Interactive Browser-Based Genomics Data Visualization Tools for Translational and Clinical Laboratory Applications

Comut-viz: efficiently creating and browsing comutation plots online

BACKGROUND

Comutation plot is a widely used visualization method to deliver a global view of the mutation landscape of large-scale genomic studies. Current tools for creating comutation plot are either offline packages that require coding or online web servers with varied features. When a package is used, it often requires repetitive runs of code to adjust a single feature that might only be a few clicks in a web app. But web apps mostly have limited capacity for customization and cannot handle very large genomic files.

RESULTS

To improve on existing tools, we identified features that are most frequently adjusted in creating a plot and incorporate them in Comut-viz that interactively filters and visualizes mutation data as downloadable plots. It includes colored labels for numeric metadata, a preloaded palette for changing colors and two input boxes for adjusting width and height. It accepts standard mutation annotation format (MAF) files as input and can handle large MAF files with more than 200 k rows. As a front-end only app, Comut-viz guarantees privacy of user data and no latency in the analysis.

CONCLUSIONS

Comut-viz is a highly responsive and extensible web app to make comutation plots. It provides customization for frequently adjusted features and accepts large genomic files as input. It is suitable for genomic studies with more than a thousand samples.

The dominant TP53 hotspot mutation in -mutant astrocytoma, R273C, has distinctive pathologic features and sex-specific prognostic implications

Background

Infiltrative astrocytic tumors with and without isocitrate dehydrogenase (IDH) mutation frequently contain mutations in the TP53 tumor suppressor gene. Disruption of normal p53 protein activity confers neoplastic cells with a number of oncogenic properties and is a common feature of aggressive malignancies. However, the high prevalence of TP53 mutation and its pathogenic role in IDH-mutant (IDHmut) astrocytoma is not well understood.

Methods

We performed a retrospective analysis of molecular and clinical data from patients with IDHmut astrocytoma at the University of Pittsburgh Medical Center between 2015 and 2019 as our initial cohort. We validated and expanded our findings using molecular and clinical data from The Cancer Genome Atlas.

Results

We show that the TP53 mutational spectrum in IDHmut astrocytomas is dominated by a single hotspot mutation that codes for the R273C amino acid change. This mutation is not enriched in IDH-wildtype astrocytomas. The high prevalence of TP53^R273C mutation is not readily explained by known mutagenic mechanisms, and TP53^R273C mutant tumors have lower transcriptional levels of proliferation-related genes compared to IDHmut astrocytomas harboring other forms of mutant p53. Despite lower proliferation, TP53^R273C mutant tumors tend to progress more quickly and have a shorter overall survival than those with other TP53 mutations, particularly in male patients.

Conclusions

Our findings suggest that compared to other TP53 mutations, IDHmut astrocytomas may select for TP53^R273C mutations during tumorigenesis. The genotype, sex, and mutation-specific findings are clinically relevant and should prompt further investigation of TP53^R273C.

MutScape: an analytical toolkit for probing the mutational landscape in cancer genomics

Cancer genomics has been evolving rapidly, fueled by the emergence of numerous studies and public databases through next-generation sequencing technologies. However, the downstream programs used to preprocess and analyze data on somatic mutations are scattered in different tools, most of which require specific input formats. Here, we developed a user-friendly Python toolkit, MutScape, which provides a comprehensive pipeline of filtering, combination, transformation, analysis and visualization for researchers, to easily explore the cohort-based mutational characterization for studying cancer genomics when obtaining somatic mutation data. MutScape not only can preprocess millions of mutation records in a few minutes, but also offers various analyses simultaneously, including driver gene detection, mutational signature, large-scale alteration identification and actionable biomarker annotation. Furthermore, MutScape supports somatic variant data in both variant call format and mutation annotation format, and leverages caller combination strategies to quickly eliminate false positives. With only two simple commands, robust results and publication-quality images are generated automatically. Herein, we demonstrate the ability of MutScape to correctly reproduce known results using breast cancer samples from The Cancer Genome Atlas. More significantly, discovery of novel results in cancer genomic studies is enabled through the advanced features in MutScape. MutScape is freely available on GitHub, at https://github.com/anitalu724/MutScape.

CoMut: visualizing integrated molecular information with comutation plots

MOTIVATION

Large-scale sequencing studies have created a need to succinctly visualize genomic characteristics of patient cohorts linked to widely variable phenotypic information. This is often done by visualizing the co-occurrence of variants with comutation plots. Current tools lack the ability to create highly customizable and publication quality comutation plots from arbitrary user data.

RESULTS

We developed CoMut, a stand-alone, object-oriented Python package that creates comutation plots from arbitrary input data, including categorical data, continuous data, bar graphs, side bar graphs and data that describes relationships between samples.

AVAILABILITY AND IMPLEMENTATION

The CoMut package is open source and is available at https://github.com/vanallenlab/comut under the MIT License, along with documentation and examples. A no installation, easy-to-use implementation is available on Google Colab (see GitHub).