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Sep 15, 2010 (Vol. 30, No. 16)

Finding Promising Protein Biomarkers

Discovery Aided by Availability of Large Patient Databases and Robust Data-Mining Practices

  • Not Reinventing the Wheel

    Click Image To Enlarge +
    Three dimensional culture enhances drug screening: Co-culturing of lymphoma cells with neonatal stroma components allows amplification of the cancer cells. This aggregate of isolated cancer cells can be used to screen drugs that are currently used in the clinic. The drug or the combination of drugs that is most effective in killing cancer cells becomes the candidate for anticancer therapy for the patient. [The John Theurer Cancer Center at Hackensack University Medical Center]

    Difficulty finding enough good patient data for screening was also a main theme for Stephen Suh, Ph.D., scientific director for the Cancer Research Program at The John Theurer Cancer Center at Hackensack University Medical Center.

    Dr. Suh noted that 85% of Americans receive their medical care at regional or community medical centers rather than academic medical centers. These regional and community medical centers do not always have the infrastructure required to procure samples from patients. This was one of the first observations Dr. Suh made while mining for data on ovarian cancer biomarkers in various scientific databases.

    Dr. Suh used the BioXM software platform from Sophic Alliance to mine the NCI Cancer Gene Index (CGI) for potential cancer biomarkers. The CGI contains 6,955 manually curated cancer genes, and 2,200 of them met the NCI Thesaurus criteria as “biomarker genes.”

    The CGI was created through a collaborative effort by Sophic, Biomax Informatics, and the NCI. The massive project took five years and entailed mining 18 million medline abstracts and manually applying millions of classifiers to the 6,955 candidate cancer genes. The final list of highest potential biomarker genes was generated by using the evidence codes for genomic databases that Sophic organized into confidence tiers.

    “By using a bioinformatics approach to identify putative biomarkers, for example lymphoma or ovarian cancer, we didn’t have to reinvent the wheel by performing multiple omics experiments,” said Dr. Suh. “Sometimes, it is efficient and productive to just mine the data that is out there and validate the candidate biomarkers with patient samples to identify clinically relevant biomarkers.”

    During his investigation, Dr. Suh pursued only the most robust and consistent biomarkers. This keeps the total number of biomarkers small enough to be manageable in a clinic. “For a typical biomarker project, we find less than a half dozen for diagnostic purposes, about a half dozen for prognostic purposes, and another half dozen predictive biomarkers.”

    In projects focusing on mantle cell lymphoma and Hodgkin lymphoma, this process identified markers such as CD23, IGHE, and CCND1. In studies to predict clinical outcomes in a small sample set, biomarkers identified in this way performed well.

    “For screening, we only work with patient samples with known clinical outcomes. So we only select markers that are different between poor to favorable outcomes,” explained Dr. Suh.

    This means that because he constructed the clinical database first, a few of the selected markers would be expected to retain robustness in predicting outcomes. This is the opposite of many biomarker data-mining projects where the biomarkers are identified first and applied prospectively to clinical samples.

    Scientists are gradually moving closer to the goal of having strong diagnostic biomarkers for challenging diseases like cancer. Innovative screening approaches and better data-mining techniques can facilitate the search for new biomarkers, while mass spectrometry and MRM continue to be leading technologies for overcoming the problem of dynamic range. Success in developing biomarkers depends on large quantities of quality clinical sample data and the application of intelligence and ingenuity in the search.


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