Research article

Genomic variation in myeloma: design, content, and initial application of the Bank On A Cure SNP Panel to detect associations with progression-free survival

Brian Van Ness¹ , Christine Ramos¹ , Majda Haznadar¹ , Antje Hoering² , Jeff Haessler² , John Crowley² , Susanna Jacobus³ , Martin Oken⁴ , Vincent Rajkumar⁵ , Philip Greipp⁵ , Bart Barlogie⁶ , Brian Durie⁷ , Michael Katz⁸ , Gowtham Atluri⁹ , Gang Fang⁹ , Rohit Gupta⁹ , Michael Steinbach⁹ , Vipin Kumar⁹ , Richard Mushlin¹⁰ , David Johnson¹¹ and Gareth Morgan¹¹

¹Cancer Center, University of Minnesota, Minneapolis, MN, USA

²Cancer Research and Biostatistics, Seattle, WA, USA

³Dana Farber Cancer Institute, Boston, MA, USA

⁴North Memorial Hospital, Minneapolis, MN, USA

⁵Hematology, Mayo Clinic, Rochester, MN, USA

⁶University of Arkansas Medical Sciences Center, Little Rock, AK, USA

⁷Cedar Sinai Medical Center, Los Angeles, CA, USA

⁸International Myeloma Foundation, Hollywood, CA, USA

⁹Electrical Engineering & Computer Science, University of Minnesota, Minneapolis, MN, USA

¹⁰IBM Research, TJ Watson Research Center, Yorktown Heights, NY, USA

¹¹Royal Marsden Hospital, London, UK

author email corresponding author email

BMC Medicine 2008, 6:26doi:10.1186/1741-7015-6-26

Published:	8 September 2008

Abstract

Background

We have engaged in an international program designated the Bank On A Cure, which has established DNA banks from multiple cooperative and institutional clinical trials, and a platform for examining the association of genetic variations with disease risk and outcomes in multiple myeloma.

We describe the development and content of a novel custom SNP panel that contains 3404 SNPs in 983 genes, representing cellular functions and pathways that may influence disease severity at diagnosis, toxicity, progression or other treatment outcomes. A systematic search of national databases was used to identify non-synonymous coding SNPs and SNPs within transcriptional regulatory regions. To explore SNP associations with PFS we compared SNP profiles of short term (less than 1 year, n = 70) versus long term progression-free survivors (greater than 3 years, n = 73) in two phase III clinical trials.

Results

Quality controls were established, demonstrating an accurate and robust screening panel for genetic variations, and some initial racial comparisons of allelic variation were done. A variety of analytical approaches, including machine learning tools for data mining and recursive partitioning analyses, demonstrated predictive value of the SNP panel in survival. While the entire SNP panel showed genotype predictive association with PFS, some SNP subsets were identified within drug response, cellular signaling and cell cycle genes.

Conclusion

A targeted gene approach was undertaken to develop an SNP panel that can test for associations with clinical outcomes in myeloma. The initial analysis provided some predictive power, demonstrating that genetic variations in the myeloma patient population may influence PFS.