Abstract
Exploratory analysis of cancer consortia data curated by the cBioPortal repository typically requires advanced programming skills and expertise to identify novel genomic prognostic markers that have the potential for both diagnostic and therapeutic exploitation. We developed GNOSIS (GeNomics explOrer using StatistIcal and Survival analysis in R), an R Shiny App incorporating a range of R packages enabling users to efficiently explore and visualise such clinical and genomic data. GNOSIS provides an intuitive graphical user interface and multiple tab panels supporting a range of functionalities, including data upload and initial exploration, data recoding and subsetting, data visualisations, statistical analysis, mutation analysis and, in particular, survival analysis to identify prognostic markers. GNOSIS also facilitates reproducible research by providing downloadable input logs and R scripts from each session, and so offers an excellent means of supporting clinician-researchers in developing their statistical computing skills.
Highlights
Cancer diagnosis, classification and treatment generally follows an integrative approach combining clinical features and tissue-based biomarkers[1,2]
Such a precision oncology paradigm has been fostered by the extensive efforts of many cancer genomics consortia, yielding extraordinarily rich repositories of genomic and associated clinical data of hundreds to, in some cases, thousands of cancer patients[4,5]
GNOSIS was initially developed to enable the exploration, visualisation and analysis of the METABRIC clinical and copy number alteration (CNA) summary data obtained from cBioPortal, as detailed in King et al (2021)[8], and the following description of its operational capabilities have their basis in that study
Summary
Classification and treatment generally follows an integrative approach combining clinical features and tissue-based biomarkers[1,2]. CBioPortal’s exploratory capabilities have their limitations, requiring the implementation of a more sophisticated ‘off site’ analysis that typically requires significant prior programming experience This remains arguably the greatest barrier for many clinician-researchers wishing to explore hypotheses in precision oncology. An ideal solution to this bottleneck would be the availability of a software environment supporting the integration of cBioPortal-hosted data products, their visualisation and tractable manipulation using standard biostatistical methodologies. Such an environment would provide a convenient means of testing exploratory hypotheses, those assessed in the context of survival analysis, in a way that would be both reproducible and interpretable. Given its open source basis and foundation in the R statistical programming environment, GNOSIS offers a means for third parties to enhance and develop its functionality for broader clinical genomics
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