The authors mapped an entire field of research to find out how certain cancer types are associated with each other.!--h2>
Our goal for this analysis was to ask questions such as:
—How are selected cancer types associated with each other? —Which molecular entities are "associated" with specific disease types? —Which combination(s) of molecular entities can provide a unique signature for specific disease types? —Can the publications dataset predict which signatures are most promising as biomarkers?
In order to answer the questions, we built a broad database of cancer biomarker publications using PubMed data, and then categorized and segmented the database into 36 cancer types and 441 molecular entities.
Highlights of this report:
The ability to map an entire field of research is powerful as it allows us to uncover associations not apparent if we focus in on a niche space.
We have utilized a methodology whereby not only do we evaluate the publications record across various cancer classes, but also can probe specific patterns of “molecular entities” as a means to ask whether a particular disease class express this pattern or not—we can then iterate these patterns to evaluate many different combinations of molecular classes.
This approach allows us to make specific predictions of patterns, which can subsequently be tested empirically—in this manner, we can produce a short list of potential marker combinations which can then be evaluated on clinically annotated sample collections to validate these predicted patterns.
This methodology is scalable and goes beyond the oncology space. We have presented specific cases (focusing primarily on breast cancer) to illustrate the approach; however, we can leverage this approach to other disease classes, and this is crucial as the field of diagnostics and personalized medicine is moving beyond the realm of cancer to include other disease classes. Indeed, we believe that over the course of this decade, the companion diagnostics field will expand beyond the 12 U.S. FDA-approved nucleic acid-based companion diagnostics entities to a much larger number beyond cancer and into cardiovascular disease, CNS disease, infectious disease, metabolic disease, etc. The current pace of deal-making in the companion diagnostics space suggests that some of these partnerships will result in marketed products in 5–7 years downstream.
Enal Razvi, Ph.D., conducted his doctoral work on viral immunology and subsequent to receiving his Ph.D. went on to the Rockefeller University in New York to serve as Aaron Diamond Post-doctoral fellow under Professor Ralph Steinman [Nobel Prize Winner in 2011 for his discovery of dendritic cells in the early-70s with Zanvil Cohn]. Subsequently, Dr. Razvi completed his research fellowship at Harvard Medical School. For the last two decades Dr. Razvi has worked with small and large companies and consulted for more than 100 clients worldwide. He currently serves as Biotechnology Analyst and Managing Director of SelectBio U.S. He can be reached at [email protected].
Gary M. Oosta holds a Ph.D. in Biophysics from Massachusetts Institute of Technology and a B.A. in Chemistry from E. Mich. Univ. He has 25 years of industrial research experience in various technology areas including medical diagnostics, thin-layer coating, bio-effects of electromagnetic radiation, and blood coagulation. Dr. Oosta has authored 20 technical publications and is an inventor on 77 patents worldwide. In addition, he has managed research groups that were responsible for many other patented innovations. Dr. Oosta has a long-standing interest in using patents and publications as strategic technology indicators for future technology selection and new product development.