TY - JOUR
T1 - CaPSSA
T2 - Visual evaluation of cancer biomarker genes for patient stratification and survival analysis using mutation and expression data
AU - Jang, Yeongjun
AU - Seo, Jihae
AU - Jang, Insu
AU - Lee, Byungwook
AU - Kim, Sun
AU - Lee, Sanghyuk
N1 - Publisher Copyright:
© 2019 The Author(s) 2019. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: [email protected].
PY - 2019/12/15
Y1 - 2019/12/15
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85077771109&partnerID=8YFLogxK
U2 - 10.1093/bioinformatics/btz516
DO - 10.1093/bioinformatics/btz516
M3 - Article
C2 - 31228188
AN - SCOPUS:85077771109
SN - 1367-4803
VL - 35
SP - 5341
EP - 5343
JO - Bioinformatics
JF - Bioinformatics
IS - 24
ER -