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Abstract Background With the growing number of public repositories for high-throughput genomic data, it is of great interest to combine the results produced by independent research groups. Such a combination allows the identification of common genomic factors across multiple cancer types and provides new insights into the disease process. In the framework of the proportional hazards model, classical procedures, which consist of ranking genes according to the estimated hazard ratio or the p-value obtained from a test statistic of no association between survival and gene expression level, are not suitable for gene selection across multiple genomic datasets with different sample sizes. We propose a novel index for identifying genes with a common effect across heterogeneous genomic studies designed to remain stable whatever the sample size and which has a straightforward interpretation in terms of the percentage of separability between patients according to their survival times and gene expression measurements. Results The simulations results show that the proposed index is not substantially affected by the sample size of the study and the censoring. They also show that its separability performance is higher than indices of predictive accuracy relying on the likelihood function. A simulated example illustrates the good operating characteristics of our index. In addition, we demonstrate that it is linked to the score statistic and possesses a biologically relevant interpretation. The practical use of the index is illustrated for identifying genes with common effects across eight independent genomic cancer studies of different sample sizes. The meta-selection allows the identification of four genes (ESPL1, KIF4A, HJURP, LRIG1) that are biologically relevant to the carcinogenesis process and have a prognostic impact on survival outcome across various solid tumors. Conclusion The proposed index is a promising tool for identifying factors having a prognostic impact across a collection of heterogeneous genomic datasets of various sizes.

Abstract Background Genomic copy number alteration (CNA) that are recurrent across multiple samples often harbor critical genes that can drive either the initiation or the progression of cancer disease. Up to now, most researchers investigating recurrent CNAs consider separately the marginal frequencies for copy gain or loss and select the areas of interest based on arbitrary cut-off thresholds of these frequencies. In practice, these analyses ignore the interdependencies between the propensity of being deleted or amplified for a clone. In this context, a joint analysis of the copy number changes across tumor samples may bring new insights about patterns of recurrent CNAs. Methods We propose to identify patterns of recurrent CNAs across tumor samples from high-resolution comparative genomic hybridization microarrays. Clustering is achieved by modeling the copy number state (loss, no-change, gain) as a multinomial distribution with probabilities parameterized through a latent class model leading to nine patterns of recurrent CNAs. This model gives us a powerful tool to identify clones with contrasting propensity of being deleted or amplified across tumor samples. We applied this model to a homogeneous series of 65 lung adenocarcinomas. Results Our latent class model analysis identified interesting patterns of chromosomal aberrations. Our results showed that about thirty percent of the genomic clones were classified either as "exclusively" deleted or amplified recurrent CNAs and could be considered as non random chromosomal events. Most of the known oncogenes or tumor suppressor genes associated with lung adenocarcinoma were located within these areas. We also describe genomic areas of potential interest and show that an increase of the frequency of amplification in these particular areas is significantly associated with poorer survival. Conclusion Analyzing jointly deletions and amplifications through our latent class model analysis allows highlighting specific genomic areas with exclusively amplified or deleted recurrent CNAs which are good candidate for harboring oncogenes or tumor suppressor genes.

Abstract Background In the context of genomic association studies, for which a large number of statistical tests are performed simultaneously, the local False Discovery Rate (lFDR), which quantifies the evidence of a specific gene association with a clinical or biological variable of interest, is a relevant criterion for taking into account the multiple testing problem. The lFDR not only allows an inference to be made for each gene through its specific value, but also an estimate of Benjamini-Hochberg's False Discovery Rate (FDR) for subsets of genes. Results In the framework of estimating procedures without any distributional assumption under the alternative hypothesis, a new and efficient procedure for estimating the lFDR is described. The results of a simulation study indicated good performances for the proposed estimator in comparison to four published ones. The five different procedures were applied to real datasets. Conclusion A novel and efficient procedure for estimating lFDR was developed and evaluated.

Background: With the growth in use of biotherapic drugs in various medical fields, the occurrence of anti-drug antibodies represents nowadays a serious issue. This immune response against a drug can be due either to pre-existing antibodies or to the novel production of antibodies from B-cell clones by a fraction of the exposed subjects. Identifying genetic markers associated with the immunogenicity of biotherapeutic drugs may provide new opportunities for risk stratification before the introduction of the drug. However, real-world investigations should take into account that the population under study is a mixture of pre-immune, immune-reactive and immune-tolerant subjects.Method: In this work, we propose a novel test for assessing the effect of genetic markers on drug immunogenicity taking into account that the population under study is a mixed one. This test statistic is derived from a novel two-part semiparametric improper survival model which relies on immunological mechanistic considerations.Results: Simulation results show the good behavior of the proposed statistic as compared to a two-part logrank test. In a study on drug immunogenicity, our results highlighted findings that would have been discarded when considering classical tests.Conclusion: We propose a novel test that can be used for analyzing drug immunogenicity and is easy to implement with standard softwares. This test is also applicable for situations where one wants to test the equality of improper survival distributions of semi-continuous outcomes between two or more independent groups. [ABSTRACT FROM AUTHOR]

Background: In genomic medical studies, one of the major objectives is to identify genomic factors with a prognostic impact on time-to-event outcomes so as to provide new insights into the disease process. Selection usually relies on statistical univariate indices based on the Cox model. Such model assumes proportional hazards (PH) which is unlikely to hold for each genomic marker. Methods: In this paper, we introduce a novel pseudo-R² measure derived from a crossing hazards model and designed for the selection of markers with crossing effects. The proposed index is related to the score statistic and quantifies the extent of a genomic factor to separate patients according to their survival times and marker measurements. We also show the importance of considering genomic markers with crossing effects as they potentially reflect the complex interplay between markers belonging to the same pathway. Results: Simulations show that our index is not affected by the censoring and the sample size of the study. It also performs better than classical indices under the crossing hazards assumption. The practical use of our index is illustrated in a lung cancer study. The use of the proposed pseudo-R² allows the identification of cell-cycle dependent genes not identified when relying on the PH assumption. Conclusions: The proposed index is a novel and promising tool for selecting markers with crossing hazards effects. [ABSTRACT FROM AUTHOR]

Background: With the growing number of public repositories for high-throughput genomic data, it is of great interest to combine the results produced by independent research groups. Such a combination allows the identification of common genomic factors across multiple cancer types and provides new insights into the disease process. In the framework of the proportional hazards model, classical procedures, which consist of ranking genes according to the estimated hazard ratio or the p-value obtained from a test statistic of no association between survival and gene expression level, are not suitable for gene selection across multiple genomic datasets with different sample sizes. We propose a novel index for identifying genes with a common effect across heterogeneous genomic studies designed to remain stable whatever the sample size and which has a straightforward interpretation in terms of the percentage of separability between patients according to their survival times and gene expression measurements. Results: The simulations results show that the proposed index is not substantially affected by the sample size of the study and the censoring. They also show that its separability performance is higher than indices of predictive accuracy relying on the likelihood function. A simulated example illustrates the good operating characteristics of our index. In addition, we demonstrate that it is linked to the score statistic and possesses a biologically relevant interpretation. The practical use of the index is illustrated for identifying genes with common effects across eight independent genomic cancer studies of different sample sizes. The meta-selection allows the identification of four genes (ESPL1, KIF4A, HJURP, LRIG1) that are biologically relevant to the carcinogenesis process and have a prognostic impact on survival outcome across various solid tumors. Conclusion: The proposed index is a promising tool for identifying factors having a prognostic impact across a collection of heterogeneous genomic datasets of various sizes. [ABSTRACT FROM AUTHOR]