Genotypic differences greatly influence susceptibility and resistance to disease. Understanding genotype-phenotype relationships requires that phenotypes be viewed as manifestations of network properties, rather than simply as the result of individual genomic variations. Genome sequencing efforts have identified numerous germline mutations associated with cancer predisposition and large numbers of somatic genomic alterations. However, it remains challenging to distinguish between background, or passenger and causal, or driver cancer mutations in these datasets. Human viruses intrinsically depend on their host cell during the course of infection and can elicit pathological phenotypes similar to those arising from mutations. To test the hypothesis that genomic variations and tumour viruses may cause cancer via related mechanisms, we systematically examined host interactome and transcriptome network perturbations caused by DNA tumour virus proteins. The resulting integrated viral perturbation data reflects rewiring of the host cell networks, and highlights pathways that go awry in cancer, such as Notch signalling and apoptosis. We show that systematic analyses of host targets of viral proteins can identify cancer genes with a success rate on par with their identification through functional genomics and large-scale cataloguing of tumour mutations. These complementary approaches together result in increased specificity for cancer gene identification. Combining systems-level studies of pathogen-encoded gene products with genomic approaches will facilitate prioritization of cancer-causing driver genes so as to advance understanding of the genetic basis of human cancer.
Interpreting cancer genomes using systematic host network perturbations by tumour virus proteins.
Cell lineView Samples
We obtained gene expression data and HD-SNP6.0 copy number data from PTL, PCNSL and PMLBCL samples and performed an integrative analysis on them. RNA was whole genome amplified using Nugen.
Targetable genetic features of primary testicular and primary central nervous system lymphomas.
Disease, Disease stageView Samples
We performed gene expression profiling of laser capture microdissected normal non-neoplastic prostate (cystoprostatectomies) epithelial tissue and compared it to non-transformed and neoplastic low and high grade prostate epithelial tissue from radical prostatectomies, each with its immediately surrounding stroma.
Stromal and epithelial transcriptional map of initiation progression and metastatic potential of human prostate cancer.
No sample metadata fieldsView Samples
To understand the molecular curcuits perturbed by BET bromodoman inhibtion we obtained gene expression profiling of five DLBCL cell lines, SU-DHL6, OCI-Ly1, OCI-Ly4, Toledo and HBL-1, which were treated with either 500nM JQ1 or DMSO for 0,2,6,12,24 and 48hr.
Discovery and characterization of super-enhancer-associated dependencies in diffuse large B cell lymphoma.
Specimen part, Cell line, Treatment, TimeView Samples
Gene Expression profiling of 170 newly diagnosed Multiple Myeloma patients
A small molecule inhibitor of ubiquitin-specific protease-7 induces apoptosis in multiple myeloma cells and overcomes bortezomib resistance.
We obtained gene experssion profiles of 52 newly diagnosed diffuse large B-cell lymphoma (DLBCL).
Molecular subtypes of diffuse large B cell lymphoma are associated with distinct pathogenic mechanisms and outcomes.
Specimen partView Samples
Predictors built from gene expression data accurately predict ER, PR, and HER2 status, and divide tumor grade into high-grade and low-grade clusters; intermediate-grade tumors are not a unique group. In contrast, gene expression data cannot be used to predict tumor size or lymphatic-vascular invasion.
Predicting features of breast cancer with gene expression patterns.
No sample metadata fieldsView Samples
Urothelial carcinoma of the bladder is characterized by significant variability in clinical outcomes depending on stage and grade. The addition of molecular information may improve our understanding of such heterogeneity and enhance prognostic prediction. The purpose of this study was to validate and improve published prognostic signatures for high-risk bladder cancer.
Combination of a novel gene expression signature with a clinical nomogram improves the prediction of survival in high-risk bladder cancer.