My personal mutanome: a computational genomic medicine platform for searching network perturbing alleles linking genotype to phenotype

AbstractMassive genome sequencing data have inspired new challenges in personalized treatments and facilitated oncological drug discovery. We present a comprehensive database, My Personal Mutanome (MPM), for accelerating the development of precision cancer medicine protocols. MPM contains 490,245 mutations from over 10,800 tumor exomes across 33 cancer types in The Cancer Genome Atlas mapped to 94,563 structure-resolved/predicted protein-protein interaction interfaces (“edgetic”) and 311,022 functional sites (“nodetic”), including ligand-protein binding sites and 8 types of protein posttranslational modifications. In total, 8884 survival results and 1,271,132 drug responses are obtained for these mapped interactions. MPM is available at https://mutanome.lerner.ccf.org.

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The landscape of high-affinity human antibodies against intratumoral antigens

10.1101/2021.02.06.430058 ◽

2021 ◽

Author(s):

Goran Rakocevic ◽

Irina Glotova ◽

Ines de Santiago ◽

Berke Cagkan Toptas ◽

Milena Popovic ◽

...

Keyword(s):

Mammalian Cells ◽

The Cancer Genome Atlas ◽

Sequencing Data ◽

Human Antibodies ◽

Cell Responses ◽

High Affinity ◽

Human Proteins ◽

Cancer Genome Atlas ◽

Cancer Types ◽

Nanomolar Range

AbstractIntratumoral expression of antibody transcripts can be detected across different cancer types and is correlated with positive clinical outcomes. However, the antigens recognized by these antibodies remain largely unknown. Here we inferred the paired sequence of thousands of clonally expanded antibodies using bulk RNA sequencing data from solid tumors in The Cancer Genome Atlas (TCGA). After expressing 283 candidate antibodies in mammalian cells, we individually tested them against a library of twenty thousands full-length human proteins and a library of five thousands membrane-only proteins. Using surface plasmon resonance we confirmed 13 high-affinity antibodies that bind to their targets with KD in the low nanomolar range. Our work provides insights into the antigens that drive B cell responses in human cancers while also directly identifying fully human, high-affinity antibodies against them.

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TumorFusions: an integrative resource for reporting cancer-associated transcript fusions in 33 tumor types

10.1101/162180 ◽

2017 ◽

Author(s):

Xin Hu ◽

Qianghu Wang ◽

Floris Barthel ◽

Ming Tang ◽

Samirkumar Amin ◽

...

Keyword(s):

The Cancer Genome Atlas ◽

Whole Genome Sequencing Data ◽

Fusion Genes ◽

Sequencing Data ◽

Structural Variations ◽

Cancer Genome Atlas ◽

Cancer Types ◽

Tumor Types ◽

Fusion Detection ◽

Genome Atlas

Fusion genes, particularly those involving kinases, have been demonstrated as drivers and are frequent therapeutic targets in cancer1. Here, we describe our results on detecting transcript fusions across 33 cancer types from The Cancer Genome Atlas (TCGA), totaling 9,966 cancer samples and 648 normal samples2. Preprocessing, including read alignment to both genome and transcriptome, and fusion detection were carried out using a uniform pipeline3. To validate the resultant fusions, we also called somatic structural variations for 561 cancers from whole genome sequencing data. A summary of the data used in this study is provided in Table S1. Our results can be accessed per our portal at http://www.tumorfusions.org.

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SuperFreq: Integrated mutation detection and clonal tracking in cancer

10.1101/380097 ◽

2018 ◽

Cited By ~ 1

Author(s):

Christoffer Flensburg ◽

Tobias Sargeant ◽

Alicia Oshlack ◽

Ian Majewski

Keyword(s):

Clonal Evolution ◽

The Cancer Genome Atlas ◽

Sequencing Analysis ◽

Sequencing Data ◽

Single Nucleotide Variants ◽

Multiple Cancer ◽

Cancer Genome Atlas ◽

Scope Of Application ◽

Cancer Types ◽

Multiple Samples

AbstractAnalysing multiple cancer samples from an individual patient can provide insight into the way the disease evolves. Monitoring the expansion and contraction of distinct clones helps to reveal the mutations that initiate the disease and those that drive progression. Existing approaches for clonal tracking from sequencing data typically require the user to combine multiple tools that are not purpose-built for this task. Furthermore, most methods require a matched normal (non-tumour) sample, which limits the scope of application. We developed SuperFreq, a cancer exome sequencing analysis pipeline that integrates identification of somatic single nucleotide variants (SNVs) and copy number alterations (CNAs) and clonal tracking for both. SuperFreq does not require a matched normal and instead relies on unrelated controls. When analysing multiple samples from a single patient, SuperFreq cross checks variant calls to improve clonal tracking, which helps to separate somatic from germline variants, and to resolve overlapping CNA calls. To demonstrate our software we analysed 304 cancer-normal exome samples across 33 cancer types in The Cancer Genome Atlas (TCGA) and evaluated the quality of the SNV and CNA calls. We simulated clonal evolution through in silico mixing of cancer and normal samples in known proportion. We found that SuperFreq identified 93% of clones with a cellular fraction of at least 50% and mutations were assigned to the correct clone with high recall and precision. In addition, SuperFreq maintained a similar level of performance for most aspects of the analysis when run without a matched normal. SuperFreq is highly versatile and can be applied in many different experimental settings for the analysis of exomes and other capture libraries. We demonstrate an application of SuperFreq to leukaemia patients with diagnosis and relapse samples.SuperFreq is implemented in R and available on github at https://github.com/ChristofferFlensburg/SuperFreq.

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Upgrading the Repertoire of miRNAs in Gastric Adenocarcinoma to Provide a New Resource for Biomarker Discovery

International Journal of Molecular Sciences ◽

10.3390/ijms20225697 ◽

2019 ◽

Vol 20 (22) ◽

pp. 5697 ◽

Cited By ~ 2

Author(s):

Michelle E. Pewarchuk ◽

Mateus C. Barros-Filho ◽

Brenda C. Minatel ◽

David E. Cohn ◽

Florian Guisier ◽

...

Keyword(s):

Gastric Adenocarcinoma ◽

Biomarker Discovery ◽

The Cancer Genome Atlas ◽

Small Rna Sequencing ◽

Sequencing Data ◽

Specific Expression ◽

Novel Mirna ◽

Cancer Genome Atlas ◽

Context Specific ◽

Independent Cohort

Recent studies have uncovered microRNAs (miRNAs) that have been overlooked in early genomic explorations, which show remarkable tissue- and context-specific expression. Here, we aim to identify and characterize previously unannotated miRNAs expressed in gastric adenocarcinoma (GA). Raw small RNA-sequencing data were analyzed using the miRMaster platform to predict and quantify previously unannotated miRNAs. A discovery cohort of 475 gastric samples (434 GA and 41 adjacent nonmalignant samples), collected by The Cancer Genome Atlas (TCGA), were evaluated. Candidate miRNAs were similarly assessed in an independent cohort of 25 gastric samples. We discovered 170 previously unannotated miRNA candidates expressed in gastric tissues. The expression of these novel miRNAs was highly specific to the gastric samples, 143 of which were significantly deregulated between tumor and nonmalignant contexts (p-adjusted < 0.05; fold change > 1.5). Multivariate survival analyses showed that the combined expression of one previously annotated miRNA and two novel miRNA candidates was significantly predictive of patient outcome. Further, the expression of these three miRNAs was able to stratify patients into three distinct prognostic groups (p = 0.00003). These novel miRNAs were also present in the independent cohort (43 sequences detected in both cohorts). Our findings uncover novel miRNA transcripts in gastric tissues that may have implications in the biology and management of gastric adenocarcinoma.

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Hyaluronan-mediated motility receptor expression functions as a prognostic biomarker in uterine carcinosarcoma based on bioinformatics analysis

Journal of International Medical Research ◽

10.1177/03000605211021043 ◽

2021 ◽

Vol 49 (6) ◽

pp. 030006052110210

Author(s):

Hui Sun ◽

Li Ma ◽

Jie Chen

Keyword(s):

Interaction Network ◽

Maximal Clique ◽

Receptor Expression ◽

The Cancer Genome Atlas ◽

Uterine Carcinosarcoma ◽

Sequencing Data ◽

Hub Genes ◽

Protein Protein Interaction ◽

Normal Tissues ◽

Potential Biomarker

Objective Uterine carcinosarcoma (UCS) is a rare, aggressive tumour with a high metastasis rate and poor prognosis. This study aimed to explore potential key genes associated with the prognosis of UCS. Methods Transcriptional expression data were downloaded from the Gene Expression Profiling Interactive Analysis database and differentially expressed genes (DEGs) were subjected to Gene Ontology and Kyoto Encyclopedia of Genes and Genomes analyses using Metascape. A protein–protein interaction network was constructed using the STRING website and Cytoscape software, and the top 30 genes obtained through the Maximal Clique Centrality algorithm were selected as hub genes. These hub genes were validated by clinicopathological and sequencing data for 56 patients with UCS from The Cancer Genome Atlas database. Results A total of 1894 DEGs were identified, and the top 30 genes were considered as hub genes. Hyaluronan-mediated motility receptor (HMMR) expression was significantly higher in UCS tissues compared with normal tissues, and elevated expression of HMMR was identified as an independent prognostic factor for shorter survival in patients with UCS. Conclusions These results suggest that HMMR may be a potential biomarker for predicting the prognosis of patients with UCS.

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Heme Biosynthesis mRNA Expression Signature: Towards a Novel Prognostic Biomarker in Patients with Diffusely Infiltrating Gliomas

Cancers ◽

10.3390/cancers13040662 ◽

2021 ◽

Vol 13 (4) ◽

pp. 662

Author(s):

Mario Mischkulnig ◽

Barbara Kiesel ◽

Daniela Lötsch ◽

Thomas Roetzer ◽

Martin Borkovec ◽

...

Keyword(s):

Overall Survival ◽

Mrna Expression ◽

Postoperative Management ◽

Heme Biosynthesis ◽

The Cancer Genome Atlas ◽

Sequencing Data ◽

Who Grade ◽

Expression Signature ◽

Cancer Genome Atlas ◽

The Impact

Diffusely infiltrating gliomas are characterized by a variable clinical course, and thus novel prognostic biomarkers are needed. The heme biosynthesis cycle constitutes a fundamental metabolic pathway and might play a crucial role in glioma biology. The aim of this study was thus to investigate the role of the heme biosynthesis mRNA expression signature on prognosis in a large glioma patient cohort. Glioma patients with available sequencing data on heme biosynthesis expression were retrieved from The Cancer Genome Atlas (TCGA). In each patient, the heme biosynthesis mRNA expression signature was calculated and categorized into low, medium, and high expression subgroups. Differences in progression-free and overall survival between these subgroups were investigated including a multivariate analysis correcting for WHO grade, tumor subtype, and patient age and sex. In a total of 693 patients, progression-free and overall survival showed a strictly monotonical decrease with increasing mRNA expression signature subgroups. In detail, median overall survival was 134.2 months in the low, 79.9 months in the intermediate, and 16.5 months in the high mRNA expression signature subgroups, respectively. The impact of mRNA expression signature on progression-free and overall survival was independent of the other analyzed prognostic factors. Our data indicate that the heme biosynthesis mRNA expression signature might serve as an additional novel prognostic marker in patients with diffusely infiltrating gliomas to optimize postoperative management.

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Effect of Different Expression of Immune-Related lncRNA on Colon Adenocarcinoma and Its Relation to Prognosis

BioMed Research International ◽

10.1155/2020/6942740 ◽

2020 ◽

Vol 2020 ◽

pp. 1-9

Author(s):

Meiwei Mu ◽

Yi Tang ◽

Zheng Yang ◽

Yuling Qiu ◽

Xiaohong Li ◽

...

Keyword(s):

Cox Regression ◽

Colon Adenocarcinoma ◽

The Cancer Genome Atlas ◽

Risk Scores ◽

Future Research ◽

Sequencing Data ◽

Tissue Samples ◽

Gene Sets ◽

Cancer Genome Atlas ◽

Immune Related Genes

Objective. To explore the expression of immune-related lncRNAs in colon adenocarcinoma and find out the effect on how these lncRNAs influence the development and prognosis of colon adenocarcinoma. Method. Transcriptome data of colon adenocarcinoma from The Cancer Genome Atlas (TCGA) were downloaded, and gene sets “IMMUNE RESPONSE” and “IMMUNE SYSTEM PROCESS” were sought from the Molecular Signatures Database (MSigDB). The expression of immune-related genes was extracted that were immune-related mRNAs. Then, the immune-related lncRNAs were sought out by utilizing of the above data. Clinical traits were combined with immune-related lncRNAs, so that prognostic-related lncRNAs were identified by Cox regression. Multivariate Cox regression was built to calculate risk scores. Relationships between clinical traits and immune-related lncRNAs were also calculated. Result. A total of 480 colorectal adenocarcinoma patients and 41 normal control patients’ transcriptome sequencing data of tissue samples were obtained from TCGA database. 918 immune-related lncRNAs were screened. Cox regression showed that 34 immune-related lncRNAs were associated with colon adenocarcinoma prognosis. Seven lncRNAs were independent risk factors. Conclusion. This study revealed that some lncRNAs can affect the development and prognosis of colon adenocarcinoma. It may provide new theory evidence of molecular mechanism for the future research and molecular targeted therapy of colon adenocarcinoma.

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Exome-Wide Pan-Cancer Analysis of Germline Variants in 8,719 Individuals Finds Little Evidence of Rare Variant Associations

Human Heredity ◽

10.1159/000519355 ◽

2021 ◽

pp. 1-10

Author(s):

Zoe Guan ◽

Ronglai Shen ◽

Colin B. Begg

Keyword(s):

Rare Variant ◽

Rare Variants ◽

Association Studies ◽

The Cancer Genome Atlas ◽

Considerable Proportion ◽

Genome Wide Association Studies ◽

Sequencing Data ◽

Risk Variants ◽

Cancer Types ◽

Pan Cancer

Background: Many cancer types show considerable heritability, and extensive research has been done to identify germline susceptibility variants. Linkage studies have discovered many rare high-risk variants, and genome-wide association studies (GWAS) have discovered many common low-risk variants. However, it is believed that a considerable proportion of the heritability of cancer remains unexplained by known susceptibility variants. The “rare variant hypothesis” proposes that much of the missing heritability lies in rare variants that cannot reliably be detected by linkage analysis or GWAS. Until recently, high sequencing costs have precluded extensive surveys of rare variants, but technological advances have now made it possible to analyze rare variants on a much greater scale. Objectives: In this study, we investigated associations between rare variants and 14 cancer types. Methods: We ran association tests using whole-exome sequencing data from The Cancer Genome Atlas (TCGA) and validated the findings using data from the Pan-Cancer Analysis of Whole Genomes Consortium (PCAWG). Results: We identified four significant associations in TCGA, only one of which was replicated in PCAWG (BRCA1 and ovarian cancer). Conclusions: Our results provide little evidence in favor of the rare variant hypothesis. Much larger sample sizes may be needed to detect undiscovered rare cancer variants.

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An R Package for Divergence Analysis of Omics Data

10.1101/720391 ◽

2019 ◽

Author(s):

Wikum Dinalankara ◽

Qian Ke ◽

Donald Geman ◽

Luigi Marchionni

Keyword(s):

High Throughput Sequencing ◽

R Package ◽

The Cancer Genome Atlas ◽

High Dimensional ◽

Omics Data ◽

Sequencing Data ◽

High Throughput Sequencing Data ◽

Ternary Code ◽

Cancer Genome Atlas ◽

Level Analysis

AbstractGiven the ever-increasing amount of high-dimensional and complex omics data becoming available, it is increasingly important to discover simple but effective methods of analysis. Divergence analysis transforms each entry of a high-dimensional omics profile into a digitized (binary or ternary) code based on the deviation of the entry from a given baseline population. This is a novel framework that is significantly different from existing omics data analysis methods: it allows digitization of continuous omics data at the univariate or multivariate level, facilitates sample level analysis, and is applicable on many different omics platforms. The divergence package, available on the R platform through the Bioconductor repository collection, provides easy-to-use functions for carrying out this transformation. Here we demonstrate how to use the package with sample high throughput sequencing data from the Cancer Genome Atlas.

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High RPS27A Expression Predicts Poor Prognosis in Patients With HPV Type 16 Cervical Cancer

Frontiers in Oncology ◽

10.3389/fonc.2021.752974 ◽

2021 ◽

Vol 11 ◽

Author(s):

Qiming Wang ◽

Yan Cai ◽

Xuewen Fu ◽

Liang Chen

Keyword(s):

Cervical Cancer ◽

Poor Prognosis ◽

Biological Significance ◽

Enrichment Analysis ◽

Hpv Infection ◽

Gene Set Enrichment Analysis ◽

The Cancer Genome Atlas ◽

Protein Protein Interaction ◽

Cervical Cancer Cells ◽

Cancer Genome Atlas

In recent years, the incidence and the mortality rate of cervical cancer have been gradually increasing, becoming one of the major causes of cancer-related death in women. In particular, patients with advanced and recurrent cervical cancers present a very poor prognosis. In addition, the vast majority of cervical cancer cases are caused by human papillomavirus (HPV) infection, of which HPV16 infection is the main cause and squamous cell carcinoma is the main presenting type. In this study, we performed screening of differentially expressed genes (DEGs) based on The Cancer Genome Atlas (TCGA) database and GSE6791, constructed a protein–protein interaction (PPI) network to screen 34 hub genes, filtered to the remaining 10 genes using the CytoHubba plug-in, and used survival analysis to determine that RPS27A was most associated with the prognosis of cervical cancer patients and has prognostic and predictive value for cervical cancer. The most significant biological functions and pathways of RPS27A enrichment were subsequently investigated with gene set enrichment analysis (GSEA), and integration of TCGA and GTEx database analyses revealed that RPS27A was significantly expressed in most cancer types. In this study, our analysis revealed that RPS27A can be used as a prognostic biomarker for HPV16 cervical cancer and has biological significance for the growth of cervical cancer cells.

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