scholarly journals SuperFreq: Integrated mutation detection and clonal tracking in cancer

2018 ◽  
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.

scholarly journals Pan-cancer analysis reveals complex tumor-specific alternative polyadenylation

2017 ◽  
Author(s):  
Zhuyi Xue ◽  
René L Warren ◽  
Ewan A Gibb ◽  
Daniel MacMillan ◽  
Johnathan Wong ◽  
...  
Keyword(s):  
Alternative Polyadenylation ◽  
Length Change ◽  
The Cancer Genome Atlas ◽  
Cancer Type ◽  
Rna Seq ◽  
Multiple Cancer ◽  
Tissue Samples ◽  
Cancer Genome Atlas ◽  
Specific Alternative ◽  
Cancer Types

AbstractAlternative polyadenylation (APA) of 3’ untranslated regions (3’ UTRs) has been implicated in cancer development. Earlier reports on APA in cancer primarily focused on 3’ UTR length modifications, and the conventional wisdom is that tumor cells preferentially express transcripts with shorter 3’ UTRs. Here, we analyzed the APA patterns of 114 genes, a select list of oncogenes and tumor suppressors, in 9,939 tumor and 729 normal tissue samples across 33 cancer types using RNA-Seq data from The Cancer Genome Atlas, and we found that the APA regulation machinery is much more complicated than what was previously thought. We report 77 cases (gene-cancer type pairs) of differential 3’ UTR cleavage patterns between normal and tumor tissues, involving 33 genes in 13 cancer types. For 15 genes, the tumor-specific cleavage patterns are recurrent across multiple cancer types. While the cleavage patterns in certain genes indicate apparent trends of 3’ UTR shortening in tumor samples, over half of the 77 cases imply 3’ UTR length change trends in cancer that are more complex than simple shortening or lengthening. This work extends the current understanding of APA regulation in cancer, and demonstrates how large volumes of RNA-seq data generated for characterizing cancer cohorts can be mined to investigate this process.

scholarly journals Pan-cancer driver copy number alterations identified by joint expression/CNA data analysis

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Gaojianyong Wang ◽  
Dimitris Anastassiou
Keyword(s):  
Gene Expression ◽  
Copy Number ◽  
The Cancer Genome Atlas ◽  
Copy Number Alterations ◽  
Multiple Cancer ◽  
Cancer Driver ◽  
Large Gene ◽  
Cancer Genome Atlas ◽  
Cancer Types ◽  
Pan Cancer

Abstract Analysis of large gene expression datasets from biopsies of cancer patients can identify co-expression signatures representing particular biomolecular events in cancer. Some of these signatures involve genomically co-localized genes resulting from the presence of copy number alterations (CNAs), for which analysis of the expression of the underlying genes provides valuable information about their combined role as oncogenes or tumor suppressor genes. Here we focus on the discovery and interpretation of such signatures that are present in multiple cancer types due to driver amplifications and deletions in particular regions of the genome after doing a comprehensive analysis combining both gene expression and CNA data from The Cancer Genome Atlas.

scholarly journals Integrative Analysis of Cancer Omics Data for Prognosis Modeling

Genes ◽  
2019 ◽  
Vol 10 (8) ◽  
pp. 604 ◽  
Author(s):  
Wang ◽  
Wu ◽  
Ma
Keyword(s):  
Integrative Analysis ◽  
The Cancer Genome Atlas ◽  
Cancer Prognosis ◽  
Sufficient Information ◽  
Cancer Type ◽  
Multiple Cancer ◽  
Cancer Genome Atlas ◽  
Cancer Types ◽  
Information Borrowing ◽  
Penalization Technique

Prognosis modeling plays an important role in cancer studies. With the development of omics profiling, extensive research has been conducted to search for prognostic markers for various cancer types. However, many of the existing studies share a common limitation by only focusing on a single cancer type and suffering from a lack of sufficient information. With potential molecular similarity across cancer types, one cancer type may contain information useful for the analysis of other types. The integration of multiple cancer types may facilitate information borrowing so as to more comprehensively and more accurately describe prognosis. In this study, we conduct marginal and joint integrative analysis of multiple cancer types, effectively introducing integration in the discovery process. For accommodating high dimensionality and identifying relevant markers, we adopt the advanced penalization technique which has a solid statistical ground. Gene expression data on nine cancer types from The Cancer Genome Atlas (TCGA) are analyzed, leading to biologically sensible findings that are different from the alternatives. Overall, this study provides a novel venue for cancer prognosis modeling by integrating multiple cancer types.

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

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Yadi Zhou ◽  
Junfei Zhao ◽  
Jiansong Fang ◽  
William Martin ◽  
Lang Li ◽  
...  
Keyword(s):  
Posttranslational Modifications ◽  
Genomic Medicine ◽  
The Cancer Genome Atlas ◽  
Sequencing Data ◽  
Protein Binding Sites ◽  
Functional Sites ◽  
Protein Protein Interaction ◽  
Cancer Genome Atlas ◽  
Cancer Types ◽  
Protein Posttranslational Modifications

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.

scholarly journals The landscape of high-affinity human antibodies against intratumoral antigens

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.

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

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.

scholarly journals Somatic Copy Number Alterations in Human Cancers: An Analysis of Publicly Available Data From The Cancer Genome Atlas

2021 ◽  
Vol 11 ◽  
Author(s):  
Luuk Harbers ◽  
Federico Agostini ◽  
Marcin Nicos ◽  
Dimitri Poddighe ◽  
Magda Bienko ◽  
...  
Keyword(s):  
Copy Number ◽  
Cancer Genome ◽  
The Cancer Genome Atlas ◽  
Copy Number Alterations ◽  
Multiple Cancer ◽  
Human Cancers ◽  
Cancer Genome Atlas ◽  
Cancer Types ◽  
Somatic Copy Number Alterations ◽  
Genome Atlas

Somatic copy number alterations (SCNAs) are a pervasive trait of human cancers that contributes to tumorigenesis by affecting the dosage of multiple genes at the same time. In the past decade, The Cancer Genome Atlas (TCGA) and the International Cancer Genome Consortium (ICGC) initiatives have generated and made publicly available SCNA genomic profiles from thousands of tumor samples across multiple cancer types. Here, we present a comprehensive analysis of 853,218 SCNAs across 10,729 tumor samples belonging to 32 cancer types using TCGA data. We then discuss current models for how SCNAs likely arise during carcinogenesis and how genomic SCNA profiles can inform clinical practice. Lastly, we highlight open questions in the field of cancer-associated SCNAs.

scholarly journals TIMER2.0 for analysis of tumor-infiltrating immune cells

2020 ◽  
Vol 48 (W1) ◽  
pp. W509-W514 ◽  
Author(s):  
Taiwen Li ◽  
Jingxin Fu ◽  
Zexian Zeng ◽  
David Cohen ◽  
Jing Li ◽  
...  
Keyword(s):  
Immune Cells ◽  
Large Scale ◽  
Immune Cell ◽  
The Cancer Genome Atlas ◽  
Multiple Cancer ◽  
Public Data ◽  
Cancer Genome Atlas ◽  
Cancer Types ◽  
Transcriptomic Changes ◽  
Web Platform

Abstract Tumor progression and the efficacy of immunotherapy are strongly influenced by the composition and abundance of immune cells in the tumor microenvironment. Due to the limitations of direct measurement methods, computational algorithms are often used to infer immune cell composition from bulk tumor transcriptome profiles. These estimated tumor immune infiltrate populations have been associated with genomic and transcriptomic changes in the tumors, providing insight into tumor–immune interactions. However, such investigations on large-scale public data remain challenging. To lower the barriers for the analysis of complex tumor–immune interactions, we significantly improved our previous web platform TIMER. Instead of just using one algorithm, TIMER2.0 (http://timer.cistrome.org/) provides more robust estimation of immune infiltration levels for The Cancer Genome Atlas (TCGA) or user-provided tumor profiles using six state-of-the-art algorithms. TIMER2.0 provides four modules for investigating the associations between immune infiltrates and genetic or clinical features, and four modules for exploring cancer-related associations in the TCGA cohorts. Each module can generate a functional heatmap table, enabling the user to easily identify significant associations in multiple cancer types simultaneously. Overall, the TIMER2.0 web server provides comprehensive analysis and visualization functions of tumor infiltrating immune cells.

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

2019 ◽  
Vol 20 (22) ◽  
pp. 5697 ◽  
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.

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

Cancers ◽  
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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