Kikuya Kato

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.

Quantitative detection of EGFR mutations in circulating tumor DNA derived from lung adenocarcinomas.

Purpose: Examination of somatic epidermal growth factor receptor (EGFR) mutations is now a diagnostic routine for treatment of cancer using EGFR tyrosine kinase inhibitors (EGFR-TKI). Circulating tumor DNA is a promising target for noninvasive diagnostics. We evaluated its utility by quantitatively detecting activating and resistant mutations, which were measured with BEAMing (beads, emulsion, amplification, and magnetics). Experimental Design: Twenty-three patients with lung cancer with progressive disease after EGFR-TKI treatment and 21 patients who had never been treated with EGFR-TKIs were studied. Their primary tumors were confirmed to have activating mutations. In the plasma DNA of each patient, the activating mutation found in the corresponding primary tumor and the T790M resistance mutation were quantified by BEAMing. Results: In 32 of 44 patients, activating mutations were detected in the plasma DNA [72.7%; 95% confidence interval (CI), 58.0%–83.6%]. The T790M mutation was detected in 10 of 23 patients in the first group (43.5%; 95% CI, 25.6%–53.4%). The ratio of T790M to activating mutations ranged from 13.3% to 94.0%. The peak of the distribution of the mutation allele fraction in the plasma DNA was in the 0.1% to 1% range. Conclusions: The major advantage of BEAMing is its ability to calculate the fraction of T790M-positive alleles from the alleles with activating mutations. This feature enables the detection of increases and decreases in the number of T790M mutations in cancer cells, regardless of normal cell DNA contamination, which may be useful for monitoring disease progression. Circulating tumor DNA could potentially be used as an alternative method for EGFR mutation detection. Clin Cancer Res; 17(24); 7808–15. ©2011 AACR.

Intratumor heterogeneity of epidermal growth factor receptor mutations in lung cancer and its correlation to the response to gefitinib

Somatic mutations introduced into the epidermal growth factor receptor (EGFR) gene in non‐small‐cell lung cancer (NSCLC) are important factors to determine therapeutic responses to gefitinib. The current diagnostic test measures the overall EGFR mutation status of the cancer tissue, and may ignore the presence of non‐mutated, gefitinib‐unresponsive cancer cells. Twenty‐one NSCLC patients with EGFR mutations were recruited for the study. All patients were treated with gefitinib after surgical treatment. Fifty to sixty areas of NSCLC tumors were sampled from each tissue, and their EGFR mutation states were determined by a primer extension assay. This assay discriminates between EGFR mutation‐positive and ‐negative cancer cells within a single tumor tissue. Fifteen tissues consisted only of cells with EGFR mutations, but the remaining six tissues contained both mutated and non‐mutated cells. Time to disease progression and overall survival after gefitinib treatment were significantly shorter in those patients with EGFR heterogeneity (P = 0.009 and P = 0.003, respectively). A considerable proportion of NSCLC contains a heterogeneous population of both EGFR mutated and non‐mutated cancer cells, resulting in a reduced response to gefitinib. The intratumor genetic heterogeneity of a target molecule such as EGFR would be an important factor to consider when treating patients with molecular target agents. (Cancer Sci 2008; 99: 929–935)

High Thioredoxin Expression Is Associated with Resistance to Docetaxel in Primary Breast Cancer

Purpose: Thioredoxin overexpression is suggested to be associated with resistance to several chemotherapeutic agents in vitro. In the present study, it has been studied whether or not high thioredoxin expression is associated with resistance to docetaxel therapy in breast cancer patients. Patients and Methods: Sixty-three primary breast cancer patients were treated with docetaxel (60 mg/m2, q3w) for four cycles in the neoadjuvant setting. Expression of thioredoxin, estrogen receptor (ER), p53, BRCA-1, and Bcl-2 in tumor tissues obtained before docetaxel therapy was studied by immunohistochemistry (thioredoxin, p53, BRCA-1, and Bcl-2) and enzyme immunoassay (ER), and relationship of expression of these biomarkers with a pathologic response was investigated. Results: There was no significant correlation between the expression of p53, BRCA-1, or Bcl-2 and a response to docetaxel. However, tumors with high thioredoxin expression showed a significantly lower response rate (0%) than those with low thioredoxin expression (30.6%; P = 0.018) and ER-negative tumors showed a significantly higher response rate (32.4%) than ER-positive tumors (10.7%; P = 0.043). Thioredoxin expression significantly increased after docetaxel therapy (mean, 56.1%) as compared with that before docetaxel therapy (mean, 28.6%; P < 0.0001) but there was no significant association between the extent of increase in thioredoxin expression and response. Conclusion: High thioredoxin expression in prechemotherapy tumor samples, but not the increase in thioredoxin expression induced by docetaxel, is associated with resistance to docetaxel in breast cancer. Thioredoxin and ER might be clinically useful in the prediction of a response to docetaxel.

RPN2 gene confers docetaxel resistance in breast cancer

Drug resistance acquired by cancer cells has led to treatment failure. To understand the regulatory network underlying docetaxel resistance in breast cancer cells and to identify molecular targets for therapy, we tested small interfering RNAs (siRNAs) against 36 genes whose expression was elevated in human nonresponders to docetaxel for the ability to promote apoptosis of docetaxel-resistant human breast cancer cells (MCF7-ADR cells). The results indicate that the downregulation of the gene encoding ribopholin II (RPN2), which is part of an N-oligosaccharyl transferase complex, most efficiently induces apoptosis of MCF7-ADR cells in the presence of docetaxel. RPN2 silencing induced reduced glycosylation of the P-glycoprotein, as well as decreased membrane localization, thereby sensitizing MCF7-ADR cells to docetaxel. Moreover, in vivo delivery of siRNA specific for RPN2 markedly reduced tumor growth in two types of models for drug resistance. Thus, RPN2 silencing makes cancer cells hypersensitive response to docetaxel, and RPN2 might be a new target for RNA interference–based therapeutics against drug resistance.

A strategy for large-scale phosphoproteomics and SRM-based validation of human breast cancer tissue samples

Protein phosphorylation is a key mechanism of cellular signaling pathways and aberrant phosphorylation has been implicated in a number of human diseases. Thus, approaches in phosphoproteomics can contribute to the identification of key biomarkers to assess disease pathogenesis and drug targets. Moreover, careful validation of large-scale phosphoproteome analysis, which is lacking in the current protein-based biomarker discovery, significantly increases the value of identified biomarkers. Here, we performed large-scale differential phosphoproteome analysis using IMAC coupled with the isobaric tag for relative quantification (iTRAQ) technique and subsequent validation by selected/multiple reaction monitoring (SRM/MRM) of human breast cancer tissues in high- and low-risk recurrence groups. We identified 8309 phosphorylation sites on 3401 proteins, of which 3766 phosphopeptides (1927 phosphoproteins) were able to be quantified and 133 phosphopeptides (117 phosphoproteins) were differentially expressed between the two groups. Among them, 19 phosphopeptides were selected for further verification and 15 were successfully quantified by SRM using stable isotope peptides as a reference. The ratio of phosphopeptides between high- and low-risk groups quantified by SRM was well correlated with iTRAQ-based quantification with a few exceptions. These results suggest that large-scale phosphoproteome quantification coupled with SRM-based validation is a powerful tool for biomarker discovery using clinical samples.

Gene Expression-Based Molecular Diagnostic System for Malignant Gliomas Is Superior to Histological Diagnosis

Purpose: Current morphology-based glioma classification methods do not adequately reflect the complex biology of gliomas, thus limiting their prognostic ability. In this study, we focused on anaplastic oligodendroglioma and glioblastoma, which typically follow distinct clinical courses. Our goal was to construct a clinically useful molecular diagnostic system based on gene expression profiling. Experimental Design: The expression of 3,456 genes in 32 patients, 12 and 20 of whom had prognostically distinct anaplastic oligodendroglioma and glioblastoma, respectively, was measured by PCR array. Next to unsupervised methods, we did supervised analysis using a weighted voting algorithm to construct a diagnostic system discriminating anaplastic oligodendroglioma from glioblastoma. The diagnostic accuracy of this system was evaluated by leave-one-out cross-validation. The clinical utility was tested on a microarray-based data set of 50 malignant gliomas from a previous study. Results: Unsupervised analysis showed divergent global gene expression patterns between the two tumor classes. A supervised binary classification model showed 100% (95% confidence interval, 89.4-100%) diagnostic accuracy by leave-one-out cross-validation using 168 diagnostic genes. Applied to a gene expression data set from a previous study, our model correlated better with outcome than histologic diagnosis, and also displayed 96.6% (28 of 29) consistency with the molecular classification scheme used for these histologically controversial gliomas in the original article. Furthermore, we observed that histologically diagnosed glioblastoma samples that shared anaplastic oligodendroglioma molecular characteristics tended to be associated with longer survival. Conclusions: Our molecular diagnostic system showed reproducible clinical utility and prognostic ability superior to traditional histopathologic diagnosis for malignant glioma.

Strategy for SRM-based Verification of Biomarker Candidates Discovered by iTRAQ Method in Limited Breast Cancer Tissue Samples

Since LC-MS-based quantitative proteomics has become increasingly applied to a wide range of biological applications over the past decade, numerous studies have performed relative and/or absolute abundance determinations across large sets of proteins. In this study, we discovered prognostic biomarker candidates from limited breast cancer tissue samples using discovery-through-verification strategy combining iTRAQ method followed by selected reaction monitoring/multiple reaction monitoring analysis (SRM/MRM). We identified and quantified 5122 proteins with high confidence in 18 patient tissue samples (pooled high-risk (n=9) or low-risk (n=9)). A total of 2480 proteins (48.4%) of them were annotated as membrane proteins, 16.1% were plasma membrane and 6.6% were extracellular space proteins by Gene Ontology analysis. Forty-nine proteins with >2-fold differences in two groups were chosen for further analysis and verified in 16 individual tissue samples (high-risk (n=9) or low-risk (n=7)) using SRM/MRM. Twenty-three proteins were differentially expressed among two groups of which MFAP4 and GP2 were further confirmed by Western blotting in 17 tissue samples (high-risk (n=9) or low-risk (n=8)) and Immunohistochemistry (IHC) in 24 tissue samples (high-risk (n=12) or low-risk (n=12)). These results indicate that the combination of iTRAQ and SRM/MRM proteomics will be a powerful tool for identification and verification of candidate protein biomarkers.

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.

Downregulation of c-MYC Protein Levels Contributes to Cancer Cell Survival under Dual Deficiency of Oxygen and Glucose

The c-MYC protein participates in energy-consuming processes such as proliferation and ribosome biosynthesis, and its expression is often dysregulated in human cancers. Cancer cells distant from blood vessels in solid tumors are in short supply of oxygen and nutrition yet can adapt to the microenvironment and survive under metabolic stress. The role and regulation of c-MYC protein in the tumor microenvironment of limited energy sources are poorly understood. Here, we show that c-MYC protein levels in cancer cells are strikingly reduced in the area distant from the blood vessels in vivo and also under oxygen- and glucose-deprived conditions in vitro. The rapid reduction of c-MYC protein levels requires low levels of both oxygen and glucose, and under these conditions, downregulation is mainly achieved by enhanced degradation. Suppression of c-MYC protein levels by small hairpin RNA decreases the necrotic cell death induced by oxygen and glucose deprivation. Thus, the environmental milieu regulates c-MYC protein levels, and downregulation of c-MYC might be a strategy for cancer cells to survive under conditions of limited energy sources.