Van Ness Brian, Ramos Christine, Haznadar Majda, Hoering Antje, Haessler Jeff, Crowley John, Jacobus Susanna, Oken Martin, Rajkumar Vincent, Greipp Philip, Barlogie Bart, Durie Brian, Katz Michael, Atluri Gowtham, Fang Gang, Gupta Rohit, Steinbach Michael, Kumar Vipin, Mushlin Richard, Johnson David, Morgan Gareth
Cancer Center, University of Minnesota, Minneapolis, MN, USA.
BMC Med. 2008 Sep 8;6:26. doi: 10.1186/1741-7015-6-26.
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.
我们参与了一项名为“治愈希望之库”的国际项目,该项目已从多个合作和机构的临床试验中建立了DNA库,并搭建了一个平台,用于研究基因变异与多发性骨髓瘤疾病风险及预后之间的关联。我们描述了一种新型定制SNP芯片的开发过程和内容,该芯片包含983个基因中的3404个SNP,代表了可能影响诊断时疾病严重程度、毒性、进展或其他治疗结果的细胞功能和信号通路。通过系统检索国家数据库来识别非同义编码SNP和转录调控区域内的SNP。为了探索SNP与无进展生存期(PFS)的关联,我们在两项III期临床试验中比较了短期(少于1年,n = 70)与长期无进展生存者(超过3年,n = 73)的SNP谱。
建立了质量控制,证明该基因变异筛选芯片准确且可靠,并进行了一些等位基因变异的初步种族比较。多种分析方法,包括用于数据挖掘的机器学习工具和递归划分分析,证明了SNP芯片在生存方面的预测价值。虽然整个SNP芯片显示出基因型与PFS的预测关联,但在药物反应、细胞信号传导和细胞周期基因中鉴定出了一些SNP子集。
我们采用了靶向基因方法来开发一种SNP芯片,该芯片可用于测试与骨髓瘤临床结局的关联。初步分析提供了一定的预测能力,表明骨髓瘤患者群体中的基因变异可能影响PFS。