Nearest Template Prediction: A Single-Sample-Based Flexible Class Prediction with Confidence Assessment

Gene-expression signature-based disease classification and clinical outcome prediction has not been widely introduced in clinical medicine as initially expected, mainly due to the lack of extensive validation needed for its clinical deployment. Obstacles include variable measurement in microarray assay, inconsistent assay platform, analytical requirement for comparable pair of training and test datasets, etc. Furthermore, as medical device helping clinical decision making, the prediction needs to be made for each single patient with a measure of its reliability. To address these issues, there is a need for flexible prediction method less sensitive to difference in experimental and analytical conditions, applicable to each single patient, and providing measure of prediction confidence. The nearest template prediction (NTP) method provides a convenient way to make class prediction with assessment of prediction confidence computed in each single patient's gene-expression data using only a list of signature genes and a test dataset. We demonstrate that the method can be flexibly applied to cross-platform, cross-species, and multiclass predictions without any optimization of analysis parameters

Ploidy variation in Kluyveromyces marxianus separates dairy and non-dairy isolates

Kluyveromyces marxianus is traditionally associated with fermented dairy products, but can also be isolated from diverse non-dairy environments. Because of thermotolerance, rapid growth and other traits, many different strains are being developed for food and industrial applications but there is, as yet, little understanding of the genetic diversity or population genetics of this species. K. marxianus shows a high level of phenotypic variation but the only phenotype that has been clearly linked to a genetic polymorphism is lactose utilisation, which is controlled by variation in the LAC12 gene. The genomes of several strains have been sequenced in recent years and, in this study, we sequenced a further nine strains fromdifferent origins. Analysis of the Single Nucleotide Polymorphisms (SNPs) in 14 strains was carried out to examine genome structure and genetic diversity. SNP diversity in K. marxianus is relatively high, with up to 3% DNA sequence divergence between alleles. It was found that the isolates include haploid, diploid, and triploid strains, as shown by both SNP analysis and flow cytometry. Diploids and triploids contain long genomic tracts showing loss of heterozygosity (LOH). All six isolates from dairy environments were diploid or triploid, whereas 6 out 7 isolates from non-dairy environment were haploid. This also correlated with the presence of functional LAC12 alleles only in dairy haplotypes. The diploids were hybrids between a non-dairy and a dairy haplotype, whereas triploids included three copies of a dairy haplotype

Renal cell carcinoma of native kidney in Chinese renal transplant recipients: a report of 12 cases and a review of the literature

Objectives To present and discuss the epidemiological and clinical aspects, as well as therapeutic options and outcome of de novo renal cell carcinoma (RCC) of the native kidneys in a series of Chinese renal transplant recipients. Patients and Methods A retrospective, cohort study examining all renal transplant recipients with the diagnosis of RCC of native kidney followed up in two major regional hospitals in Hong Kong between January 2000 and December 2009. Clinical data includedage, gender, cause of renal failure, symptoms at presentation, duration of transplantation, immunosuppressive therapy, and history of acquired cystic kidney disease (ACKD). Laboratory, radiographic, operative, and pathology reports were used to assess the tumor extent. Results Among the 1,003 renal transplant recipients recruited, 12 transplant recipients had a nephrectomy for a total of 13 RCC. The prevalence of de novo RCC was 1.3%. The mean age at diagnosis of RCC was 48.4 years, and the median time from transplantation to diagnosis was 6.1 years. ACKD was found in 6 (50%) of the patients. All patients except one were asymptomatic. pT1 disease was found in ten patients with a mean tumor size of 3.2 cm. All patients were treated successfully with radical nephrectomy. After a median follow-up of 38 months, two patients (16.7%) died. One died of sepsis, and the other died of metastatic carcinoma. Conclusions With increasing data showing a better prognosis if RCC is detected early by screening, it is time to consider screening all kidney transplant recipients for ACKD and RCC. © The Author(s) 2011. This article is published with open access at Springerlink.com.published_or_final_versionSpringer Open Choice, 21 Feb 201

Host Genetics Predict Clinical Deterioration in HCV-Related Cirrhosis

Single nucleotide polymorphisms (SNPs) in the epidermal growth factor (EGF, rs4444903), patatin-like phospholipase domain-containing protein 3 (PNPLA3, rs738409) genes, and near the interleukin-28B (IL28B, rs12979860) gene are linked to treatment response, fibrosis, and hepatocellular carcinoma (HCC) in chronic hepatitis C. Whether these SNPs independently or in combination predict clinical deterioration in hepatitis C virus (HCV)-related cirrhosis is unknown. We genotyped SNPs in EGF, PNPLA3, and IL28B from liver tissue from 169 patients with biopsy-proven HCV cirrhosis. We estimated risk of clinical deterioration, defined as development of ascites, encephalopathy, variceal hemorrhage, HCC, or liver-related death using Cox proportional hazards modeling. During a median follow-up of 6.6 years, 66 of 169 patients experienced clinical deterioration. EGF non-AA, PNPLA3 non-CC, and IL28B non-CC genotypes were each associated with increased risk of clinical deterioration in age, sex, and race-adjusted analysis. Only EGF non-AA genotype was independently associated with increased risk of clinical deterioration (hazard ratio [HR] 2.87; 95% confidence interval [CI] 1.31–6.25) after additionally adjusting for bilirubin, albumin, and platelets. Compared to subjects who had 0–1 unfavorable genotypes, the HR for clinical deterioration was 1.79 (95%CI 0.96–3.35) for 2 unfavorable genotypes and 4.03 (95%CI 2.13–7.62) for unfavorable genotypes for all three loci (Ptrend<0.0001). In conclusion, among HCV cirrhotics, EGF non-AA genotype is independently associated with increased risk for clinical deterioration. Specific PNPLA3 and IL28B genotypes also appear to be associated with clinical deterioration. These SNPs have potential to identify patients with HCV-related cirrhosis who require more intensive monitoring for decompensation or future therapies preventing disease progression

A Cross-Species Analysis of a Mouse Model of Breast Cancer-Specific Osteolysis and Human Bone Metastases Using Gene Expression Profiling

<p>Abstract</p> <p>Background</p> <p>Breast cancer is the second leading cause of cancer-related death in women in the United States. During the advanced stages of disease, many breast cancer patients suffer from bone metastasis. These metastases are predominantly osteolytic and develop when tumor cells interact with bone. <it>In vivo </it>models that mimic the breast cancer-specific osteolytic bone microenvironment are limited. Previously, we developed a mouse model of tumor-bone interaction in which three mouse breast cancer cell lines were implanted onto the calvaria. Analysis of tumors from this model revealed that they exhibited strong bone resorption, induction of osteoclasts and intracranial penetration at the tumor bone (TB)-interface.</p> <p>Methods</p> <p>In this study, we identified and used a TB microenvironment-specific gene expression signature from this model to extend our understanding of the metastatic bone microenvironment in human disease and to predict potential therapeutic targets.</p> <p>Results</p> <p>We identified a TB signature consisting of 934 genes that were commonly (among our 3 cell lines) and specifically (as compared to tumor-alone area within the bone microenvironment) up- and down-regulated >2-fold at the TB interface in our mouse osteolytic model. By comparing the TB signature with gene expression profiles from human breast metastases and an <it>in vitro </it>osteoclast model, we demonstrate that our model mimics both the human breast cancer bone microenvironment and osteoclastogenesis. Furthermore, we observed enrichment in various signaling pathways specific to the TB interface; that is, TGF-β and myeloid self-renewal pathways were activated and the Wnt pathway was inactivated. Lastly, we used the TB-signature to predict cyclopenthiazide as a potential inhibitor of the TB interface.</p> <p>Conclusion</p> <p>Our mouse breast cancer model morphologically and genetically resembles the osteoclastic bone microenvironment observed in human disease. Characterization of the gene expression signature specific to the TB interface in our model revealed signaling mechanisms operative in human breast cancer metastases and predicted a therapeutic inhibitor of cancer-mediated osteolysis.</p

Immunological and Molecular Correlates of Disease Recurrence after Liver Resection for Hepatocellular Carcinoma

The definition of the risk of hepatocellular carcinoma (HCC) recurrence after resection represents a central issue to improve the clinical management of patients. In this study we examined the prognostic relevance of infiltrating immune cell subsets in the tumor (TIL) and in nontumorous (NT) liver (LIL), and the expression of immune-related and lineage-specific mRNAs in HCC and NT liver derived from 42 patients. The phenotype of infiltrating cells was analyzed by flow cytometry, and mRNA expression in liver tissue was examined by real-time reverse transcription (RT)-PCR. The tumor immune microenvironment was enriched in inhibitory and dysfunctional cell subsets. Enrichment in CD4+ T-cells and in particular CD4 and CD8+ memory subsets within TIL was predictive of better overall survival (OS) and time to recurrence (TTR). Increased programmed death ligand 1 (PDL1) mRNA content and higher prevalence of invariant NKT (iNKT) cells were associated with shorter OS and TTR, respectively. By combined evaluation of infiltrating cell subsets along with mRNA profiling of immune and tumor related genes, we identified the intratumoral frequency of memory T-cells and iNKT-cells as well as PDL1 expression as the best predictors of clinical outcome. HCC infiltrate is characterized by the expression of molecules with negative regulatory function that may favor tumor recurrence and poor survival

Progenitor cell markers predict outcome of patients with hepatocellular carcinoma beyond Milan criteria undergoing liver transplantation.

BACKGROUND & AIMS: In patients with hepatocellular carcinoma (HCC), liver transplantation (LT) is an excellent therapy if tumor characteristics are within the Milan criteria. We aimed to define genomic features enabling to identify HCC patients beyond Milan criteria who have acceptable transplant outcomes. METHODS: Among 770 consecutive HCC patients transplanted between 1990 and 2013, 132 had tumors exceeding Milan criteria on pathology and were enrolled in the study; 44% of the patients satisfied the 'up-to-7 rule' [7=sum of the size of the largest tumor and the number of tumors]. Explant tumors were assessed for genomic signatures and immunohistochemical markers associated with poor outcome. RESULTS: At a median follow-up of 88months, 64 patients had died and 45 recurred; the 5-year overall survival (OS) and recurrence rates were 57% and 35%, respectively. Cytokeratin 19 (CK19) gene signature was independently associated with recurrence [Hazard ratio (HR)=2.95, p<0.001], along with tumor size (HR=3.37, p=0.023) and presence of satellites (HR=2.98, p=0.001). S2 subclass signature was independently associated with poor OS (HR=3.18, p=0.001), along with tumor size (HR=5.06, p<0.001) and up-to-7 rule (HR=2.50, p=0.002). Using the presence of progenitor cell markers (either CK19 or S2 signatures) patients were classified into poor prognosis (n=58; 5-year recurrence 53%, survival 45%) and good prognosis (n=74; 5-year recurrence 19%, survival 67%) (HR=3.16, p<0.001 for recurrence, and HR=1.72, p=0.04 for OS). CONCLUSIONS: HCC patients transplanted beyond Milan criteria without gene signatures of progenitor markers (CK19 and S2) achieved survival rates similar as those within Milan criteria. Once prospectively validated, these markers may support a limited expansion of LT indications

Epstein Barr Virus-positive large T-cell lymphoma presenting as acute appendicitis 17 years after cadaveric renal transplant: a case report

<p>Abstract</p> <p>Introduction</p> <p>The majority of post-transplant lymphoproliferative disorders in renal transplant patients are of the B-cell phenotype, while the T-cell phenotype is rare. We report a case of Epstein Barr Virus-positive, T-cell lymphoma in a renal transplant patient, presenting unusually as acute appendicitis.</p> <p>Case presentation</p> <p>A 45-year-old Hispanic male renal transplant patient presented with right-side abdominal pain 17 years after transplant. The laboratory studies were unremarkable. Laparoscopic exploration showed an inflamed appendix so a laparoscopic appendectomy was performed. Pathology of the appendix showed large cells positive for CD3, CD56 and Epstein Barr Virus-encoded RNA staining, and negative for CD20 and CD30. The tissue tested positive for T-cell receptor gene rearrangement by polymerase chain reaction analysis. Treatment management involved reduction of immunosuppression and initiation of chemotherapy with cisplatin, etoposide, gemcitabine, and solumedrol followed by cyclophosphamide, hydroxydaunorubicin, vincristine and prednisone). He recovered and the allo-grafted kidney is fully functional.</p> <p>Conclusion</p> <p>We report a rare case of post-renal transplant large T-cell lymphoma, with an unusual presentation of acute appendicitis and Epstein Barr Virus-positivity, which responded well to chemotherapy.</p

DeBi: Discovering Differentially Expressed Biclusters using a Frequent Itemset Approach

<p>Abstract</p> <p>Background</p> <p>The analysis of massive high throughput data via clustering algorithms is very important for elucidating gene functions in biological systems. However, traditional clustering methods have several drawbacks. Biclustering overcomes these limitations by grouping genes and samples simultaneously. It discovers subsets of genes that are co-expressed in certain samples. Recent studies showed that biclustering has a great potential in detecting marker genes that are associated with certain tissues or diseases. Several biclustering algorithms have been proposed. However, it is still a challenge to find biclusters that are significant based on biological validation measures. Besides that, there is a need for a biclustering algorithm that is capable of analyzing very large datasets in reasonable time.</p> <p>Results</p> <p>Here we present a fast biclustering algorithm called DeBi (Differentially Expressed BIclusters). The algorithm is based on a well known data mining approach called frequent itemset. It discovers maximum size homogeneous biclusters in which each gene is strongly associated with a subset of samples. We evaluate the performance of DeBi on a yeast dataset, on synthetic datasets and on human datasets.</p> <p>Conclusions</p> <p>We demonstrate that the DeBi algorithm provides functionally more coherent gene sets compared to standard clustering or biclustering algorithms using biological validation measures such as Gene Ontology term and Transcription Factor Binding Site enrichment. We show that DeBi is a computationally efficient and powerful tool in analyzing large datasets. The method is also applicable on multiple gene expression datasets coming from different labs or platforms.</p