CaPSSA: Visual evaluation of cancer biomarker genes for patient stratification and survival analysis using mutation and expression data

Yeongjun Jang, Jihae Seo, Insu Jang, Byungwook Lee, Sun Kim, Sanghyuk Lee

Research output: Contribution to journalArticlepeer-review

7 Scopus citations

Abstract

Predictive biomarkers for patient stratification play critical roles in realizing the paradigm of precision medicine. Molecular characteristics such as somatic mutations and expression signatures represent the primary source of putative biomarker genes for patient stratification. However, evaluation of such candidate biomarkers is still cumbersome and requires multistep procedures especially when using massive public omics data. Here, we present an interactive web application that divides patients from large cohorts (e.g. The Cancer Genome Atlas, TCGA) dynamically into two groups according to the mutation, copy number variation or gene expression of query genes. It further supports users to examine the prognostic value of resulting patient groups based on survival analysis and their association with the clinical features as well as the previously annotated molecular subtypes, facilitated with a rich and interactive visualization. Importantly, we also support custom omics data with clinical information.

Original languageEnglish
Pages (from-to)5341-5343
Number of pages3
JournalBioinformatics
Volume35
Issue number24
DOIs
StatePublished - 15 Dec 2019

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