scholarly journals Convergent architecture of the transcriptome of human cancer

2015 ◽  
Author(s):  
Lihua Zou
Keyword(s):  
Large Scale ◽  
Functional Organization ◽  
Human Cancer ◽  
Adaptive Immune Response ◽  
Cancer Genome ◽  
The Cancer Genome Atlas ◽  
System Level ◽  
Data Sets ◽  
Using Data ◽  
Conserved Gene

Despite large-scale efforts to systematically map the cancer genome, little is known about how the interplay of genetic and epigenetic alternations shapes the architecture of the transcriptome of human cancer. With the goal of constructing a system-level view of the deregulated pathways in cancer cells, we systematically investigated the functional organization of the transcriptomes of 10 tumor types using data sets generated by The Cancer Genome Atlas project (TCGA). Our analysis indicates that the human cancer transcriptome is organized into well-conserved modules of co-expressed genes. In particular, our analysis identified a set of conserved gene modules with distinct cancer hallmark themes involving cell cycle regulation, angiogenesis, innate and adaptive immune response, differentiation, metabolism and regulation of protein phosphorylation. Our analysis provided global views of convergent transcriptome architecture of human cancer. The result of our analysis can serve as a foundation to link diverse genomic alternations to common transcriptomic features in human cancer.

scholarly journals Large-scale profiling of microRNAs for The Cancer Genome Atlas

2015 ◽  
Vol 44 (1) ◽  
pp. e3-e3 ◽  
Author(s):  
Andy Chu ◽  
Gordon Robertson ◽  
Denise Brooks ◽  
Andrew J. Mungall ◽  
Inanc Birol ◽  
...  
Keyword(s):  
Large Scale ◽  
Cancer Genome ◽  
The Cancer Genome Atlas ◽  
Cancer Genome Atlas ◽  
Genome Atlas

Molecular profiling in cancer research and personalized medicine

2019 ◽  
pp. 347-362
Author(s):  
Pieter-Jan van Dam ◽  
Steven Van Laere
Keyword(s):  
Personalized Medicine ◽  
High Throughput ◽  
Cancer Biology ◽  
Complex Disease ◽  
Human Cancer ◽  
Molecular Profiling ◽  
Cancer Genome ◽  
The Cancer Genome Atlas ◽  
Molecular Heterogeneity ◽  
Profiling Techniques

Recent efforts by worldwide consortia such as The Cancer Genome Atlas and the International Cancer Genome Consortium have greatly accelerated our knowledge of human cancer biology. Nowadays, complete sets of human tumours that have been characterized at the genomic, epigenomic, transcriptomic, or proteomic level are available to the research community. The generation of these data was made possible thanks to the application of high-throughput molecular profiling techniques such as microarrays and next-generation sequencing. The primary conclusion from current profiling experiments is that human cancer is a complex disease characterized by extreme molecular heterogeneity, both between and within the classical, tissue-defined cancer types. This molecular variety necessitates a paradigm shift in patient management, away from generalized therapy schemes and towards more personalized treatments. This chapter provides an overview of how molecular cancer profiling can assist in facilitating this transition. First, the state-of-the-art of molecular breast cancer profiling is reviewed to provide a general background. Then, the most pertinent high-throughput molecular profiling techniques along with various data mining techniques (i.e. unsupervised clustering, statistical learning) are discussed. Finally, the challenges and perspectives with respect to molecular cancer profiling, also from the perspective of personalized medicine, are summarized.

scholarly journals FIREVAT: finding reliable variants without artifacts in human cancer samples using etiologically relevant mutational signatures

2019 ◽  
Vol 11 (1) ◽  
Author(s):  
Hyunbin Kim ◽  
Andy Jinseok Lee ◽  
Jongkeun Lee ◽  
Hyonho Chun ◽  
Young Seok Ju ◽  
...  
Keyword(s):  
Data Storage ◽  
Human Cancer ◽  
Point Mutations ◽  
Variant Calling ◽  
Human Tumor ◽  
Cancer Genome ◽  
The Cancer Genome Atlas ◽  
Precision Oncology ◽  
Mutational Signatures ◽  
Accurate Identification

Abstract Background Accurate identification of real somatic variants is a primary part of cancer genome studies and precision oncology. However, artifacts introduced in various steps of sequencing obfuscate confidence in variant calling. Current computational approaches to variant filtering involve intensive interrogation of Binary Alignment Map (BAM) files and require massive computing power, data storage, and manual labor. Recently, mutational signatures associated with sequencing artifacts have been extracted by the Pan-cancer Analysis of Whole Genomes (PCAWG) study. These spectrums can be used to evaluate refinement quality of a given set of somatic mutations. Results Here we introduce a novel variant refinement software, FIREVAT (FInding REliable Variants without ArTifacts), which uses known spectrums of sequencing artifacts extracted from one of the largest publicly available catalogs of human tumor samples. FIREVAT performs a quick and efficient variant refinement that accurately removes artifacts and greatly improves the precision and specificity of somatic calls. We validated FIREVAT refinement performance using orthogonal sequencing datasets totaling 384 tumor samples with respect to ground truth. Our novel method achieved the highest level of performance compared to existing filtering approaches. Application of FIREVAT on additional 308 The Cancer Genome Atlas (TCGA) samples demonstrated that FIREVAT refinement leads to identification of more biologically and clinically relevant mutational signatures as well as enrichment of sequence contexts associated with experimental errors. FIREVAT only requires a Variant Call Format file (VCF) and generates a comprehensive report of the variant refinement processes and outcomes for the user. Conclusions In summary, FIREVAT facilitates a novel refinement strategy using mutational signatures to distinguish artifactual point mutations called in human cancer samples. We anticipate that FIREVAT results will further contribute to precision oncology efforts that rely on accurate identification of variants, especially in the context of analyzing mutational signatures that bear prognostic and therapeutic significance. FIREVAT is freely available at https://github.com/cgab-ncc/FIREVAT

scholarly journals Comprehensive characterization of protein-protein interaction network perturbations by human disease mutations

2020 ◽  
Author(s):  
Feixiong Cheng ◽  
Junfei Zhao ◽  
Yang Wang ◽  
Weiqiang Lu ◽  
Zehui Liu ◽  
...  
Keyword(s):  
Protein Interaction ◽  
Large Scale ◽  
Human Cancer ◽  
The Cancer Genome Atlas ◽  
Binary Interaction ◽  
Missense Mutations ◽  
Cancer Subtypes ◽  
Protein Protein Interaction ◽  
Protein Interfaces ◽  
Disease Mutations

AbstractTechnological and computational advances in genomics and interactomics have made it possible to identify rapidly how disease mutations perturb interaction networks within human cells. In this study, we investigate at large-scale the effects of network perturbations caused by disease mutations within the human three-dimensional (3D), structurally-resolved macromolecular interactome. We show that disease-associated germline mutations are significantly enriched in sequences encoding protein-protein interfaces compared to mutations identified in healthy subjects from the 1000 Genomes and ExAC projects; these interface mutations correspond to protein-protein interaction (PPI)-perturbing alleles including p.Ser127Arg in PCSK9 at the PCSK9-LDLR interface. In addition, somatic missense mutations are significantly enriched in PPI interfaces compared to non-interfaces in 10,861 human exomes across 33 cancer subtypes/types from The Cancer Genome Atlas. Using a binomial statistical model, we computationally identified 470 PPIs harboring a statistically significant excess number of missense mutations at protein-protein interfaces (termed putative oncoPPIs) in pan-cancer analysis. We demonstrate that the oncoPPIs, including histone H4 complex in individual cancer types, are highly correlated with patient survival and drug resistance/sensitivity in human cancer cell lines and patient-derived xenografts. We experimentally validate the network effects of 13 oncoPPIs using a systematic binary interaction assay. We further showed that ALOX5 p.Met146Lys at the ALOX5-MAD1L1 interface and RXRA p.Ser427Phe at the RXRA-PPARG interface promote significant tumor cell growth using cell line-based functional assays, providing a functional proof-of-concept. In summary, if broadly applied, this human 3D interactome network analysis offers a powerful tool for prioritizing alleles with mutations altering PPIs that may contribute to the pathobiology of human diseases, and may offer disease-specific targets for genotype-informed therapeutic discovery.

scholarly journals Identification of ST7 Alteration Profile, Frequency of Alteration and Correlation with ST7-Related Genes using TCGA Data

2019 ◽  
Vol 16 (3) ◽  
pp. 217-230
Author(s):  
Nurdina CHARONG ◽  
Moltira PROMKAN
Keyword(s):  
Disease Free Survival ◽  
Cancer Genome ◽  
The Cancer Genome Atlas ◽  
Free Survival ◽  
Cancer Genome Atlas ◽  
Stomach Adenocarcinoma ◽  
Using Data ◽  
Liver Hepatocellular Carcinoma ◽  
Disease Free ◽  
Genome Atlas

ST7 (Suppression of Tumorigenicity 7) was reported as a protein playing a role in maintaining cellular structure. This study aims to investigate the ST7 alteration profiles and frequency of alteration in different cancers using data from The Cancer Genome Atlas (TCGA). The correlation between alterations of ST7 and angiogenesis-related genes, SERPINE1, MMP13, and VEGFA, was determined and the relation between ST7 and genes involved in suppression of ST7 transcription, PRMT5 and SMARCA4, were also analyzed. Data of 6 cancer groups from The Cancer Genome Atlas (TCGA) including ovarian serous cystadenocarcinoma (OSC), liver hepatocellular carcinoma (LHC), bladder urothelial adenocarcinoma (BUA), stomach adenocarcinoma (SC), prostate adenocarcinoma (PRAD) and glioblastoma multiforme (GBM) were downloaded for this study. The results indicated that 3 alteration patterns including amplification, missense mutation, and deletion were observed in 6 cancer studies. Gene pair between ST7 and SERPINE1 indicated the co-occurrent alteration in BUC, OSC and SC (p < 0.05). However, no association between alterations of these 2 genes and survival events in our study was observed. Shorter overall survival rate and disease-free survival were found in BUC patients with ST7, PRMT5, and  SMARCA4 alterations. These findings suggest that using TCGA data can target the potential genes involved in carcinogenesis. Combining ST7 with PRMT5 and SMARCA4 could be used as indicators for analyzing the patient survival in BUC patients and may serve as the potential therapeutic target for cancer in the future.

Analysing large scale TGCT data from The Cancer Genome Atlas (TCGA): Challenges and considerations

2019 ◽  
Vol 18 (4) ◽  
pp. 38
Author(s):  
S. Smith ◽  
K. Amin ◽  
S. Fang ◽  
T. Morrison ◽  
N. Coleman ◽  
...  
Keyword(s):  
Large Scale ◽  
Cancer Genome ◽  
The Cancer Genome Atlas ◽  
Cancer Genome Atlas ◽  
Genome Atlas

scholarly journals Combining asynchronous data sets in regional body-wave tomography

2020 ◽  
Vol 224 (1) ◽  
pp. 401-415
Author(s):  
Valérie Maupin
Keyword(s):  
Large Scale ◽  
Reference Model ◽  
Body Wave ◽  
Body Waves ◽  
Joint Analysis ◽  
Data Sets ◽  
Asynchronous Network ◽  
Tomographic Method ◽  
Wave Tomography ◽  
Using Data

SUMMARY Regional body-wave tomography is a very popular tomographic method consisting in inverting relative traveltime residuals of teleseismic body waves measured at regional networks. It is well known that the resulting inverse seismic model is relative to an unknown vertically varying reference model. If jointly inverting data obtained with networks in the vicinity of each other but operating at different times, the relative velocity anomalies in different areas of the model may have different reference levels, possibly introducing large-scale biases in the model that may compromise the interpretation. This is very unfortunate as we have numerous examples of asynchronous network deployments which would benefit from a joint analysis. We show here how a simple improvement in the formulation of the sensitivity kernels allows us to mitigate this problem. Using sensitivity kernels that take into account that data processing implies a zero mean residual for each event, the large-scale biases that otherwise arise in the inverse model using data from asynchronous station deployment are largely removed. We illustrate this first with a very simple 3-station example, and then compare the results obtained using the usual and the relative kernels in synthetic tests with more realistic station coverage, simulating data acquisition at two neighbouring asynchronous networks.

scholarly journals RNF125 Modulates Tumor Immunity by Promoting Ubiquitination of PD-L1

2021 ◽  
Author(s):  
Yumeng Peng ◽  
Huan Yang ◽  
Zihui Li ◽  
Huilong Li ◽  
Xiaolin Qiu ◽  
...  
Keyword(s):  
Tumor Immunity ◽  
Human Cancer ◽  
Ring Finger ◽  
Ring Domain ◽  
The Cancer Genome Atlas ◽  
Comprehensive Treatment ◽  
Cancer Genome Atlas ◽  
Using Data ◽  
The Stability ◽  
L1 Protein

Abstract In recent years, the incidence of tumors has been increasing, and the overall cure rate by traditional treatment methods does not exceed 20%. One of the most effective and promising strategies for comprehensive treatment of tumors is immunotherapy, such as treatment with the PD-1/PD-L1 antibody. Here, we showed that ring finger protein 125 (RNF125), an E3 ligase in the RING domain family, could interact with PD-L1 to reduce the stability of PD-L1 protein. In addition, RNF125 downregulated the expression of PD-L1 by promoting its ubiquitination at K48, whereas a mutation in the RING domain of RNF125 disrupted this function. A significant positive correlation between RNF125 and genes involved with tumor immunity was determined in cancer samples, as determined using data from The Cancer Genome Atlas (TCGA). Furthermore, we elucidated the effects of RNF125 on the occurrence and development of tumors in mice. Analyses of wild-type and RNF125 knockout mice transplanted with MC-38 cells revealed enhanced MC-38 tumor growth in KO mice. These data indicated that RNF125 could participate in tumor immunity by promoting the K48-linked ubiquitination of PD-L1 to affect the occurrence and development of tumors, providing a potential target for enhancing therapeutic efficacy for human cancer treatment.

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