Kazuya Taniguchi

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)

Quantitative Identification of Mutant Alleles Derived from Lung Cancer in Plasma Cell-Free DNA via Anomaly Detection Using Deep Sequencing Data

The detection of rare mutants using next generation sequencing has considerable potential for diagnostic applications. Detecting circulating tumor DNA is the foremost application of this approach. The major obstacle to its use is the high read error rate of next-generation sequencers. Rather than increasing the accuracy of final sequences, we detected rare mutations using a semiconductor sequencer and a set of anomaly detection criteria based on a statistical model of the read error rate at each error position. Statistical models were deduced from sequence data from normal samples. We detected epidermal growth factor receptor (EGFR) mutations in the plasma DNA of lung cancer patients. Single-pass deep sequencing (>100,000 reads) was able to detect one activating mutant allele in 10,000 normal alleles. We confirmed the method using 22 prospective and 155 retrospective samples, mostly consisting of DNA purified from plasma. A temporal analysis suggested potential applications for disease management and for therapeutic decision making to select epidermal growth factor receptor tyrosine kinase inhibitors (EGFR-TKI).

A multi-class predictor based on a probabilistic model: application to gene expression profiling-based diagnosis of thyroid tumors

BackgroundAlthough microscopic diagnosis has been playing the decisive role in cancer diagnostics, there have been cases in which it does not satisfy the clinical need. Differential diagnosis of malignant and benign thyroid tissues is one such case, and supplementary diagnosis such as that by gene expression profile is expected.ResultsWith four thyroid tissue types, i.e., papillary carcinoma, follicular carcinoma, follicular adenoma, and normal thyroid, we performed gene expression profiling with adaptor-tagged competitive PCR, a high-throughput RT-PCR technique. For differential diagnosis, we applied a novel multi-class predictor, introducing probabilistic outputs. Multi-class predictors were constructed using various combinations of binary classifiers. The learning set included 119 samples, and the predictors were evaluated by strict leave-one-out cross validation. Trials included classical combinations, i.e., one-to-one, one-to-the-rest, but the predictor using more combination exhibited the better prediction accuracy. This characteristic was consistent with other gene expression data sets. The performance of the selected predictor was then tested with an independent set consisting of 49 samples. The resulting test prediction accuracy was 85.7%.ConclusionMolecular diagnosis of thyroid tissues is feasible by gene expression profiling, and the current level is promising towards the automatic diagnostic tool to complement the present medical procedures. A multi-class predictor with an exhaustive combination of binary classifiers could achieve a higher prediction accuracy than those with classical combinations and other predictors such as multi-class SVM. The probabilistic outputs of the predictor offer more detailed information for each sample, which enables visualization of each sample in low-dimensional classification spaces. These new concepts should help to improve the multi-class classification including that of cancer tissues.

Differentiation of Follicular Thyroid Adenoma from Carcinoma by Means of Gene Expression Profiling with Adapter-Tagged Competitive Polymerase Chain Reaction

Objective: Since preoperative differentiation between follicular thyroid adenoma (FTA) and carcinoma (FTC) remains very difficult, the purpose of this study was to identify the genes differentially expressed in FTA and FTC in order to construct a diagnostic system based on such genes for differentiation of FTA and FTC. Methods:Gene expression profiles of 45 FTAs and 22 FTCs were analyzed by means of adapter-tagged competitive polymerase chain reaction (ATAC-PCR) with 2,516 genes (learning set). The genes differentially expressed in FTAs and FTCs were then used to construct a diagnostic system based on the weighted-voting algorithm. In addition, a validation study of this diagnostic system was conducted using 12 FTAs and 6 FTCs (validation set). Results: The diagnostic system for differentiation of FTA and FTC, constructed with the aid of the learning set samples, was based on 60 genes differentially expressed in FTA and FTC, which included several genes previously identified as overexpressed in FTC (DPP4, KRT19 and IGFBP3) or FTA (trefoil factor 3 and thyroid peroxidase).The leave-one-out cross-validation study showed that the accuracy of this diagnostic system was as high as 90% (sensitivity: 77.3% and specificity: 95.6%), and was confirmed by the validation study (diagnostic accuracy: 83.3%; 95% confidence interval: 62.8–95.4%, sensitivity: 66.7% and specificity: 91.2%). Conclusions: This diagnostic system using the ATAC-PCR assay is expected to be clinically useful for preoperative differentiation between FTA and FTC since ATAC-PCR can be used for the small amount of RNA obtained from fine needle aspiration biopsy.

Prognostic factors for gefitinib-treated postoperative recurrence in non-small cell lung cancer.

Background and Objectives: The association between epidermal growth factor receptor (EGFR) mutations and response to EGFR tyrosine kinase inhibitor (TKI) has been consistently confirmed in a number of studies. However, it is still unclear whether a response to TKI treatment translates into increased survival for patients with non-small cell lung cancer (NSCLC). Methods: EGFR mutations were analyzed in 169 primary lung cancer tissues by RT-PCR and sequencing of multiple clones. The association between EGFR mutation status and the clinical outcome of gefitinib treatment was investigated. For mutation-positive cases, the percentage of mutated clones from the total number of clones was calculated. This ratio was used as the quantitative index of EGFR mutations. Results: We identified mutations in 71 of 169 patients with NSCLC. 46 patients were treated with gefitinib for postoperative recurrence. Progression-free survival and overall survival after initial gefitinib were significantly longer in patients with mutation than with wild type (univariate analysis, p < 0.001 for both). Multivariate analyses identified EGFR mutations and longer disease-free intervals after surgery as significant prognostic factors for survival. By quantitative analysis of mutation-positive cases, the increased ratio of mutated EGFR transcripts significantly associated with longer survival after gefitinib. Conclusions: EGFR mutation status and disease-free interval were associated with prolonged progression-free survival and overall survival after gefitinib treatment for postoperative recurrence of NSCLC. Quantitative analysis of mutated EGFR transcripts provided additional information for the stratification of patients with mutated EGFR.

Genetic and epigenetic characteristics of human multiple hepatocellular carcinoma

BackgroundMultiple carcinogenesis is one of the major characteristics of human hepatocellular carcinoma (HCC). The history of multiple tumors, that is, whether they derive from a common precancerous or cancerous ancestor or individually from hepatocytes, is a major clinical issue. Multiple HCC is clinically classified as either intratumor metastasis (IM) or multicentric carcinogenesis (MC). Molecular markers that differentiate IM and MC are of interest to clinical practitioners because the clinical diagnoses of IM and MC often lead to different therapies.MethodsWe analyzed 30 multiple tumors from 15 patients for somatic mutations of cancer-related genes, chromosomal aberrations, and promoter methylation of tumor suppressor genes using techniques such as high-resolution melting, array-comparative genomic hybridization (CGH), and quantitative methylation-specific PCR.ResultsSomatic mutations were found in TP53 and CTNNB1 but not in CDKN2A or KRAS. Tumors from the same patient did not share the same mutations. Array-CGH analysis revealed variations in the number of chromosomal aberrations, and the detection of common aberrations in tumors from the same patient was found to depend on the total number of chromosomal aberrations. A promoter methylation analysis of genes revealed dense methylation in HCC but not in the adjacent non-tumor tissue. The correlation coefficients (r) of methylation patterns between tumors from the same patient were more similar than those between tumors from different patients. In total, 47% of tumor samples from the same patients had an r ≥ 0.8, whereas, in contrast, only 18% of tumor samples from different patients had an r ≥ 0.8 (p = 0.01). All IM cases were highly similar; that is, r ≥ 0.8 (p = 0.025).ConclusionsThe overall scarcity of common somatic mutations and chromosomal aberrations suggests that biological IM is likely to be rare. Tumors from the same patient had a methylation pattern that was more similar than those from different patients. As all clinical IM cases exhibited high similarity, the methylation pattern may be applicable to support the clinical diagnosis of IM and MC.

Prognostic prediction of glioblastoma by quantitative assessment of the methylation status of the entire MGMT promoter region

BackgroundO6-methylguanine-DNA methyltransferase (MGMT) promoter methylation is reported to be a prognostic and predictive factor of alkylating chemotherapy for glioblastoma patients. Methylation specific PCR (MSP) has been most commonly used when the methylation status of MGMT is assessed. However, technical obstacles have hampered the implementation of MSP-based diagnostic tests. We quantitatively analyzed the methylation status of the entire MGMT promoter region and applied this information for prognostic prediction using sequencing technology.MethodsBetween 1998 and 2012, the genomic DNA of 85 tumor samples from newly diagnosed glioblastoma patients was subjected to bisulfite treatment and subdivided into a training set, consisting of fifty-three samples, and a test set, consisting of thirty-two samples. The training set was analyzed by deep Sanger sequencing with a sequencing coverage of up to 96 clones per sample. This analysis quantitatively revealed the degree of methylation of each cytidine phosphate guanosine (CpG) site. Based on these data, we constructed a prognostic prediction system for glioblastoma patients using a supervised learning method. We then validated this prediction system by deep sequencing with a next-generation sequencer using a test set of 32 samples.ResultsThe methylation status of the MGMT promoter was correlated with progression-free survival (PFS) in our patient population in the training set. The degree of correlation differed among the CpG sites. Using the data from the top twenty CpG sites, we constructed a prediction system for overall survival (OS) and PFS. The system successfully classified patients into good and poor prognosis groups in both the training set (OS, p = 0.0381; PFS, p = 0.00122) and the test set (OS, p = 0.0476; PFS, p = 0.0376). Conventional MSP could not predict the prognosis in either of our sets. (training set: OS; p = 0.993 PFS; p = 0.113, test set: OS; p = 0.326 PFS; p = 0.342).ConclusionsThe prognostic ability of our prediction system using sequencing data was better than that of methylation-specific PCR (MSP). Advances in sequencing technologies will make this approach a plausible option for diagnoses based on MGMT promotor methylation.

Conversion of a molecular classifier obtained by gene expression profiling into a classifier based on real-time PCR: a prognosis predictor for gliomas

BackgroundThe advent of gene expression profiling was expected to dramatically improve cancer diagnosis. However, despite intensive efforts and several successful examples, the development of profile-based diagnostic systems remains a difficult task. In the present work, we established a method to convert molecular classifiers based on adaptor-tagged competitive PCR (ATAC-PCR) (with a data format that is similar to that of microarrays) into classifiers based on real-time PCR.MethodsPreviously, we constructed a prognosis predictor for glioma using gene expression data obtained by ATAC-PCR, a high-throughput reverse-transcription PCR technique. The analysis of gene expression data obtained by ATAC-PCR is similar to the analysis of data from two-colour microarrays. The prognosis predictor was a linear classifier based on the first principal component (PC1) score, a weighted summation of the expression values of 58 genes. In the present study, we employed the delta-delta Ct method for measurement by real-time PCR. The predictor was converted to a Ct value-based predictor using linear regression.ResultsWe selected UBL5 as the reference gene from the group of genes with expression patterns that were most similar to the median expression level from the previous profiling study. The number of diagnostic genes was reduced to 27 without affecting the performance of the prognosis predictor. PC1 scores calculated from the data obtained by real-time PCR showed a high linear correlation (r = 0.94) with those obtained by ATAC-PCR. The correlation for individual gene expression patterns (r = 0.43 to 0.91) was smaller than for PC1 scores, suggesting that errors of measurement were likely cancelled out during the weighted summation of the expression values. The classification of a test set (n = 36) by the new predictor was more accurate than histopathological diagnosis (log rank p-values, 0.023 and 0.137, respectively) for predicting prognosis.ConclusionWe successfully converted a molecular classifier obtained by ATAC-PCR into a Ct value-based predictor. Our conversion procedure should also be applicable to linear classifiers obtained from microarray data. Because errors in measurement are likely to be cancelled out during the calculation, the conversion of individual gene expression is not an appropriate procedure. The predictor for gliomas is still in the preliminary stages of development and needs analytical clinical validation and clinical utility studies.

Abstract A149: Quantitative detection of the EGFR activating and resistance mutations in plasma DNA of lung cancer patients.

EGFR-TKIs are effective therapeutic agents for EGFR-mutation-positive lung cancer patients, but most patients develop resistance to EGFR-TKIs. A secondary EGFR T790M mutation is reported to correlate with acquired resistance and accounts for about half of the resistance cases. Circulating tumor DNA (ctDNA) is a promising target for monitoring T790M mutations in non-invasive cancer diagnostics and evaluations, although detection of rare mutation alleles is technically demanding. BEAMing (beads, emulsion, amplification, magnetics) is currently the most sensitive technique which can quantitate the mutated allele. With the technique, we can detect one mutated allele in 10,000 normal alleles. Activating mutations were identified from plasma DNA in 72% (31/43). The T790M mutation was identified in 43% (10/23) of the patients with progressive disease during EGFR-TKI treatment. The T790M mutation appeared in 13–94% of alleles with activating mutations. Judging from this incidence, BEAMing could detect T790M in the majority of eligible cases. The major advantage over other techniques is the ability to calculate the fraction of T790M-positive alleles in cancer cells, irrespective of normal cell DNA contamination. Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the AACR-NCI-EORTC International Conference: Molecular Targets and Cancer Therapeutics; 2011 Nov 12-16; San Francisco, CA. Philadelphia (PA): AACR; Mol Cancer Ther 2011;10(11 Suppl):Abstract nr A149.