A Genomic Analysis Pipeline and Its Application to Pediatric Cancers

We present a cancer genomic analysis pipeline which takessequencing reads for both germline and tumor genomes as input andoutputs prioritized lists of the most affected genes in the tumorgenome. Using publicly available datasets and literature specificto each patient, we extract out clinically relevant informationto be used in a novel reporting and ranking system in order toidentify the most affected genes and pathways within apatient. Network-based approaches that integrate protein-protein,protein-TF, and protein-drug interaction data are used toidentify potentially therapeutic drugs and their targets. Effectsof genetic variations on gene expression, as profiled by RNA-seqin tumor samples, are used to provide further evidence of“driver” mutations -- those mutations responsible for tumorprogression. Additionally, previously implicated small and largevariations (including gene fusions) are reported.Results are presented in a collaborative interface that combinesall evidence for the top ranking genes and pathways. Affectedgenes in and around protein coding sequences are investigatedfurther using sequence-level features such as predicted secondarystructure, solvent accessibility, phosphorylation status, andprotein domains. By using an integrative approach, effects ofgenetic variations on gene expression are used to provide furtherevidence of “driver” mutations.This pipeline has been developed with the aim to be used inassisting in the analysis of pediatric tumors, as an unbiased andautomated method. We present results that agree with previousliterature and highlight specific findings in a fewpatients. Portions of this pipeline have been successfully reusedin the analysis of other high-throughput sequencing data innon-cancer related projects. This work provides a basis for whichfuture personalized medicine pipelines can be systematicallyperformed in order to assist in the treatment of newly diagnosedcancer patients