Generating evidence-backed hypotheses in silico for novel prostate cancer biomarkers, Part I

The story of how, within just a few weeks, a scientist can uncover plausible biomarkers for a complex disease using IPA® for in silico research.

We also invite you to register for a webinar to hear Dr. Billaud present this in silico study live on September 7 or 21 or to listen to a pre-recorded video recording of his presentation.

By Sheila Colby, Ph.D., Sr. Scientific Product Marketing Manager, IPA. August 30, 2011.

Part I: Introduction

With more than 53,000 publications on prostate cancer over the past 40 years, how could a scientist, during just six weeks of research done completely in silico and no wet lab experiments—possibly uncover several potential prognostic, diagnostic, and disease progression biomarkers for prostate cancer? Over the next four weeks, Dr. Jean-Noel Billaud, Ph.D., Associate Staff Scientist at Ingenuity, will share his in silico journey to generate evidence-backed hypotheses using IPA.

Dr. Billaud identified several new mRNA-miRNA pairs based upon previously published information that enabled him to generate hypotheses regarding several plausible prostate cancer biomarkers. These hypotheses are backed by a great deal of published evidence from the literature and ready to be followed up in the lab. This study demonstrates the power of in silico research done in IPA and shows how IPA can leverage years worth of experimentally observed scientific knowledge to leap ahead in science.

Dr. Billaud’s look into the complex biology of prostate cancer starts with the use of the Ingenuity® Knowledge Base as a source for scientific findings and publications pertinent to prostate cancer to narrow quickly into what is already known about this disease, for example, canonical signaling, molecular players, and drugs, biomarker (See Figure 1). He shows how IPA can be used throughout his work to understand other biological aspects of this very complex disease, including biomarker information, clinical aspects provided by “functions and diseases,” and biological processes involved in the development of prostate cancer (PCa). He will then show how IPA helps analyze published expression data (microarray mRNA and microRNA), and how he combined these two data sets using the new MicroRNA Target Filter in IPA. He finally combines these datasets to select hypothetical biomarkers that include new and known biological parameters of this cancer. Dr. Billaud successfully connected many diverse pieces of knowledge on this complex disease by using IPA and gained remarkable insights that resulted in evidence-backed hypotheses worthy of laboratory investigation.

Click here to go to the home page.