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

A Beta-mixture model for dimensionality reduction, sample classification and analysis

<p>Abstract</p> <p>Background</p> <p>Patterns of genome-wide methylation vary between tissue types. For example, cancer tissue shows markedly different patterns from those of normal tissue. In this paper we propose a beta-mixture model to describe genome-wide methylation patterns based on probe data from methylation microarrays. The model takes dependencies between neighbour probe pairs into account and assumes three broad categories of methylation, low, medium and high. The model is described by 37 parameters, which reduces the dimensionality of a typical methylation microarray significantly. We used methylation microarray data from 42 colon cancer samples to assess the model.</p> <p>Results</p> <p>Based on data from colon cancer samples we show that our model captures genome-wide characteristics of methylation patterns. We estimate the parameters of the model and show that they vary between different tissue types. Further, for each methylation probe the posterior probability of a methylation state (low, medium or high) is calculated and the probability that the state is correctly predicted is assessed. We demonstrate that the model can be applied to classify cancer tissue types accurately and that the model provides accessible and easily interpretable data summaries.</p> <p>Conclusions</p> <p>We have developed a beta-mixture model for methylation microarray data. The model substantially reduces the dimensionality of the data. It can be used for further analysis, such as sample classification or to detect changes in methylation status between different samples and tissues.</p

A New Experimental Infection Model in Ferrets Based on Aerosolised Mycobacterium bovis

There is significant interest in developing vaccines to control bovine tuberculosis, especially in wildlife species where this disease continues to persist in reservoir species such as the European Badger (Meles meles). However, gaining access to populations of badgers (protected under UK law) is problematic and not always possible. In this study, a new infection model has been developed in ferrets (Mustela furo), a species which is closely related to the badger. Groups of ferrets were infected using a Madison infection chamber and were examined postmortem for the presence of tuberculous lesions and to provide tissue samples for confirmation of Mycobacterium bovis by culture. An infectious dose was defined, that establishes infection within the lungs and associated lymph nodes with subsequent spread to the mesentery lymph nodes. This model, which emphasises respiratory tract infection, will be used to evaluate vaccines for the control of bovine tuberculosis in wildlife species

Genotyping and annotation of Affymetrix SNP arrays

In this paper we develop a new method for genotyping Affymetrix single nucleotide polymorphism (SNP) array. The method is based on (i) using multiple arrays at the same time to determine the genotypes and (ii) a model that relates intensities of individual SNPs to each other. The latter point allows us to annotate SNPs that have poor performance, either because of poor experimental conditions or because for one of the alleles the probes do not behave in a dose–response manner. Generally, our method agrees well with a method developed by Affymetrix. When both methods make a call they agree in 99.25% (using standard settings) of the cases, using a sample of 113 Affymetrix 10k SNP arrays. In the majority of cases where the two methods disagree, our method makes a genotype call, whereas the method by Affymetrix makes a no call, i.e. the genotype of the SNP is not determined. By visualization it is indicated that our method is likely to be correct in majority of these cases. In addition, we demonstrate that our method produces more SNPs that are in concordance with Hardy–Weinberg equilibrium than the method by Affymetrix. Finally, we have validated our method on HapMap data and shown that the performance of our method is comparable to other methods

Gel-Based Proteomics of Clinical Samples Identifies Potential Serological Biomarkers for Early Detection of Colorectal Cancer

The burden of colorectal cancer (CRC) is considerable—approximately 1.8 million people are diagnosed each year with CRC and of these about half will succumb to the disease. In the case of CRC, there is strong evidence that an early diagnosis leads to a better prognosis, with metastatic CRC having a 5-year survival that is only slightly greater than 10% compared with up to 90% for stage I CRC. Clearly, biomarkers for the early detection of CRC would have a major clinical impact. We implemented a coherent gel-based proteomics biomarker discovery platform for the identification of clinically useful biomarkers for the early detection of CRC. Potential protein biomarkers were identified by a 2D gel-based analysis of a cohort composed of 128 CRC and site-matched normal tissue biopsies. Potential biomarkers were prioritized and assays to quantitatively measure plasma expression of the candidate biomarkers were developed. Those biomarkers that fulfilled the preset criteria for technical validity were validated in a case-control set of plasma samples, including 70 patients with CRC, adenomas, or non-cancer diseases and healthy individuals in each group. We identified 63 consistently upregulated polypeptides (factor of four-fold or more) in our proteomics analysis. We selected 10 out of these 63 upregulated polypeptides, and established assays to measure the concentration of each one of the ten biomarkers in plasma samples. Biomarker levels were analyzed in plasma samples from healthy individuals, individuals with adenomas, CRC patients, and patients with non-cancer diseases and we identified one protein, tropomyosin 3 (Tpm3) that could discriminate CRC at a significant level (p = 0.0146). Our results suggest that at least one of the identified proteins, Tpm3, could be used as a biomarker in the early detection of CRC, and further studies should provide unequivocal evidence for the real-life clinical validity and usefulness of Tpm3

3 '-UTR poly(T/U) repeat of EWSR1 is altered in microsatellite unstable colorectal cancer with nearly perfect sensitivity

Approximately 15 % of colorectal cancers exhibit instability of short nucleotide repeat regions, microsatellites. These tumors display a unique clinicopathologic profile and the microsatellite instability status is increasingly used to guide clinical management as it is known to predict better prognosis as well as resistance to certain chemotherapeutics. A panel of five repeats determined by the National Cancer Institute, the Bethesda panel, is currently the standard for determining the microsatellite instability status in colorectal cancer. Recently, a quasimonomorphic mononucleotide repeat 16T/U at the 3' untranslated region of the Ewing sarcoma breakpoint region 1 gene was reported to show perfect sensitivity and specificity in detecting mismatch repair deficient colorectal, endometrial, and gastric cancers in two independent populations. To confirm this finding, we replicated the analysis in 213 microsatellite unstable colorectal cancers from two independent populations, 148 microsatellite stable colorectal cancers, and the respective normal samples by PCR and fragment analysis. The repeat showed nearly perfect sensitivity for microsatellite unstable colorectal cancer as it was altered in 212 of the 213 microsatellite unstable (99.5 %) and none of the microsatellite stable colorectal tumors. This repeat thus represents the first potential single marker for detecting microsatellite instability.Peer reviewe