Gene expression fingerprint of uterine serous papillary carcinoma: identification of novel molecular markers for uterine serous cancer diagnosis and therapy

Uterine serous papillary cancer (USPC) represents a rare but highly aggressive variant of endometrial cancer, the most common gynecologic tumour in women. We used oligonucleotide microarrays that interrogate the expression of some 10 000 known genes to profile 10 highly purified primary USPC cultures and five normal endometrial cells (NEC). We report that unsupervised analysis of mRNA fingerprints readily distinguished USPC from normal endometrial epithelial cells and identified 139 and 390 genes that exhibited >5-fold upregulation and downregulation, respectively, in primary USPC when compared to NEC. Many of the genes upregulated in USPC were found to represent adhesion molecules, secreted proteins and oncogenes, such as L1 cell adhesion molecule, claudin-3 and claudin-4, kallikrein 6 (protease M) and kallikrein 10 (NES1), interleukin-6 and c-erbB2. Downregulated genes in USPC included SEMACAP3, ras homolog gene family, member I (ARHI), and differentially downregulated in ovarian carcinoma gene 1. Quantitative RT–PCR was used to validate differences in gene expression between USPC and NEC for several of these genes. Owing to its potential as a novel therapeutic marker, expression of the high-affinity epithelial receptor for Clostridium perfringens enterotoxin (CPE) claudin-4 was further validated through immunohistochemical analysis of formalin-fixed paraffin-embedded specimens from which the primary USPC cultures were obtained, as well as an independent set of archival USPC specimens. Finally, the sensitivity of primary USPC to the administration of scalar doses of CPE in vitro was also demonstrated. Our results highlight the novel molecular features of USPC and provide a foundation for the development of new type-specific therapies against this highly aggressive variant of endometrial cancer

Conserved Molecular Underpinnings and Characterization of a Role for Caveolin-1 in the Tumor Microenvironment of Mature T-Cell Lymphomas

Neoplasms of extra-thymic T-cell origin represent a rare and difficult population characterized by poor clinical outcome, aggressive presentation, and poorly defined molecular characteristics. Much work has been done to gain greater insights into distinguishing features among malignant subtypes, but there also exists a need to identify unifying characteristics to assist in rapid diagnosis and subsequent potential treatment. Herein, we investigated gene expression data of five different mature T-cell lymphoma subtypes (n = 187) and found 21 genes to be up- and down-regulated across all malignancies in comparison to healthy CD4+ and CD8+ T-cell controls (n = 52). From these results, we sought to characterize a role for caveolin-1 (CAV1), a gene with previous description in the progression of both solid and hematological tumors. Caveolin-1 was upregulated, albeit with a heterogeneous nature, across all mature T-cell lymphoma subtypes, a finding confirmed using immunohistochemical staining on an independent sampling of mature T-cell lymphoma biopsies (n = 65 cases). Further, stratifying malignant samples in accordance with high and low CAV1 expression revealed that higher expression of CAV1 in mature T-cell lymphomas is analogous with an enhanced inflammatory and invasive gene expression profile. Taken together, these results demonstrate a role for CAV1 in the tumor microenvironment of mature T-cell malignancies and point toward potential prognostic implications

Genomic profiling using array comparative genomic hybridization define distinct subtypes of diffuse large b-cell lymphoma: a review of the literature

Diffuse large B-cell lymphoma (DLBCL) is the most common type of non-Hodgkin Lymphoma comprising of greater than 30% of adult non-Hodgkin Lymphomas. DLBCL represents a diverse set of lymphomas, defined as diffuse proliferation of large B lymphoid cells. Numerous cytogenetic studies including karyotypes and fluorescent in situ hybridization (FISH), as well as morphological, biological, clinical, microarray and sequencing technologies have attempted to categorize DLBCL into morphological variants, molecular and immunophenotypic subgroups, as well as distinct disease entities. Despite such efforts, most lymphoma remains undistinguishable and falls into DLBCL, not otherwise specified (DLBCL-NOS). The advent of microarray-based studies (chromosome, RNA, gene expression, etc) has provided a plethora of high-resolution data that could potentially facilitate the finer classification of DLBCL. This review covers the microarray data currently published for DLBCL. We will focus on these types of data; 1) array based CGH; 2) classical CGH; and 3) gene expression profiling studies. The aims of this review were three-fold: (1) to catalog chromosome loci that are present in at least 20% or more of distinct DLBCL subtypes; a detailed list of gains and losses for different subtypes was generated in a table form to illustrate specific chromosome loci affected in selected subtypes; (2) to determine common and distinct copy number alterations among the different subtypes and based on this information, characteristic and similar chromosome loci for the different subtypes were depicted in two separate chromosome ideograms; and, (3) to list re-classified subtypes and those that remained indistinguishable after review of the microarray data. To the best of our knowledge, this is the first effort to compile and review available literatures on microarray analysis data and their practical utility in classifying DLBCL subtypes. Although conventional cytogenetic methods such as Karyotypes and FISH have played a major role in classification schemes of lymphomas, better classification models are clearly needed to further understanding the biology, disease outcome and therapeutic management of DLBCL. In summary, microarray data reviewed here can provide better subtype specific classifications models for DLBCL

Testing and confidence intervals for high dimensional proportional hazards models

The paper considers the problem of hypothesis testing and confidence intervals in high dimensional proportional hazards models. Motivated by a geometric projection principle, we propose a unified likelihood ratio inferential framework, including score, Wald and partial likelihood ratio statistics for hypothesis testing. Without assuming model selection consistency, we derive the asymptotic distributions of these test statistics, establish their semiparametric optimality and conduct power analysis under Pitman alternatives. We also develop new procedures to construct pointwise confidence intervals for the baseline hazard function and conditional hazard function. Simulation studies show that all tests proposed perform well in controlling type I errors. Moreover, the partial likelihood ratio test is empirically more powerful than the other tests. The methods proposed are illustrated by an example of a gene expression data set