Person:

Adelstein, Stanley

Background: With the arrival of the postgenomic era, there is increasing interest in the discovery of biomarkers for the accurate diagnosis, prognosis, and early detection of cancer. Blood-borne cancer markers are favored by clinicians, because blood samples can be obtained and analyzed with relative ease. We have used a combined mining strategy based on an integrated cancer microarray platform, Oncomine, and the biomarker module of the Ingenuity Pathways Analysis (IPA) program to identify potential blood-based markers for six common human cancer types. Methodology/Principal Findings: In the Oncomine platform, the genes overexpressed in cancer tissues relative to their corresponding normal tissues were filtered by Gene Ontology keywords, with the extracellular environment stipulated and a corrected Q value (false discovery rate) cut-off implemented. The identified genes were imported to the IPA biomarker module to separate out those genes encoding putative secreted or cell-surface proteins as blood-borne (blood/serum/plasma) cancer markers. The filtered potential indicators were ranked and prioritized according to normalized absolute Student t values. The retrieval of numerous marker genes that are already clinically useful or under active investigation confirmed the effectiveness of our mining strategy. To identify the biomarkers that are unique for each cancer type, the upregulated marker genes that are in common between each two tumor types across the six human tumors were also analyzed by the IPA biomarker comparison function. Conclusion/Significance: The upregulated marker genes shared among the six cancer types may serve as a molecular tool to complement histopathologic examination, and the combination of the commonly upregulated and unique biomarkers may serve as differentiating markers for a specific cancer. This approach will be increasingly useful to discover diagnostic signatures as the mass of microarray data continues to grow in the ‘omics’ era.

We are developing a noninvasive approach for targeting imaging and therapeutic radionuclides to prostate cancer. Our method, Enzyme-Mediated Cancer Imaging and Therapy (EMCIT), aims to use enzyme-dependent, site-specific, in vivo precipitation of a radioactive molecule within the extracellular space of solid tumors. Advanced methods for data mining of the literature, protein databases, and knowledge bases (IT.Omics LSGraph and Ingenuity Systems) identified prostatic acid phosphatase (PAP) as an enzyme overexpressed in prostate cancer and secreted in the extracellular space. Using AutoDock 3.0 software, the prodrug ammonium 2-(2'-phosphoryloxyphenyl)-6-iodo-4-(3H)-quinazolinone (IQ(2-P)) was docked in silico into the X-ray structure of PAP. The data indicate that IQ(2-P) docked into the PAP active site with a calculated inhibition constant (K-i) more favorable than that of the PAP inhibitor alpha-benzylaminobenzylphosphonic acid. When (125)IQ(2-P), the radioiodinated form of the water-soluble prodrug, was incubated with PAP, rapid hydrolysis of the compound was observed as exemplified by formation of the water-insoluble 2-(2'-hydroxyphenyl)-6-[I-125] iodo-4-(3H)-quinazolinone ((125)IQ(2-OH))- Similarly, the incubation of IQ(2-P) with human LNCaP, PC-3, and 22110 prostate tumor cells resulted in the formation of large fluorescent IQ(2-OH) crystals. No hydrolysis was seen in the presence of normal human cells. Autoradiography of tumor cells incubated with (125)IQ(2-P) showed accumulation of radioactive grains ((125)IQ(2-OH)) around the cells. We anticipate that the EMCIT approach will enable the active in vivo entrapment of radioimaging and radio-therapeutic compounds within the extracellular spaces of primary prostate tumors and their metastases.

Background: We present an effective, rapid, systematic data mining approach for identifying genes or proteins related to a particular interest. A selected combination of programs exploring PubMed abstracts, universal gene/protein knowledge bases (LSGraph and Ingenuity Pathway Analysis) was assembled to distinguish enzymes with hydrolytic activities that are expressed in the extracellular space of cancer cells. Proteins were identified with respect to six types of cancer occurring in the prostate, breast, lung, colon, ovary, and pancreas.Results: The data mining method identified previously undetected targets. Our combined strategy applied to each cancer type identified a minimum of 375 proteins expressed within the extracellular space and/or attached to the plasma membrane. The method led to the recognition of human cancer-related hydrolases (on average, similar to 35 per cancer type), among which were prostatic acid phosphatase, prostate-specific antigen, and sulfatase 1.Conclusion: The combined data mining of several databases overcame many of the limitations of querying a single database and enabled the facile identification of gene products. In the case of cancer-related targets, it produced a list of putative extracellular, hydrolytic enzymes that merit additional study as candidates for cancer radioimaging and radiotherapy. The proposed data mining strategy is of a general nature and can be applied to other biological databases for understanding biological functions and diseases.