PROMO: An interactive tool for analyzing clinically-labeled multi-omic cancer datasets

Abstract

BackgroundAnalysis of large genomic datasets along with their accompanying clinical information has shown great promise in cancer research over the last decade. Such datasets typically include thousands of samples, each measured by one or several high-throughput technologies ('omics') and annotated with extensive clinical information. While instrumental for fulfilling the promise of personalized medicine, the analysis and visualization of such large datasets is challenging and necessitates programming skills and familiarity with a large array of software tools to be used for the various steps of the analysis. ResultsWe developed PROMO (Profiler of Multi-Omic data), a friendly, fully interactive stand-alone software for analyzing large genomic cancer datasets together with their associated clinical information. The tool provides an array of built-in methods and algorithms for importing, preprocessing, visualizing, clustering, clinical label enrichment testing and survival analysis that can be performed on a single or multi-omic dataset. The tool can be used for quick exploration and for stratification of tumor samples taken from patients into clinically significant molecular subtypes. Identification of prognostic biomarkers and generation of simple subtype classifiers are additional important features. We review PROMO's main features and demonstrate its analysis capabilities on a breast cancer cohort from TCGA. ConclusionsPROMO provides a single integrated solution for swiftly performing a complete analysis of cancer genomic data for subtype discovery and biomarker identification without writing a single line of code, and can, therefore, make the analysis of these data much easier for cancer biologists and biomedical researchers.PROMO is freely available for download at http://acgt.cs.tau.ac.il/promo/.

Classification of node-positive melanomas into prognostic subgroups using keratin, immune, and melanogenesis expression patterns

“…To train a molecular classifier for predicting melanoma subgroups, we used the expression levels of the 2000 most variable genes on the set of 469 melanoma samples. We used Matlab's implementation (R2019a) (accessed through PROMO [54]) to grow a classification tree using a curvature test as the method for splitting predictors [55,56]. The training procedure consisted of two steps.…”

Yin Yang 1 promotes aggressive cell growth in high‐grade breast cancer by directly transactivating kinectin 1

“…To investigate the upstream transcription factors that modulate the KTN1 gene, we predicted transcription factors that might bind to the KTN1 promoter by the PROMO database (http://algge n.lsi.upc.es/cgi‐bin/promo_v3/promo/promoinit.cgi?dirDB = TF8.3) to screen for known transcription factor binding sites 21 . The score from random expectation (RE) analysis identified the top ranked genes as NR3C1 , YY1 , and ESR1 (conforming to RE equally ≥ 2 & RE query ≥ 2, Figure 1C).…”

“…To investigate the upstream transcription factors that modulate the KTN1 gene, we predicted transcription factors that might bind to the KTN1 promoter by the PROMO database (http://algge n.lsi.upc.es/cgibin/promo_v3/promo/promoinit.cgi?dirDB = TF8.3) to screen for known transcription factor binding sites. 21 The score from random expectation (RE) analysis identified the top ranked genes as NR3C1, YY1, and ESR1 (conforming to RE equally ≥ 2 & RE query ≥ 2, Figure 1C). Next, quantitative real-time reverse transcription (qRT-PCR) analysis showed that knockdown of NR3C1 using NR3C1 siRNA oligonucleotides (siNR3C1) had no significant effect on the regulation KTN1 expression compared with the negative control group (siNC).…”

Integrative Network Fusion: A Multi-Omics Approach in Molecular Profiling

“…The problem of data integration in computational biology is far from having a consolidated and shared solution. Many long-standing obstacles are still far from being overcome, and the increasing availability of data [e.g., TCGA, ( 46 )] and computational tools [see for instance ( 47 – 51 ) and https://github.com/mikelove/awesome-multi-omics ], also interactive [e.g., ( 52 )], is raising new issues that need to be addressed. In fact, not only are existing datasets still lacking standardization protocols to deal with their complexity and heterogeneity, but also the reliability, reproducibility and interpretability of new computational methods are emerging as urgent and relevant questions ( 53 ).…”