Work

AI-integrated RNA therapeutics in precision oncology

Year: 2026

Type: review

Abstract: RNA therapeutics can silence oncogenic transcripts, modulate RNA processing, restore protein expression, or encode tumor antigens. Their development, however, is constrained by target selection, molecular design, delivery, immunogenicity, and inter-patient heterogeneity. Artificial intelligence (AI) is increasingly applied to these bottlenecks through multi-omics target prioritization, RNA structure and sequence modeling, delivery optimization, neoantigen selection, patient stratification, and manufacturing quality control. The maturity of these applications is uneven: within oncology, messenger RNA (mRNA) cancer vaccines are the most clinically advanced RNA-based strategy, whereas therapeutic approaches involving long non-coding RNAs and circular RNAs remain predominantly preclinical or early clinical, and most AI-enabled methods lack prospective clinical validation. In this review, we evaluate AI across the RNA therapeutic development pipeline, distinguish evidence-supported applications from preclinical and speculative uses, and discuss the validation, regulatory, and data-governance requirements for translation into precision oncology.

Source: Cell Reports Medicine

DOI: 10.1016/j.xcrm.2026.103100

Authors: William S.O. Symmans, Ziyi Li, George A. Calin, Akanksha Samuel

Institution: The University of Texas MD Anderson Cancer Center

Language: English

Topic: RNA Interference and Gene Delivery

Subfield: Molecular Biology

Field: Biochemistry, Genetics and Molecular Biology

Domain: Life Sciences

SDG: Good health and well-being

Open Access status: gold

Estimated APC paid: $5,620