Open Access 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

Author Affiliations

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

2 Cancer Research and Biostatistics, Seattle, WA, USA

3 Dana Farber Cancer Institute, Boston, MA, USA

4 North Memorial Hospital, Minneapolis, MN, USA

5 Hematology, Mayo Clinic, Rochester, MN, USA

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

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

8 International Myeloma Foundation, Hollywood, CA, USA

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

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

11 Royal Marsden Hospital, London, UK

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

Published: 8 September 2008



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.


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.


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.