NAD(P)H:quinone oxidoreductase 1 (NQO1) P187S polymorphism and prostate cancer risk in Caucasians

NAD(P)H:quinone oxidoreductase 1 (NQO1) catalyses the reduction of quinoid compounds to hydroquinones, preventing the generation of free radicals and reactive oxygen. A “C” to “T” transversion at position 609 of NQO1, leading to a nonsynonymous amino acid change (Pro187Ser, P187S), results in an altered enzyme activity. No NQO1 protein activity was detected in NQO1 609TT genotype, and low to intermediate activity was detected in NQO1 609CT genotype compared with 609CC genotype. Thus, this polymorphism may result in altered cancer predisposition. For prostate cancer, only sparse data are available. We therefore analyzed the distribution of the NQO1 P187S SNP (single nucleotide polymorphism) in prostate cancer patients and a healthy control group. Allelic variants were determined using RFLP analysis. Overall, 232 patients without any malignancy and 119 consecutive prostate cancer patients were investigated. The genotype distribution in our cohorts followed the Hardy–Weinberg equilibrium in cases and controls. The distribution of the NQO1 codon 187 SNP did not differ significantly between prostate cancer patients and the control group (p = 0.242). There was also no association between the allelic variants and stage or Gleason score of the tumors. The NQO1 P187S SNP was not significantly associated with an increased prostate cancer risk in our cohorts. The SNP has also no influence on histopathological characteristics of the tumors. A combined analysis of all available data from published European studies also showed no significant differences in the genotype distribution between controls and prostate cancer patients. Our data suggest a minor role of the NQO1 nucleotide 609 polymorphism in prostate carcinogenesis

Mismatch Repair Proteins hMLH1 and hMSH2 Are Differently Expressed in the Three Main Subtypes of Sporadic Renal Cell Carcinoma

Objectives: We studied the role of minor mismatch repair proteins (MMR) human MutL homologue 1 (hMLH1) and human MutS homologue 2 (hMSH2) in the main subtypes of renal cell carcinoma (RCC). Methods: Expression of MMR proteins hMLH1 and hMSH2 were investigated in 166 RCC tumors, containing the main subtypes by immunohistochemistry. Furthermore, each tumor was screened for microsatellite instability (MSI) using the National Cancer Institute consensus panel for hereditary non-polyposis colon carcinoma as well as for elevated microsatellite alterations at selected tetranucleotide repeats (EMAST) by 10 additional markers. Results: MSI was found only in 2.0% of analyzable cases and EMAST was detected only in 1 patient. hMLH1 and hMSH2 expression was reduced in 83.7 (118/141) and 51.2% (65/127) of cases, respectively, in a subtype-specific manner. None of the clear cell RCC tumors retained a high hMLH1 expression and 92.0% lost hMLH1 completely, while papillary and chromophobe RCC preserved the expression in 25.0 and 33.3% of cases (p < 0.001). Subtype specificity was also present in hMSH2 staining, where chromophobe RCC retained a high expression in 41.7% of cases, while clear cell and papillary tumors did not (29.9 and 23.1%; p = 0.01). Conclusion: MSI and EMAST are rare events in sporadic RCC, whereas diminished MMR protein expression is linked to tumor entity and might contribute to the different biological behavior of the RCC subtypes

Pan-Cancer Analysis of lncRNA Regulation Supports Their Targeting of Cancer Genes in Each Tumor Context

Long noncoding RNAs (lncRNAs) are commonly dys-regulated in tumors, but only a handful are known toplay pathophysiological roles in cancer. We inferredlncRNAs that dysregulate cancer pathways, onco-genes, and tumor suppressors (cancer genes) bymodeling their effects on the activity of transcriptionfactors, RNA-binding proteins, and microRNAs in5,185 TCGA tumors and 1,019 ENCODE assays.Our predictions included hundreds of candidateonco- and tumor-suppressor lncRNAs (cancerlncRNAs) whose somatic alterations account for thedysregulation of dozens of cancer genes and path-ways in each of 14 tumor contexts. To demonstrateproof of concept, we showed that perturbations tar-geting OIP5-AS1 (an inferred tumor suppressor) andTUG1 and WT1-AS (inferred onco-lncRNAs) dysre-gulated cancer genes and altered proliferation ofbreast and gynecologic cancer cells. Our analysis in-dicates that, although most lncRNAs are dysregu-lated in a tumor-specific manner, some, includingOIP5-AS1, TUG1, NEAT1, MEG3, and TSIX, synergis-tically dysregulate cancer pathways in multiple tumorcontexts

Pan-cancer Alterations of the MYC Oncogene and Its Proximal Network across the Cancer Genome Atlas

Although theMYConcogene has been implicated incancer, a systematic assessment of alterations ofMYC, related transcription factors, and co-regulatoryproteins, forming the proximal MYC network (PMN),across human cancers is lacking. Using computa-tional approaches, we define genomic and proteo-mic features associated with MYC and the PMNacross the 33 cancers of The Cancer Genome Atlas.Pan-cancer, 28% of all samples had at least one ofthe MYC paralogs amplified. In contrast, the MYCantagonists MGA and MNT were the most frequentlymutated or deleted members, proposing a roleas tumor suppressors.MYCalterations were mutu-ally exclusive withPIK3CA,PTEN,APC,orBRAFalterations, suggesting that MYC is a distinct onco-genic driver. Expression analysis revealed MYC-associated pathways in tumor subtypes, such asimmune response and growth factor signaling; chro-matin, translation, and DNA replication/repair wereconserved pan-cancer. This analysis reveals insightsinto MYC biology and is a reference for biomarkersand therapeutics for cancers with alterations ofMYC or the PMN

Genomic, Pathway Network, and Immunologic Features Distinguishing Squamous Carcinomas

This integrated, multiplatform PanCancer Atlas study co-mapped and identified distinguishing molecular features of squamous cell carcinomas (SCCs) from five sites associated with smokin

Spatial Organization and Molecular Correlation of Tumor-Infiltrating Lymphocytes Using Deep Learning on Pathology Images

Beyond sample curation and basic pathologic characterization, the digitized H&E-stained images of TCGA samples remain underutilized. To highlight this resource, we present mappings of tumorinfiltrating lymphocytes (TILs) based on H&E images from 13 TCGA tumor types. These TIL maps are derived through computational staining using a convolutional neural network trained to classify patches of images. Affinity propagation revealed local spatial structure in TIL patterns and correlation with overall survival. TIL map structural patterns were grouped using standard histopathological parameters. These patterns are enriched in particular T cell subpopulations derived from molecular measures. TIL densities and spatial structure were differentially enriched among tumor types, immune subtypes, and tumor molecular subtypes, implying that spatial infiltrate state could reflect particular tumor cell aberration states. Obtaining spatial lymphocytic patterns linked to the rich genomic characterization of TCGA samples demonstrates one use for the TCGA image archives with insights into the tumor-immune microenvironment

Genetic Networks of Liver Metabolism Revealed by Integration of Metabolic and Transcriptional Profiling

Although numerous quantitative trait loci (QTL) influencing disease-related phenotypes have been detected through gene mapping and positional cloning, identification of the individual gene(s) and molecular pathways leading to those phenotypes is often elusive. One way to improve understanding of genetic architecture is to classify phenotypes in greater depth by including transcriptional and metabolic profiling. In the current study, we have generated and analyzed mRNA expression and metabolic profiles in liver samples obtained in an F2 intercross between the diabetes-resistant C57BL/6 leptinob/ob and the diabetes-susceptible BTBR leptinob/ob mouse strains. This cross, which segregates for genotype and physiological traits, was previously used to identify several diabetes-related QTL. Our current investigation includes microarray analysis of over 40,000 probe sets, plus quantitative mass spectrometry-based measurements of sixty-seven intermediary metabolites in three different classes (amino acids, organic acids, and acyl-carnitines). We show that liver metabolites map to distinct genetic regions, thereby indicating that tissue metabolites are heritable. We also demonstrate that genomic analysis can be integrated with liver mRNA expression and metabolite profiling data to construct causal networks for control of specific metabolic processes in liver. As a proof of principle of the practical significance of this integrative approach, we illustrate the construction of a specific causal network that links gene expression and metabolic changes in the context of glutamate metabolism, and demonstrate its validity by showing that genes in the network respond to changes in glutamine and glutamate availability. Thus, the methods described here have the potential to reveal regulatory networks that contribute to chronic, complex, and highly prevalent diseases and conditions such as obesity and diabetes

Integrated Genomic Analysis of the Ubiquitin Pathway across Cancer Types

Protein ubiquitination is a dynamic and reversibleprocess of adding single ubiquitin molecules orvarious ubiquitin chains to target proteins. Here,using multidimensional omic data of 9,125 tumorsamples across 33 cancer types from The CancerGenome Atlas, we perform comprehensive molecu-lar characterization of 929 ubiquitin-related genesand 95 deubiquitinase genes. Among them, we sys-tematically identify top somatic driver candidates,including mutatedFBXW7with cancer-type-specificpatterns and amplifiedMDM2showing a mutuallyexclusive pattern withBRAFmutations. Ubiquitinpathway genes tend to be upregulated in cancermediated by diverse mechanisms. By integratingpan-cancer multiomic data, we identify a group oftumor samples that exhibit worse prognosis. Thesesamples are consistently associated with the upre-gulation of cell-cycle and DNA repair pathways, char-acterized by mutatedTP53,MYC/TERTamplifica-tion, andAPC/PTENdeletion. Our analysishighlights the importance of the ubiquitin pathwayin cancer development and lays a foundation fordeveloping relevant therapeutic strategies

Machine Learning Identifies Stemness Features Associated with Oncogenic Dedifferentiation.

Cancer progression involves the gradual loss of a differentiated phenotype and acquisition of progenitor and stem-cell-like features. Here, we provide novel stemness indices for assessing the degree of oncogenic dedifferentiation. We used an innovative one-class logistic regression (OCLR) machine-learning algorithm to extract transcriptomic and epigenetic feature sets derived from non-transformed pluripotent stem cells and their differentiated progeny. Using OCLR, we were able to identify previously undiscovered biological mechanisms associated with the dedifferentiated oncogenic state. Analyses of the tumor microenvironment revealed unanticipated correlation of cancer stemness with immune checkpoint expression and infiltrating immune cells. We found that the dedifferentiated oncogenic phenotype was generally most prominent in metastatic tumors. Application of our stemness indices to single-cell data revealed patterns of intra-tumor molecular heterogeneity. Finally, the indices allowed for the identification of novel targets and possible targeted therapies aimed at tumor differentiation