Mycophenolate mofetil-associated enterocolitis.
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Cecum↗
Biomedical subjects
Thomas C Liu
Publications and source records attributed to Thomas C Liu.
Effects of nonselective cyclooxygenase inhibition with low-dose ibuprofen on tumor growth, angiogenesis, metastasis, and survival in a mouse model of colorectal cancer.
PURPOSE: To determine whether the nonselective and relatively inexpensive nonsteroidal anti-inflammatory drug ibuprofen would be effective in inhibiting colorectal cancer and might improve mortality in a mouse model. EXPERIMENTAL DESIGN: The effects of ibuprofen on tumor growth inhibition and animal survival have been examined in both mouse and human colorectal cancer tumor models. Angiogenesis was measured by in vitro endothelial cell tube formation and immunohistochemistry. RESULTS: Ibuprofen significantly inhibited cell proliferation in mouse (MC-26) and human (HT-29) colorectal cancer cell lines. In vitro angiogenesis assays also indicated that ibuprofen decreased both cell proliferation and tube formation. The administration of chow containing 1,360 ppm ibuprofen, which achieved an average plasma concentration of ibuprofen lower than the peak level achieved in humans at therapeutic doses, inhibited tumor growth by 40% to 82%. Fewer liver metastases were found in the ibuprofen group compared with the control group. In combination therapy with the standard antineoplastic agents, 5-fluorouracil, or irinotecan (CPT-11), tumor volumes in the groups with ibuprofen +/- CPT-11 or 5-fluorouracil were smaller than in the control group. Ibuprofen was similar to the cyclooxygenase-2 selective inhibitor rofecoxib in its ability to suppress tumor growth and improve overall survival. CONCLUSIONS: Ibuprofen, in part by modulating tumor angiogenesis, decreases both tumor growth and metastatic potential in mice. The ibuprofen doses were in the low range of therapeutic human plasma concentrations. Ibuprofen potentiates the antitumor properties of CPT-11 and improves survival of mice without increasing gastrointestinal toxicity.
Animals↗
Application of cDNA microarrays to generate a molecular taxonomy capable of distinguishing between colon cancer and normal colon.
In order to discover global gene expression patterns characterizing subgroups of colon cancer, microarrays were hybridized to labeled RNAs obtained from seventeen colonic specimens (nine carcinomas and eight normal samples). Using a hierarchical agglomerative method, the samples grouped naturally into two major clusters, in perfect concordance with pathological reports (colon cancer versus normal colon). Using a variant of the unpaired t-test, selected genes were ordered according to an index of importance. In order to confirm microarray data, we performed quantitative, real-time reverse transcriptase-polymerase chain reaction (TaqMan RT-PCR) on RNAs from 13 colorectal tumors and 13 normal tissues (seven of which were matched normal-tumor pairs). RT-PCR was performed on the gro1, B-factor, adlican, and endothelin converting enzyme-1 genes and confirmed microarray findings. Two hundred and fifty genes were identified, some of which were previously reported as being involved in colon cancer. We conclude that cDNA microarraying, combined with bioinformatics tools, can accurately classify colon specimens according to current histopathological taxonomy. Moreover, this technology holds promise of providing invaluable insight into specific gene roles in the development and progression of colon cancer. Our data suggests that a large-scale approach may be undertaken with the purpose of identifying biomarkers relevant to cancer progression.
Aged↗
Artificial neural networks and gene filtering distinguish between global gene expression profiles of Barrett's esophagus and esophageal cancer.
cDNAmicroarrays, combined with bioinformatics analyses, are becomingincreasingly used in current medical research. Existing analytic methods,particularly those that are unsupervised, often have difficulty recognizing subtle differences among predefined subgroups. In contrast, supervised methods, such as Artificial Neural Networks (ANNs), are able to recognize subtly different biological entities. We applied ANNs in a proof-of-principle study of cDNA microarray data in esophageal cancer (CA) and premalignancy. cDNA microarrays, each containing 8064 clones, were hybridized to RNAs from 22 esophageal lesions, including 14 Barrett's esophagus (BA) metaplasias and 8 esophageal carcinomas (3 squamous cell carcinomas and 5 adenocarcinomas). Scanned cDNA microarray data were analyzed using the bioinformatics software Cluster/TreeView, Significance Analysis of Microarrays (SAM), and ANNs. Cluster analysis based on all 8064 clones on the microarrays was unable to correctly distinguish BA specimens from CA specimens. SAM then selected 160 differentially expressed genes between Barrett's and cancer. Cluster analysis based on this reduced set still misclassified 2 Barrett's as cancers. The ANN was trained on 12 samples and tested against the remaining 10 samples. Using the 160 selected genes, the ANN correctly diagnosed all 10 samples in the test set. Finally, the 160 genes selected by SAM may merit further study as biomarkers of neoplastic progression in the esophagus, as well as in elucidating pathological mechanisms underlying BA and CA.
Adenocarcinoma↗
Artificial neural networks distinguish among subtypes of neoplastic colorectal lesions.
BACKGROUND & AIMS: There is a subtle distinction between sporadic colorectal adenomas and cancers (SAC) and inflammatory bowel disease (IBD)-associated dysplasias and cancers. However, this distinction is clinically important because sporadic adenomas are usually managed by polypectomy alone, whereas IBD-related high-grade dysplasias mandate subtotal colectomy. The current study evaluated the ability of artificial neural networks (ANNs) based on complementary DNA (cDNA) microarray data to discriminate between these 2 types of colorectal lesions. METHODS: We hybridized cDNA microarrays, each containing 8064 cDNA clones, to RNAs derived from 39 colorectal neoplastic specimens. Hierarchical clustering was performed, and an ANN was constructed and trained on a set of 5 IBD-related dysplasia or cancer (IBDNs) and 22 SACs. RESULTS: Hierarchical clustering based on all 8064 clones failed to correctly categorize the SACs and IBDNs. However, the ANN correctly diagnosed 12 of 12 blinded samples in a test set (3 IBDNs and 9 SACs). Furthermore, using an iterative process based on the computer programs GeneFinder, Cluster, and MATLAB, we reduced the number of clones used for diagnosis from 8064 to 97. Even with this reduced clone set, the ANN retained its capacity for correct diagnosis. Moreover, cluster analysis performed with these 97 clones now separated the 2 types of lesions. CONCLUSIONS: Our results suggest that ANNs have the potential to discriminate among subtly different clinical entities, such as IBDNs and SACs, as well as to identify gene subsets having the power to make these diagnostic distinctions.
Adenoma↗
Direct tandem mass spectrometry reveals limitations in protein profiling experiments for plasma biomarker discovery.
The low molecular weight plasma proteome and its biological relevance are not well defined; therefore, experiments were conducted to directly sequence and identify peptides observed in plasma and serum protein profiles. Protein fractionation, matrix-assisted laser desorption ionization mass spectrometry (MALDI-MS) profiling, and liquid-chromatography coupled to MALDI tandem mass spectrometry (MS/MS) sequencing were used to analyze the low molecular weight proteome of heparinized plasma. Four fractionation techniques using functionally derivatized 96-well plates were used to extract peptides from plasma. Tandem TOF was successful for identifying peptides up to m/z 5500 with no prior knowledge of the sequence and was also used to verify the sequence assignments for larger ion signals. The peptides (n>250) sequenced in these profiles came from a surprisingly small number of proteins (n approximately 20), which were all common to plasma, including fibrinogen, complement components, antiproteases, and carrier proteins. The cleavage patterns were consistent with those of known plasma proteases, including initial cleavages by thrombin, plasmin and complement proteins, followed by aminopeptidase and carboxypeptidase activity. On the basis of these data, we discuss limitations in biomarker discovery in the low molecular weight plasma or serum proteome using crude fractionation coupled to MALDI-MS profiling.
Biomarkers↗