Inferring Loss-of-Heterozygosity from Unpaired Tumors Using High-Density Oligonucleotide SNP Arrays

Loss of heterozygosity (LOH) of chromosomal regions bearing tumor suppressors is a key event in the evolution of epithelial and mesenchymal tumors. Identification of these regions usually relies on genotyping tumor and counterpart normal DNA and noting regions where heterozygous alleles in the normal DNA become homozygous in the tumor. However, paired normal samples for tumors and cell lines are often not available. With the advent of oligonucleotide arrays that simultaneously assay thousands of single-nucleotide polymorphism (SNP) markers, genotyping can now be done at high enough resolution to allow identification of LOH events by the absence of heterozygous loci, without comparison to normal controls. Here we describe a hidden Markov model-based method to identify LOH from unpaired tumor samples, taking into account SNP intermarker distances, SNP-specific heterozygosity rates, and the haplotype structure of the human genome. When we applied the method to data genotyped on 100 K arrays, we correctly identified 99% of SNP markers as either retention or loss. We also correctly identified 81% of the regions of LOH, including 98% of regions greater than 3 megabases. By integrating copy number analysis into the method, we were able to distinguish LOH from allelic imbalance. Application of this method to data from a set of prostate samples without paired normals identified known regions of prevalent LOH. We have developed a method for analyzing high-density oligonucleotide SNP array data to accurately identify of regions of LOH and retention in tumors without the need for paired normal samples

Impact of Body Composition in Overweight and Obese Patients With Localised Renal Cell Carcinoma

International audienceBackground/aim: To investigate the impact of body composition on morbidity and mortality at the initial diagnosis of localised renal cell carcinoma (RCC) in patients with overweight or obesity. Patients and methods: Sarcopenia was defined using sex-specific cut-off points and other body composition parameters by median values with computed tomography imaging. Results: Among the 96 patients, 40 had sarcopenia (43.0%) at diagnosis. Body composition had no effect on morbidity and 5-year disease-free survival contrary to the classic factors (p<0.05). In the subgroup of obese patients, those with sarcopenia had a poor prognosis (p=0.04) but not in the population with overweight (p=0.9). Conclusion: Sarcopenia was frequently associated with localised RCC at the initial diagnosis. Body composition did not affect morbidity or outcomes. BMI was involved in morbidity and there was paradoxically longer survival in the obesity group

Identification et impact de la sarcopénie chez les patients en surpoids et obèses avec un carcinome à cellules rénales localisé.

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Transforming growth factor β-receptor II protein expression in benign prostatic hyperplasia is associated with prostate volume and inflammation.: TGFBR2 in benign prostatic hyperplasia

International audienceUNLABELLED: What's known on the subject? and What does the study add? Inflammation is known to play a role in BPH. Growth factors such as IGF and FGF are also known to play a role in the development of BPH. The paper shows that (1) TGFBR2, a member of the TGF family, has a protein expression related to prostate gland volume, and (2) highlights the interaction between inflammation and TGFBR2 for the development of BPH. OBJECTIVE: *  To assess transforming growth factor β-receptor II (TGFBRII) protein expression in benign prostatic hyperplasia (BPH) using immunohistochemistry analysis, and to compare the analysis with phenotypic properties. METHODS: *  TGFBRII protein expression was profiled using three clinical outcome tissue microarrays (TMAs), sampled from 231 patients who underwent surgery for BPH. *  Using these TMAs, five inflammatory cell markers were also assessed, including CD3, CD4, CD8, CD20, and CD163. *  The surgical procedure was open prostatectomy in 95 patients and transurethral resection of the prostate in 136 patients. RESULTS: *  TGFBRII protein expression was found in BPH epithelium cells for both basal and secretory cells, as well as in fibromuscular stromal cells. TGFBRII staining was also strong in most of the lymphocytes infiltrating the prostate. *  TGFBRII stromal staining was found to be significantly associated with prostate volume (P = 0.04), whereas TGFBRII epithelial staining was found to be significantly associated with 5-α-reductase-inhibitor medical therapy received by patients before surgery (P = 0.004). *  Both stromal and epithelial TGFBRII staining were found to be associated with CD4 T-lymphocyte infiltrate, independently of prostate volume (P < 0.001 and P = 0.002). CONCLUSIONS: *  TGFBRII protein expression in BPH is associated with prostate gland volume and with CD4 T-lymphocyte prostatitis. *  TGFBRII might be a promising therapeutic target to prevent prostate enlargement or even to decrease prostate volume

Correspondence between LOH and Copy Number

<p>For each inferred copy number (<i>x</i>-axis), the proportion of SNP markers (<i>y</i>-axis) observed in the 10 K dataset of tumor/normal pairs to have undergone LOH (blue) or retention (red) are shown.</p

Accounting for LD by the LD-HMM Significantly Reduces False LOH Inferences from Data Obtained at High Marker Density

<div><p>(A) Inferences from the basic HMM applied to 100 K SNP array data are shown for Chromosome 4 in normal samples. Data are shown as in <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.0020041#pcbi-0020041-g003" target="_blank">Figure 3</a>.</p><p>(B) The genotypes of one region of falsely inferred LOH reveal a region of linkage disequilibrium (dashed red box), also identified by the HapMap project. The sample in column “D” contains one haplotype, the samples in columns “E” through “K” contain another haplotype, and the samples in columns “A” through “C” are heterozygous.</p><p>(C) Improved LOH inferences after application of the LD-HMM.</p></div

The Elements Included in the HMM for LOH Inference

<p>Unobserved LOH states (LOSS or RET) of SNP markers generate observed genotype calls via emission probabilities. The solid arrows indicate the transition probabilities between LOH states, and the dashed arrows indicate LD-induced dependencies between consecutive SNP genotypes.</p