Biomedical subjects

Miyuki Suguro

Publications and source records attributed to Miyuki Suguro.

9 recordsLinked to original sources

PubMed2006

Expression profiling analysis of the CD5+ diffuse large B-cell lymphoma subgroup: development of a CD5 signature.

Diffuse large B-cell lymphoma (DLBCL) accounts for 30% of non-Hodgkin's lymphomas and is known to comprise heterogeneous groups. We previously reported that CD5+ DLBCL is a clinically distinct subgroup of these tumors that is associated with poor prognosis. In our current study, we have used gene expression profiling technology in an attempt to identify new markers and to further characterize the biological features of CD5+ DLBCL. Candidate genes, which showed the greatest difference in expression between 22 CD5+ and 26 CD5- DLBCL cases, were selected from our screening and subjected to clustering analysis. This resulted in identification of a specific mRNA profile (a CD5 signature) for CD5+ DLBCL. The CD5 signature included downregulated extracellular matrix genes such as POSTN, SPARC, COL1A1, COL3A1, CTSK, MMP9 and LAMB3, and comprised upregulated genes including TRPM4. We tested this CD5 signature for its potential use as a relevant marker for CD5+ DLBCL and found that it did indeed recognize this subgroup. The tumors identified by the CD5 signature contained most of the CD5+ DLBCL cases and some CD5- DLBCL cases. Moreover, the subgroup of cases with this CD5 signature showed a poorer prognosis. The subsequent application of the CD5 signature to the analysis of an independent series of DLBCL microarray data resulted in identification of a subgroup of DLBCL cases with a similar clinical outcome, further suggesting that the CD5 signature can be used as a clinically relevant marker of this disease.

Biomarkers, Tumor↗

PubMed2005

Comparison of genome profiles for identification of distinct subgroups of diffuse large B-cell lymphoma.

Diffuse large B-cell lymphoma (DLBCL) comprises molecularly distinct subgroups such as activated B-cell-like (ABC) and germinal center B-cell-like (GCB) DLBCLs. We previously reported that CD5(+) and CD5(-)CD10(+) DLBCL constitute clinically relevant subgroups. To determine whether these 2 subgroups are related to ABC and GCB DLBCLs, we analyzed the genomic imbalance of 99 cases (36 CD5(+), 19 CD5(-)CD10(+), and 44 CD5(-)CD10(-)) using array-based comparative genomic hybridization (CGH). Forty-six of these cases (22 CD5(+), 7 CD5(-)CD10(+), and 17 CD5(-)CD10(-)) were subsequently subjected to gene-expression profiling, resulting in their division into 28 ABC (19 CD5(+) and 9 CD5(-)CD10(-)) and 18 GCB (3 CD5(+), 7 CD5(-)CD10(+), and 8 CD5(-)CD10(-)) types. A comparison of genome profiles of distinct subgroups of DLBCL demonstrated that (1) ABC DLBCL is characterized by gain of 3q, 18q, and 19q and loss of 6q and 9p21, and GCB DLBCL is characterized by gain of 1q, 2p, 7q, and 12q; (2) the genomic imbalances characteristic of the CD5(+) and CD5(-)CD10(+) groups were similar to those of the ABC and GCB types, respectively. These findings suggest that CD5(+) and CD5(-)CD10(+) subgroups are included, respectively, in the ABC and GCB types. Finally, when searching for genomic imbalances that affect patients' prognosis, we found that 9p21 loss (p16(INK4a) locus) marks the most aggressive type of DLBCL.

Adult↗

PubMed2004

Contig array CGH at 3p14.2 points to the FRA3B/FHIT common fragile region as the target gene in diffuse large B-cell lymphoma.

Deletions of the 3p arm have been detected in various solid tumors, but no study to date has investigated this deletion in diffuse large B-cell lymphoma (DLBCL). Recently, we demonstrated that 3p14.2 was deleted in approximately 30% of DLBCL cases by use of a genome-wide array-comparative genomic hybridization (CGH). For a more detailed examination of the genomic losses at 3p14.2, here we made use of contig BAC array for 3p14.2, and found that 12 DLBCL samples displayed losses. All of the deleted regions were located within the fragile histidine triad (FHIT) gene, and the most frequent region of loss was mapped to 0.4 Mbp of the region encompassing the introns 4 and 5 and exon 5 of the FHIT gene. Concomitant analysis of transcripts showed that the FHIT gene was aberrantly transcribed in 31% of the DLBCL samples examined and that the lost exons of the aberrant transcripts were correlated with genomic deletions. These findings indicate that (1) loss of genomic material at 3q14.2 is responsible for exon losses of the FHIT gene, and (2) genomic loss of the FHIT gene is one of the causes of the generation of aberrant transcripts.

Acid Anhydride Hydrolases↗

PubMed2004

Genome-wide array-based comparative genomic hybridization of diffuse large B-cell lymphoma: comparison between CD5-positive and CD5-negative cases.

Diffuse large B-cell lymphoma (DLBCL) is the most common type of non-Hodgkin's lymphoma and exhibits aggressive and heterogeneous clinical behavior. To genetically characterize DLBCL, we established our own array-based comparative genomic hybridization and analyzed a total of 70 cases [26 CD-positive (CD5+) DLBCL and 44 CD5-negative (CD5-) DLBCL cases]. Regions of genomic aberrations observed in >20% of cases of both the CD5+ and CD5- groups were gains of 1q21-q31, 1q32, 3p25-q29, 5p13, 6p21-p25, 7p22-q31, 8q24, 11q23-q24, 12q13-q21, 16p13, 18, and X and losses of 1p36, 3p14, 6q14-q25, 6q27, 9p21, and 17p11-p13. Because CD5 expression marks a subgroup with poor prognosis, we subsequently analyzed genomic gains and losses of CD5+ DLBCL compared with those of CD5-. Although both groups showed similar genomic patterns of gains and losses, gains of 10p14-p15 and 19q13 and losses of 1q43-q44 and 8p23 were found to be characteristic of CD5+ DLBCL. By focusing on the gain of 13q21-q34 and loss of 1p34-p36, we were also able to identify prognostically distinct subgroups among CD5+ DLBCL cases. These results suggest that array-based comparative genomic hybridization analysis provides a platform of genomic aberrations of DLBCL both common and specific to clinically distinct subgroups.

CD5 Antigens↗

PubMed2004

Analysis of chromosomal imbalances in de novo CD5-positive diffuse large-B-cell lymphoma detected by comparative genomic hybridization.

We recently demonstrated that the prognosis for de novo CD5-positive (CD5+) diffuse large-B-cell lymphoma (DLBCL) is markedly worse than that for CD5-negative (CD5-) DLBCL. Our findings also suggested that on the basis of its clinical features CD5+ DLBCL may constitute a unique disease category. However, the genetic basis for these two categories has not been established. Therefore, we performed comparative genomic hybridization analysis (CGH) of 26 cases of CD5+ DLBCL and 44 cases of CD5- DLBCL. Several identical changes in CD5+ and CD5- DLBCLs were found, such as gains of 3q, 9p, 12q, 13q, and 18q and losses of 1p, 6q, 17p, and 19p. However, distinct differences between the two categories were also detected. These included gains of 11q21-q24 (P=0.032) and 16p (P=0.005) in CD5+ DLBCL, and loss of 16p (P=0.028) in CD5- DLBCL. A comparison with results reported for mantle cell lymphoma, chronic lymphocytic leukemia, and Richter's syndrome demonstrated that the CGH pattern of CD5+ DLBCL was markedly different. This indicates that CD5+ DLBCL constitutes a disease category distinct from that of CD5- DLBCL and other CD5+ malignancies.

Adult↗

PubMed2003

Selection of causal gene sets for lymphoma prognostication from expression profiling and construction of prognostic fuzzy neural network models.

To assess the response of lymphomas to chemotherapy, gene expression profiling data from DNA microarrays were analyzed using the fuzzy neural network (FNN) modeling method. We used the FNN modeling method to produce 10 noninferior models. Using these models, we were able to predict diffuse large B-cell lymphoma (DLBCL) patient outcome with 93% accuracy. Of the 37 genes in the 10 models, 13 genes were repeatedly selected, indicating that these genes are important for prognostication. On Kaplan-Meier plots of overall survival, patients predicted by the FNN model to be cured survived significantly longer than those predicted to be refractory (P<0.0001), indicating that the FNN could successfully identify patients with a relatively poor prognosis among low-clinical-risk patients. The FNN modeling method presented here is able to precisely extract significant biological markers affecting prognosis.

Journal Article↗

PubMed2003

Multiple fuzzy neural network system for outcome prediction and classification of 220 lymphoma patients on the basis of molecular profiling.

A fuzzy neural network (FNN) using gene expression profile data can select combinations of genes from thousands of genes, and is applicable to predict outcome for cancer patients after chemotherapy. However, wide clinical heterogeneity reduces the accuracy of prediction. To overcome this problem, we have proposed an FNN system based on majoritarian decision using multiple noninferior models. We used transcriptional profiling data, which were obtained from "Lymphochip" DNA microarrays (http://llmpp.nih.gov/DLBCL), reported by Rosenwald (N Engl J Med 2002; 346: 1937-47). When the data were analyzed by our FNN system, accuracy (73.4%) of outcome prediction using only 1 FNN model with 4 genes was higher than that (68.5%) of the Cox model using 17 genes. Higher accuracy (91%) was obtained when an FNN system with 9 noninferior models, consisting of 35 independent genes, was used. The genes selected by the system included genes that are informative in the prognosis of Diffuse large B-cell lymphoma (DLBCL), such as genes showing an expression pattern similar to that of CD10 and BCL-6 or similar to that of IRF-4 and BCL-4. We classified 220 DLBCL patients into 5 groups using the prediction results of 9 FNN models. These groups may correspond to DLBCL subtypes. In group A containing half of the 220 patients, patients with poor outcome were found to satisfy 2 rules, i.e., high expression of MAX dimerization with high expression of unknown A (LC_26146), or high expression of MAX dimerization with low expression of unknown B (LC_33144). The present paper is the first to describe the multiple noninferior FNN modeling system. This system is a powerful tool for predicting outcome and classifying patients, and is applicable to other heterogeneous diseases.

Cluster Analysis↗

PubMed2002

Prognostic factors in elderly patients with acute myelogenous leukemia: a single center study in Japan.

We retrospectively analyzed data of 47 patients aged 60 years or older, hospitalized in our institution with the diagnosis of acute myelogenous leukemia (AML), and searched for prognostic factors. Induction with anthracyclines significantly correlated with better complete remission (CR) rate (P = 0.0016) and overall survival (OS) (P < 0.001). Another factor significantly affecting CR rate was higher age (> 70 years) (P = 0.042). Therapy-non-related factors predictive for shorter OS in univariate analyses were age older than 70 years (P = 0.003), percentage of blasts in bone marrow more than 80% (P = 0.048), serum lactate dehydrogenase level higher than 250 U l(-1) (P = 0.032). In stepwise cox proportional hazard regression model, all the four factors predictive for poor OS remained to be independently and significantly prognostic for shorter OS. Only two patients receiving anthracyclines died within 30 days and the frequency was not different from that in patients not receiving anthracyclines. The use of anthracyclines as induction therapy is recommended even in the elderly patients.

Age Factors↗

PubMed2002

Fuzzy neural network applied to gene expression profiling for predicting the prognosis of diffuse large B-cell lymphoma.

Diffuse large B-cell lymphoma (DLBCL) is the largest category of aggressive lymphomas. Less than 50% of patients can be cured by combination chemotherapy. Microarray technologies have recently shown that the response to chemotherapy reflects the molecular heterogeneity in DLBCL. On the basis of published microarray data, we attempted to develop a long-overdue method for the precise and simple prediction of survival of DLBCL patients. We developed a fuzzy neural network (FNN) model to analyze gene expression profiling data for DLBCL. From data on 5857 genes, this model identified four genes (CD10, AA807551, AA805611 and IRF-4) that could be used to predict prognosis with 93% accuracy. FNNs are powerful tools for extracting significant biological markers affecting prognosis, and are applicable to various kinds of expression profiling data for any malignancy.

DNA-Binding Proteins↗