Identifying Cancer Biomarkers Research Articles

Comparing the DNA Hypermethylome with Gene Mutations in Human Colorectal Cancer

We have developed a transcriptome-wide approach to identify genes affected by promoter CpG island DNA hypermethylation and transcriptional silencing in colorectal cancer. By screening cell lines and validating tumor-specific hypermethylation in a panel of primary human colorectal cancer samples, we estimate that nearly 5% or more of all known genes may be promoter methylated in an individual tumor. When directly compared to gene mutations, we find larger numbers of genes hypermethylated in individual tumors, and a higher frequency of hypermethylation within individual genes harboring either genetic or epigenetic changes. Thus, to enumerate the full spectrum of alterations in the human cancer genome, and to facilitate the most efficacious grouping of tumors to identify cancer biomarkers and tailor therapeutic approaches, both genetic and epigenetic screens should be undertaken.

Dependence network modeling for biomarker identification

Our purpose is to develop a statistical modeling approach for cancer biomarker discovery and provide new insights into early cancer detection. We propose the concept of dependence network, apply it for identifying cancer biomarkers, and study the difference between the protein or gene samples from cancer and non-cancer subjects based on mass-spectrometry (MS) and microarray data. Three MS and two gene microarray datasets are studied. Clear differences are observed in the dependence networks for cancer and non-cancer samples. Protein/gene features are examined three at one time through an exhaustive search. Dependence networks are constructed by binding triples identified by the eigenvalue pattern of the dependence model, and are further compared to identify cancer biomarkers. Such dependence-network-based biomarkers show much greater consistency under 10-fold cross-validation than the classification-performance-based biomarkers. Furthermore, the biological relevance of the dependence-network-based biomarkers using microarray data is discussed. The proposed scheme is shown promising for cancer diagnosis and prediction. See supplements: http://dsplab.eng.umd.edu/~genomics/dependencenetwork/

Characterization of Proteins in Human Pancreatic Cancer Serum Using Differential Gel Electrophoresis and Tandem Mass Spectrometry

The purpose of this study was to develop techniques for identifying cancer biomarkers in human serum using differential in-gel electrophoresis (DIGE), and characterizing the protein biomarkers using tandem mass spectrometry (MS/MS). A major problem in profiling protein expression by DIGE comes from the presence of high concentrations of a small number of proteins. Therefore, serum samples were first chromatographed using an immunoaffinity HPLC column (Agilent Technologies), to selectively remove albumin, immunoglobulins, transferrin, haptoglobin, and antitrypsin. Serum samples from three individuals with pancreatic cancer and three individuals without cancer were compared. Serum samples were processed using the immunoaffinity column. Differential protein analysis was performed using DIGE. A total of 56 protein spot-features were found to be significantly increased and 43 significantly decreased in cancer serum samples. These spot features were excised, trypsin digested, and analyzed by MALDI/TOF/TOF (4700 Proteomics Analyzer, Applied Biosystems). We identified 24 unique proteins that were increased and 17 unique proteins that were decreased in cancer serum samples. Western blot analysis confirmed increased levels of several of these proteins in the pancreatic cancer serum samples. In an independent series of serum samples from 20 patients with pancreatic cancer and 14 controls, increased levels of apolipoprotein E, alpha-1-antichymotrypsin, and inter-alpha-trypsin inhibitor were found to be associated with pancreatic cancer. These results suggest that affinity column enrichment and 2-D DIGE can be used to identify numerous proteins differentially expressed in serum from individuals with pancreatic cancer.