Abstract
We describe open, reproducible pipelines that create an integrated genomic profile of a cancer and use the profile to find mutations associated with disease and potentially useful drugs. These pipelines analyze high-throughput cancer exome and transcriptome sequence data together with public databases to find relevant mutations and drugs. The three pipelines that we have developed are: (1) an exome analysis pipeline, which uses whole or targeted tumor exome sequence data to produce a list of putative variants (no matched normal data are needed); (2) a transcriptome analysis pipeline that processes whole tumor transcriptome sequence (RNA-seq) data to compute gene expression and find potential gene fusions; and (3) an integrated variant analysis pipeline that uses the tumor variants from the exome pipeline and tumor gene expression from the transcriptome pipeline to identify deleterious and druggable mutations in all genes and in highly expressed genes. These pipelines are integrated into the popular Web platform Galaxy at http://usegalaxy.org/cancer to make them accessible and reproducible, thereby providing an approach for doing standardized, distributed analyses in clinical studies. We have used our pipeline to identify similarities and differences between pancreatic adenocarcinoma cancer cell lines and primary tumors.
Highlights
A promising path toward personalizing cancer treatment is using genomic features of tumors to guide treatment
Using the Galaxy API and Bioblend [36], we developed Python scripts to automatically execute the pipelines on sequencing data from numerous pancreatic cancer samples, the results of which we discuss in detail below
All three cell lines are included in the Cancer Cell Line Encyclopedia (CCLE) [15]; the CCLE includes a mutational profile for known oncogenes and drug response information for each cell line
Summary
A promising path toward personalizing cancer treatment is using genomic features of tumors to guide treatment. Tumor features such as gene mutations [1, 2], differential gene expression [3, 4], and structural variation [5, 6] have proven useful in predicting and personalizing cancer treatment. Developing effective treatments for many tumor types requires multiple targeted approaches informed by comprehensive tumor profiles merged with public and private patient data to identify precise targets [7]. Comprehensive genomic profiles of tumors derived from high-throughput sequencing data holds significant promise for better understanding the biology which drives their growth and resistance to standard therapies [8, 9].
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