UC Irvine

We present a cancer genomic analysis pipeline which takes

sequencing reads for both germline and tumor genomes as input and

outputs prioritized lists of the most affected genes in the tumor

genome. Using publicly available datasets and literature specific

to each patient, we extract out clinically relevant information

to be used in a novel reporting and ranking system in order to

identify the most affected genes and pathways within a

patient. Network-based approaches that integrate protein-protein,

protein-TF, and protein-drug interaction data are used to

identify potentially therapeutic drugs and their targets. Effects

of genetic variations on gene expression, as profiled by RNA-seq

in tumor samples, are used to provide further evidence of

“driver” mutations -- those mutations responsible for tumor

progression. Additionally, previously implicated small and large

variations (including gene fusions) are reported.

Results are presented in a collaborative interface that combines

all evidence for the top ranking genes and pathways. Affected

genes in and around protein coding sequences are investigated

further using sequence-level features such as predicted secondary

structure, solvent accessibility, phosphorylation status, and

protein domains. By using an integrative approach, effects of

genetic variations on gene expression are used to provide further

evidence of “driver” mutations.

This pipeline has been developed with the aim to be used in

assisting in the analysis of pediatric tumors, as an unbiased and

automated method. We present results that agree with previous

literature and highlight specific findings in a few

patients. Portions of this pipeline have been successfully reused

in the analysis of other high-throughput sequencing data in

non-cancer related projects. This work provides a basis for which

future personalized medicine pipelines can be systematically

performed in order to assist in the treatment of newly diagnosed

cancer patients.