Christine Ramos

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

BackgroundWe 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.ResultsQuality 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.ConclusionA 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.

Pervasive Purifying Selection Characterizes the Evolution of I2 Homologs

We sampled 384 sequences related to the Solanum pimpinellifolium (=Lycopersicon pimpinellifolium) disease resistance (R) gene 12 from six species, potato, S. demissum, tomato, eggplant, pepper, and tobacco. These species represent increasing phylogenetic distance from potato to tobacco, within the family Solanaceae. Using sequence data from the nucleotide binding site (NBS) region of this gene, we tested models of gene family evolution and inferred patterns of selection acting on the NBS gene region and I2 gene family. We find that the I2 family has diversified within the family Solanaceae for at least 14 million years and evolves through a slow birth-and-death process requiring approximately 12 million years to homogenize gene copies within a species. Analyses of selection resolved a general pattern of strong purifying selection acting on individual codon positions within the NBS and on NBS lineages through time. Surprisingly, we find nine codon positions strongly affected by positive selection and six pairs of codon positions demonstrating correlated amino acid substitutions. Evolutionary analyses serve as bioinformatic tools with which to sort through the vast R gene diversity in plants and find candidates for new resistance specificities or to identify specific amino acid positions important for biochemical function. The slow birth-and-death evolution of I2 genes suggests that some NBS-leucine rich repeat-mediated resistances may not be overcome rapidly by virulence evolution and that the natural diversity of R genes is a potentially valuable source for durable resistance.

Genetic polymorphisms of EPHX1, Gsk3β, TNFSF8 and myeloma cell DKK-1 expression linked to bone disease in myeloma

Bone disease in myeloma occurs as a result of complex interactions between myeloma cells and the bone marrow microenvironment. A custom-built DNA single nucleotide polymorphism (SNP) chip containing 3404 SNPs was used to test genomic DNA from myeloma patients classified by the extent of bone disease. Correlations identified with a Total Therapy 2 (TT2) (Arkansas) data set were validated with Eastern Cooperative Oncology Group (ECOG) and Southwest Oncology Group (SWOG) data sets. Univariate correlates with bone disease included: EPHX1, IGF1R, IL-4 and Gsk3β. SNP signatures were linked to the number of bone lesions, log2 DKK-1 myeloma cell expression levels and patient survival. Using stepwise multivariate regression analysis, the following SNPs: EPHX1 (P=0.0026); log2 DKK-1 expression (P=0.0046); serum lactic dehydrogenase (LDH) (P=0.0074); Gsk3β (P=0.02) and TNFSF8 (P=0.04) were linked to bone disease. This assessment of genetic polymorphisms identifies SNPs with both potential biological relevance and utility in prognostic models of myeloma bone disease.