Ryo Matoba

Prediction of Docetaxel Response in Human Breast Cancer by Gene Expression Profiling

PURPOSE Docetaxel is one of the most effective anticancer drugs available in the treatment of breast cancer. Nearly half of the treated patients, however, do not respond to chemotherapy and suffer from side effects. The ability to reliably predict a patients response based on tumor gene expression will improve therapeutic decision making and save patients from unnecessary side effects. PATIENTS AND METHODS A total of 44 breast tumor tissues were sampled by biopsy before treatment with docetaxel, and the response to therapy was clinically evaluated by the degree of reduction in tumor size. Gene expression profiling of the biopsy samples was performed with 2,453 genes using a high-throughput reverse transcriptase polymerase chain reaction technique. Using genes differentially expressed between responders and nonresponders, a diagnostic system based on the weighted-voting algorithm was constructed. RESULTS This system predicted the clinical response of 26 previously unanalyzed samples with over 80% accuracy, a level promising for clinical applications. Diagnostic profiles in nonresponders were characterized by elevated expression of genes controlling the cellular redox environment (ie, redox genes, such as thioredoxin, glutathione-S-transferase, and peroxiredoxin). Overexpression of these genes protected cultured mammary tumor cells from docetaxel-induced cell death, suggesting that enhancement of the redox system plays a major role in docetaxel resistance. CONCLUSION These results suggest that the clinical response to docetaxel can be predicted by gene expression patterns in biopsy samples. The results also suggest that one of the molecular mechanisms of the resistance is activation of a group of redox genes.

Identification of expressed genes linked to malignancy of human colorectal carcinoma by parametric clustering of quantitative expression data

BackgroundIndividual human carcinomas have distinct biological and clinical properties: gene-expression profiling is expected to unveil the underlying molecular features. Particular interest has been focused on potential diagnostic and therapeutic applications. Solid tumors, such as colorectal carcinoma, present additional obstacles for experimental and data analysis.ResultsWe analyzed the expression levels of 1,536 genes in 100 colorectal cancer and 11 normal tissues using adaptor-tagged competitive PCR, a high-throughput reverse transcription-PCR technique. A parametric clustering method using the Gaussian mixture model and the Bayes inference revealed three groups of expressed genes. Two contained large numbers of genes. One of these groups correlated well with both the differences between tumor and normal tissues and the presence or absence of distant metastasis, whereas the other correlated only with the tumor/normal difference. The third group comprised a small number of genes. Approximately half showed an identical expression pattern, and cancer tissues were classified into two groups by their expression levels. The high-expression group had strong correlation with distant metastasis, and a poorer survival rate than the low-expression group, indicating possible clinical applications of these genes. In addition to c-yes, a homolog of a viral oncogene, prognostic indicators included genes specific to glial cells, which gives a new link between malignancy and ectopic gene expression.ConclusionsThe malignancy of human colorectal carcinoma is correlated with a unique expression pattern of a specific group of genes, allowing the classification of tumor tissues into two clinically distinct groups.

Molecular Prediction of Response to 5-Fluorouracil and Interferon-α Combination Chemotherapy in Advanced Hepatocellular Carcinoma

Purpose: The prognosis of hepatocellular carcinoma (HCC) is very poor, particularly in patients with tumors that have invaded the major branches of the portal vein. Combination chemotherapy with intra-arterial 5-fluorouracil and subcutaneous interferon-α has shown promising results for such advanced HCC, but it is important to develop the ability to accurately predict chemotherapeutic responses. Experimental Design: We analyzed the expression of 3,080 genes using a polymerase chain reaction-based array in 20 HCC patients who were treated with combination chemotherapy after reduction surgery. After unsupervised analyses, a supervised classification method for predicting chemotherapeutic responses was constructed. To minimize the number of predictive genes, we used a random permutation test to select only significant (P < 0.01) genes. A leave-one-out cross-validation confirmed the gene selection. We also prepared an additional 11 cases for validation of predictive performance. Results: Hierarchical clustering analysis and principal component analysis with all 3,080 genes revealed distinct gene expression patterns in responders (those with complete response or partial response) and nonresponders (those with stable disease or progressive disease) to the combination chemotherapy. Using a weighted-voting classification method with either all genes or only significant genes as assessed by permutation testing, the objective responses to treatment were correctly predicted in 17 of 20 cases (accuracy, 85%; positive predictive value, 100%; negative predictive value, 80%). Moreover, patients in the validation dataset could be classified into two distinct prognostic groups using 63 predictive genes. Conclusions: Molecular analysis of 63 genes can predict the response of patients with advanced HCC and major portal vein tumor thrombi to combination chemotherapy with 5-fluorouracil and interferon-α.

Prediction of efficacy of anti-TNF biologic agent, infliximab, for rheumatoid arthritis patients using a comprehensive transcriptome analysis of white blood cells.

Introduction of biologics, such as infliximab, to the therapy of rheumatoid arthritis (RA) patients has revolutionized the treatment of this disease. However, biomarkers for predicting the efficacy of the drug at an early phase of treatment for selecting real responders have not been found. We here present predictive markers based on a thorough transcriptome analysis of white blood cells from RA patients. RNA from whole blood cells of consecutive 42 patients before the first infusion was analyzed with microarrays for training studies. Samples from the subsequent 26 consecutive patients were used for a prospective study. We categorized the results into no inflammation and residual inflammation groups using the serum C-reactive protein (CRP) level at 14weeks after the first infusion. The accuracy of prediction in our study was 65.4%.

Dicer Is Required for Maintaining Adult Pancreas

Dicer1, an essential component of RNA interference and the microRNA pathway, has many important roles in the morphogenesis of developing tissues. Dicer1 null mice have been reported to die at E7.5; therefore it is impossible to study its function in adult tissues. We previously reported that Dicer1-hypomorphic mice, whose Dicer1 expression was reduced to 20% in all tissues, were unexpectedly viable. Here we analyzed these mice to ascertain whether the down-regulation of Dicer1 expression has any influence on adult tissues. Interestingly, all tissues of adult (8–10 week old) Dicer1-hypomorphic mice were histologically normal except for the pancreas, whose development was normal at the fetal and neonatal stages; however, morphologic abnormalities in Dicer1-hypomorphic mice were detected after 4 weeks of age. This suggested that Dicer1 is important for maintaining the adult pancreas.

Molecular features of non-B, non-C hepatocellular carcinoma: a PCR-array gene expression profiling study

BACKGROUND/AIMS Hepatocellular carcinoma (HCC) usually develops following chronic liver inflammation caused by hepatitis C or B virus. Through expression profiling in a rare type of HCC, for which the causes are unknown, we sought to find key genes responsible for each step of hepatocarcinogenesis in the absence of viral influence. METHODS We used 68 non-B, non-C liver tissues (20 HCC, 17 non-tumor, 31 normal liver) for expression profiling with PCR-array carrying 3072 genes known to be expressed in liver tissues. To select the differentially expressed genes, we performed random permutation testing. A weighted voting classification algorithm was used to confirm the reliability of gene selection. We then compared these genes with the results of previous expression profiling studies. RESULTS A total of 220 differentially expressed genes were selected by random permutation tests. The classification accuracies using these genes were 91.8, 92.0 and 100.0% by a leave-one-out cross-validation, an additional PCR-array dataset and a Stanford DNA microarray dataset, respectively. By comparing our results with previous reports on virus-infected HCC, four genes (ALB, A2M, ECHS1 and IGFBP3) were commonly selected in some studies. CONCLUSIONS The 220 differentially expressed genes selected by PCR-array are potentially responsible for hepatocarcinogenesis in the absence of viral influence.

Cancer gene expression database (CGED): a database for gene expression profiling with accompanying clinical information of human cancer tissues

Gene expression profiling of cancer tissues is expected to contribute to our understanding of cancer biology as well as developments of new methods of diagnosis and therapy. Our collaborative efforts in Japan have been mainly focused on solid tumors such as breast, colorectal and hepatocellular cancers. The expression data are obtained by a high-throughput RT–PCR technique, and patients are recruited mainly from a single hospital. In the cancer gene expression database (CGED), the expression and clinical data are presented in a way useful for scientists interested in specific genes or biological functions. The data can be retrieved either by gene identifiers or by functional categories defined by Gene Ontology terms or the Swiss-Prot annotation. Expression patterns of multiple genes, selected by names or similarity search of the patterns, can be compared. Visual presentation of the data with sorting function enables users to easily recognize of relationships between gene expression and clinical parameters. Data for other cancers such as lung and thyroid cancers will be added in the near future. The URL of CGED is http://cged.hgc.jp.

Gene expression profiling of mouse postnatal cerebellar development

Expression patterns of 1,869 genes were determined using adapter-tagged competitive PCR (ATAC-PCR) at 6 time points during mouse postnatal cerebellar development. The expression patterns were classified into 12 clusters that were further assembled into 3 groups by hierarchical cluster analysis. Among the 1,869 genes, 1,053 known genes were assigned to 90 functional categories. Statistically significant correlation between the clusters or groups of gene expression and the functional categories was ascertained. Genes involved in oncogenesis or protein synthesis were highly expressed during the earlier stages of development. Those responsible for brain functions such as neurotransmitter receptor and synapse components were more active during the later stages of development. Many other genes also showed expression patterns in accordance with literature information. The gene expression patterns and the inferred functions were in good agreement with anatomical as well as physiological observations made during the developmental process.

Correlation between gene functions and developmental expression patterns in the mouse cerebellum.

Quantitative changes of 419 gene transcripts during postnatal mouse cerebellar development were accurately determined with a novel polymerase chain reaction (PCR)‐based technique. About 70% of the genes showed differences in expression levels, and the magnitude of difference was relatively small. By hierarchic cluster analysis of developmental expression patterns, the genes were categorized into 19 clusters, which were subsequently assembled into four major groups: group 1, with elevation of gene expression throughout the time course; group 2, with relatively unchanged levels; group 3, with transiently high expression at ∼ 12 days; and group 4, with highest expression at ∼ 4 days. Genes related to brain functions were segregated into several clusters of group 1 and group 3: the same clusters in which cerebellum‐specific genes were also segregated. Genes for protein synthesis belonged to group 4. Genes with housekeeping functions belonged to group 2. Western blotting analysis of representative protein products of each group revealed correlation with the mRNA level for those belonging to group 1 and group 4, but not necessarily in the other groups. The close correlation of algorithmically categorized temporal expression patterns of genes with their functions will be useful for estimating the functions of thousands of novel genes.

Genome-Wide Analysis of DNA Methylation and Expression of MicroRNAs in Breast Cancer Cells

DNA methylation of promoters is linked to transcriptional silencing of protein-coding genes, and its alteration plays important roles in cancer formation. For example, hypermethylation of tumor suppressor genes has been seen in some cancers. Alteration of methylation in the promoters of microRNAs (miRNAs) has also been linked to transcriptional changes in cancers; however, no systematic studies of methylation and transcription of miRNAs have been reported. In the present study, to clarify the relation between DNA methylation and transcription of miRNAs, next-generation sequencing and microarrays were used to analyze the methylation and expression of miRNAs, protein-coding genes, other non-coding RNAs (ncRNAs), and pseudogenes in the human breast cancer cell lines MCF7 and the adriamycin (ADR) resistant cell line MCF7/ADR. DNA methylation in the proximal promoter of miRNAs is tightly linked to transcriptional silencing, as it is with protein-coding genes. In protein-coding genes, highly expressed genes have CpG-rich proximal promoters whereas weakly expressed genes do not. This is only rarely observed in other gene categories, including miRNAs. The present study highlights the epigenetic similarities and differences between miRNA and protein-coding genes.