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

Van Ness B, Ramos C, Haznadar M, Hoering A, Haessler J, Crowley J, Jacobus S, Oken M, Rajkumar V, Greipp P, Barlogie B, Durie B, Katz M, Atluri G, Fang G, Gupta R, Steinbach M, Kumar V, Mushlin R, Johnson D, Morgan G - BMC Med (2008)

Bottom Line: A systematic search of national databases was used to identify non-synonymous coding SNPs and SNPs within transcriptional regulatory regions.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.

View Article: PubMed Central - HTML - PubMed

Affiliation: Cancer Center, University of Minnesota, Minneapolis, MN, USA. vanne001@umn.edu

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.

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Related in: MedlinePlus

Racial allelic frequency patterns. A) Diagonal plot comparing minor allele frequencies between BOAC SNPs of Caucasian versus African American myeloma patients. Note high rate of allelic variation. B) Diagonal plot comparing minor allele frequencies between BOAC SNPs of Caucasian myeloma patients versus unaffected Caucasians.
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Figure 2: Racial allelic frequency patterns. A) Diagonal plot comparing minor allele frequencies between BOAC SNPs of Caucasian versus African American myeloma patients. Note high rate of allelic variation. B) Diagonal plot comparing minor allele frequencies between BOAC SNPs of Caucasian myeloma patients versus unaffected Caucasians.

Mentions: It has been well established that there are significant allelic frequency differences by race, or ethnic and regional origins [29]. Part of the SNP panel design included the admixture SNP panel that shows significant racial variation. Figure 2A shows a diagonal plot in which each SNP minor allelic frequency is plotted by frequency in the Caucasian (n = 92) versus the African American (n = 27) myeloma populations. Equivalent frequencies would be expected to cluster on the 45 degree angle; and it is readily apparent that frequencies of many of the SNPs vary widely between races. Indeed, the racial disparities in allelic frequencies were far more significant than could be assessed in case-control or outcome studies, so that subsequent initial survival analyses were done only on a single racial group. Moreover, given the inclusion criteria of SNPs (included if greater than 5% in one racial group), it is noteworthy that 401 SNPs show allelic variation only in the African Americans (ie, no variations seen in Caucasian). In contrast, in a comparison of unaffected samples with affected samples, restricted to Caucasians, there is high concordance across the total panel of SNPs (Figure 2B). This provides an opportunity to examine smaller clusters or functional associations with disease that may be masked by the larger multi-racial pools. However, the object of this study was not to compare variations within different ethnic patient populations.


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

Van Ness B, Ramos C, Haznadar M, Hoering A, Haessler J, Crowley J, Jacobus S, Oken M, Rajkumar V, Greipp P, Barlogie B, Durie B, Katz M, Atluri G, Fang G, Gupta R, Steinbach M, Kumar V, Mushlin R, Johnson D, Morgan G - BMC Med (2008)

Racial allelic frequency patterns. A) Diagonal plot comparing minor allele frequencies between BOAC SNPs of Caucasian versus African American myeloma patients. Note high rate of allelic variation. B) Diagonal plot comparing minor allele frequencies between BOAC SNPs of Caucasian myeloma patients versus unaffected Caucasians.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC2553089&req=5

Figure 2: Racial allelic frequency patterns. A) Diagonal plot comparing minor allele frequencies between BOAC SNPs of Caucasian versus African American myeloma patients. Note high rate of allelic variation. B) Diagonal plot comparing minor allele frequencies between BOAC SNPs of Caucasian myeloma patients versus unaffected Caucasians.
Mentions: It has been well established that there are significant allelic frequency differences by race, or ethnic and regional origins [29]. Part of the SNP panel design included the admixture SNP panel that shows significant racial variation. Figure 2A shows a diagonal plot in which each SNP minor allelic frequency is plotted by frequency in the Caucasian (n = 92) versus the African American (n = 27) myeloma populations. Equivalent frequencies would be expected to cluster on the 45 degree angle; and it is readily apparent that frequencies of many of the SNPs vary widely between races. Indeed, the racial disparities in allelic frequencies were far more significant than could be assessed in case-control or outcome studies, so that subsequent initial survival analyses were done only on a single racial group. Moreover, given the inclusion criteria of SNPs (included if greater than 5% in one racial group), it is noteworthy that 401 SNPs show allelic variation only in the African Americans (ie, no variations seen in Caucasian). In contrast, in a comparison of unaffected samples with affected samples, restricted to Caucasians, there is high concordance across the total panel of SNPs (Figure 2B). This provides an opportunity to examine smaller clusters or functional associations with disease that may be masked by the larger multi-racial pools. However, the object of this study was not to compare variations within different ethnic patient populations.

Bottom Line: A systematic search of national databases was used to identify non-synonymous coding SNPs and SNPs within transcriptional regulatory regions.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.

View Article: PubMed Central - HTML - PubMed

Affiliation: Cancer Center, University of Minnesota, Minneapolis, MN, USA. vanne001@umn.edu

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.

Show MeSH
Related in: MedlinePlus