Expression quantitative trait loci of genes predicting outcome are associated with survival of multiple myeloma patients

Canadian Institutes of Health Research, Grant/ Award Number: 81274; Huntsman Cancer Institute Pilot Funds; Leukemia Lymphoma Society, Grant/Award Number: 6067-09; the National Institute of Health/National Cancer Institute, Grant/Award Numbers: P30 CA016672, P30 CA042014, P30 CA13148, P50 CA186781, R01 CA107476, R01 CA134674, R01 CA168762, R01 CA186646, R01 CA235026, R21 CA155951, R25 CA092049, R25 CA47888, U54 CA118948; Utah Population Database, Utah Cancer Registry, Huntsman Cancer Center Support Grant, Utah State Department of Health, University of Utah; VicHealth, Cancer Council Victoria, Australian National Health and Medical Research Council, Grant/Award Numbers: 1074383, 209057, 396414; Victorian Cancer Registry, Australian Institute of Health and Welfare, Australian National Death Index, Australian Cancer Database; Mayo Clinic Cancer Center; University of Pisa and DKFZThe authors thank all site investigators that contributed to the studies within the Multiple Myeloma Working Group (Interlymph Consortium), staff involved at each site and, most importantly, the study participants for their contributions that made our study possible. This work was partially supported by intramural funds of University of Pisa and DKFZ. This work was supported in part by the National Institute of Health/National Cancer Institute (R25 CA092049, P30 CA016672, R01 CA134674, P30 CA042014, R01 CA186646, R21 CA155951, U54 CA118948, P30 CA13148, R25 CA47888, R01 CA235026, R01 CA107476, R01 CA168762, P50 CA186781 and the NCI Intramural Research Program), Leukemia Lymphoma Society (6067-09), Huntsman Cancer Institute Pilot Funds, Utah PopulationDatabase, Utah Cancer Registry, Huntsman Cancer Center Support Grant, Utah StateDepartment of Health, University of Utah, Canadian Institutes of Health Research (Grant number 81274), VicHealth, Cancer Council Victoria, Australian National Health and Medical Research Council (Grants 209057, 396414, 1074383), Victorian Cancer Registry, Australian Institute of Health and Welfare, Australian National Death Index, Australian Cancer Database and the Mayo Clinic Cancer Center.Open Access funding enabled and organized by ProjektDEAL.The data that support the findings of this study are available on request from the corresponding author. The data are not publicly available due to privacy or ethical restrictions.Gene expression profiling can be used for predicting survival in multiple myeloma (MM) and identifying patients who will benefit from particular types of therapy. Some germline single nucleotide polymorphisms (SNPs) act as expression quantitative trait loci (eQTLs) showing strong associations with gene expression levels. We performed an association study to test whether eQTLs of genes reported to be associated with prognosis of MM patients are directly associated with measures of adverse outcome. Using the genotype-tissue expression portal, we identified a total of 16 candidate genes with at least one eQTL SNP associated with their expression with P < 10(-7) either in EBV-transformed B-lymphocytes or whole blood. We genotyped the resulting 22 SNPs in 1327 MM cases from the International Multiple Myeloma rESEarch (IMMEnSE) consortium and examined their association with overall survival (OS) and progression-free survival (PFS), adjusting for age, sex, country of origin and disease stage. Three polymorphisms in two genes (TBRG4-rs1992292, TBRG4-rs2287535 and ENTPD1-rs2153913) showed associations with OS at P < .05, with the former two also associated with PFS. The associations of two polymorphisms in TBRG4 with OS were replicated in 1277 MM cases from the International Lymphoma Epidemiology (InterLymph) Consortium. A meta-analysis of the data from IMMEnSE and InterLymph (2579 cases) showed that TBRG4-rs1992292 is associated with OS (hazard ratio = 1.14, 95% confidence interval 1.04-1.26, P = .007). In conclusion, we found biologically a plausible association between a SNP in TBRG4 and OS of MM patients.Canadian Institutes of Health Research (CIHR) 81274Huntsman Cancer Institute Pilot FundsLeukemia and Lymphoma Society 6067-09United States Department of Health & Human Services National Institutes of Health (NIH) - USA NIH National Cancer Institute (NCI) P30 CA016672 P30 CA042014 P30 CA13148 P50 CA186781 R01 CA107476 R01 CA134674 R01 CA168762 R01 CA186646 R01 CA235026 R21 CA155951 R25 CA092049 R25 CA47888 U54 CA118948Utah Population Database, Utah Cancer Registry, Huntsman Cancer Center Support Grant, Utah State Department of Health, University of UtahVicHealth, Cancer Council Victoria, Australian National Health and Medical Research Council 1074383 209057 396414Victorian Cancer Registry, Australian Institute of Health and Welfare, Australian National Death Index, Australian Cancer DatabaseMayo Clinic Cancer CenterUniversity of PisaHelmholtz Associatio

Structure-Based Predictive Models for Allosteric Hot Spots

In allostery, a binding event at one site in a protein modulates the behavior of a distant site. Identifying residues that relay the signal between sites remains a challenge. We have developed predictive models using support-vector machines, a widely used machine-learning method. The training data set consisted of residues classified as either hotspots or non-hotspots based on experimental characterization of point mutations from a diverse set of allosteric proteins. Each residue had an associated set of calculated features. Two sets of features were used, one consisting of dynamical, structural, network, and informatic measures, and another of structural measures defined by Daily and Gray [1]. The resulting models performed well on an independent data set consisting of hotspots and non-hotspots from five allosteric proteins. For the independent data set, our top 10 models using Feature Set 1 recalled 68–81% of known hotspots, and among total hotspot predictions, 58–67% were actual hotspots. Hence, these models have precision P = 58–67% and recall R = 68–81%. The corresponding models for Feature Set 2 had P = 55–59% and R = 81–92%. We combined the features from each set that produced models with optimal predictive performance. The top 10 models using this hybrid feature set had R = 73–81% and P = 64–71%, the best overall performance of any of the sets of models. Our methods identified hotspots in structural regions of known allosteric significance. Moreover, our predicted hotspots form a network of contiguous residues in the interior of the structures, in agreement with previous work. In conclusion, we have developed models that discriminate between known allosteric hotspots and non-hotspots with high accuracy and sensitivity. Moreover, the pattern of predicted hotspots corresponds to known functional motifs implicated in allostery, and is consistent with previous work describing sparse networks of allosterically important residues