Project description:Background: Septic shock heterogeneity has important implications for the conduct of clinical trials and individual patient management. We previously addressed this heterogeneity by indentifying 3 putative subclasses of children with septic shock based on a 100-gene expression signature corresponding to adaptive immunity and glucocorticoid receptor signaling. Herein we attempted to prospectively validate the existence of these gene expression-based subclasses in a validation cohort. Methods: Gene expression mosaics were generated from the 100 class-defining genes for 82 individual patients in the validation cohort. Patients were classified into 1 of 3 subclasses (“A”, “B”, or “C”) based on color and pattern similarity relative to reference mosaics generated from the original derivation cohort. Separate classifications were conducted by 21 individual clinicians and a computer-based algorithm. After subclassification the clinical database was mined for clinical phenotyping. Results: In the final consensus subclassification generated by clinicians, subclass A patients had a higher illness severity, as measured by illness severity scores and maximal organ failure, relative to subclasses B and C. The k coefficient across all possible inter-evaluator comparisons was 0.633. Similar observations were made based on the computer-generated subclassification. Patients in subclass A were also characterized by repression of a large number of genes having functional annotations related to zinc biology. Conclusions: We have validated the existence of subclasses of children with septic shock based on a biologically relevant, 100-gene expression signature. The subclasses can be indentified by clinicians without formal bioinformatics training, at a clinically relevant time point, and have clinically relevant phenotypic differences. Expression data from 82 children with septic shock and 21 normal controls were generated using whole blood-derived RNA samples representing the first 24 hours of admission to the pediatric intensive care unit. The controls were used for normalization. Subsequently, we used the expression data from 100 class defining genes to validate the existence of pediatric septic shock subclasses having phenotypic differences.

Project description:Background: Septic shock is a heterogeneous syndrome within which probably exist several biological subclasses. Discovery and identification of septic shock subclasses could provide the foundation for the design of more specifically targeted therapies. Herein we tested the hypothesis that pediatric septic shock subclasses can be discovered through genome-wide expression profiling. Methods: Genome-wide expression profiling was conducted using whole blood-derived RNA from 98 children with septic shock, followed by a series of bioinformatic approaches targeted at subclass discovery and characterization. Results: Three putative subclasses (subclasses A, B, and C) were initially identified based on an empiric, discovery-oriented expression filter and unsupervised hierarchical clustering. Statistical comparison of the 3 putative subclasses (ANOVA, Bonferonni correction, p < 0.05) identified 6,934 differentially regulated genes. K means clustering of these 6,934 genes generated 10 coordinately regulated gene clusters corresponding to multiple signaling and metabolic pathways, all of which were differentially regulated across the 3 subclasses. Leave one out cross validation procedures indentified 100 genes having the strongest predictive values for subclass identification. Forty-four of these 100 genes corresponded to signaling pathways relevant to the adaptive immune system and glucocorticoid receptor signaling, the majority of which were repressed in subclass A patients. Subclass A patients were also characterized by repression of genes corresponding to zinc-related biology. Phenotypic analyses revealed that subclass A patients were younger, had a higher illness severity, and a higher mortality rate than patients in subclasses B and C. Conclusions: Genome-wide expression profiling can identify pediatric septic shock subclasses having clinically relevant phenotypes. Expression data from 98 children with septic shock and 32 normal controls were generated using whole blood-derived RNA samples representing the first 24 hours of admission to the pediatric intensive care unit. The controls were used for normalization. Subsequently, we used the expression data to derive expression-based subclasses of patients using discovery oriented expression and statistical filters, followed by unsupervised hierarchical clustering.

Project description:Purpose: The majority of patients with epithelial ovarian cancer (EOC) is diagnosed at advanced stage and has a poor prognosis. A proportion of these patients though will fare well, with a prognosis similar to patients with early stage disease while others die very quickly. Clinicopathological prognostic factors do not allow precise identification of these subgroups. Thus we have validated a molecular subclassification as prognostic factor in EOC. Experimental Design: One hundred ninety-four patients with EOC stage II to IV were characterized by whole-genome expression profiling of tumor tissues and classified using a published 112 gene-set, derived from a FIGO stage directed supervised classification approach. Results: The 194 tumor samples were classified into two subclasses of 95 (subclass 1) and 99 (subclass 2) tumors, grouping all 9 FIGO II tumors in subclass 1 (p=0.001). Subclass 2 (54% of advanced stage tumors) correlated significantly with peritoneal carcinomatosis and non-optimal debulking. Patients with subclass 2 tumors had a worse progression free survival (HR 1.67, p=0.005) by univariate analysis, but it was not an independent factor in multiple analysis. However, overall survival was impaired both, univariate (HR 3.68, p<0.001) and in models corrected for relevant clinicopathologic parameters (HR 3.13, p<0.001). Significance analysis of microarrays revealed 2,115 genes differentially expressed in both subclasses (FDR 5%). Conclusion: In this validation study we showed that in advanced-stage epithelial ovarian cancer two approximately equally large molecular subtypes exist, independent from classical clinocopathological parameters presenting with highly different whole genome expression profiles and an impressively different overall survival. Purpose: The majority of patients with epithelial ovarian cancer (EOC) is diagnosed at advanced stage and has a poor prognosis. A proportion of these patients though will fare well, with a prognosis similar to patients with early stage disease while others die very quickly. Clinicopathological prognostic factors do not allow precise identification of these subgroups. Thus we have validated a molecular subclassification as prognostic factor in EOC. Experimental Design: One hundred ninety-four patients with EOC stage II to IV were characterized by whole-genome expression profiling of tumor tissues and classified using a published 112 gene-set, derived from a FIGO stage directed supervised classification approach. Results: The 194 tumor samples were classified into two subclasses of 95 (subclass 1) and 99 (subclass 2) tumors, grouping all 9 FIGO II tumors in subclass 1 (p=0.001). Subclass 2 (54% of advanced stage tumors) correlated significantly with peritoneal carcinomatosis and non-optimal debulking. Patients with subclass 2 tumors had a worse progression free survival (HR 1.67, p=0.005) by univariate analysis, but it was not an independent factor in multiple analysis. However, overall survival was impaired both, univariate (HR 3.68, p<0.001) and in models corrected for relevant clinicopathologic parameters (HR 3.13, p<0.001). Significance analysis of microarrays revealed 2,115 genes differentially expressed in both subclasses (FDR 5%). Conclusion: In this validation study we showed that in advanced-stage epithelial ovarian cancer two approximately equally large molecular subtypes exist, independent from classical clinocopathological parameters presenting with highly different whole genome expression profiles and an impressively different overall survival. Targeted therapies in second line treatment gain more and more importance in managing recurrent or progressive carcinomas, particularly in ovarian cancer, a cancer entity characterized by a very high recurrence rate. One step ahead, it is necessary to define new therapeutic targets and to select patients who might benefit from these therapies already in first line settings. A robust molecular subclassification could provide both, an adequate patient selection and potential new targets. Notably, the validation of such a subclassification is of outstanding importance to obtain a reliable basis for a specific clinical decision and a rational for the expensive development of new targeted therapies. This work provides a comprehensive basis for both. The expression values of 204 epithelial ovarian cancer tissues are determined and used for the validation of a subclassification approach (Cancer Sci. 2009 Aug;100(8):1421-8.)

Project description:Rationale: We previously generated genome-wide expression data in children with septic shock, based on whole blood-derive RNA, having the potential to lead the field into novel areas of investigation. Objective: Herein we seek to validate our data through a bioinformatic approach centered on a validation patient cohort. Methods: Microarray- and bioinformatics-centered analyses involving our previous data as a training data set (n = 42) and a new, validation cohort (n = 30) as the test data set. Measurements and Main Results: Class prediction modeling using the training data set and the previously reported genome-wide expression signature of pediatric septic shock correctly identified 93 to 100% of septic shock patients in the test data set, depending on the class prediction algorithm and the gene selection method. Subjecting the test data set to an identical filtering strategy as that used for the training data set, demonstrated 72% concordance between the two gene lists. Subjecting the test data set to a purely statistical filtering strategy, with highly stringent correction for multiple comparisons, demonstrated less than 50% concordance with the previous gene filtering strategy. However, functional analysis of this statistics-based gene list demonstrated similar functional annotations and signaling pathways as that seen in the learning data set. In particular, we validated that pediatric septic shock is characterized by large scale repression of genes related to zinc homeostasis and lymphocyte function. Conclusions: These data demonstrate that the previously reported genome-wide expression signature of pediatric septic shock is applicable to a validation cohort of patients. Experiment Overall Design: Table 1: Clinical and demographic data for all subjects in test data set. Experiment Overall Design: Controls Septic Shock Experiment Overall Design: No. of individual subjects 15 30 Experiment Overall Design: Mean age (years) Â± S.D. 3.1 Â± 3.5 3.2 Â± 2.9 Experiment Overall Design: Mean PRISM Score Â± S.D. n/a 18.9 Â± 12.3 Experiment Overall Design: Gender (Male/Female) 8/7 16/14 Experiment Overall Design: Race (no.) A.A./Black (6) A.A./Black (2) Experiment Overall Design: Asian (4) White (26) White (5) Unreported (2)

Project description:Purpose: The majority of patients with epithelial ovarian cancer (EOC) is diagnosed at advanced stage and has a poor prognosis. A proportion of these patients though will fare well, with a prognosis similar to patients with early stage disease while others die very quickly. Clinicopathological prognostic factors do not allow precise identification of these subgroups. Thus we have validated a molecular subclassification as prognostic factor in EOC. Experimental Design: One hundred ninety-four patients with EOC stage II to IV were characterized by whole-genome expression profiling of tumor tissues and classified using a published 112 gene-set, derived from a FIGO stage directed supervised classification approach. Results: The 194 tumor samples were classified into two subclasses of 95 (subclass 1) and 99 (subclass 2) tumors, grouping all 9 FIGO II tumors in subclass 1 (p=0.001). Subclass 2 (54% of advanced stage tumors) correlated significantly with peritoneal carcinomatosis and non-optimal debulking. Patients with subclass 2 tumors had a worse progression free survival (HR 1.67, p=0.005) by univariate analysis, but it was not an independent factor in multiple analysis. However, overall survival was impaired both, univariate (HR 3.68, p<0.001) and in models corrected for relevant clinicopathologic parameters (HR 3.13, p<0.001). Significance analysis of microarrays revealed 2,115 genes differentially expressed in both subclasses (FDR 5%). Conclusion: In this validation study we showed that in advanced-stage epithelial ovarian cancer two approximately equally large molecular subtypes exist, independent from classical clinocopathological parameters presenting with highly different whole genome expression profiles and an impressively different overall survival. Purpose: The majority of patients with epithelial ovarian cancer (EOC) is diagnosed at advanced stage and has a poor prognosis. A proportion of these patients though will fare well, with a prognosis similar to patients with early stage disease while others die very quickly. Clinicopathological prognostic factors do not allow precise identification of these subgroups. Thus we have validated a molecular subclassification as prognostic factor in EOC. Experimental Design: One hundred ninety-four patients with EOC stage II to IV were characterized by whole-genome expression profiling of tumor tissues and classified using a published 112 gene-set, derived from a FIGO stage directed supervised classification approach. Results: The 194 tumor samples were classified into two subclasses of 95 (subclass 1) and 99 (subclass 2) tumors, grouping all 9 FIGO II tumors in subclass 1 (p=0.001). Subclass 2 (54% of advanced stage tumors) correlated significantly with peritoneal carcinomatosis and non-optimal debulking. Patients with subclass 2 tumors had a worse progression free survival (HR 1.67, p=0.005) by univariate analysis, but it was not an independent factor in multiple analysis. However, overall survival was impaired both, univariate (HR 3.68, p<0.001) and in models corrected for relevant clinicopathologic parameters (HR 3.13, p<0.001). Significance analysis of microarrays revealed 2,115 genes differentially expressed in both subclasses (FDR 5%). Conclusion: In this validation study we showed that in advanced-stage epithelial ovarian cancer two approximately equally large molecular subtypes exist, independent from classical clinocopathological parameters presenting with highly different whole genome expression profiles and an impressively different overall survival. Targeted therapies in second line treatment gain more and more importance in managing recurrent or progressive carcinomas, particularly in ovarian cancer, a cancer entity characterized by a very high recurrence rate. One step ahead, it is necessary to define new therapeutic targets and to select patients who might benefit from these therapies already in first line settings. A robust molecular subclassification could provide both, an adequate patient selection and potential new targets. Notably, the validation of such a subclassification is of outstanding importance to obtain a reliable basis for a specific clinical decision and a rational for the expensive development of new targeted therapies. This work provides a comprehensive basis for both.

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Expression data from validation cohort of children with septic shock

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