Cross-species analysis of genetically engineered mouse models of MAPK-driven colorectal cancer identifies hallmarks of the human disease

Effective treatment options for advanced colorectal cancer (CRC) are limited, survival rates are poor and this disease continues to be a leading cause of cancer-related deaths worldwide. Despite being a highly heterogeneous disease, a large subset of individuals with sporadic CRC typically harbor relatively few established ‘driver’ lesions. Here, we describe a collection of genetically engineered mouse models (GEMMs) of sporadic CRC that combine lesions frequently altered in human patients, including well-characterized tumor suppressors and activators of MAPK signaling. Primary tumors from these models were profiled, and individual GEMM tumors segregated into groups based on their genotypes. Unique allelic and genotypic expression signatures were generated from these GEMMs and applied to clinically annotated human CRC patient samples. We provide evidence that a Kras signature derived from these GEMMs is capable of distinguishing human tumors harboring KRAS mutation, and tracks with poor prognosis in two independent human patient cohorts. Furthermore, the analysis of a panel of human CRC cell lines suggests that high expression of the GEMM Kras signature correlates with sensitivity to targeted pathway inhibitors. Together, these findings implicate GEMMs as powerful preclinical tools with the capacity to recapitulate relevant human disease biology, and support the use of genetic signatures generated in these models to facilitate future drug discovery and validation efforts

Test of Four Colon Cancer Risk-Scores in Formalin Fixed Paraffin Embedded Microarray Gene Expression Data

Background Prognosis prediction for resected primary colon cancer is based on the T-stage Node Metastasis (TNM) staging system. We investigated if four well-documented gene expression risk scores can improve patient stratification. Methods Microarray-based versions of risk-scores were applied to a large independent cohort of 688 stage II/III tumors from the PETACC-3 trial. Prognostic value for relapse-free survival (RFS), survival after relapse (SAR), and overall survival (OS) was assessed by regression analysis. To assess improvement over a reference, prognostic model was assessed with the area under curve (AUC) of receiver operating characteristic (ROC) curves. All statistical tests were two-sided, except the AUC increase. Results All four risk scores (RSs) showed a statistically significant association (single-test, P < .0167) with OS or RFS in univariate models, but with HRs below 1.38 per interquartile range. Three scores were predictors of shorter RFS, one of shorter SAR. Each RS could only marginally improve an RFS or OS model with the known factors T-stage, N-stage, and microsatellite instability (MSI) status (AUC gains < 0.025 units). The pairwise interscore discordance was never high (maximal Spearman correlation = 0.563) A combined score showed a trend to higher prognostic value and higher AUC increase for OS (HR = 1.74, 95% confidence interval [CI] = 1.44 to 2.10, P < .001, AUC from 0.6918 to 0.7321) and RFS (HR = 1.56, 95% CI = 1.33 to 1.84, P < .001, AUC from 0.6723 to 0.6945) than any single score. Conclusions The four tested gene expression-based risk scores provide prognostic information but contribute only marginally to improving models based on established risk factors. A combination of the risk scores might provide more robust information. Predictors of RFS and SAR might need to be differen

Modèle linéaire mixte avec segmentation : application à la détection de changements dans les dates de vendanges

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Joint segmentation of multivariate Gaussian processes using mixed linear models

International audienceWe consider the joint segmentation of multiple series. We use a mixed linear model to account for both covariates and correlations between signals. We propose an estimation algorithm based on EM which involves dynamic programming for the segmentation step. We show the computational e±ciency of this procedure. An application to microarray CGH profiles from multiple patients is presented

Additional file 1 of ToPASeq: an R package for topology-based pathway analysis of microarray and RNA-Seq data

Supplementary material.pdf. The file contains additional details on the following: i) common principles of the multivariable and univariable topology-based methods; ii) the functions for pathway creation and manipulation (desciption as well as demostration); iii) comparison of ToPASeq with existing tools. (1013 Kb

Modèle linéaire mixte avec segmentation : application à la détection de changements dans les dates de vendanges

International audienceNous nous intéressons à la détection de changements dans les dates de vendanges de plusieurs stations qui seraient dûs à des changements de pratiques et non à des changements climatiques. Ces séries sont analysées simultanément à l'aide d'un modèle linéaire mixte avec ruptures qui permet de prendre en compte à la fois des covariables et des corrélations entre series. Pour obtenir les paramètres du maximum de vraisemblance, nous utilisons un algorithme EM et proposons un nouvel algorithme de programmation dynamique pour l'étape de segmentation. Cependant, se pose la question du choix du nombre de segments. Ici nous généralisons trois critères de sélection de modèles, qui avaient été proposés dans le cas de la segmentation d'une serie, à la segmentation jointe de plusieurs series. Nous comparons ces critères par une étude de simulation

Overview of the eight controlled experiments (Ex. 1-8) performed.

<p>Overview of the eight controlled experiments (Ex. 1-8) performed.</p

Overview of the experiments performed to evaluate methods’ performance.

<p>Overview of the experiments performed to evaluate methods’ performance.</p

Effect of pre-processing of pathway topologies on simulated data—Overexpression of single gene.

<p>Each point represents a single gene. Only genes common for pathway topologies from graphite package (+GPT) and method-specific pathway topologies (MSPT) are displayed. Points on diagonal represent genes with the same influence in +GPT and MSPT. Points below (above) diagonal represent genes with higher (lower) influence in MSPT.</p

Effect of topological motifs in SPIA.

<p>Proportions of differentially expressed pathways (DEPs) for individual motifs (columns) at variable induced log2 fold-changes (rows) are displayed as a heatmap. Color bars on the top show influence of the motif, its size and topology (see <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0191154#pone.0191154.s001" target="_blank">S1 Text</a> for details). Note, that colors used for motif topology are unique only among motifs of the same size. The bottom panel shows the influence of the genes in a representation of a topological motif as discovered in Experiment 3.</p