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TJU Researcher Wins $200K Grant to Improve qRT-PCR Data Analysis


This article was originally published on June 28.

A Thomas Jefferson University researcher has been awarded $200,000 from the National Institutes of Health for the first year of a two-year project that aims to use mathematical modeling to improve the use of quantitative real-time PCR in molecular diagnostics and cancer prognostics.

Under the grant, administered by the National Cancer Institute, Inna Chervoneva, an associate professor at TJU, will develop and validate "new and universal methods of efficiency-adjusted relative qRT-PCR quantification that would be universally applicable to various qRT-PCR equipment platforms and chemistries," according to the grant's abstract.

Although the use of qRT-PCR in molecular diagnostics and cancer prognostics is burgeoning, and the detection of biomarkers is becoming a part of routine clinical practice, "the actual quantification of tumor burden, for example in lymph nodes or serum, is still in the developmental phase," the grant's abstract states. "In order for such actual quantification to become clinically useful, it is necessary to have in place data-analysis methods that would provide high accuracy and precision of relative qRT-PCR quantification of low-abundance transcripts across various equipment platforms and chemistries used for qRT-PCR assays."

In an e-mail to PCR Insider, Chervoneva that she won't be personally conducting experiments using various instrument platforms and chemistries, but instead will be conducting "various data analyses with existing data either published or coming from my collaborators."

According to the grant's abstract, the new quantification methods will be based on novel semi-parametric models of qRT-PCR kinetic data that "flexibly represent amplification history using smoothing splines and incorporate the model for dynamics of qRT-PCR efficiency through the penalty defined by [a] suitable differential equation."

More specifically, Chervoneva and colleagues plan to investigate the utility of a Michaelis-Menten model — a frequently used model of enzyme kinetics — and its extensions to describe the dynamics of qRT-PCR efficiency. The researchers will also develop semi-parametric models for qRT-PCR kinetic data that incorporate the dynamics of PCR efficiency using the "profiled penalty estimation approach in functional data analysis;" and will develop new universal methods for efficiency-adjusted relative qRT-PCR quantification based on those semi-parametric models, the abstract states.

Lastly, Chervonova and colleagues will compare the accuracy and precision of the new methods using simulations and a wide range of both publicly available kinetic qRT-PCR data and data collected during the course of two large NCI-sponsored studies of the expression of the GUCY2C gene, which encodes for the enzyme guanylyl cyclase, in fresh tissue and blood from colorectal cancer patients.

The grant was awarded under the NIH's R21 grant mechanism for "Exploratory Innovations in Biomedical Computational Science and Technology." Under the funding mechanism, direct research costs are limited to $275,000 over a two-year period, with no more than $200,000 in direct costs allowed in any single year.