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Claudin-2 promotes breast cancer liver metastasis by enabling seeding and early cancer cell survival. We now demonstrate that Claudin-2 is functionally required for colorectal cancer liver metastasis and that Claudin-2 expression in primary colorectal cancers is associated with poor overall and liver metastasis-free survival. We have examined the role of Claudin-2, and other claudin family members, as potential prognostic biomarkers of the desmoplastic and replacement histopathological growth pattern associated with colorectal cancer liver metastases. Immunohistochemical analysis revealed higher Claudin-2 levels in replacement type metastases when compared to those with desmoplastic features. In contrast, Claudin-8 was highly expressed in desmoplastic colorectal cancer liver metastases. Similar observations were made following immunohistochemical staining of patient-derived xenografts (PDXs) that we have established, which faithfully retain the histopathology of desmoplastic or replacement type colorectal cancer liver metastases. We provide evidence that Claudin-2 status in patient-derived extracellular vesicles may serve as a relevant prognostic biomarker to predict whether colorectal cancer patients have developed replacement type liver metastases. Such a biomarker will be a valuable tool in designing optimal treatment strategies to better manage patients with colorectal cancer liver metastases.

BACKGROUND We aimed to develop a gene expression-based prognostic signature for isocitrate dehydrogenase (IDH) wild-type glioblastoma using clinical trial datasets representative of glioblastoma clinical trial populations. METHODS Samples were collected from newly diagnosed patients with IDH wild-type glioblastoma in the ARTE, TAMIGA, EORTC 26101 (referred to as "ATE"), AVAglio, and GLARIUS trials, or treated at UCLA. Transcriptional profiling was achieved with the NanoString gene expression platform. To identify genes prognostic for overall survival (OS), we built an elastic net penalized Cox proportional hazards regression model using the discovery ATE dataset. For validation in independent datasets (AVAglio, GLARIUS, UCLA), we combined elastic net-selected genes into a robust z-score signature (ATE score) to overcome gene expression platform differences between discovery and validation cohorts. RESULTS NanoString data were available from 512 patients in the ATE dataset. Elastic net identified a prognostic signature of 9 genes (CHEK1, GPR17, IGF2BP3, MGMT, MTHFD1L, PTRH2, SOX11, S100A9, and TFRC). Translating weighted elastic net scores to the ATE score conserved the prognostic value of the genes. The ATE score was prognostic for OS in the ATE dataset (P < 0.0001), as expected, and in the validation cohorts (AVAglio, P < 0.0001; GLARIUS, P = 0.02; UCLA, P = 0.004). The ATE score remained prognostic following adjustment for O6-methylguanine-DNA methyltransferase (MGMT) promoter methylation status and corticosteroid use at baseline. A positive correlation between ATE score and proneural/proliferative subtypes was observed in patients with MGMT non-methylated promoter status. CONCLUSIONS The ATE score showed prognostic value and may enable clinical trial stratification for IDH wild-type glioblastoma.

With this book, you will learn not just about R, but how to use R to answer conceptual, scientific, and experimental questions.Beginning with an overview of fundamental R concepts, you'll learn how R can be used to achieve the most commonly needed scientific data analysis tasks: testing for statistically significant differences between groups and model relationships in data. You will delve into linear algebra and matrix operations with an emphasis not on the R syntax, but on how these operations can be used to address common computational or analytical needs. This book also covers the application of matrix operations for the purpose of finding structure in high-dimensional data using the principal component, exploratory factor, and confirmatory factor analysis in addition to structural equation modeling. You will also master methods for simulation and learn about an advanced analytical method.

With this book, you will learn not just about R, but how to use R to answer conceptual, scientific, and experimental questions.Beginning with an overview of fundamental R concepts, you'll learn how R can be used to achieve the most commonly needed scientific data analysis tasks: testing for statistically significant differences between groups and model relationships in data. You will delve into linear algebra and matrix operations with an emphasis not on the R syntax, but on how these operations can be used to address common computational or analytical needs. This book also covers the application of matrix operations for the purpose of finding structure in high-dimensional data using the principal component, exploratory factor, and confirmatory factor analysis in addition to structural equation modeling. You will also master methods for simulation and learn about an advanced analytical method.

With this book, you will learn not just about R, but how to use R to answer conceptual, scientific, and experimental questions.Beginning with an overview of fundamental R concepts, you'll learn how R can be used to achieve the most commonly needed scientific data analysis tasks: testing for statistically significant differences between groups and model relationships in data. You will delve into linear algebra and matrix operations with an emphasis not on the R syntax, but on how these operations can be used to address common computational or analytical needs. This book also covers the application of matrix operations for the purpose of finding structure in high-dimensional data using the principal component, exploratory factor, and confirmatory factor analysis in addition to structural equation modeling. You will also master methods for simulation and learn about an advanced analytical method.