Identifying prognostic markers for multiple myeloma through integration and analysis of MMRF-CoMMpass data

Multiple myeloma (MM) is the second most frequent haematological malignancy in the world although therelated pathogenesis remains unclear. The study of how gene expression profiling (GEP) is correlated with patients' survival could be important for understanding the initiation and progression of MM. In order to aid researchers in identifying new prognostic RNA biomarkers as targets for functional cell-based studies, the use ofappropriate bioinformatic tools for integrative analysis is required. In this context, TCGABiolinks package represents a valid tool for integrative analysis of MM data if its functions are properly adapted for handling MMRFdata.This paper aims to extend largely our previous work [1] in which we introduced some bridging functions tomake TCGABiolinks package able to deal with Multiple Myeloma Research Foundation (MMRF) CoMMpass studydata available at the NCI's Genomic Data Commons (GDC) Data Portal.Here we present an integrative analysis workflow based on the usage of a novel R-package, calledMMRFBiolinks, that collects the set of the previously mentioned bridging functions besides of extending them.Our workflow leads towards a comparative analysis of MMRF data stored at GDC Data Portal that allows tocarry out a Kaplan Meier (KM) Survival Analysis and an enrichment analysis for a differential gene expression(DGE) gene set.Furthermore, it leads towards an integrative analysis of MMRF Research Gateway (MMRF-RG) data. In orderto show the potential of our workflow, we present two case studies. The former deals with RNA-Seq data of MMBone Marrow sample types available at GDC Data Portal. The latter deals with MMRF-RG data for analyzing thecorrelation between canonical variants in a gene set obtained from the case study 1 and the treatment outcome aswell as the treatment class.