Biomedical subjects

Ichiro Takemasa

Publications and source records attributed to Ichiro Takemasa.

6 recordsLinked to original sources

PubMed2025

Molecular Residual Disease and Recurrence in Rectal Cancer Patients Undergoing Upfront Surgery: A Prospective Cohort Study.

OBJECTIVE: To evaluate the prognostic utility of postoperative circulating tumor DNA (ctDNA) for recurrence and treatment response in patients with rectal cancer undergoing upfront surgery. BACKGROUND: ctDNA-based molecular residual disease (MRD) testing shows promise in colorectal cancer, but its role in patients with rectal cancer not receiving neoadjuvant therapy is unclear. This study evaluates whether postoperative ctDNA predicts disease-free survival (DFS) and guides adjuvant chemotherapy (ACT) decisions. METHODS: We analyzed ctDNA from patients with stage II to III rectal cancer (N=250) enrolled in the GALAXY study, a multicenter registry in Japan. A clinically validated, personalized, tumor-informed 16-plex PCR next-generation sequencing assay (Signatera) was used to detect and quantify ctDNA. The primary outcome was DFS, defined as the time from landmark to recurrence, death, or the latest radiologic assessment. RESULTS: In the MRD window (2-10 wk postsurgery, before ACT), 14.2% (35/246) of patients were ctDNA-positive and had significantly shorter DFS (HR: 9.96, 95% CI: 5.76-17.2, P <0.0001). Among patients who were ctDNA-positive in the MRD window, a significant benefit from ACT was observed (HR: 0.28, 95% CI: 0.09-0.89, P =0.031), whereas no benefit was seen in ctDNA-negative patients (HR: 0.59, 95% CI: 0.26-1.35, P =0.211). When analyzing ctDNA dynamics from the MRD window to 6 months postsurgery, recurrence risk was higher in patients who converted from ctDNA-negative to positive (HR: 8.22, 95% CI: 1.86-36.32, P =0.0055) and who remained ctDNA-positive (HR: 45.48, 95% CI: 14.31-144.57, P <0.0001) compared with serially ctDNA-negative patients. CONCLUSIONS: Postoperative ctDNA status is a robust biomarker predicting recurrence risk and ACT benefit in patients with rectal cancer undergoing upfront surgery.

Humans↗

PubMed2004

Molecular prediction of response to 5-fluorouracil and interferon-alpha 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-alpha 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-alpha.

Adult↗

PubMed2004

Molecular-based prediction of early recurrence in hepatocellular carcinoma.

BACKGROUND/AIMS: Hepatocellular carcinoma (HCC) has a very poor prognosis, due to the high incidence of tumor recurrence. As the current morphological indicators are often insufficient for therapeutic decisions, we sought to identify additional biologic indicators for early recurrence. METHODS: We analyzed gene expression using a PCR-based array of 3,072 genes in 100 HCC patients. Informative genes predicting early intrahepatic recurrence were selected by random permutation testing, and a weighted voting prediction method was constructed. Following estimation of prediction accuracy, a multivariate Cox analysis was performed. RESULTS: By permutation testing, we selected 92 genes demonstrated distinct expression patterns differing significantly between recurrence cases and recurrence-free cases. Our prediction method, using the 20 top-ranked genes, correctly predicted the early intrahepatic recurrence for 29 of 40 cases within the validation group, and the odds ratio was 6.8 (95%CI 1.7-27.5, P = 0.010). The 2-year recurrence rates in the patients with the good signature and those with the poor signature were 29.4 and 73.9%, respectively. Multivariate Cox analysis revealed that molecular-signature was an independent indicator for recurrence (hazard ratio 3.82, 95%CI 1.44-10.10, P = 0.007). CONCLUSIONS: Our molecular-based prediction method using 20 genes is clinically useful to predict early recurrence of HCC.

Aged↗

PubMed2003

A Bayesian missing value estimation method for gene expression profile data.

MOTIVATION: Gene expression profile analyses have been used in numerous studies covering a broad range of areas in biology. When unreliable measurements are excluded, missing values are introduced in gene expression profiles. Although existing multivariate analysis methods have difficulty with the treatment of missing values, this problem has received little attention. There are many options for dealing with missing values, each of which reaches drastically different results. Ignoring missing values is the simplest method and is frequently applied. This approach, however, has its flaws. In this article, we propose an estimation method for missing values, which is based on Bayesian principal component analysis (BPCA). Although the methodology that a probabilistic model and latent variables are estimated simultaneously within the framework of Bayes inference is not new in principle, actual BPCA implementation that makes it possible to estimate arbitrary missing variables is new in terms of statistical methodology. RESULTS: When applied to DNA microarray data from various experimental conditions, the BPCA method exhibited markedly better estimation ability than other recently proposed methods, such as singular value decomposition and K-nearest neighbors. While the estimation performance of existing methods depends on model parameters whose determination is difficult, our BPCA method is free from this difficulty. Accordingly, the BPCA method provides accurate and convenient estimation for missing values. AVAILABILITY: The software is available at http://hawaii.aist-nara.ac.jp/~shige-o/tools/.

Algorithms↗

PubMed2003

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

BACKGROUND: Individual 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. RESULTS: We 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. CONCLUSIONS: The 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.

Cluster Analysis↗

PubMed2003

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.

Aged↗