More emphasis on pathways whose member genes show fold changes that

More emphasis on pathways whose member genes show fold changes that are congruent with underlying interaction topology. We also performed the GO analysis to supplement limited information obtained from IF’s KEGG-dependency.Survival Analyses Using Public aCGH and GEP DataPrognostic utility of CINGEC and CINGECS was assessed through survival analysis using overall survival (OS). For CINGEC survival analysis, we used CINGEC scores estimated on aCGH datasets from the Mayo clinic (Mayo; Agilent 44K chip) [17] and from the University of Arkansas Medical School (UAMS; Agilent 22K chip) [18] separately. For CINGECS survival analyses, we used University of Arkansas dataset (UAMS; GSE2658; HG-U133 Plus 2.0) [19,20] of 559 newly diagnosed MM patients treated with total therapy II III, APEX clinical trial dataset (APEX;GSE9782; HG-U133 A/B) of 188 relapsed patients treated with bortezomib [21], and HOVON-65/GMMG-HD4 clinical trial dataset (HOVON; GSE19784; HG-U133 Plus 2.0) of 290 newly diagnosed MM patients [22,23]. For APEX dataset, we used only HG-U133 A chip probesets in this study. Besides CINGECS, we used prognostic GEP signatures known to be statistically significant in MM and 2 previously reported CIN signatures that were predictive of patient prognosis in diverse cancers for comparison: 70-gene survival index developed by 16985061 researchers from the University of Arkansas Medical School (UAMS70) [20], MedChemExpress 58-49-1 proliferation index (PI) [24], centrosome 18204824 index (CNTI) [25,26], 15-gene survival index from Intergroupe Francophone du Myelome study (IFM) [27], cell death genes affected by homozygous deletion (HZDCD) [28], 7-gene survival index from a detailed study of IL-6 dependent and independent MM cell lines (HMCL7) [29], 92-gene survival index from HOVON-65/ GMMG-HD4 study (EMC92) [23], CIN index by Carter et al. (CIN70) [30] and CIN index from sarcoma study (CINSARC) [31]. (See Table S4 for full list of member probesets). For each GEP dataset, we normalized the expression profile for a probeset by dividing individual expression values with theChromosome Instability and Prognosis in MMInput genes Pathway genes Input genes Pathway in pathway on chip in pathway genes in (number) (number) ( ) input ( )median across all samples. We, then, estimated the univariate CINGECS index of a sample by CINGECS = U ?D where U is the logarithm (base 2) of median of normalized expression values of up-regulated CINGECS members while D is the logarithm (base 2) of median of normalized expression values over downregulated CINGECS member genes, respectively. For other indices, the estimation was done as follows: Gracillin indices UAMS70 and CNTI were estimated as indicated in their respective original publications. All other indices were estimated using log2transformed median-normalized MAS5 signals as expression levels. For signatures where probesets are split into up- or downregulation groups such as IFM and HZDCD, indices were estimated as done in CINGECS. All other indices were estimated as the median of expression levels of member probesets. For each dataset, we performed univariate and multivariate survival analyses using Cox proportional hazard model. First, values from CINGEC index or GEP signature indices were respectively split into 4 quartile groups and survival tests using univariate Cox proportional hazard model were performed. For multivariate Cox analysis, we first examined three CIN-associated signatures (CINGECS, CIN70, CINSARC) to remove possible confounding effects due.More emphasis on pathways whose member genes show fold changes that are congruent with underlying interaction topology. We also performed the GO analysis to supplement limited information obtained from IF’s KEGG-dependency.Survival Analyses Using Public aCGH and GEP DataPrognostic utility of CINGEC and CINGECS was assessed through survival analysis using overall survival (OS). For CINGEC survival analysis, we used CINGEC scores estimated on aCGH datasets from the Mayo clinic (Mayo; Agilent 44K chip) [17] and from the University of Arkansas Medical School (UAMS; Agilent 22K chip) [18] separately. For CINGECS survival analyses, we used University of Arkansas dataset (UAMS; GSE2658; HG-U133 Plus 2.0) [19,20] of 559 newly diagnosed MM patients treated with total therapy II III, APEX clinical trial dataset (APEX;GSE9782; HG-U133 A/B) of 188 relapsed patients treated with bortezomib [21], and HOVON-65/GMMG-HD4 clinical trial dataset (HOVON; GSE19784; HG-U133 Plus 2.0) of 290 newly diagnosed MM patients [22,23]. For APEX dataset, we used only HG-U133 A chip probesets in this study. Besides CINGECS, we used prognostic GEP signatures known to be statistically significant in MM and 2 previously reported CIN signatures that were predictive of patient prognosis in diverse cancers for comparison: 70-gene survival index developed by 16985061 researchers from the University of Arkansas Medical School (UAMS70) [20], proliferation index (PI) [24], centrosome 18204824 index (CNTI) [25,26], 15-gene survival index from Intergroupe Francophone du Myelome study (IFM) [27], cell death genes affected by homozygous deletion (HZDCD) [28], 7-gene survival index from a detailed study of IL-6 dependent and independent MM cell lines (HMCL7) [29], 92-gene survival index from HOVON-65/ GMMG-HD4 study (EMC92) [23], CIN index by Carter et al. (CIN70) [30] and CIN index from sarcoma study (CINSARC) [31]. (See Table S4 for full list of member probesets). For each GEP dataset, we normalized the expression profile for a probeset by dividing individual expression values with theChromosome Instability and Prognosis in MMInput genes Pathway genes Input genes Pathway in pathway on chip in pathway genes in (number) (number) ( ) input ( )median across all samples. We, then, estimated the univariate CINGECS index of a sample by CINGECS = U ?D where U is the logarithm (base 2) of median of normalized expression values of up-regulated CINGECS members while D is the logarithm (base 2) of median of normalized expression values over downregulated CINGECS member genes, respectively. For other indices, the estimation was done as follows: indices UAMS70 and CNTI were estimated as indicated in their respective original publications. All other indices were estimated using log2transformed median-normalized MAS5 signals as expression levels. For signatures where probesets are split into up- or downregulation groups such as IFM and HZDCD, indices were estimated as done in CINGECS. All other indices were estimated as the median of expression levels of member probesets. For each dataset, we performed univariate and multivariate survival analyses using Cox proportional hazard model. First, values from CINGEC index or GEP signature indices were respectively split into 4 quartile groups and survival tests using univariate Cox proportional hazard model were performed. For multivariate Cox analysis, we first examined three CIN-associated signatures (CINGECS, CIN70, CINSARC) to remove possible confounding effects due.