Purpose: Immune components of the tumor microenvironment (TME) have been associated with disease outcome. We prospectively evaluated the association of an immune-related gene signature (GS) with... Show morePurpose: Immune components of the tumor microenvironment (TME) have been associated with disease outcome. We prospectively evaluated the association of an immune-related gene signature (GS) with clinical outcome in melanoma and non-small cell lung cancer (NSCLC) tumor samples from two phase III studies.Experimental Design: The GS was prospectively validated using an adaptive signature design to optimize it for the sample type and technology used in phase III studies. One-third of the samples were used as "training set"; the remaining two thirds, constituting the "test set," were used for the prospective validation of the GS.Results: In the melanoma training set, the expression level of eight Th1/IFN gamma-related genes in tumor-positive lymph node tissue predicted the duration of disease-free survival (DFS) and overall survival (OS) in the placebo arm. This GS was prospectively and independently validated as prognostic in the test set. Building a multivariate Cox model in the test set placebo patients from clinical covariates and the GS score, an increased number of melanoma-involved lymph nodes and the GS were associated with DFS and OS. This GS was not associated with DFS in NSCLC, although expression of the Th1/IFN gamma-related genes was associated with the presence of lymphocytes in tumor samples in both indications.Conclusions: These findings provide evidence that expression of Th1/IFN gamma genes in the TME, as measured by this GS, is associated with clinical outcome in melanoma. This suggests that, using this GS, patients with stage IIIB/C melanoma can be classified into different risk groups. Show less
OBJECTIVE: Standard methods for linkage analysis ignore the phenotype of the parents when they are not genotyped. However, this information can be useful for gene mapping. In this paper we propose... Show moreOBJECTIVE: Standard methods for linkage analysis ignore the phenotype of the parents when they are not genotyped. However, this information can be useful for gene mapping. In this paper we propose methods for age at onset genetic linkage analysis in sibling pairs, taking into account parental age at onset. METHODS: Two new score statistics are derived, one from an additive gamma frailty model and one from a log-normal frailty model. The score statistics are classical non-parametric linkage (NPL) statistics weighted by a function of the age at onset of the four family members. The weight depends on information from registries (age-specific incidences) and family studies (sib-sib and father-mother correlation). RESULTS: In order to investigate how age at onset of sibs and their parents affect the information for linkage analysis the weight functions were studied for rare and common disease models, realistic models for breast cancer and human lifespan. We studied the performance of the weighted NPL methods by simulations. As illustration, the score statistics were applied to the GAW12 data. The results show that it is useful to include parental age at onset information in genetic linkage analysis. Show less
Objective: Standard methods for linkage analysis ignore the phenotype of the parents when they are not genotyped. However, this information can be useful for gene mapping. In this paper we propose... Show moreObjective: Standard methods for linkage analysis ignore the phenotype of the parents when they are not genotyped. However, this information can be useful for gene mapping. In this paper we propose methods for age at onset genetic linkage analysis in sibling pairs, taking into account parental age at onset. Methods: Two new score statistics are derived, one from an additive gamma frailty model and one from a log-normal frailty model. The score statistics are classical non-parametric linkage (NPL) statistics weighted by a function of the age at onset of the four family members. The weight depends on information from registries (age-specific incidences) and family studies (sib-sib and father-mother correlation). Results: In order to investigate how age at onset of sibs and their parents affect the information for linkage analysis the weight functions were studied for rare and common disease models, realistic models for breast cancer and human lifespan. We studied the performance of the weighted NPL methods by simulations. As illustration, the score statistics were applied to the GAW12 data. The results show that it is useful to include parental age at onset information in genetic linkage analysis. Copyright (C) 2009 S. Karger AG, Basel Show less
P>When conducting genetic studies for complex traits, large samples are commonly required to detect new genetic factors. A possible strategy to decrease the sample size is to reduce... Show moreP>When conducting genetic studies for complex traits, large samples are commonly required to detect new genetic factors. A possible strategy to decrease the sample size is to reduce heterogeneity using available information. In this paper we propose a new class of model-free linkage analysis statistics which takes into account the information given by the ungenotyped affected relatives (positive family history). This information is included into the scoring function of classical allele-sharing statistics. We studied pedigrees of affected sibling pairs with one ungenotyped affected relative. We show that, for rare allele common complex diseases, the proposed method increases the expected power to detect linkage. Allele-sharing methods were applied to the symptomatic osteoarthritis GARP study where taking into account the family-history increased considerably the evidence of linkage in the region of the DIO2 susceptibility locus. Show less
The largest part of this thesis is devoted to newly developed statistical methods for age at onset linkage analysis. We used frailty models in which random effects were introduced to model the... Show moreThe largest part of this thesis is devoted to newly developed statistical methods for age at onset linkage analysis. We used frailty models in which random effects were introduced to model the dependence between outcomes of relatives due to sharing of marker alleles Identical By Descent. From the retrospective likelihood of the marker data conditional on the phenotypes, we derived score tests for genetic linkage analysis. The score statistics appear to be classical Non-Parametric Linkage statistics weighted by functions of the age at onset (or age at censoring) of the family members. These tests are based on allele-sharing, they can be applied to families ascertained through their phenotypes, and they do not require specification of genetic models or penetrance functions. Further, they can incorporate both affected and unaffected family members. In fact, the age at disease onset of the affecteds and the age at censoring of the unaffecteds are considered by this approach. Finally, with respect to the likelihood-ratio tests proposed in the literature the derived score tests are computationally faster, locally most powerful, and robust. For all these reasons, the proposed weighted NPL statistics provide a practical solution for mapping genes for complex diseases with variable age at onset. Show less
In order to study family-based association in the presence of linkage, we extend a generalized linear mixed model proposed for genetic linkage analysis (Lebrec and van Houwelingen (2007), Human... Show moreIn order to study family-based association in the presence of linkage, we extend a generalized linear mixed model proposed for genetic linkage analysis (Lebrec and van Houwelingen (2007), Human Heredity 64, 5-15) by adding a genotypic effect to the mean. The corresponding score test is a weighted family-based association tests statistic, where the weight depends on the linkage effect and on other genetic and shared environmental effects. For testing of genetic association in the presence of gene-covariate interaction, we propose a linear regression method where the family-specific score statistic is regressed on family-specific covariates. Both statistics are straightforward to compute. Simulation results show that adjusting the weight for the within-family variance structure may be a powerful approach in the presence of environmental effects. The test statistic for genetic association in the presence of gene-covariate interaction improved the power for detecting association. For illustration, we analyze the rheumatoid arthritis data from GAW15. Adjusting for smoking and anti-cyclic citrullinated peptide increased the significance of the association with the DR locus. Show less
In order to study family-based association in the presence of linkage, we extend a generalized linear mixed model proposed for genetic linkage analysis (Lebrec and van Houwelingen (2007), Human... Show moreIn order to study family-based association in the presence of linkage, we extend a generalized linear mixed model proposed for genetic linkage analysis (Lebrec and van Houwelingen (2007), Human Heredity 64, 5-15) by adding a genotypic effect to the mean. The corresponding score test is a weighted family-based association tests statistic, where the weight depends on the linkage effect and on other genetic and shared environmental effects. For testing of genetic association in the presence of gene-covariate interaction, we propose a linear regression method where the family-specific score statistic is regressed on family-specific covariates. Both statistics are straightforward to compute. Simulation results show that adjusting the weight for the within-family variance structure may be a powerful approach in the presence of environmental effects. The test statistic for genetic association in the presence of gene-covariate interaction improved the power for detecting association. For illustration, we analyze the rheumatoid arthritis data from GAW15. Adjusting for smoking and anti-cyclic citrullinated peptide increased the significance of the association with the DR locus. Show less
