Prognostic Molecular Signatures Research Articles

Quantitative immunocytochemical profile to predict early outcome of disease in triple-negative breast carcinomas

We aimed in this study at identifying prognostic immunohistochemical molecular signatures indicative of disease outcome, also relevant for development of new specific therapies, in triple-negative (ER, PR, c-erbB2- negative) breast carcinoma subtypes. We evaluated 42 markers in tissue micro-arrays from a series of 924 breast carcinomas including 184 triple-negative tumors using standardized quantitative immunocytochemical assays and correlated the data with patients' outcome (mean follow-up of 79 months). When 27/42 markers including basal-like markers first found to be individually significant for prognosis in a univariate analysis (log-rank test) in 924 tumors, were secondly evaluated in the triple-negative tumor subtype (184/924), eleven including maspin, P21, P27, PTEN, caveolin, EGFR, FAK, P38, pMAPK, STAT1 and CD10 were 89.2% predictive of disease outcome in logistic regression. When markers reported in the literature as expressed in basal-like subtype were evaluated in the 924 series, only eight (EGFR, CK14, moesin, caveolin, cMet, ckit, CD44v6, C10) were prognosis predictive in univariate analysis (log-rank test) and in logistic regression were predictive of disease outcome in 66.3% independently of ER, PR and c-erbB2 expression and in 72% in triple-negative tumor subset. The results suggest that the category of 'triple-negative' breast carcinomas does not exactly overlap the basal-like subtype, and that immunoprofiling of triple-negative tumors (not similar to that of basal-like tumors) may be helpful to select patients for more aggressive treatment and provides a basis for development of tailored therapy.

Prediction of future metastasis and molecular characterization of head and neck squamous-cell carcinoma based on transcriptome and genome analysis by microarrays

Propensity for subsequent distant metastasis in head and neck squamous-cell carcinoma (HNSCC) was analysed using 186 primary tumours from patients initially treated by surgery that developed (M) or did not develop (NM) metastases as the first recurrent event. Transcriptome (Affymetrix HGU133_Plus2, QRT-PCR) and array-comparative genomic hybridization data were collected. Non-supervised hierarchical clustering based on Affymetrix data distinguished tumours differing in pathological differentiation, and identified associated functional changes. Propensity for metastasis was not associated with these subgroups. Using QRT-PCR data we identified a four-gene model (PSMD10, HSD17B12, FLOT2 and KRT17) that predicts M/NM status with 77% success in a separate 79-sample validation group of HNSCC samples. This prediction is independent of clinical criteria (age, lymph node status, stage, differentiation and localization). The most significantly altered transcripts in M versus NM were significantly associated to metastasis-related functions, including adhesion, mobility and cell survival. Several genomic modifications were significantly associated with M/NM status (most notably gains at 4q11-22 and Xq12-28; losses at 11q14-24 and 17q11 losses) and partly linked to transcription modifications. This work yields a basis for the development of prognostic molecular signatures, markers and therapeutic targets for HNSCC metastasis.

Bayesian profiling of molecular signatures to predict event times

BackgroundIt is of particular interest to identify cancer-specific molecular signatures for early diagnosis, monitoring effects of treatment and predicting patient survival time. Molecular information about patients is usually generated from high throughput technologies such as microarray and mass spectrometry. Statistically, we are challenged by the large number of candidates but only a small number of patients in the study, and the right-censored clinical data further complicate the analysis.ResultsWe present a two-stage procedure to profile molecular signatures for survival outcomes. Firstly, we group closely-related molecular features into linkage clusters, each portraying either similar or opposite functions and playing similar roles in prognosis; secondly, a Bayesian approach is developed to rank the centroids of these linkage clusters and provide a list of the main molecular features closely related to the outcome of interest. A simulation study showed the superior performance of our approach. When it was applied to data on diffuse large B-cell lymphoma (DLBCL), we were able to identify some new candidate signatures for disease prognosis.ConclusionThis multivariate approach provides researchers with a more reliable list of molecular features profiled in terms of their prognostic relationship to the event times, and generates dependable information for subsequent identification of prognostic molecular signatures through either biological procedures or further data analysis.

Molecular profiles of prostate cancer versus today’s diagnostic tools

14659 Background: Tumor size, nodal status, the Gleason grading system and serum PSA values are today’s available prognostic tools of localized prostate cancer and the only help for adjuvant therapy. Based on the results of a feasibility study we continued the evaluation of the recently developed prognostic molecular signature. Methods: Of 60 CaP patients, who underwent primary prostatectomy in 2003 to 2005, fresh frozen samples of the tumor were asserved. The RNA extracted from cryocuts was tested. The quantitative RNA expression levels of 90 relevant genes involved in the different tumor hallmarks were simultaneously assessed. Results: Unsupervised agglomerative clustering of the obtained molecular profiles revealed different signatures. Correlations between these groups and the known TNM staging as well as Gleason scores were strongly present. Of interest was that all recurrences observed within this short period of time were found in a single cluster expressing higher levels of proliferation markers. Conclusions: The molecular profile of primary prostate cancer by quantitative RT-PCR is a powerful tool describing the biology of an individual tumor. Gene expression profiling can be precisely quantified and seems to be better reproducible than pathological judgments of the Gleason scores. Moreover, the gene panel is partially based on drug target genes and therefore of predictive value. Finally the method could be applied also in core biopsies. [Table: see text]

Association Between Microarray Gene Expression Signature and Extravascular Matrix Patterns in Primary Uveal Melanomas

To determine whether there is an association between gene expression profile and looping extravascular matrix patterns in primary uveal melanomas. Laboratory investigation. Formalin-fixed, paraffin-embedded sections from 22 primary uveal melanomas that previously were analyzed by microarray gene expression profiling were stained for periodic acid-Schiff and scored in a masked fashion for the presence of looping extravascular matrix patterns. A strong association was observed between looping patterns and the unfavorable class 2 molecular prognostic signature (P < .001). Looping extravascular matrix patterns occur almost exclusively in class 2 uveal melanomas. This finding may provide new insights into the pathogenesis and management of uveal melanoma.

ArrayVigil: A Methodology for Statistical Comparison of Gene Signatures Using Segregated-one-tailed (SOT) Wilcoxon's Signed-rank Test

Due to versatile diagnostic and prognostic fidelity molecular signatures or fingerprints are anticipated as the most powerful tools for cancer management in the near future. Notwithstanding the experimental advancements in microarray technology, methods for analyzing either whole arrays or gene signatures have not been firmly established. Recently, an algorithm, ArraySolver has been reported by Khan for two-group comparison of microarray gene expression data using two-tailed Wilcoxon signed-rank test. Most of the molecular signatures are composed of two sets of genes (hybrid signatures) wherein up-regulation of one set and down-regulation of the other set collectively define the purpose of a gene signature. Since the direction of a selected gene's expression (positive or negative) with respect to a particular disease condition is known, application of one-tailed statistics could be a more relevant choice. A novel method, ArrayVigil, is described for comparing hybrid signatures using segregated-one-tailed (SOT) Wilcoxon signed-rank test and the results compared with integrated-two-tailed (ITT) procedures (SPSS and ArraySolver). ArrayVigil resulted in lower P values than those obtained from ITT statistics while comparing real data from four signatures.

Arraying the Orchestration of Allograft Pathology

Microarrays, or gene chips, are exciting investigative tools for analyzing expression changes across thousands of genes in concert in tissues and cells of interest. Despite the relatively recent application of microarrays to transplant research, they hold great promise for unraveling the staging of rejection, stratifying patients towards more individualized treatment regimes, and discovering noninvasive biomarkers for monitoring of intragraft events. Bioinformatics tools are being developed to sift through the large data sets generated as ‘genomic fingerprints’ of the underlying biologic pathways. Gene clustering and class prediction tools allow discovery of diagnostic and prognostic molecular signatures of health and disease. Oligonucleotide-based microarrays also have utility in genotyping polymorphic markers. This report reviews the current literature of microarray use in transplantation research, compares currently available array platforms, and discusses future application of this technology to clinical organ transplantation.