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

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

<p>Abstract</p> <p>Background</p> <p>The 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.</p> <p>Methods</p> <p>Previously, 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.</p> <p>Results</p> <p>We selected <it>UBL5 </it>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.</p> <p>Conclusion</p> <p>We 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.</p

Genetic and epigenetic characteristics of human multiple hepatocellular carcinoma

<p>Abstract</p> <p>Background</p> <p>Multiple 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.</p> <p>Methods</p> <p>We 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.</p> <p>Results</p> <p>Somatic mutations were found in <it>TP53 </it>and <it>CTNNB1 </it>but not in <it>CDKN2A </it>or <it>KRAS</it>. 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 (<it>r</it>) 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 <it>r </it>≥ 0.8, whereas, in contrast, only 18% of tumor samples from different patients had an <it>r </it>≥ 0.8 (p = 0.01). All IM cases were highly similar; that is, <it>r </it>≥ 0.8 (<it>p </it>= 0.025).</p> <p>Conclusions</p> <p>The 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.</p

Dynamics of cancer cell subpopulations in primary and metastatic colorectal tumors

Intratumor heterogeneity—heterogeneity of cancer cells within a single tumor—is considered one of the most problematic factors of treatment. Genetic heterogeneity, such as in somatic mutations and chromosome aberrations, is a common characteristic of human solid tumors and is probably the basis of biological heterogeneity. Using mutations in APC, TP53 and KRAS as markers to identify distinct colorectal cancer subpopulations, we analyzed a total of 42 primary colorectal cancer tissues and six paired liver metastases with multipoint microsampling, which enabled analysis of mutation patterns and allelic imbalances with a resolution of 0.01 mm2 (about 200 cells). There was usually more than one subpopulation in each primary tumor. Only two of 15 (13.3%) cases with three gene mutations and eight of 27 (29.6%) cases with two gene mutations had a single subpopulation. Cells with mutations in all of the examined genes usually constituted the major population. Multipoint microsampling of six primary and metastatic tumor pairs revealed that the majority of discrepancies in mutation patterns found with the bulk tissue analysis were due to loss of subpopulations in the metastatic tissues. In addition, multipoint microsampling uncovered substantial changes in subpopulations that were not detected with bulk tissue analysis. Specifically, the proportion of KRAS mutation-negative subpopulations increased in the metastatic tumors of four cases. Because KRAS mutation status is linked to cetuximab/panitumumab efficacy, subpopulation dynamics could lead to differences in response to cetuximab/panitumumab in primary versus metastatic tumors

Quantitative detection of ALK fusion breakpoints in plasma cell-free DNA from patients with non-small cell lung cancer using PCR-based target sequencing with a tiling primer set and two-step mapping/alignment.

BackgroundTyrosine kinase inhibitors targeted to anaplastic lymphoma kinase (ALK) have been demonstrated to be effective for lung cancer patients with an ALK fusion gene. Application of liquid biopsy, i.e., detection and quantitation of the fusion product in plasma cell-free DNA (cfDNA), could improve clinical practice. To detect ALK fusions, because fusion breakpoints occur somewhere in intron 19 of the ALK gene, sequencing of the entire intron is required to locate breakpoints.ResultsWe constructed a target sequencing system using an adapter and a set of primers that cover the entire ALK intron 19. This system can amplify fragments, including breakpoints, regardless of fusion partners. The data analysis pipeline firstly detected fusions by alignment to selected target sequences, and then quantitated the fusion alleles aligning to the identified breakpoint sequences. Performance was validated using 20 cfDNA samples from ALK-positive non-small cell lung cancer patients and samples from 10 healthy volunteers. Sensitivity and specificity were 50 and 100%, respectively.ConclusionsWe demonstrated that PCR-based target sequencing using a tiling primer set and two-step mapping/alignment quantitatively detected ALK fusions in cfDNA from lung cancer patients. The system offers an alternative to existing approaches based on hybridization capture