Survival analysis using gene expression to derive predictive gene signatures is a commonly used feature in research studies employing high throughput genomic data. Gene signatures predictive of overall, relapse free or metastasis free survival are popular and several such signatures are published periodically and the data submitted to public repositories. Data from such studies which is available on the public domain can be leveraged to identify prognostic markers in different cancer types.
Provides a web resource for exploring, visualizing, and analyzing multidimensional cancer genomics data. cBioPortal reduces molecular profiling data from cancer tissues and cell lines into readily understandable genetic, epigenetic, gene expression, and proteomic events. The query interface, combined with customized data storage, enables researchers to interactively explore genetic alterations across samples, genes, and pathways and, when available in the underlying data, to link these to clinical outcomes.
Provides various next-generation sequencing (NGS) data analysis applications which are developed or optimized by Illumina, or from a growing ecosystem of third-party app providers. BAseSpace is a cloud platform that can be integrated with the industry’s leading sequencing platforms, without cumbersome or time consuming data transfer steps.
Assesses the effect of genes on survival using cancer samples. KM plotter is a web application, developed for meta-analysis-based biomarker assessment, that can be used for breast, ovarian, lung, gastric, and liver cancer. The software includes patients with a mean follow-up of 69 / 40/ 49 /33 months. The subtool miRpower enables the validation of the prognostic relevance of microRNAs (miRNAs) in breast and liver cancer.
Provides functions to assess and statistically compare the performance of survival/risk prediction models. survcomp implements state-of-the-art statistics to (i) measure the performance of risk prediction models; (ii) combine these statistical estimates from multiple datasets using a meta-analytical framework; and (iii) statistically compare the performance of competitive models.
A multi-tiered compendium of bioinformatics algorithms and gene signatures for molecular subtyping and prognostication in breast cancer. Genefu provides bioinformatics implementations of classification algorithms to identify molecular subtypes, as well as prognostic predictors along with their published gene signatures. It also includes other functions to facilitate quick manipulation of gene expression datasets, including gene selection and probe-gene mapping across microarray platforms. The genefu package provides a unified framework for integration of molecular subtype and survival analysis of breast cancer. We have demonstrated how the package can be utilized to perform both meta-analyses across datasets and across algorithms, to facilitate integrated analysis of breast cancer gene expression profiles.
A versatile free tool to perform validation of multi-gene biomarkers for gene expression in human cancers. We generated a cancer database collecting more than 20,000 samples and 130 datasets with censored clinical information covering tumors over 20 tissues. We implemented a web interface to perform biomarker validation and comparisons in this database, where a multivariate survival analysis can be accomplished in about one minute. SurvExpress is a valuable and comprehensive web tool and cancer database with clinical outcomes tailored to rapidly evaluate gene expression biomarkers.
A user-friendly online tool that allows rapid assessment of gene expression levels, identification of co-expressed genes and association with outcome for single genes, gene sets or gene signatures in an 1881-sample breast cancer data set. Moreover, GOBO offers the possibility of investigation of gene expression levels in breast cancer subgroups and breast cancer cell lines for gene sets, as well as creation of potential metagenes based on iterative correlation analysis to a prototype gene. The design and implementation of GOBO facilitate easy incorporation of additional query functions and applications, as well as additional data sets irrespective of tumor type and array platform in the form of precompiled R-data sets.