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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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Recursive partitioning tree from S9321 and E9486. The classification prediction was calculated for one trial and tested on the other as validation.
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Figure 4: Recursive partitioning tree from S9321 and E9486. The classification prediction was calculated for one trial and tested on the other as validation.

Mentions: The first survival separation was analyzed for clinical trial S9321, and the top 20 rank ordered SNPs are presented in Table 3 (more extended rank order presented in Table 2S of [28]). Figure 4 shows the pruned recursive partitioning tree, resulting in two SNPs with the highest classification prediction of survival groups. One SNP is in catechol methyl transferase (COMT) and one is in Ghrelin precursor (GHRL). The potential significance of these SNPs on outcomes is discussed below. The correct classification rate (survival prediction) was 71%, which dropped to 58% on validation testing with the E9486 trial. The specificity and sensitivities are also presented. The converse analysis was done (E9497 training set; S9321 validation); and the rank order of SNPs was determined (Table 4 and an expanded Table 3S in [28]). A recursive partitioning tree of two SNPs showed 79% classification on the training E9487 set, and 56% on the S9321 validation (Figure 4). The SNPs identified in this trial were farnesyl transferase (FDFT) and ABCC1 (in the family of ATP transporters). The potential significance is also provided in the Discussion.


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)

Recursive partitioning tree from S9321 and E9486. The classification prediction was calculated for one trial and tested on the other as validation.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

Figure 4: Recursive partitioning tree from S9321 and E9486. The classification prediction was calculated for one trial and tested on the other as validation.
Mentions: The first survival separation was analyzed for clinical trial S9321, and the top 20 rank ordered SNPs are presented in Table 3 (more extended rank order presented in Table 2S of [28]). Figure 4 shows the pruned recursive partitioning tree, resulting in two SNPs with the highest classification prediction of survival groups. One SNP is in catechol methyl transferase (COMT) and one is in Ghrelin precursor (GHRL). The potential significance of these SNPs on outcomes is discussed below. The correct classification rate (survival prediction) was 71%, which dropped to 58% on validation testing with the E9486 trial. The specificity and sensitivities are also presented. The converse analysis was done (E9497 training set; S9321 validation); and the rank order of SNPs was determined (Table 4 and an expanded Table 3S in [28]). A recursive partitioning tree of two SNPs showed 79% classification on the training E9487 set, and 56% on the S9321 validation (Figure 4). The SNPs identified in this trial were farnesyl transferase (FDFT) and ABCC1 (in the family of ATP transporters). The potential significance is also provided in the Discussion.

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