Survival analysis tools in genomics research

Chen, Xintong; Sun, Xiaochen; Hoshida, Yujin

doi:10.1186/preaccept-1020290243146153

Cited by 4 publications

(7 citation statements)

References 12 publications

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“…We observed several common pitfalls in many publicly available survival analysis web tools that are used for putative biomarker validation or analysis of the association between survival and gene expression data. These tools have been previously described [5] and have been used by researchers in the community with the analysis results presented in publications. Because of the frequency and potential impact of such cases, we feel it is important to bring their pitfalls to the attention of the community in more detail.…”

Section: Resultsmentioning

confidence: 99%

“…These biomarkers can be generalized to represent variables that are categorical with a limited number of discrete values. Conveniently, categorized or discrete covariates including clinical features such as pathological cancer stage or genomic features like gene mutation status can be directly used to classify patients for survival analysis (reviewed in [5]). Such categorical variables that can be classified into two or more categories based on corresponding covariates, e.g., KRAS mutation vs. wild type, can be subjected to the Kaplan-Meier estimator method to produce survival curves and a logrank test can be performed to assess the significance of the difference in survival outcome between the groups [1].…”

Section: Introductionmentioning

confidence: 99%

“…As a result, it is very common to cast gene expression from a continuous to a binary variable or represent it as a multi-segmented covariate to assess the prognostic impact on survival outcome. Median or quartile-based cutpoint approaches are commonly used to classify the samples into two or more groups to assess differences in their survival outcome in many publicly available survival analysis tools (reviewed in [5]). The same approach has resulted in many published reports as described below with the obvious problem that the cutpoint selection becomes critical.…”

Section: Introductionmentioning

confidence: 99%

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GradientScanSurv—An exhaustive association test method for gene expression data with censored survival outcome

Zhu

Stephens

2018

PLoS ONE

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Accurate assessment of the association between continuous variables such as gene expression and survival is a critical aspect of precision medicine. In this report, we provide a review of some of the available survival analysis and validation tools by referencing published studies that have utilized these tools. We have identified pitfalls associated with the assumptions inherent in those applications that have the potential to impact scientific research through their potential bias. In order to overcome these pitfalls, we have developed a novel method that enables the logrank test method to handle continuous variables that comprehensively evaluates survival association with derived aggregate statistics. This is accomplished by exhaustively considering all the cutpoints across the full expression gradient. Direct side-by-side comparisons, global ROC analysis, and evaluation of the ability to capture relevant biological themes based on current understanding of RAS biology all demonstrated that the new method shows better consistency between multiple datasets of the same disease, better reproducibility and robustness, and better detection power to uncover biological relevance within the selected datasets over the available survival analysis methods on univariate gene expression and penalized linear model-based methods.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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GradientScanSurv—An exhaustive association test method for gene expression data with censored survival outcome

Zhu

Stephens

2018

PLoS ONE

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“…PRECOG has unique advantages over related resources 49 . First, multiple datasets are included for most human cancers, and the use of a robust survival meta-z approach to integrate studies reduces the potential for erroneous conclusions drawn from single datasets.…”

Section: Discussionmentioning

confidence: 99%

The prognostic landscape of genes and infiltrating immune cells across human cancers

Gentles

Newman

Liu

et al. 2015

Nat Med

2,580

2,388

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Molecular profiles of tumors and tumor-associated cells hold great promise as biomarkers of clinical outcomes. However, existing datasets are fragmented and difficult to analyze systematically. Here we present a pan-cancer resource and meta-analysis of expression signatures from ~18,000 human tumors with overall survival outcomes across 39 malignancies. Using this resource, we identified a FOXM1 regulatory network as a major predictor of adverse outcomes, and found that expression of favorably prognostic genes, including KLRB1, largely reflect tumor-associated leukocytes. By applying CIBERSORT, a computational approach for inferring leukocyte representation in bulk tumor transcriptomes, we identified complex associations between 22 distinct leukocyte subsets and cancer survival. For example, tumor-associated neutrophil and plasma cell signatures emerged as significant but opposite predictors of survival for diverse solid tumors, including breast and lung adenocarcinomas. This resource and associated analytical tools (http://precog.stanford.edu) may help delineate prognostic genes and leukocyte subsets within and across cancers, shed light on the impact of tumor heterogeneity on cancer outcomes, and discover biomarkers and therapeutic targets.

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“…We briefly summarized the differences between these tools in Table 2 . A comprehensive comparison of these translational bioinformatics platforms including G-DOC has been summarized in these papers [ 64 – 66 ].…”

Section: Resultsmentioning

confidence: 99%

G-DOC Plus – an integrative bioinformatics platform for precision medicine

et al. 2016

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BackgroundG-DOC Plus is a data integration and bioinformatics platform that uses cloud computing and other advanced computational tools to handle a variety of biomedical BIG DATA including gene expression arrays, NGS and medical images so that they can be analyzed in the full context of other omics and clinical information.ResultsG-DOC Plus currently holds data from over 10,000 patients selected from private and public resources including Gene Expression Omnibus (GEO), The Cancer Genome Atlas (TCGA) and the recently added datasets from REpository for Molecular BRAin Neoplasia DaTa (REMBRANDT), caArray studies of lung and colon cancer, ImmPort and the 1000 genomes data sets. The system allows researchers to explore clinical-omic data one sample at a time, as a cohort of samples; or at the level of population, providing the user with a comprehensive view of the data.G-DOC Plus tools have been leveraged in cancer and non-cancer studies for hypothesis generation and validation; biomarker discovery and multi-omics analysis, to explore somatic mutations and cancer MRI images; as well as for training and graduate education in bioinformatics, data and computational sciences. Several of these use cases are described in this paper to demonstrate its multifaceted usability.ConclusionG-DOC Plus can be used to support a variety of user groups in multiple domains to enable hypothesis generation for precision medicine research. The long-term vision of G-DOC Plus is to extend this translational bioinformatics platform to stay current with emerging omics technologies and analysis methods to continue supporting novel hypothesis generation, analysis and validation for integrative biomedical research. By integrating several aspects of the disease and exposing various data elements, such as outpatient lab workup, pathology, radiology, current treatments, molecular signatures and expected outcomes over a web interface, G-DOC Plus will continue to strengthen precision medicine research. G-DOC Plus is available at: https://gdoc.georgetown.edu.Electronic supplementary materialThe online version of this article (doi:10.1186/s12859-016-1010-0) contains supplementary material, which is available to authorized users.

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Survival analysis tools in genomics research

Cited by 4 publications

References 12 publications

GradientScanSurv—An exhaustive association test method for gene expression data with censored survival outcome

GradientScanSurv—An exhaustive association test method for gene expression data with censored survival outcome

The prognostic landscape of genes and infiltrating immune cells across human cancers

G-DOC Plus – an integrative bioinformatics platform for precision medicine

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