation markers, age, gender and type of hematuria resulted in an area under the curve (AUC) of 0.88 and an optimism corrected AUC of 0.84 after internal validation by bootstrapping. Using a cut-off value of 0.307 allowed stratification of patients in a low-risk and high-risk group, resulting in a sensitivity of 82% (44/54) and a specificity of 82% (94/115). Most aggressive tumors were found in patients in the high-risk group. The addition of cytology to the prediction model, improved the AUC from 0.88 to 0.89, with a sensitivity and specificity of 85% (39/46) and 87% (80/92), retrospectively.Conclusions:This newly developed prediction model could be a helpful tool in risk stratification of patients presenting with painless hematuria. Accurate risk prediction might result in less extensive examination of low risk patients and thereby, reducing patient burden and costs. Further validation in a large prospective patient cohort is necessary to prove the true clinical value of this model

Selection of microsatellite markers for bladder cancer diagnosis without the need for corresponding blood

Microsatellite markers are used for loss-of-heterozygosity, allelic imbalance and clonality analyses in cancers. Usually, tumor DNA is compared to corresponding normal DNA. However, normal DNA is not always available and can display aberrant allele ratios due to copy number variations in the genome. Moreover, stutter peaks may complicate the analysis. To use microsatellite markers for diagnosis of recurrent bladder cancer, we aimed to select markers without stutter peaks and a constant ratio between alleles, thereby avoiding the need for a control DNA sample. We investigated 49 microsatellite markers with tri- and tetranucleotide repeats in regions commonly lost in bladder cancer. Based on analysis of 50 blood DNAs the 12 best performing markers were selected with few stutter peaks and a constant ratio between peaks heights. Per marker upper and lower cut off values for allele ratios were determined. LOH of the markers was observed in 59/104 tumor DNAs. We then determined the sensitivity of the marker panel for detection of recurrent bladder cancer by assaying 102 urine samples of these patients. Sensitivity was 63% when patients were stratified for LOH in their primary tumors. We demonstrate that up-front selection of microsatellite markers obliterates the need for a corresponding blood sample. For diagnosis of bladder cancer recurrences in urine this significantly reduces costs. Moreover, this approach facilitates retrospective analysis of archival tumor samples for allelic imbalance

Gene expression profiling of noninvasive primary urothelial tumours using microarrays

At present, the mechanism leading to bladder cancer is still poorly understood, and our knowledge about early events in tumorigenesis is limited. This study describes the changes in gene expression occurring during the neoplastic transition from normal bladder urothelium to primary Ta tumours. Using DNA microarrays, we identified novel differentially expressed genes in Ta tumours compared to normal bladder, and genes that were altered in high-grade tumours. Among the mostly changed genes between normal bladder and Ta tumours, we found genes related to the cytoskeleton (keratin 7 and syndecan 1), and transcription (high mobility group AT-hook 1). Altered genes in high-grade tumours were related to cell cycle (cyclin-dependent kinase 4) and transcription (jun d proto-oncogene). Furthermore, we showed the presence of high keratin 7 transcript expression in bladder cancer, and Western blotting analysis revealed three major molecular isoforms of keratin 7 in the tissues. These could be detected in urine sediments from bladder tumour patients

Identification of Common Differentially Expressed Genes in Urinary Bladder Cancer

BACKGROUND: Current diagnosis and treatment of urinary bladder cancer (BC) has shown great progress with the utilization of microarrays. PURPOSE: Our goal was to identify common differentially expressed (DE) genes among clinically relevant subclasses of BC using microarrays. METHODOLOGY/PRINCIPAL FINDINGS: BC samples and controls, both experimental and publicly available datasets, were analyzed by whole genome microarrays. We grouped the samples according to their histology and defined the DE genes in each sample individually, as well as in each tumor group. A dual analysis strategy was followed. First, experimental samples were analyzed and conclusions were formulated; and second, experimental sets were combined with publicly available microarray datasets and were further analyzed in search of common DE genes. The experimental dataset identified 831 genes that were DE in all tumor samples, simultaneously. Moreover, 33 genes were up-regulated and 85 genes were down-regulated in all 10 BC samples compared to the 5 normal tissues, simultaneously. Hierarchical clustering partitioned tumor groups in accordance to their histology. K-means clustering of all genes and all samples, as well as clustering of tumor groups, presented 49 clusters. K-means clustering of common DE genes in all samples revealed 24 clusters. Genes manifested various differential patterns of expression, based on PCA. YY1 and NFκB were among the most common transcription factors that regulated the expression of the identified DE genes. Chromosome 1 contained 32 DE genes, followed by chromosomes 2 and 11, which contained 25 and 23 DE genes, respectively. Chromosome 21 had the least number of DE genes. GO analysis revealed the prevalence of transport and binding genes in the common down-regulated DE genes; the prevalence of RNA metabolism and processing genes in the up-regulated DE genes; as well as the prevalence of genes responsible for cell communication and signal transduction in the DE genes that were down-regulated in T1-Grade III tumors and up-regulated in T2/T3-Grade III tumors. Combination of samples from all microarray platforms revealed 17 common DE genes, (BMP4, CRYGD, DBH, GJB1, KRT83, MPZ, NHLH1, TACR3, ACTC1, MFAP4, SPARCL1, TAGLN, TPM2, CDC20, LHCGR, TM9SF1 and HCCS) 4 of which participate in numerous pathways. CONCLUSIONS/SIGNIFICANCE: The identification of the common DE genes among BC samples of different histology can provide further insight into the discovery of new putative markers

Gene expression signatures for colorectal cancer microsatellite status and HNPCC

The majority of microsatellite instable (MSI) colorectal cancers are sporadic, but a subset belongs to the syndrome hereditary nonpolyposis colorectal cancer (HNPCC). Microsatellite instability is caused by dysfunction of the mismatch repair (MMR) system that leads to a mutator phenotype, and MSI is correlated to prognosis and response to chemotherapy. Gene expression signatures as predictive markers are being developed for many cancers, and the identification of a signature for MMR deficiency would be of interest both clinically and biologically. To address this issue, we profiled the gene expression of 101 stage II and III colorectal cancers (34 MSI, 67 microsatellite stable (MSS)) using high-density oligonucleotide microarrays. From these data, we constructed a nine-gene signature capable of separating the mismatch repair proficient and deficient tumours. Subsequently, we demonstrated the robustness of the signature by transferring it to a real-time RT-PCR platform. Using this platform, the signature was validated on an independent test set consisting of 47 tumours (10 MSI, 37 MSS), of which 45 were correctly classified. In a second step, we constructed a signature capable of separating MMR-deficient tumours into sporadic MSI and HNPCC cases, and validated this by a mathematical cross-validation approach. The demonstration that this two-step classification approach can identify MSI as well as HNPCC cases merits further gene expression studies to identify prognostic signatures

An evaluation of urinary microRNA reveals a high sensitivity for bladder cancer

Background: Urinary biomarkers are needed to improve the care and reduce the cost of managing bladder cancer. Current biomarkers struggle to identify both high and low-grade cancers due to differing molecular pathways. Changes in microRNA (miR) expression are seen in urothelial carcinogenesis in a phenotype-specific manner. We hypothesised that urinary miRs reflecting low- and high-grade pathways could detect bladder cancers and overcome differences in genetic events seen within the disease. Methods: We investigated urinary samples (n ¼ 121) from patients with bladder cancer (n ¼ 68) and age-matched controls (n ¼ 53). Fifteen miRs were quantified using real-time PCR. Results: We found that miR is stable within urinary cells despite adverse handling and detected differential expression of 10 miRs from patients with cancer and controls (miRs 15a/15b/24-1/27b/100/135b/203/212/328/1224, ANOVA Po0.05). Individually, miR-1224-3p had the best individual performance with specificity, positive and negative predictive values and concordance of 83%, 83%, 75% and 77%, respectively. The combination of miRs-135b/15b/1224-3p detected bladder cancer with a high sensitivity (94.1%), sufficient specificity (51%) and was correct in 86% of patients (concordance). Conclusion: The use of this panel in patients with haematuria would have found 94% of urothelial cell carcinoma, while reducing cystoscopy rates by 26%. However, two invasive cancers (3%) would have been missed

Cross-Platform Comparison of Microarray-Based Multiple-Class Prediction

High-throughput microarray technology has been widely applied in biological and medical decision-making research during the past decade. However, the diversity of platforms has made it a challenge to re-use and/or integrate datasets generated in different experiments or labs for constructing array-based diagnostic models. Using large toxicogenomics datasets generated using both Affymetrix and Agilent microarray platforms, we carried out a benchmark evaluation of cross-platform consistency in multiple-class prediction using three widely-used machine learning algorithms. After an initial assessment of model performance on different platforms, we evaluated whether predictive signature features selected in one platform could be directly used to train a model in the other platform and whether predictive models trained using data from one platform could predict datasets profiled using the other platform with comparable performance. Our results established that it is possible to successfully apply multiple-class prediction models across different commercial microarray platforms, offering a number of important benefits such as accelerating the possible translation of biomarkers identified with microarrays to clinically-validated assays. However, this investigation focuses on a technical platform comparison and is actually only the beginning of exploring cross-platform consistency. Further studies are needed to confirm the feasibility of microarray-based cross-platform prediction, especially using independent datasets