New RNA-based Biomarker Discovery and Diagnostics
Inventors: Dimitri Pestov, PhD; Ekaterina Kashkina, PhD (Department of Cell & Molecular Biology, Rowan University)
Brief Description
A molecular diagnostic platform that enables characterization of cellular stress and damage. This technology may be applied to a broad range of clinical settings, from assessing the efficacy of cancer therapies to detecting inflammatory and autoimmune disorders, ischemia/reperfusion injury, and toxic environmental exposures.
Problem
There is a growing demand for accurate molecular diagnostics to assess cellular damage for disease monitoring and treatment. However, traditional types of biomarkers often face challenges such as limited interoperability, low sensitivity, and a lengthy, costly discovery process. These issues create a bottleneck in the development and implementation of effective new diagnostics.
Solution
Our RNA-based diagnostic platform eliminates the need for cell- and organ-specific gene panels, focusing instead on a single universal class of RNA molecules. This novel approach can generate digital, AI/ML-ready biomarkers adaptable to diverse operational workflows, including digital pathology and biofluid-based laboratory tests.
Technology
Our approach integrates advanced molecular biology, next-generation sequencing (NGS) and computational analysis to produce unique diagnostic biosignatures from ribosomal RNA (rRNA)—a universal and abundant type of RNA. Additionally, we aim to use machine learning (ML) algorithms to facilitate the translation of this technology into practical applications for clinical diagnostics.
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Advantages
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- Faster, simpler biomarker discovery: produces a digital signature from a single RNA type, reducing time and cost compared to traditional approaches
- Broad clinical applicability: rRNA is ubiquitous, universally occurring, and sequence-conserved across cell types
- High analytical sensitivity: rRNA abundance supports detection from low sample inputs, compatible with minimally invasive liquid biopsies and small solid-tissue specimens
- Flexible digital readout: outputs are visually interpretable 2D barcodes or numerical matrices ready for machine learning classifiers
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Intellectual Property
Provisional patent filed in October, 2025 through Rowan Office of Technology Commercialization. Patent pending.
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Stage of Development
- Bench-tested / Preclinical validation
(proof-of-principle established in cell culture models)
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Contact
Neal Lemon, PhD, MBA
AVP for Innovation and Technology Commercialization
Cooper University Health and Rowan University
lemonna@rowan.edu
lemon-neal@cooperhealth.edu
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Included: visuals that support the innovation.
