Alternative splice variants, a new class of protein cancer
biomarker candidates: Findings in pancreatic cancer and breast cancer with
systems biology implications
Affiliations: University of Michigan Center for Computational
Medicine and Bioinformatics and Michigan Proteomics Alliance for Cancer
Research, Ann Arbor, MI, USA | Departments of Internal Medicine, Human Genetics and
School of Public Health, University of Michigan, Ann Arbor, MI, USA | Michigan Center for Translational Pathology,
University of Michigan, Ann Arbor, MI, USA
Note: [] Corresponding author: Gilbert S. Omenn, M.D., Ph.D., Professor
of Internal Medicine, Human Genetics, Bioinformatics and Public Health;
Director, Center for Computational Medicine and Bioinformatics, University of
Michigan, 2017F Palmer Commons, 100 Washtenaw Avenue, Ann Arbor, MI 48109-2218,
USA. Tel.: +1 734 763 7583; Fax: +1 734 615 6553; E-mail: [email protected]
Abstract: Alternative splicing plays an important role in protein diversity
without increasing genome size. Earlier thought to be uncommon, splicing
appears to affect the majority of genes. Alternative splice variants have been
detected at the mRNA level in many diseases. We have designed and demonstrated
a discovery pipeline for alternative splice variant (ASV) proteins from tandem
MS/MS datasets. We created a modified ECgene database with entries from
exhaustive three-frame translation of Ensembl transcripts and gene models from
ECgene, with periodic updates. The human database has 14 million entries; the
mouse database, 10 million entries. We match MS/MS findings against these
potential translation products to identify and quantify known and novel ASVs.
In this review, we summarize findings and systems biology implications of
biomarker candidates from a mouse model of human pancreatic ductal
adenocarcinoma [28] and a mouse model of human Her2/neu-induced breast
cancer [27]. The same approach is being applied to human tumors, plasma,
and cell line studies of other cancers.
Keywords: Alternative splicing, splice variants, protein isoforms, breast cancer, pancreatic cancer, mouse models, proteomics, protein interaction networks, systems biology
