Isoform-level gene signature improves prognostic stratification and accurately classifies glioblastoma subtypes.

Molecular stratification of tumors is essential for developing personalized therapies. Although patient stratification strategies have been successful; computational methods to accurately translate the gene-signature from high-throughput platform to a clinically adaptable low-dimensional platform are currently lacking. Here, we describe PIGExClass (platform-independent isoform-level gene-expression based classification-system), a novel computational approach to derive and then transfer gene-signatures from one analytical platform to another. We applied PIGExClass to design a reverse transcriptase-quantitative polymerase chain reaction (RT-qPCR) based molecular-subtyping assay for glioblastoma multiforme (GBM), the most aggressive primary brain tumors. Unsupervised clustering of TCGA (the Cancer Genome Altas Consortium) GBM samples, based on isoform-level gene-expression profiles, recaptured the four known molecular subgroups but switched the subtype for 19% of the samples, resulting in significant (P = 0.0103) survival differences among the refined subgroups. PIGExClass derived four-class classifier, which requires only 121 transcript-variants, assigns GBM patients' molecular subtype with 92% accuracy. This classifier was translated to an RT-qPCR assay and validated in an independent cohort of 206 GBM samples. Our results demonstrate the efficacy of PIGExClass in the design of clinically adaptable molecular subtyping assay and have implications for developing robust diagnostic assays for cancer patient stratification

GliomaPredict: A Clinically Useful Tool for Assigning Glioma Patients to Specific Molecular Subtypes

Background: Advances in generating genome-wide gene expression data have accelerated the development of molecular-based tumor classification systems. Tools that allow the translation of such molecular classification schemas from research into clinical applications are still missing in the emerging era of personalized medicine. Results: We developed GliomaPredict as a computational tool that allows the fast and reliable classification of glioma patients into one of six previously published stratified subtypes based on sets of extensively validated classifiers derived from hundreds of glioma transcriptomic profiles. Our tool utilizes a principle component analysis (PCA)-based approach to generate a visual representation of the analyses, quantifies the confidence of the underlying subtype assessment and presents results as a printable PDF file. GliomaPredict tool is implemented as a plugin application for the widely-used GenePattern framework. Conclusions: GliomaPredict provides a user-friendly, clinically applicable novel platform for instantly assigning gene expression-based subtype in patients with gliomas thereby aiding in clinical trial design and therapeutic decisionmaking. Implemented as a user-friendly diagnostic tool, we expect that in time GliomaPredict, and tools like it, will become routinely used in translational/clinical research and in the clinical care of patients with gliomas

Pan-urologic cancer genomic subtypes that transcend tissue of origin

AbstractUrologic cancers include cancers of the bladder, kidney, prostate, and testes, with common molecular features spanning different types. Here, we show that 1954 urologic cancers can be classified into nine major genomic subtypes, on the basis of multidimensional and comprehensive molecular characterization (including DNA methylation and copy number, and RNA and protein expression). Tissue dominant effects are first removed computationally in order to define these subtypes, which reveal common processes—reflecting in part tumor microenvironmental influences—driving cellular behavior across tumor lineages. Six of the subtypes feature a mixture of represented cancer types as defined by tissue or cell of origin. Differences in patient survival and in the manifestation of specific pathways—including hypoxia, metabolism, NRF2-ARE, Hippo, and immune checkpoint—can further distinguish the subtypes. Immune checkpoint markers and molecular signatures of macrophages and T cell infiltrates are relatively high within distinct subsets of each cancer type studied. The pan-urologic cancer genomic subtypes would facilitate information sharing involving therapeutic implications between tissue-oriented domains.</jats:p

Integrative analysis of the inter-tumoral heterogeneity of triple-negative breast cancer.

Triple-negative breast cancers (TNBC) lack estrogen and progesterone receptors and HER2 amplification, and are resistant to therapies that target these receptors. Tumors from TNBC patients are heterogeneous based on genetic variations, tumor histology, and clinical outcomes. We used high throughput genomic data for TNBC patients (n = 137) from TCGA to characterize inter-tumor heterogeneity. Similarity network fusion (SNF)-based integrative clustering combining gene expression, miRNA expression, and copy number variation, revealed three distinct patient clusters. Integrating multiple types of data resulted in more distinct clusters than analyses with a single datatype. Whereas most TNBCs are classified by PAM50 as basal subtype, one of the clusters was enriched in the non-basal PAM50 subtypes, exhibited more aggressive clinical features and had a distinctive signature of oncogenic mutations, miRNAs and expressed genes. Our analyses provide a new classification scheme for TNBC based on multiple omics datasets and provide insight into molecular features that underlie TNBC heterogeneity

Prevalence and genetic characterization of Cryptosporidium spp. In diarrheic children from Gonbad Kavoos city, Iran

Background: Cryptosporidium is an intestinal protozean parasite causing water-borne and foodborne outbreaks of diarrheal diseases. The present study was per-formed in order to find prevalence and subtypes of Cryptosporidium among children with diarrhea in Gonbad Kavoos City, Northern Iran. Methods: Diarrheic samples were collected from 547 children. The initial parasi-tological diagnosis was made based on detection of oocysts using the modified Ziehl-Neelsen acid-fast staining method. The positive microscopically samples were selected for sequence analysis of partial 60 kDa glycoprotein (gp60) gene. Results: Out of 547 collected samples, 27 (4.94) were positive for Cryptosporid-ium oocysts. Fifteen from 27 positive samples successfully amplified in PCR. Se-quences analysis of gp60 gene in 15 Cryptosporidium isolates revealed that all of them (100) were C. parvum. The results showed three subtypes of IIa subtype family (7 cases) including IIaA16G2R1, IIaA17G1R1, IIaA22G3R1 and one subtype of IId subtype family (8 cases). The most common allele was IId A17G1d (53.3). Conclusion: The predominance of zoonotic subtype families of C. parvum species (IIa, IId) in the present study is in concordance with previous studies in Iran and emphasizes the significance of zoonotic transmission of cryptosporidiosis in the country. © 2015, Tehran University of Medical Sciences (TUMS). All rights Reserved

Typing tumors using pathways selected by somatic evolution.

Many recent efforts to analyze cancer genomes involve aggregation of mutations within reference maps of molecular pathways and protein networks. Here, we find these pathway studies are impeded by molecular interactions that are functionally irrelevant to cancer or the patient's tumor type, as these interactions diminish the contrast of driver pathways relative to individual frequently mutated genes. This problem can be addressed by creating stringent tumor-specific networks of biophysical protein interactions, identified by signatures of epistatic selection during tumor evolution. Using such an evolutionarily selected pathway (ESP) map, we analyze the major cancer genome atlases to derive a hierarchical classification of tumor subtypes linked to characteristic mutated pathways. These pathways are clinically prognostic and predictive, including the TP53-AXIN-ARHGEF17 combination in liver and CYLC2-STK11-STK11IP in lung cancer, which we validate in independent cohorts. This ESP framework substantially improves the definition of cancer pathways and subtypes from tumor genome data

INTEGRATIVE ANALYSIS OF OMICS DATA IN ADULT GLIOMA AND OTHER TCGA CANCERS TO GUIDE PRECISION MEDICINE

Transcriptomic profiling and gene expression signatures have been widely applied as effective approaches for enhancing the molecular classification, diagnosis, prognosis or prediction of therapeutic response towards personalized therapy for cancer patients. Thanks to modern genome-wide profiling technology, scientists are able to build engines leveraging massive genomic variations and integrating with clinical data to identify “at risk” individuals for the sake of prevention, diagnosis and therapeutic interventions. In my graduate work for my Ph.D. thesis, I have investigated genomic sequencing data mining to comprehensively characterise molecular classifications and aberrant genomic events associated with clinical prognosis and treatment response, through applying high-dimensional omics genomic data to promote the understanding of gene signatures and somatic molecular alterations contributing to cancer progression and clinical outcomes. Following this motivation, my dissertation has been focused on the following three topics in translational genomics. 1) Characterization of transcriptomic plasticity and its association with the tumor microenvironment in glioblastoma (GBM). I have integrated transcriptomic, genomic, protein and clinical data to increase the accuracy of GBM classification, and identify the association between the GBM mesenchymal subtype and reduced tumorpurity, accompanied with increased presence of tumor-associated microglia. Then I have tackled the sole source of microglial as intrinsic tumor bulk but not their corresponding neurosphere cells through both transcriptional and protein level analysis using a panel of sphere-forming glioma cultures and their parent GBM samples.FurthermoreI have demonstrated my hypothesis through longitudinal analysis of paired primary and recurrent GBM samples that the phenotypic alterations of GBM subtypes are not due to intrinsic proneural-to-mesenchymal transition in tumor cells, rather it is intertwined with increased level of microglia upon disease recurrence. Collectively I have elucidated the critical role of tumor microenvironment (Microglia and macrophages from central nervous system) contributing to the intra-tumor heterogeneity and accurate classification of GBM patients based on transcriptomic profiling, which will not only significantly impact on clinical perspective but also pave the way for preclinical cancer research. 2) Identification of prognostic gene signatures that stratify adult diffuse glioma patientsharboring1p/19q co-deletions. I have compared multiple statistical methods and derived a gene signature significantly associated with survival by applying a machine learning algorithm. Then I have identified inflammatory response and acetylation activity that associated with malignant progression of 1p/19q co-deleted glioma. In addition, I showed this signature translates to other types of adult diffuse glioma, suggesting its universality in the pathobiology of other subset gliomas. My efforts on integrative data analysis of this highly curated data set usingoptimizedstatistical models will reflect the pending update to WHO classification system oftumorsin the central nervous system (CNS). 3) Comprehensive characterization of somatic fusion transcripts in Pan-Cancers. I have identified a panel of novel fusion transcripts across all of TCGA cancer types through transcriptomic profiling. Then I have predicted fusion proteins with kinase activity and hub function of pathway network based on the annotation of genetically mobile domains and functional domain architectures. I have evaluated a panel of in -frame gene fusions as potential driver mutations based on network fusion centrality hypothesis. I have also characterised the emerging complexity of genetic architecture in fusion transcripts through integrating genomic structure and somatic variants and delineating the distinct genomic patterns of fusion events across different cancer types. Overall my exploration of the pathogenetic impact and clinical relevance of candidate gene fusions have provided fundamental insights into the management of a subset of cancer patients by predicting the oncogenic signalling and specific drug targets encoded by these fusion genes. Taken together, the translational genomic research I have conducted during my Ph.D. study will shed new light on precision medicine and contribute to the cancer research community. The novel classification concept, gene signature and fusion transcripts I have identified will address several hotly debated issues in translational genomics, such as complex interactions between tumor bulks and their adjacent microenvironments, prognostic markers for clinical diagnostics and personalized therapy, distinct patterns of genomic structure alterations and oncogenic events in different cancer types, therefore facilitating our understanding of genomic alterations and moving us towards the development of precision medicine

Phylogeography, Risk Factors and Genetic History of Hepatitis C Virus in Gabon, Central Africa

BACKGROUND: The epidemiological and molecular characteristics of hepatitis C virus (HCV) infection in the general population have been poorly investigated in Africa. The aim of this study was to determine the prevalence, genotype distribution and epidemic history of HCV in the Gabonese general population. METHODS/PRINCIPAL FINDINGS: A total of 4042 sera collected from adults in 220 villages in all nine administrative areas of the country were screened for antibodies to HCV. HCV NS5B region sequencing was performed for molecular characterization and population genetic analyses. Of 4042 tested sera, 455 (11.2%) were positive. The seroprevalence of HCV varied significantly by administrative area, with the highest rate in Ogooue-Lolo province (20.4%) and the lowest in Ogooue-Maritine province (3.7%). History of parenteral injections, past hospital admission and age over 55 years were independent risk factors for HCV infection (p<0.0001). Phylogenetic analyses showed that 91.9% of the strains were genotype 4 (HCV-4), 5.7% genotype 1 and 2.2% genotype 2. HCV-4 strains were highly heterogeneous, with more than eight subtypes; subtype 4e predominated (57.3%). Coalescence analyses indicated that subtype 4e was the oldest, with an estimated most recent common ancestor of 1702 [95% CI, 1418-1884]. The epidemic profile indicated that it spread exponentially during the first part of the 20th century, probably by iatrogenic transmission. CONCLUSIONS/SIGNIFICANCE: These results confirm the endemicity of HCV subtype 4e in Gabon and show that its spread is due to a cohort effect, with previous, possibly iatrogenic events. More extensive epidemiological studies are needed to better characterize the route of transmission and the dissemination of HCV in Gabon

Comprehensive transcriptomic analysis of cell lines as models of primary tumors across 22 tumor types.

Cancer cell lines are a cornerstone of cancer research but previous studies have shown that not all cell lines are equal in their ability to model primary tumors. Here we present a comprehensive pan-cancer analysis utilizing transcriptomic profiles from The Cancer Genome Atlas and the Cancer Cell Line Encyclopedia to evaluate cell lines as models of primary tumors across 22 tumor types. We perform correlation analysis and gene set enrichment analysis to understand the differences between cell lines and primary tumors. Additionally, we classify cell lines into tumor subtypes in 9 tumor types. We present our pancreatic cancer results as a case study and find that the commonly used cell line MIA PaCa-2 is transcriptionally unrepresentative of primary pancreatic adenocarcinomas. Lastly, we propose a new cell line panel, the TCGA-110-CL, for pan-cancer studies. This study provides a resource to help researchers select more representative cell line models