Abstract
While there are certain common clinical features in myeloma, the disease shows significant heterogeneity with regard to disease progression, and responses to therapy, affecting both survival and toxicities. Heritable variations in a wide variety of genes and pathways affecting cellular functions and drug responses likely impact patient outcomes. In the Bank On A Cure (BOAC) program we have developed a custom chip that assesses 3,404 SNPs representing variations in cellular functions and pathways that may be involved in myeloma progression and response. The chip has gone through rigorous quality controls checks for high call rates, accuracy, and reproducibility that will be presented. Using the BOAC chip, we have conducted studies to look for SNPs that may identify biologic variations that are associated with good or poor response across a variety of treatments. In this study we looked for SNPs that may distinguish short term and long term survivors in two phase III clinical trials: ECOG E9486 and intergroup trial S9321. E9487 patients were treated with VBMCP followed by randomization to no further treatment, IFN-alpha, or cylcophosphamide; and, although there was variation in survival, no significant differences in survival were noted among the 3 arms of the trial. Patients included in this SNP study from S9321 received VAD induction followed by randomization to VBMCP or high dose melphalan + TBI. SNP profiles were obtained for patients with less than 1 year EFS (n=20 in E9487; n=50 in S9321) and patients showing greater than 3 years EFS (n=32 in E9486; n=41 in S9321). Statistical approaches were performed to identify single and groups of SNPs that best discriminated the survival groups. Previous studies have suggested genetic variations in drug metabolism genes, p-glycoprotein transport, and DNA repair genes may influence survival outcomes. Our results show significant survival associations of genetic variations in genes within these functional categories (eg. GST, XRCC, ABCB, and CYP genes). Although genetic variations were found that were uniquely associated with each clinical trial, several of these genetic variations show survival associations that increase in significance when the two trials were examined as a conglomerate data set. Grouping genetic variations through common pathway approaches using gene set enrichment analysis, as well as clustering or partitioning algorithms, further improve the value of the SNPs as potential prognostic markers of survival outcomes. These results and statistical approaches will be presented, and represent steps toward identifying patient variations in biologic mechanisms important in predicting therapeutic outcomes.
